4547 lines
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1.6 MiB
4547 lines
No EOL
1.6 MiB
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const metas = window.document.getElementsByTagName("meta");
|
||
for (let i = 0; i < metas.length; i++) {
|
||
if (metas[i].getAttribute("name") === metaName) {
|
||
return metas[i].getAttribute("content");
|
||
}
|
||
}
|
||
return "";
|
||
}
|
||
|
||
async function findAndActivateCategories() {
|
||
const currentPagePath = offsetAbsoluteUrl(window.location.href);
|
||
const response = await fetch(offsetRelativeUrl("listings.json"));
|
||
if (response.status == 200) {
|
||
return response.json().then(function (listingPaths) {
|
||
const listingHrefs = [];
|
||
for (const listingPath of listingPaths) {
|
||
const pathWithoutLeadingSlash = listingPath.listing.substring(1);
|
||
for (const item of listingPath.items) {
|
||
if (
|
||
item === currentPagePath ||
|
||
item === currentPagePath + "index.html"
|
||
) {
|
||
// Resolve this path against the offset to be sure
|
||
// we already are using the correct path to the listing
|
||
// (this adjusts the listing urls to be rooted against
|
||
// whatever root the page is actually running against)
|
||
const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
|
||
const baseUrl = window.location;
|
||
const resolvedPath = new URL(relative, baseUrl);
|
||
listingHrefs.push(resolvedPath.pathname);
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
// Look up the tree for a nearby linting and use that if we find one
|
||
const nearestListing = findNearestParentListing(
|
||
offsetAbsoluteUrl(window.location.pathname),
|
||
listingHrefs
|
||
);
|
||
if (nearestListing) {
|
||
activateCategories(nearestListing);
|
||
} else {
|
||
// See if the referrer is a listing page for this item
|
||
const referredRelativePath = offsetAbsoluteUrl(document.referrer);
|
||
const referrerListing = listingHrefs.find((listingHref) => {
|
||
const isListingReferrer =
|
||
listingHref === referredRelativePath ||
|
||
listingHref === referredRelativePath + "index.html";
|
||
return isListingReferrer;
|
||
});
|
||
|
||
if (referrerListing) {
|
||
// Try to use the referrer if possible
|
||
activateCategories(referrerListing);
|
||
} else if (listingHrefs.length > 0) {
|
||
// Otherwise, just fall back to the first listing
|
||
activateCategories(listingHrefs[0]);
|
||
}
|
||
}
|
||
});
|
||
}
|
||
}
|
||
if (hasTitleCategories()) {
|
||
findAndActivateCategories();
|
||
}
|
||
|
||
const findNearestParentListing = (href, listingHrefs) => {
|
||
if (!href || !listingHrefs) {
|
||
return undefined;
|
||
}
|
||
// Look up the tree for a nearby linting and use that if we find one
|
||
const relativeParts = href.substring(1).split("/");
|
||
while (relativeParts.length > 0) {
|
||
const path = relativeParts.join("/");
|
||
for (const listingHref of listingHrefs) {
|
||
if (listingHref.startsWith(path)) {
|
||
return listingHref;
|
||
}
|
||
}
|
||
relativeParts.pop();
|
||
}
|
||
|
||
return undefined;
|
||
};
|
||
|
||
const manageSidebarVisiblity = (el, placeholderDescriptor) => {
|
||
let isVisible = true;
|
||
|
||
return (hiddenRegions) => {
|
||
if (el === null) {
|
||
return;
|
||
}
|
||
|
||
// Find the last element of the TOC
|
||
const lastChildEl = el.lastElementChild;
|
||
|
||
if (lastChildEl) {
|
||
// Find the top and bottom o the element that is being managed
|
||
const elTop = el.offsetTop;
|
||
const elBottom =
|
||
elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
|
||
|
||
// Converts the sidebar to a menu
|
||
const convertToMenu = () => {
|
||
for (const child of el.children) {
|
||
child.style.opacity = 0;
|
||
child.style.overflow = "hidden";
|
||
}
|
||
|
||
const toggleContainer = window.document.createElement("div");
|
||
toggleContainer.style.width = "100%";
|
||
toggleContainer.classList.add("zindex-over-content");
|
||
toggleContainer.classList.add("quarto-sidebar-toggle");
|
||
toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
|
||
toggleContainer.id = placeholderDescriptor.id;
|
||
toggleContainer.style.position = "fixed";
|
||
|
||
const toggleIcon = window.document.createElement("i");
|
||
toggleIcon.classList.add("quarto-sidebar-toggle-icon");
|
||
toggleIcon.classList.add("bi");
|
||
toggleIcon.classList.add("bi-caret-down-fill");
|
||
|
||
const toggleTitle = window.document.createElement("div");
|
||
const titleEl = window.document.body.querySelector(
|
||
placeholderDescriptor.titleSelector
|
||
);
|
||
if (titleEl) {
|
||
toggleTitle.append(titleEl.innerText, toggleIcon);
|
||
}
|
||
toggleTitle.classList.add("zindex-over-content");
|
||
toggleTitle.classList.add("quarto-sidebar-toggle-title");
|
||
toggleContainer.append(toggleTitle);
|
||
|
||
const toggleContents = window.document.createElement("div");
|
||
toggleContents.classList = el.classList;
|
||
toggleContents.classList.add("zindex-over-content");
|
||
toggleContents.classList.add("quarto-sidebar-toggle-contents");
|
||
for (const child of el.children) {
|
||
if (child.id === "toc-title") {
|
||
continue;
|
||
}
|
||
|
||
const clone = child.cloneNode(true);
|
||
clone.style.opacity = 1;
|
||
clone.style.display = null;
|
||
toggleContents.append(clone);
|
||
}
|
||
toggleContents.style.height = "0px";
|
||
toggleContainer.append(toggleContents);
|
||
el.parentElement.prepend(toggleContainer);
|
||
|
||
// Process clicks
|
||
let tocShowing = false;
|
||
// Allow the caller to control whether this is dismissed
|
||
// when it is clicked (e.g. sidebar navigation supports
|
||
// opening and closing the nav tree, so don't dismiss on click)
|
||
const clickEl = placeholderDescriptor.dismissOnClick
|
||
? toggleContainer
|
||
: toggleTitle;
|
||
|
||
const closeToggle = () => {
|
||
if (tocShowing) {
|
||
toggleContainer.classList.remove("expanded");
|
||
toggleContents.style.height = "0px";
|
||
tocShowing = false;
|
||
}
|
||
};
|
||
|
||
const positionToggle = () => {
|
||
// position the element (top left of parent, same width as parent)
|
||
const elRect = el.getBoundingClientRect();
|
||
toggleContainer.style.left = `${elRect.left}px`;
|
||
toggleContainer.style.top = `${elRect.top}px`;
|
||
toggleContainer.style.width = `${elRect.width}px`;
|
||
};
|
||
|
||
// Get rid of any expanded toggle if the user scrolls
|
||
window.document.addEventListener(
|
||
"scroll",
|
||
throttle(() => {
|
||
closeToggle();
|
||
}, 50)
|
||
);
|
||
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
positionToggle();
|
||
}, 50)
|
||
);
|
||
positionToggle();
|
||
|
||
// Process the click
|
||
clickEl.onclick = () => {
|
||
if (!tocShowing) {
|
||
toggleContainer.classList.add("expanded");
|
||
toggleContents.style.height = null;
|
||
tocShowing = true;
|
||
} else {
|
||
closeToggle();
|
||
}
|
||
};
|
||
};
|
||
|
||
// Converts a sidebar from a menu back to a sidebar
|
||
const convertToSidebar = () => {
|
||
for (const child of el.children) {
|
||
child.style.opacity = 1;
|
||
child.style.overflow = null;
|
||
}
|
||
|
||
const placeholderEl = window.document.getElementById(
|
||
placeholderDescriptor.id
|
||
);
|
||
if (placeholderEl) {
|
||
placeholderEl.remove();
|
||
}
|
||
|
||
el.classList.remove("rollup");
|
||
};
|
||
|
||
if (isReaderMode()) {
|
||
convertToMenu();
|
||
isVisible = false;
|
||
} else {
|
||
if (!isVisible) {
|
||
// If the element is current not visible reveal if there are
|
||
// no conflicts with overlay regions
|
||
if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
|
||
convertToSidebar();
|
||
isVisible = true;
|
||
}
|
||
} else {
|
||
// If the element is visible, hide it if it conflicts with overlay regions
|
||
// and insert a placeholder toggle (or if we're in reader mode)
|
||
if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
|
||
convertToMenu();
|
||
isVisible = false;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
};
|
||
};
|
||
|
||
// Find any conflicting margin elements and add margins to the
|
||
// top to prevent overlap
|
||
const marginChildren = window.document.querySelectorAll(
|
||
".column-margin.column-container > * "
|
||
);
|
||
let lastBottom = 0;
|
||
for (const marginChild of marginChildren) {
|
||
const top = marginChild.getBoundingClientRect().top;
|
||
if (top < lastBottom) {
|
||
const margin = lastBottom - top;
|
||
marginChild.style.marginTop = `${margin}px`;
|
||
}
|
||
const styles = window.getComputedStyle(marginChild);
|
||
const marginTop = parseFloat(styles["marginTop"]);
|
||
|
||
lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
|
||
}
|
||
|
||
// Manage the visibility of the toc and the sidebar
|
||
const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
|
||
id: "quarto-toc-toggle",
|
||
titleSelector: "#toc-title",
|
||
dismissOnClick: true,
|
||
});
|
||
const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
|
||
id: "quarto-sidebarnav-toggle",
|
||
titleSelector: ".title",
|
||
dismissOnClick: false,
|
||
});
|
||
let tocLeftScrollVisibility;
|
||
if (leftTocEl) {
|
||
tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
|
||
id: "quarto-lefttoc-toggle",
|
||
titleSelector: "#toc-title",
|
||
dismissOnClick: true,
|
||
});
|
||
}
|
||
|
||
// Find the first element that uses formatting in special columns
|
||
const conflictingEls = window.document.body.querySelectorAll(
|
||
'[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
|
||
);
|
||
|
||
// Filter all the possibly conflicting elements into ones
|
||
// the do conflict on the left or ride side
|
||
const arrConflictingEls = Array.from(conflictingEls);
|
||
const leftSideConflictEls = arrConflictingEls.filter((el) => {
|
||
if (el.tagName === "ASIDE") {
|
||
return false;
|
||
}
|
||
return Array.from(el.classList).find((className) => {
|
||
return (
|
||
className !== "column-body" &&
|
||
className.startsWith("column-") &&
|
||
!className.endsWith("right") &&
|
||
!className.endsWith("container") &&
|
||
className !== "column-margin"
|
||
);
|
||
});
|
||
});
|
||
const rightSideConflictEls = arrConflictingEls.filter((el) => {
|
||
if (el.tagName === "ASIDE") {
|
||
return true;
|
||
}
|
||
|
||
const hasMarginCaption = Array.from(el.classList).find((className) => {
|
||
return className == "margin-caption";
|
||
});
|
||
if (hasMarginCaption) {
|
||
return true;
|
||
}
|
||
|
||
return Array.from(el.classList).find((className) => {
|
||
return (
|
||
className !== "column-body" &&
|
||
!className.endsWith("container") &&
|
||
className.startsWith("column-") &&
|
||
!className.endsWith("left")
|
||
);
|
||
});
|
||
});
|
||
|
||
const kOverlapPaddingSize = 10;
|
||
function toRegions(els) {
|
||
return els.map((el) => {
|
||
const top =
|
||
el.getBoundingClientRect().top +
|
||
document.documentElement.scrollTop -
|
||
kOverlapPaddingSize;
|
||
return {
|
||
top,
|
||
bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
|
||
};
|
||
});
|
||
}
|
||
|
||
const hideOverlappedSidebars = () => {
|
||
marginScrollVisibility(toRegions(rightSideConflictEls));
|
||
sidebarScrollVisiblity(toRegions(leftSideConflictEls));
|
||
if (tocLeftScrollVisibility) {
|
||
tocLeftScrollVisibility(toRegions(leftSideConflictEls));
|
||
}
|
||
};
|
||
|
||
window.quartoToggleReader = () => {
|
||
// Applies a slow class (or removes it)
|
||
// to update the transition speed
|
||
const slowTransition = (slow) => {
|
||
const manageTransition = (id, slow) => {
|
||
const el = document.getElementById(id);
|
||
if (el) {
|
||
if (slow) {
|
||
el.classList.add("slow");
|
||
} else {
|
||
el.classList.remove("slow");
|
||
}
|
||
}
|
||
};
|
||
|
||
manageTransition("TOC", slow);
|
||
manageTransition("quarto-sidebar", slow);
|
||
};
|
||
|
||
const readerMode = !isReaderMode();
|
||
setReaderModeValue(readerMode);
|
||
|
||
// If we're entering reader mode, slow the transition
|
||
if (readerMode) {
|
||
slowTransition(readerMode);
|
||
}
|
||
highlightReaderToggle(readerMode);
|
||
hideOverlappedSidebars();
|
||
|
||
// If we're exiting reader mode, restore the non-slow transition
|
||
if (!readerMode) {
|
||
slowTransition(!readerMode);
|
||
}
|
||
};
|
||
|
||
const highlightReaderToggle = (readerMode) => {
|
||
const els = document.querySelectorAll(".quarto-reader-toggle");
|
||
if (els) {
|
||
els.forEach((el) => {
|
||
if (readerMode) {
|
||
el.classList.add("reader");
|
||
} else {
|
||
el.classList.remove("reader");
|
||
}
|
||
});
|
||
}
|
||
};
|
||
|
||
const setReaderModeValue = (val) => {
|
||
if (window.location.protocol !== "file:") {
|
||
window.localStorage.setItem("quarto-reader-mode", val);
|
||
} else {
|
||
localReaderMode = val;
|
||
}
|
||
};
|
||
|
||
const isReaderMode = () => {
|
||
if (window.location.protocol !== "file:") {
|
||
return window.localStorage.getItem("quarto-reader-mode") === "true";
|
||
} else {
|
||
return localReaderMode;
|
||
}
|
||
};
|
||
let localReaderMode = null;
|
||
|
||
// Walk the TOC and collapse/expand nodes
|
||
// Nodes are expanded if:
|
||
// - they are top level
|
||
// - they have children that are 'active' links
|
||
// - they are directly below an link that is 'active'
|
||
const walk = (el, depth) => {
|
||
// Tick depth when we enter a UL
|
||
if (el.tagName === "UL") {
|
||
depth = depth + 1;
|
||
}
|
||
|
||
// It this is active link
|
||
let isActiveNode = false;
|
||
if (el.tagName === "A" && el.classList.contains("active")) {
|
||
isActiveNode = true;
|
||
}
|
||
|
||
// See if there is an active child to this element
|
||
let hasActiveChild = false;
|
||
for (child of el.children) {
|
||
hasActiveChild = walk(child, depth) || hasActiveChild;
|
||
}
|
||
|
||
// Process the collapse state if this is an UL
|
||
if (el.tagName === "UL") {
|
||
if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
|
||
el.classList.remove("collapse");
|
||
} else {
|
||
el.classList.add("collapse");
|
||
}
|
||
|
||
// untick depth when we leave a UL
|
||
depth = depth - 1;
|
||
}
|
||
return hasActiveChild || isActiveNode;
|
||
};
|
||
|
||
// walk the TOC and expand / collapse any items that should be shown
|
||
|
||
if (tocEl) {
|
||
walk(tocEl, 0);
|
||
updateActiveLink();
|
||
}
|
||
|
||
// Throttle the scroll event and walk peridiocally
|
||
window.document.addEventListener(
|
||
"scroll",
|
||
throttle(() => {
|
||
if (tocEl) {
|
||
updateActiveLink();
|
||
walk(tocEl, 0);
|
||
}
|
||
if (!isReaderMode()) {
|
||
hideOverlappedSidebars();
|
||
}
|
||
}, 5)
|
||
);
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
if (!isReaderMode()) {
|
||
hideOverlappedSidebars();
|
||
}
|
||
}, 10)
|
||
);
|
||
hideOverlappedSidebars();
|
||
highlightReaderToggle(isReaderMode());
|
||
});
|
||
|
||
// grouped tabsets
|
||
window.addEventListener("pageshow", (_event) => {
|
||
function getTabSettings() {
|
||
const data = localStorage.getItem("quarto-persistent-tabsets-data");
|
||
if (!data) {
|
||
localStorage.setItem("quarto-persistent-tabsets-data", "{}");
|
||
return {};
|
||
}
|
||
if (data) {
|
||
return JSON.parse(data);
|
||
}
|
||
}
|
||
|
||
function setTabSettings(data) {
|
||
localStorage.setItem(
|
||
"quarto-persistent-tabsets-data",
|
||
JSON.stringify(data)
|
||
);
|
||
}
|
||
|
||
function setTabState(groupName, groupValue) {
|
||
const data = getTabSettings();
|
||
data[groupName] = groupValue;
|
||
setTabSettings(data);
|
||
}
|
||
|
||
function toggleTab(tab, active) {
|
||
const tabPanelId = tab.getAttribute("aria-controls");
|
||
const tabPanel = document.getElementById(tabPanelId);
|
||
if (active) {
|
||
tab.classList.add("active");
|
||
tabPanel.classList.add("active");
|
||
} else {
|
||
tab.classList.remove("active");
|
||
tabPanel.classList.remove("active");
|
||
}
|
||
}
|
||
|
||
function toggleAll(selectedGroup, selectorsToSync) {
|
||
for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
|
||
const active = selectedGroup === thisGroup;
|
||
for (const tab of tabs) {
|
||
toggleTab(tab, active);
|
||
}
|
||
}
|
||
}
|
||
|
||
function findSelectorsToSyncByLanguage() {
|
||
const result = {};
|
||
const tabs = Array.from(
|
||
document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
|
||
);
|
||
for (const item of tabs) {
|
||
const div = item.parentElement.parentElement.parentElement;
|
||
const group = div.getAttribute("data-group");
|
||
if (!result[group]) {
|
||
result[group] = {};
|
||
}
|
||
const selectorsToSync = result[group];
|
||
const value = item.innerHTML;
|
||
if (!selectorsToSync[value]) {
|
||
selectorsToSync[value] = [];
|
||
}
|
||
selectorsToSync[value].push(item);
|
||
}
|
||
return result;
|
||
}
|
||
|
||
function setupSelectorSync() {
|
||
const selectorsToSync = findSelectorsToSyncByLanguage();
|
||
Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
|
||
Object.entries(tabSetsByValue).forEach(([value, items]) => {
|
||
items.forEach((item) => {
|
||
item.addEventListener("click", (_event) => {
|
||
setTabState(group, value);
|
||
toggleAll(value, selectorsToSync[group]);
|
||
});
|
||
});
|
||
});
|
||
});
|
||
return selectorsToSync;
|
||
}
|
||
|
||
const selectorsToSync = setupSelectorSync();
|
||
for (const [group, selectedName] of Object.entries(getTabSettings())) {
|
||
const selectors = selectorsToSync[group];
|
||
// it's possible that stale state gives us empty selections, so we explicitly check here.
|
||
if (selectors) {
|
||
toggleAll(selectedName, selectors);
|
||
}
|
||
}
|
||
});
|
||
|
||
function throttle(func, wait) {
|
||
let waiting = false;
|
||
return function () {
|
||
if (!waiting) {
|
||
func.apply(this, arguments);
|
||
waiting = true;
|
||
setTimeout(function () {
|
||
waiting = false;
|
||
}, wait);
|
||
}
|
||
};
|
||
}
|
||
</script>
|
||
<script>/**
|
||
* @popperjs/core v2.11.4 - MIT License
|
||
*/
|
||
|
||
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<script>// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
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// AnchorJS - v4.3.1 - 2021-04-17
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j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static 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t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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n=Yt(i);if("none"!==n.transform||"none"!==n.perspective||"paint"===n.contain||-1!==["transform","perspective"].indexOf(n.willChange)||e&&"filter"===n.willChange||e&&n.filter&&"none"!==n.filter)return i;i=i.parentNode}return null}(t)||e}function ee(t){return["top","bottom"].indexOf(t)>=0?"x":"y"}var ie=Math.max,ne=Math.min,se=Math.round;function oe(t,e,i){return ie(t,ne(e,i))}function re(t){return Object.assign({},{top:0,right:0,bottom:0,left:0},t)}function ae(t,e){return e.reduce((function(e,i){return e[i]=t,e}),{})}const le={name:"arrow",enabled:!0,phase:"main",fn:function(t){var e,i=t.state,n=t.name,s=t.options,o=i.elements.arrow,r=i.modifiersData.popperOffsets,a=Ut(i.placement),l=ee(a),c=[bt,_t].indexOf(a)>=0?"height":"width";if(o&&r){var h=function(t,e){return re("number"!=typeof(t="function"==typeof 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e=t.x,i=t.y,n=window.devicePixelRatio||1;return{x:se(se(e*n)/n)||0,y:se(se(i*n)/n)||0}}(r):"function"==typeof h?h(r):r,u=d.x,f=void 0===u?0:u,p=d.y,m=void 0===p?0:p,g=r.hasOwnProperty("x"),_=r.hasOwnProperty("y"),b=bt,v=mt,y=window;if(c){var w=te(i),E="clientHeight",A="clientWidth";w===Wt(i)&&"static"!==Yt(w=Gt(i)).position&&"absolute"===a&&(E="scrollHeight",A="scrollWidth"),w=w,s!==mt&&(s!==bt&&s!==_t||o!==Et)||(v=gt,m-=w[E]-n.height,m*=l?1:-1),s!==bt&&(s!==mt&&s!==gt||o!==Et)||(b=_t,f-=w[A]-n.width,f*=l?1:-1)}var T,O=Object.assign({position:a},c&&he);return l?Object.assign({},O,((T={})[v]=_?"0":"",T[b]=g?"0":"",T.transform=(y.devicePixelRatio||1)<=1?"translate("+f+"px, "+m+"px)":"translate3d("+f+"px, "+m+"px, 0)",T)):Object.assign({},O,((e={})[v]=_?m+"px":"",e[b]=g?f+"px":"",e.transform="",e))}const ue={name:"computeStyles",enabled:!0,phase:"beforeWrite",fn:function(t){var e=t.state,i=t.options,n=i.gpuAcceleration,s=void 0===n||n,o=i.adaptive,r=void 0===o||o,a=i.roundOffsets,l=void 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C=O[s];Object.keys(T).forEach((function(t){var e=[_t,gt].indexOf(t)>=0?1:-1,i=[mt,gt].indexOf(t)>=0?"y":"x";T[t]+=C[i]*e}))}return T}function Le(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=i.boundary,o=i.rootBoundary,r=i.padding,a=i.flipVariations,l=i.allowedAutoPlacements,c=void 0===l?Lt:l,h=ce(n),d=h?a?kt:kt.filter((function(t){return ce(t)===h})):yt,u=d.filter((function(t){return c.indexOf(t)>=0}));0===u.length&&(u=d);var f=u.reduce((function(e,i){return e[i]=ke(t,{placement:i,boundary:s,rootBoundary:o,padding:r})[Ut(i)],e}),{});return Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const xe={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=Ut(g),b=l||(_!==g&&p?function(t){if(Ut(t)===vt)return[];var e=ge(t);return[be(t),e,be(e)]}(g):[ge(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(Ut(i)===vt?Le(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,E=new Map,A=!0,T=v[0],O=0;O<v.length;O++){var C=v[O],k=Ut(C),L=ce(C)===wt,x=[mt,gt].indexOf(k)>=0,D=x?"width":"height",S=ke(e,{placement:C,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),N=x?L?_t:bt:L?gt:mt;y[D]>w[D]&&(N=ge(N));var I=ge(N),P=[];if(o&&P.push(S[k]<=0),a&&P.push(S[N]<=0,S[I]<=0),P.every((function(t){return t}))){T=C,A=!1;break}E.set(C,P)}if(A)for(var j=function(t){var e=v.find((function(e){var i=E.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return 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e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[bt,_t].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},Pe={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=Ce({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},je={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new 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this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
|
||
//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
|
||
<style type="text/css">@font-face {
|
||
font-family: "bootstrap-icons";
|
||
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) format("woff");
|
||
}
|
||
.bi::before,
|
||
[class^="bi-"]::before,
|
||
[class*=" bi-"]::before {
|
||
display: inline-block;
|
||
font-family: bootstrap-icons !important;
|
||
font-style: normal;
|
||
font-weight: normal !important;
|
||
font-variant: normal;
|
||
text-transform: none;
|
||
line-height: 1;
|
||
vertical-align: -.125em;
|
||
-webkit-font-smoothing: antialiased;
|
||
-moz-osx-font-smoothing: grayscale;
|
||
}
|
||
.bi-123::before { content: "\f67f"; }
|
||
.bi-alarm-fill::before { content: "\f101"; }
|
||
.bi-alarm::before { content: "\f102"; }
|
||
.bi-align-bottom::before { content: "\f103"; }
|
||
.bi-align-center::before { content: "\f104"; }
|
||
.bi-align-end::before { content: "\f105"; }
|
||
.bi-align-middle::before { content: "\f106"; }
|
||
.bi-align-start::before { content: "\f107"; }
|
||
.bi-align-top::before { content: "\f108"; }
|
||
.bi-alt::before { content: "\f109"; }
|
||
.bi-app-indicator::before { content: "\f10a"; }
|
||
.bi-app::before { content: "\f10b"; }
|
||
.bi-archive-fill::before { content: "\f10c"; }
|
||
.bi-archive::before { content: "\f10d"; }
|
||
.bi-arrow-90deg-down::before { content: "\f10e"; }
|
||
.bi-arrow-90deg-left::before { content: "\f10f"; }
|
||
.bi-arrow-90deg-right::before { content: "\f110"; }
|
||
.bi-arrow-90deg-up::before { content: "\f111"; }
|
||
.bi-arrow-bar-down::before { content: "\f112"; }
|
||
.bi-arrow-bar-left::before { content: "\f113"; }
|
||
.bi-arrow-bar-right::before { content: "\f114"; }
|
||
.bi-arrow-bar-up::before { content: "\f115"; }
|
||
.bi-arrow-clockwise::before { content: "\f116"; }
|
||
.bi-arrow-counterclockwise::before { content: "\f117"; }
|
||
.bi-arrow-down-circle-fill::before { content: "\f118"; }
|
||
.bi-arrow-down-circle::before { content: "\f119"; }
|
||
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
|
||
.bi-arrow-down-left-circle::before { content: "\f11b"; }
|
||
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
|
||
.bi-arrow-down-left-square::before { content: "\f11d"; }
|
||
.bi-arrow-down-left::before { content: "\f11e"; }
|
||
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
|
||
.bi-arrow-down-right-circle::before { content: "\f120"; }
|
||
.bi-arrow-down-right-square-fill::before { content: "\f121"; }
|
||
.bi-arrow-down-right-square::before { content: "\f122"; }
|
||
.bi-arrow-down-right::before { content: "\f123"; }
|
||
.bi-arrow-down-short::before { content: "\f124"; }
|
||
.bi-arrow-down-square-fill::before { content: "\f125"; }
|
||
.bi-arrow-down-square::before { content: "\f126"; }
|
||
.bi-arrow-down-up::before { content: "\f127"; }
|
||
.bi-arrow-down::before { content: "\f128"; }
|
||
.bi-arrow-left-circle-fill::before { content: "\f129"; }
|
||
.bi-arrow-left-circle::before { content: "\f12a"; }
|
||
.bi-arrow-left-right::before { content: "\f12b"; }
|
||
.bi-arrow-left-short::before { content: "\f12c"; }
|
||
.bi-arrow-left-square-fill::before { content: "\f12d"; }
|
||
.bi-arrow-left-square::before { content: "\f12e"; }
|
||
.bi-arrow-left::before { content: "\f12f"; }
|
||
.bi-arrow-repeat::before { content: "\f130"; }
|
||
.bi-arrow-return-left::before { content: "\f131"; }
|
||
.bi-arrow-return-right::before { content: "\f132"; }
|
||
.bi-arrow-right-circle-fill::before { content: "\f133"; }
|
||
.bi-arrow-right-circle::before { content: "\f134"; }
|
||
.bi-arrow-right-short::before { content: "\f135"; }
|
||
.bi-arrow-right-square-fill::before { content: "\f136"; }
|
||
.bi-arrow-right-square::before { content: "\f137"; }
|
||
.bi-arrow-right::before { content: "\f138"; }
|
||
.bi-arrow-up-circle-fill::before { content: "\f139"; }
|
||
.bi-arrow-up-circle::before { content: "\f13a"; }
|
||
.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
|
||
.bi-arrow-up-left-circle::before { content: "\f13c"; }
|
||
.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
|
||
.bi-arrow-up-left-square::before { content: "\f13e"; }
|
||
.bi-arrow-up-left::before { content: "\f13f"; }
|
||
.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
|
||
.bi-arrow-up-right-circle::before { content: "\f141"; }
|
||
.bi-arrow-up-right-square-fill::before { content: "\f142"; }
|
||
.bi-arrow-up-right-square::before { content: "\f143"; }
|
||
.bi-arrow-up-right::before { content: "\f144"; }
|
||
.bi-arrow-up-short::before { content: "\f145"; }
|
||
.bi-arrow-up-square-fill::before { content: "\f146"; }
|
||
.bi-arrow-up-square::before { content: "\f147"; }
|
||
.bi-arrow-up::before { content: "\f148"; }
|
||
.bi-arrows-angle-contract::before { content: "\f149"; }
|
||
.bi-arrows-angle-expand::before { content: "\f14a"; }
|
||
.bi-arrows-collapse::before { content: "\f14b"; }
|
||
.bi-arrows-expand::before { content: "\f14c"; }
|
||
.bi-arrows-fullscreen::before { content: "\f14d"; }
|
||
.bi-arrows-move::before { content: "\f14e"; }
|
||
.bi-aspect-ratio-fill::before { content: "\f14f"; }
|
||
.bi-aspect-ratio::before { content: "\f150"; }
|
||
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|
||
.bi-at::before { content: "\f152"; }
|
||
.bi-award-fill::before { content: "\f153"; }
|
||
.bi-award::before { content: "\f154"; }
|
||
.bi-back::before { content: "\f155"; }
|
||
.bi-backspace-fill::before { content: "\f156"; }
|
||
.bi-backspace-reverse-fill::before { content: "\f157"; }
|
||
.bi-backspace-reverse::before { content: "\f158"; }
|
||
.bi-backspace::before { content: "\f159"; }
|
||
.bi-badge-3d-fill::before { content: "\f15a"; }
|
||
.bi-badge-3d::before { content: "\f15b"; }
|
||
.bi-badge-4k-fill::before { content: "\f15c"; }
|
||
.bi-badge-4k::before { content: "\f15d"; }
|
||
.bi-badge-8k-fill::before { content: "\f15e"; }
|
||
.bi-badge-8k::before { content: "\f15f"; }
|
||
.bi-badge-ad-fill::before { content: "\f160"; }
|
||
.bi-badge-ad::before { content: "\f161"; }
|
||
.bi-badge-ar-fill::before { content: "\f162"; }
|
||
.bi-badge-ar::before { content: "\f163"; }
|
||
.bi-badge-cc-fill::before { content: "\f164"; }
|
||
.bi-badge-cc::before { content: "\f165"; }
|
||
.bi-badge-hd-fill::before { content: "\f166"; }
|
||
.bi-badge-hd::before { content: "\f167"; }
|
||
.bi-badge-tm-fill::before { content: "\f168"; }
|
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.bi-badge-tm::before { content: "\f169"; }
|
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.bi-badge-vo-fill::before { content: "\f16a"; }
|
||
.bi-badge-vo::before { content: "\f16b"; }
|
||
.bi-badge-vr-fill::before { content: "\f16c"; }
|
||
.bi-badge-vr::before { content: "\f16d"; }
|
||
.bi-badge-wc-fill::before { content: "\f16e"; }
|
||
.bi-badge-wc::before { content: "\f16f"; }
|
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.bi-bag-check-fill::before { content: "\f170"; }
|
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.bi-bag-check::before { content: "\f171"; }
|
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.bi-bag-dash-fill::before { content: "\f172"; }
|
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.bi-bag-dash::before { content: "\f173"; }
|
||
.bi-bag-fill::before { content: "\f174"; }
|
||
.bi-bag-plus-fill::before { content: "\f175"; }
|
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.bi-bag-plus::before { content: "\f176"; }
|
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.bi-bag-x-fill::before { content: "\f177"; }
|
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.bi-bag-x::before { content: "\f178"; }
|
||
.bi-bag::before { content: "\f179"; }
|
||
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|
||
.bi-bar-chart-line-fill::before { content: "\f17b"; }
|
||
.bi-bar-chart-line::before { content: "\f17c"; }
|
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|
||
.bi-bar-chart::before { content: "\f17e"; }
|
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|
||
.bi-basket::before { content: "\f180"; }
|
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.bi-basket2-fill::before { content: "\f181"; }
|
||
.bi-basket2::before { content: "\f182"; }
|
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.bi-basket3-fill::before { content: "\f183"; }
|
||
.bi-basket3::before { content: "\f184"; }
|
||
.bi-battery-charging::before { content: "\f185"; }
|
||
.bi-battery-full::before { content: "\f186"; }
|
||
.bi-battery-half::before { content: "\f187"; }
|
||
.bi-battery::before { content: "\f188"; }
|
||
.bi-bell-fill::before { content: "\f189"; }
|
||
.bi-bell::before { content: "\f18a"; }
|
||
.bi-bezier::before { content: "\f18b"; }
|
||
.bi-bezier2::before { content: "\f18c"; }
|
||
.bi-bicycle::before { content: "\f18d"; }
|
||
.bi-binoculars-fill::before { content: "\f18e"; }
|
||
.bi-binoculars::before { content: "\f18f"; }
|
||
.bi-blockquote-left::before { content: "\f190"; }
|
||
.bi-blockquote-right::before { content: "\f191"; }
|
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.bi-book-fill::before { content: "\f192"; }
|
||
.bi-book-half::before { content: "\f193"; }
|
||
.bi-book::before { content: "\f194"; }
|
||
.bi-bookmark-check-fill::before { content: "\f195"; }
|
||
.bi-bookmark-check::before { content: "\f196"; }
|
||
.bi-bookmark-dash-fill::before { content: "\f197"; }
|
||
.bi-bookmark-dash::before { content: "\f198"; }
|
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.bi-bookmark-fill::before { content: "\f199"; }
|
||
.bi-bookmark-heart-fill::before { content: "\f19a"; }
|
||
.bi-bookmark-heart::before { content: "\f19b"; }
|
||
.bi-bookmark-plus-fill::before { content: "\f19c"; }
|
||
.bi-bookmark-plus::before { content: "\f19d"; }
|
||
.bi-bookmark-star-fill::before { content: "\f19e"; }
|
||
.bi-bookmark-star::before { content: "\f19f"; }
|
||
.bi-bookmark-x-fill::before { content: "\f1a0"; }
|
||
.bi-bookmark-x::before { content: "\f1a1"; }
|
||
.bi-bookmark::before { content: "\f1a2"; }
|
||
.bi-bookmarks-fill::before { content: "\f1a3"; }
|
||
.bi-bookmarks::before { content: "\f1a4"; }
|
||
.bi-bookshelf::before { content: "\f1a5"; }
|
||
.bi-bootstrap-fill::before { content: "\f1a6"; }
|
||
.bi-bootstrap-reboot::before { content: "\f1a7"; }
|
||
.bi-bootstrap::before { content: "\f1a8"; }
|
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.bi-border-all::before { content: "\f1a9"; }
|
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.bi-border-bottom::before { content: "\f1aa"; }
|
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|
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|
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.bi-border-left::before { content: "\f1ad"; }
|
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.bi-border-middle::before { content: "\f1ae"; }
|
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.bi-border-outer::before { content: "\f1af"; }
|
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.bi-border-right::before { content: "\f1b0"; }
|
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|
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.bi-border-top::before { content: "\f1b2"; }
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|
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.bi-border::before { content: "\f1b4"; }
|
||
.bi-bounding-box-circles::before { content: "\f1b5"; }
|
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.bi-bounding-box::before { content: "\f1b6"; }
|
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.bi-box-arrow-down-left::before { content: "\f1b7"; }
|
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|
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|
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|
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.bi-box-arrow-in-down-right::before { content: "\f1bb"; }
|
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.bi-box-arrow-in-down::before { content: "\f1bc"; }
|
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.bi-box-arrow-in-left::before { content: "\f1bd"; }
|
||
.bi-box-arrow-in-right::before { content: "\f1be"; }
|
||
.bi-box-arrow-in-up-left::before { content: "\f1bf"; }
|
||
.bi-box-arrow-in-up-right::before { content: "\f1c0"; }
|
||
.bi-box-arrow-in-up::before { content: "\f1c1"; }
|
||
.bi-box-arrow-left::before { content: "\f1c2"; }
|
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.bi-box-arrow-right::before { content: "\f1c3"; }
|
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.bi-box-arrow-up-left::before { content: "\f1c4"; }
|
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.bi-box-arrow-up-right::before { content: "\f1c5"; }
|
||
.bi-box-arrow-up::before { content: "\f1c6"; }
|
||
.bi-box-seam::before { content: "\f1c7"; }
|
||
.bi-box::before { content: "\f1c8"; }
|
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.bi-braces::before { content: "\f1c9"; }
|
||
.bi-bricks::before { content: "\f1ca"; }
|
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.bi-briefcase-fill::before { content: "\f1cb"; }
|
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.bi-briefcase::before { content: "\f1cc"; }
|
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.bi-brightness-alt-high-fill::before { content: "\f1cd"; }
|
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.bi-brightness-alt-high::before { content: "\f1ce"; }
|
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.bi-brightness-alt-low-fill::before { content: "\f1cf"; }
|
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.bi-brightness-alt-low::before { content: "\f1d0"; }
|
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.bi-brightness-high-fill::before { content: "\f1d1"; }
|
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.bi-brightness-high::before { content: "\f1d2"; }
|
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.bi-brightness-low-fill::before { content: "\f1d3"; }
|
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.bi-brightness-low::before { content: "\f1d4"; }
|
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.bi-broadcast-pin::before { content: "\f1d5"; }
|
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.bi-broadcast::before { content: "\f1d6"; }
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.bi-brush-fill::before { content: "\f1d7"; }
|
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.bi-brush::before { content: "\f1d8"; }
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.bi-bucket-fill::before { content: "\f1d9"; }
|
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.bi-bucket::before { content: "\f1da"; }
|
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.bi-bug-fill::before { content: "\f1db"; }
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.bi-bug::before { content: "\f1dc"; }
|
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.bi-building::before { content: "\f1dd"; }
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.bi-bullseye::before { content: "\f1de"; }
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.bi-calculator-fill::before { content: "\f1df"; }
|
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.bi-calculator::before { content: "\f1e0"; }
|
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.bi-calendar-check-fill::before { content: "\f1e1"; }
|
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.bi-calendar-check::before { content: "\f1e2"; }
|
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.bi-calendar-date-fill::before { content: "\f1e3"; }
|
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.bi-calendar-date::before { content: "\f1e4"; }
|
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.bi-calendar-day-fill::before { content: "\f1e5"; }
|
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.bi-calendar-day::before { content: "\f1e6"; }
|
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.bi-calendar-event-fill::before { content: "\f1e7"; }
|
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.bi-calendar-event::before { content: "\f1e8"; }
|
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.bi-calendar-fill::before { content: "\f1e9"; }
|
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.bi-calendar-minus-fill::before { content: "\f1ea"; }
|
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.bi-calendar-minus::before { content: "\f1eb"; }
|
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.bi-calendar-month-fill::before { content: "\f1ec"; }
|
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.bi-calendar-month::before { content: "\f1ed"; }
|
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.bi-calendar-plus-fill::before { content: "\f1ee"; }
|
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.bi-calendar-plus::before { content: "\f1ef"; }
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.bi-calendar-range-fill::before { content: "\f1f0"; }
|
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.bi-calendar-range::before { content: "\f1f1"; }
|
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.bi-calendar-week-fill::before { content: "\f1f2"; }
|
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.bi-calendar-week::before { content: "\f1f3"; }
|
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.bi-calendar-x-fill::before { content: "\f1f4"; }
|
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.bi-calendar-x::before { content: "\f1f5"; }
|
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|
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|
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.bi-calendar2-check::before { content: "\f1f8"; }
|
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.bi-calendar2-date-fill::before { content: "\f1f9"; }
|
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.bi-calendar2-date::before { content: "\f1fa"; }
|
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.bi-calendar2-day-fill::before { content: "\f1fb"; }
|
||
.bi-calendar2-day::before { content: "\f1fc"; }
|
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.bi-calendar2-event-fill::before { content: "\f1fd"; }
|
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.bi-calendar2-event::before { content: "\f1fe"; }
|
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.bi-calendar2-fill::before { content: "\f1ff"; }
|
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.bi-calendar2-minus-fill::before { content: "\f200"; }
|
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.bi-calendar2-minus::before { content: "\f201"; }
|
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.bi-calendar2-month-fill::before { content: "\f202"; }
|
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.bi-calendar2-month::before { content: "\f203"; }
|
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.bi-calendar2-plus-fill::before { content: "\f204"; }
|
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.bi-calendar2-plus::before { content: "\f205"; }
|
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.bi-calendar2-range-fill::before { content: "\f206"; }
|
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.bi-calendar2-range::before { content: "\f207"; }
|
||
.bi-calendar2-week-fill::before { content: "\f208"; }
|
||
.bi-calendar2-week::before { content: "\f209"; }
|
||
.bi-calendar2-x-fill::before { content: "\f20a"; }
|
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.bi-calendar2-x::before { content: "\f20b"; }
|
||
.bi-calendar2::before { content: "\f20c"; }
|
||
.bi-calendar3-event-fill::before { content: "\f20d"; }
|
||
.bi-calendar3-event::before { content: "\f20e"; }
|
||
.bi-calendar3-fill::before { content: "\f20f"; }
|
||
.bi-calendar3-range-fill::before { content: "\f210"; }
|
||
.bi-calendar3-range::before { content: "\f211"; }
|
||
.bi-calendar3-week-fill::before { content: "\f212"; }
|
||
.bi-calendar3-week::before { content: "\f213"; }
|
||
.bi-calendar3::before { content: "\f214"; }
|
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.bi-calendar4-event::before { content: "\f215"; }
|
||
.bi-calendar4-range::before { content: "\f216"; }
|
||
.bi-calendar4-week::before { content: "\f217"; }
|
||
.bi-calendar4::before { content: "\f218"; }
|
||
.bi-camera-fill::before { content: "\f219"; }
|
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.bi-camera-reels-fill::before { content: "\f21a"; }
|
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.bi-camera-reels::before { content: "\f21b"; }
|
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.bi-camera-video-fill::before { content: "\f21c"; }
|
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|
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.bi-camera-video-off::before { content: "\f21e"; }
|
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.bi-camera-video::before { content: "\f21f"; }
|
||
.bi-camera::before { content: "\f220"; }
|
||
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|
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|
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|
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|
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|
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|
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|
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.bi-caret-down-fill::before { content: "\f229"; }
|
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|
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|
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|
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.bi-caret-left::before { content: "\f230"; }
|
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.bi-caret-right-fill::before { content: "\f231"; }
|
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|
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|
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.bi-caret-right::before { content: "\f234"; }
|
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.bi-caret-up-fill::before { content: "\f235"; }
|
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.bi-caret-up-square-fill::before { content: "\f236"; }
|
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.bi-caret-up-square::before { content: "\f237"; }
|
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|
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.bi-cart-check-fill::before { content: "\f239"; }
|
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.bi-cart-check::before { content: "\f23a"; }
|
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.bi-cart-dash-fill::before { content: "\f23b"; }
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.bi-cart-dash::before { content: "\f23c"; }
|
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.bi-cart-fill::before { content: "\f23d"; }
|
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.bi-cart-plus-fill::before { content: "\f23e"; }
|
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.bi-cart-plus::before { content: "\f23f"; }
|
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.bi-cart-x-fill::before { content: "\f240"; }
|
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.bi-cart-x::before { content: "\f241"; }
|
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.bi-cart::before { content: "\f242"; }
|
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.bi-cart2::before { content: "\f243"; }
|
||
.bi-cart3::before { content: "\f244"; }
|
||
.bi-cart4::before { content: "\f245"; }
|
||
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|
||
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|
||
.bi-cast::before { content: "\f248"; }
|
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.bi-chat-dots-fill::before { content: "\f249"; }
|
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|
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.bi-chat-fill::before { content: "\f24b"; }
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|
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|
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.bi-chat-left-fill::before { content: "\f24e"; }
|
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.bi-chat-left-quote-fill::before { content: "\f24f"; }
|
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.bi-chat-left-quote::before { content: "\f250"; }
|
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.bi-chat-left-text-fill::before { content: "\f251"; }
|
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.bi-chat-left-text::before { content: "\f252"; }
|
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.bi-chat-left::before { content: "\f253"; }
|
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.bi-chat-quote-fill::before { content: "\f254"; }
|
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.bi-chat-quote::before { content: "\f255"; }
|
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.bi-chat-right-dots-fill::before { content: "\f256"; }
|
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.bi-chat-right-dots::before { content: "\f257"; }
|
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.bi-chat-right-fill::before { content: "\f258"; }
|
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.bi-chat-right-quote-fill::before { content: "\f259"; }
|
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.bi-chat-right-quote::before { content: "\f25a"; }
|
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.bi-chat-right-text-fill::before { content: "\f25b"; }
|
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.bi-chat-right-text::before { content: "\f25c"; }
|
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.bi-chat-right::before { content: "\f25d"; }
|
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.bi-chat-square-dots-fill::before { content: "\f25e"; }
|
||
.bi-chat-square-dots::before { content: "\f25f"; }
|
||
.bi-chat-square-fill::before { content: "\f260"; }
|
||
.bi-chat-square-quote-fill::before { content: "\f261"; }
|
||
.bi-chat-square-quote::before { content: "\f262"; }
|
||
.bi-chat-square-text-fill::before { content: "\f263"; }
|
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.bi-chat-square-text::before { content: "\f264"; }
|
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.bi-chat-square::before { content: "\f265"; }
|
||
.bi-chat-text-fill::before { content: "\f266"; }
|
||
.bi-chat-text::before { content: "\f267"; }
|
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.bi-chat::before { content: "\f268"; }
|
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|
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.bi-check-circle-fill::before { content: "\f26a"; }
|
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|
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.bi-check-square-fill::before { content: "\f26c"; }
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|
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|
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|
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|
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|
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.bi-check2::before { content: "\f272"; }
|
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|
||
.bi-chevron-bar-down::before { content: "\f274"; }
|
||
.bi-chevron-bar-expand::before { content: "\f275"; }
|
||
.bi-chevron-bar-left::before { content: "\f276"; }
|
||
.bi-chevron-bar-right::before { content: "\f277"; }
|
||
.bi-chevron-bar-up::before { content: "\f278"; }
|
||
.bi-chevron-compact-down::before { content: "\f279"; }
|
||
.bi-chevron-compact-left::before { content: "\f27a"; }
|
||
.bi-chevron-compact-right::before { content: "\f27b"; }
|
||
.bi-chevron-compact-up::before { content: "\f27c"; }
|
||
.bi-chevron-contract::before { content: "\f27d"; }
|
||
.bi-chevron-double-down::before { content: "\f27e"; }
|
||
.bi-chevron-double-left::before { content: "\f27f"; }
|
||
.bi-chevron-double-right::before { content: "\f280"; }
|
||
.bi-chevron-double-up::before { content: "\f281"; }
|
||
.bi-chevron-down::before { content: "\f282"; }
|
||
.bi-chevron-expand::before { content: "\f283"; }
|
||
.bi-chevron-left::before { content: "\f284"; }
|
||
.bi-chevron-right::before { content: "\f285"; }
|
||
.bi-chevron-up::before { content: "\f286"; }
|
||
.bi-circle-fill::before { content: "\f287"; }
|
||
.bi-circle-half::before { content: "\f288"; }
|
||
.bi-circle-square::before { content: "\f289"; }
|
||
.bi-circle::before { content: "\f28a"; }
|
||
.bi-clipboard-check::before { content: "\f28b"; }
|
||
.bi-clipboard-data::before { content: "\f28c"; }
|
||
.bi-clipboard-minus::before { content: "\f28d"; }
|
||
.bi-clipboard-plus::before { content: "\f28e"; }
|
||
.bi-clipboard-x::before { content: "\f28f"; }
|
||
.bi-clipboard::before { content: "\f290"; }
|
||
.bi-clock-fill::before { content: "\f291"; }
|
||
.bi-clock-history::before { content: "\f292"; }
|
||
.bi-clock::before { content: "\f293"; }
|
||
.bi-cloud-arrow-down-fill::before { content: "\f294"; }
|
||
.bi-cloud-arrow-down::before { content: "\f295"; }
|
||
.bi-cloud-arrow-up-fill::before { content: "\f296"; }
|
||
.bi-cloud-arrow-up::before { content: "\f297"; }
|
||
.bi-cloud-check-fill::before { content: "\f298"; }
|
||
.bi-cloud-check::before { content: "\f299"; }
|
||
.bi-cloud-download-fill::before { content: "\f29a"; }
|
||
.bi-cloud-download::before { content: "\f29b"; }
|
||
.bi-cloud-drizzle-fill::before { content: "\f29c"; }
|
||
.bi-cloud-drizzle::before { content: "\f29d"; }
|
||
.bi-cloud-fill::before { content: "\f29e"; }
|
||
.bi-cloud-fog-fill::before { content: "\f29f"; }
|
||
.bi-cloud-fog::before { content: "\f2a0"; }
|
||
.bi-cloud-fog2-fill::before { content: "\f2a1"; }
|
||
.bi-cloud-fog2::before { content: "\f2a2"; }
|
||
.bi-cloud-hail-fill::before { content: "\f2a3"; }
|
||
.bi-cloud-hail::before { content: "\f2a4"; }
|
||
.bi-cloud-haze-1::before { content: "\f2a5"; }
|
||
.bi-cloud-haze-fill::before { content: "\f2a6"; }
|
||
.bi-cloud-haze::before { content: "\f2a7"; }
|
||
.bi-cloud-haze2-fill::before { content: "\f2a8"; }
|
||
.bi-cloud-lightning-fill::before { content: "\f2a9"; }
|
||
.bi-cloud-lightning-rain-fill::before { content: "\f2aa"; }
|
||
.bi-cloud-lightning-rain::before { content: "\f2ab"; }
|
||
.bi-cloud-lightning::before { content: "\f2ac"; }
|
||
.bi-cloud-minus-fill::before { content: "\f2ad"; }
|
||
.bi-cloud-minus::before { content: "\f2ae"; }
|
||
.bi-cloud-moon-fill::before { content: "\f2af"; }
|
||
.bi-cloud-moon::before { content: "\f2b0"; }
|
||
.bi-cloud-plus-fill::before { content: "\f2b1"; }
|
||
.bi-cloud-plus::before { content: "\f2b2"; }
|
||
.bi-cloud-rain-fill::before { content: "\f2b3"; }
|
||
.bi-cloud-rain-heavy-fill::before { content: "\f2b4"; }
|
||
.bi-cloud-rain-heavy::before { content: "\f2b5"; }
|
||
.bi-cloud-rain::before { content: "\f2b6"; }
|
||
.bi-cloud-slash-fill::before { content: "\f2b7"; }
|
||
.bi-cloud-slash::before { content: "\f2b8"; }
|
||
.bi-cloud-sleet-fill::before { content: "\f2b9"; }
|
||
.bi-cloud-sleet::before { content: "\f2ba"; }
|
||
.bi-cloud-snow-fill::before { content: "\f2bb"; }
|
||
.bi-cloud-snow::before { content: "\f2bc"; }
|
||
.bi-cloud-sun-fill::before { content: "\f2bd"; }
|
||
.bi-cloud-sun::before { content: "\f2be"; }
|
||
.bi-cloud-upload-fill::before { content: "\f2bf"; }
|
||
.bi-cloud-upload::before { content: "\f2c0"; }
|
||
.bi-cloud::before { content: "\f2c1"; }
|
||
.bi-clouds-fill::before { content: "\f2c2"; }
|
||
.bi-clouds::before { content: "\f2c3"; }
|
||
.bi-cloudy-fill::before { content: "\f2c4"; }
|
||
.bi-cloudy::before { content: "\f2c5"; }
|
||
.bi-code-slash::before { content: "\f2c6"; }
|
||
.bi-code-square::before { content: "\f2c7"; }
|
||
.bi-code::before { content: "\f2c8"; }
|
||
.bi-collection-fill::before { content: "\f2c9"; }
|
||
.bi-collection-play-fill::before { content: "\f2ca"; }
|
||
.bi-collection-play::before { content: "\f2cb"; }
|
||
.bi-collection::before { content: "\f2cc"; }
|
||
.bi-columns-gap::before { content: "\f2cd"; }
|
||
.bi-columns::before { content: "\f2ce"; }
|
||
.bi-command::before { content: "\f2cf"; }
|
||
.bi-compass-fill::before { content: "\f2d0"; }
|
||
.bi-compass::before { content: "\f2d1"; }
|
||
.bi-cone-striped::before { content: "\f2d2"; }
|
||
.bi-cone::before { content: "\f2d3"; }
|
||
.bi-controller::before { content: "\f2d4"; }
|
||
.bi-cpu-fill::before { content: "\f2d5"; }
|
||
.bi-cpu::before { content: "\f2d6"; }
|
||
.bi-credit-card-2-back-fill::before { content: "\f2d7"; }
|
||
.bi-credit-card-2-back::before { content: "\f2d8"; }
|
||
.bi-credit-card-2-front-fill::before { content: "\f2d9"; }
|
||
.bi-credit-card-2-front::before { content: "\f2da"; }
|
||
.bi-credit-card-fill::before { content: "\f2db"; }
|
||
.bi-credit-card::before { content: "\f2dc"; }
|
||
.bi-crop::before { content: "\f2dd"; }
|
||
.bi-cup-fill::before { content: "\f2de"; }
|
||
.bi-cup-straw::before { content: "\f2df"; }
|
||
.bi-cup::before { content: "\f2e0"; }
|
||
.bi-cursor-fill::before { content: "\f2e1"; }
|
||
.bi-cursor-text::before { content: "\f2e2"; }
|
||
.bi-cursor::before { content: "\f2e3"; }
|
||
.bi-dash-circle-dotted::before { content: "\f2e4"; }
|
||
.bi-dash-circle-fill::before { content: "\f2e5"; }
|
||
.bi-dash-circle::before { content: "\f2e6"; }
|
||
.bi-dash-square-dotted::before { content: "\f2e7"; }
|
||
.bi-dash-square-fill::before { content: "\f2e8"; }
|
||
.bi-dash-square::before { content: "\f2e9"; }
|
||
.bi-dash::before { content: "\f2ea"; }
|
||
.bi-diagram-2-fill::before { content: "\f2eb"; }
|
||
.bi-diagram-2::before { content: "\f2ec"; }
|
||
.bi-diagram-3-fill::before { content: "\f2ed"; }
|
||
.bi-diagram-3::before { content: "\f2ee"; }
|
||
.bi-diamond-fill::before { content: "\f2ef"; }
|
||
.bi-diamond-half::before { content: "\f2f0"; }
|
||
.bi-diamond::before { content: "\f2f1"; }
|
||
.bi-dice-1-fill::before { content: "\f2f2"; }
|
||
.bi-dice-1::before { content: "\f2f3"; }
|
||
.bi-dice-2-fill::before { content: "\f2f4"; }
|
||
.bi-dice-2::before { content: "\f2f5"; }
|
||
.bi-dice-3-fill::before { content: "\f2f6"; }
|
||
.bi-dice-3::before { content: "\f2f7"; }
|
||
.bi-dice-4-fill::before { content: "\f2f8"; }
|
||
.bi-dice-4::before { content: "\f2f9"; }
|
||
.bi-dice-5-fill::before { content: "\f2fa"; }
|
||
.bi-dice-5::before { content: "\f2fb"; }
|
||
.bi-dice-6-fill::before { content: "\f2fc"; }
|
||
.bi-dice-6::before { content: "\f2fd"; }
|
||
.bi-disc-fill::before { content: "\f2fe"; }
|
||
.bi-disc::before { content: "\f2ff"; }
|
||
.bi-discord::before { content: "\f300"; }
|
||
.bi-display-fill::before { content: "\f301"; }
|
||
.bi-display::before { content: "\f302"; }
|
||
.bi-distribute-horizontal::before { content: "\f303"; }
|
||
.bi-distribute-vertical::before { content: "\f304"; }
|
||
.bi-door-closed-fill::before { content: "\f305"; }
|
||
.bi-door-closed::before { content: "\f306"; }
|
||
.bi-door-open-fill::before { content: "\f307"; }
|
||
.bi-door-open::before { content: "\f308"; }
|
||
.bi-dot::before { content: "\f309"; }
|
||
.bi-download::before { content: "\f30a"; }
|
||
.bi-droplet-fill::before { content: "\f30b"; }
|
||
.bi-droplet-half::before { content: "\f30c"; }
|
||
.bi-droplet::before { content: "\f30d"; }
|
||
.bi-earbuds::before { content: "\f30e"; }
|
||
.bi-easel-fill::before { content: "\f30f"; }
|
||
.bi-easel::before { content: "\f310"; }
|
||
.bi-egg-fill::before { content: "\f311"; }
|
||
.bi-egg-fried::before { content: "\f312"; }
|
||
.bi-egg::before { content: "\f313"; }
|
||
.bi-eject-fill::before { content: "\f314"; }
|
||
.bi-eject::before { content: "\f315"; }
|
||
.bi-emoji-angry-fill::before { content: "\f316"; }
|
||
.bi-emoji-angry::before { content: "\f317"; }
|
||
.bi-emoji-dizzy-fill::before { content: "\f318"; }
|
||
.bi-emoji-dizzy::before { content: "\f319"; }
|
||
.bi-emoji-expressionless-fill::before { content: "\f31a"; }
|
||
.bi-emoji-expressionless::before { content: "\f31b"; }
|
||
.bi-emoji-frown-fill::before { content: "\f31c"; }
|
||
.bi-emoji-frown::before { content: "\f31d"; }
|
||
.bi-emoji-heart-eyes-fill::before { content: "\f31e"; }
|
||
.bi-emoji-heart-eyes::before { content: "\f31f"; }
|
||
.bi-emoji-laughing-fill::before { content: "\f320"; }
|
||
.bi-emoji-laughing::before { content: "\f321"; }
|
||
.bi-emoji-neutral-fill::before { content: "\f322"; }
|
||
.bi-emoji-neutral::before { content: "\f323"; }
|
||
.bi-emoji-smile-fill::before { content: "\f324"; }
|
||
.bi-emoji-smile-upside-down-fill::before { content: "\f325"; }
|
||
.bi-emoji-smile-upside-down::before { content: "\f326"; }
|
||
.bi-emoji-smile::before { content: "\f327"; }
|
||
.bi-emoji-sunglasses-fill::before { content: "\f328"; }
|
||
.bi-emoji-sunglasses::before { content: "\f329"; }
|
||
.bi-emoji-wink-fill::before { content: "\f32a"; }
|
||
.bi-emoji-wink::before { content: "\f32b"; }
|
||
.bi-envelope-fill::before { content: "\f32c"; }
|
||
.bi-envelope-open-fill::before { content: "\f32d"; }
|
||
.bi-envelope-open::before { content: "\f32e"; }
|
||
.bi-envelope::before { content: "\f32f"; }
|
||
.bi-eraser-fill::before { content: "\f330"; }
|
||
.bi-eraser::before { content: "\f331"; }
|
||
.bi-exclamation-circle-fill::before { content: "\f332"; }
|
||
.bi-exclamation-circle::before { content: "\f333"; }
|
||
.bi-exclamation-diamond-fill::before { content: "\f334"; }
|
||
.bi-exclamation-diamond::before { content: "\f335"; }
|
||
.bi-exclamation-octagon-fill::before { content: "\f336"; }
|
||
.bi-exclamation-octagon::before { content: "\f337"; }
|
||
.bi-exclamation-square-fill::before { content: "\f338"; }
|
||
.bi-exclamation-square::before { content: "\f339"; }
|
||
.bi-exclamation-triangle-fill::before { content: "\f33a"; }
|
||
.bi-exclamation-triangle::before { content: "\f33b"; }
|
||
.bi-exclamation::before { content: "\f33c"; }
|
||
.bi-exclude::before { content: "\f33d"; }
|
||
.bi-eye-fill::before { content: "\f33e"; }
|
||
.bi-eye-slash-fill::before { content: "\f33f"; }
|
||
.bi-eye-slash::before { content: "\f340"; }
|
||
.bi-eye::before { content: "\f341"; }
|
||
.bi-eyedropper::before { content: "\f342"; }
|
||
.bi-eyeglasses::before { content: "\f343"; }
|
||
.bi-facebook::before { content: "\f344"; }
|
||
.bi-file-arrow-down-fill::before { content: "\f345"; }
|
||
.bi-file-arrow-down::before { content: "\f346"; }
|
||
.bi-file-arrow-up-fill::before { content: "\f347"; }
|
||
.bi-file-arrow-up::before { content: "\f348"; }
|
||
.bi-file-bar-graph-fill::before { content: "\f349"; }
|
||
.bi-file-bar-graph::before { content: "\f34a"; }
|
||
.bi-file-binary-fill::before { content: "\f34b"; }
|
||
.bi-file-binary::before { content: "\f34c"; }
|
||
.bi-file-break-fill::before { content: "\f34d"; }
|
||
.bi-file-break::before { content: "\f34e"; }
|
||
.bi-file-check-fill::before { content: "\f34f"; }
|
||
.bi-file-check::before { content: "\f350"; }
|
||
.bi-file-code-fill::before { content: "\f351"; }
|
||
.bi-file-code::before { content: "\f352"; }
|
||
.bi-file-diff-fill::before { content: "\f353"; }
|
||
.bi-file-diff::before { content: "\f354"; }
|
||
.bi-file-earmark-arrow-down-fill::before { content: "\f355"; }
|
||
.bi-file-earmark-arrow-down::before { content: "\f356"; }
|
||
.bi-file-earmark-arrow-up-fill::before { content: "\f357"; }
|
||
.bi-file-earmark-arrow-up::before { content: "\f358"; }
|
||
.bi-file-earmark-bar-graph-fill::before { content: "\f359"; }
|
||
.bi-file-earmark-bar-graph::before { content: "\f35a"; }
|
||
.bi-file-earmark-binary-fill::before { content: "\f35b"; }
|
||
.bi-file-earmark-binary::before { content: "\f35c"; }
|
||
.bi-file-earmark-break-fill::before { content: "\f35d"; }
|
||
.bi-file-earmark-break::before { content: "\f35e"; }
|
||
.bi-file-earmark-check-fill::before { content: "\f35f"; }
|
||
.bi-file-earmark-check::before { content: "\f360"; }
|
||
.bi-file-earmark-code-fill::before { content: "\f361"; }
|
||
.bi-file-earmark-code::before { content: "\f362"; }
|
||
.bi-file-earmark-diff-fill::before { content: "\f363"; }
|
||
.bi-file-earmark-diff::before { content: "\f364"; }
|
||
.bi-file-earmark-easel-fill::before { content: "\f365"; }
|
||
.bi-file-earmark-easel::before { content: "\f366"; }
|
||
.bi-file-earmark-excel-fill::before { content: "\f367"; }
|
||
.bi-file-earmark-excel::before { content: "\f368"; }
|
||
.bi-file-earmark-fill::before { content: "\f369"; }
|
||
.bi-file-earmark-font-fill::before { content: "\f36a"; }
|
||
.bi-file-earmark-font::before { content: "\f36b"; }
|
||
.bi-file-earmark-image-fill::before { content: "\f36c"; }
|
||
.bi-file-earmark-image::before { content: "\f36d"; }
|
||
.bi-file-earmark-lock-fill::before { content: "\f36e"; }
|
||
.bi-file-earmark-lock::before { content: "\f36f"; }
|
||
.bi-file-earmark-lock2-fill::before { content: "\f370"; }
|
||
.bi-file-earmark-lock2::before { content: "\f371"; }
|
||
.bi-file-earmark-medical-fill::before { content: "\f372"; }
|
||
.bi-file-earmark-medical::before { content: "\f373"; }
|
||
.bi-file-earmark-minus-fill::before { content: "\f374"; }
|
||
.bi-file-earmark-minus::before { content: "\f375"; }
|
||
.bi-file-earmark-music-fill::before { content: "\f376"; }
|
||
.bi-file-earmark-music::before { content: "\f377"; }
|
||
.bi-file-earmark-person-fill::before { content: "\f378"; }
|
||
.bi-file-earmark-person::before { content: "\f379"; }
|
||
.bi-file-earmark-play-fill::before { content: "\f37a"; }
|
||
.bi-file-earmark-play::before { content: "\f37b"; }
|
||
.bi-file-earmark-plus-fill::before { content: "\f37c"; }
|
||
.bi-file-earmark-plus::before { content: "\f37d"; }
|
||
.bi-file-earmark-post-fill::before { content: "\f37e"; }
|
||
.bi-file-earmark-post::before { content: "\f37f"; }
|
||
.bi-file-earmark-ppt-fill::before { content: "\f380"; }
|
||
.bi-file-earmark-ppt::before { content: "\f381"; }
|
||
.bi-file-earmark-richtext-fill::before { content: "\f382"; }
|
||
.bi-file-earmark-richtext::before { content: "\f383"; }
|
||
.bi-file-earmark-ruled-fill::before { content: "\f384"; }
|
||
.bi-file-earmark-ruled::before { content: "\f385"; }
|
||
.bi-file-earmark-slides-fill::before { content: "\f386"; }
|
||
.bi-file-earmark-slides::before { content: "\f387"; }
|
||
.bi-file-earmark-spreadsheet-fill::before { content: "\f388"; }
|
||
.bi-file-earmark-spreadsheet::before { content: "\f389"; }
|
||
.bi-file-earmark-text-fill::before { content: "\f38a"; }
|
||
.bi-file-earmark-text::before { content: "\f38b"; }
|
||
.bi-file-earmark-word-fill::before { content: "\f38c"; }
|
||
.bi-file-earmark-word::before { content: "\f38d"; }
|
||
.bi-file-earmark-x-fill::before { content: "\f38e"; }
|
||
.bi-file-earmark-x::before { content: "\f38f"; }
|
||
.bi-file-earmark-zip-fill::before { content: "\f390"; }
|
||
.bi-file-earmark-zip::before { content: "\f391"; }
|
||
.bi-file-earmark::before { content: "\f392"; }
|
||
.bi-file-easel-fill::before { content: "\f393"; }
|
||
.bi-file-easel::before { content: "\f394"; }
|
||
.bi-file-excel-fill::before { content: "\f395"; }
|
||
.bi-file-excel::before { content: "\f396"; }
|
||
.bi-file-fill::before { content: "\f397"; }
|
||
.bi-file-font-fill::before { content: "\f398"; }
|
||
.bi-file-font::before { content: "\f399"; }
|
||
.bi-file-image-fill::before { content: "\f39a"; }
|
||
.bi-file-image::before { content: "\f39b"; }
|
||
.bi-file-lock-fill::before { content: "\f39c"; }
|
||
.bi-file-lock::before { content: "\f39d"; }
|
||
.bi-file-lock2-fill::before { content: "\f39e"; }
|
||
.bi-file-lock2::before { content: "\f39f"; }
|
||
.bi-file-medical-fill::before { content: "\f3a0"; }
|
||
.bi-file-medical::before { content: "\f3a1"; }
|
||
.bi-file-minus-fill::before { content: "\f3a2"; }
|
||
.bi-file-minus::before { content: "\f3a3"; }
|
||
.bi-file-music-fill::before { content: "\f3a4"; }
|
||
.bi-file-music::before { content: "\f3a5"; }
|
||
.bi-file-person-fill::before { content: "\f3a6"; }
|
||
.bi-file-person::before { content: "\f3a7"; }
|
||
.bi-file-play-fill::before { content: "\f3a8"; }
|
||
.bi-file-play::before { content: "\f3a9"; }
|
||
.bi-file-plus-fill::before { content: "\f3aa"; }
|
||
.bi-file-plus::before { content: "\f3ab"; }
|
||
.bi-file-post-fill::before { content: "\f3ac"; }
|
||
.bi-file-post::before { content: "\f3ad"; }
|
||
.bi-file-ppt-fill::before { content: "\f3ae"; }
|
||
.bi-file-ppt::before { content: "\f3af"; }
|
||
.bi-file-richtext-fill::before { content: "\f3b0"; }
|
||
.bi-file-richtext::before { content: "\f3b1"; }
|
||
.bi-file-ruled-fill::before { content: "\f3b2"; }
|
||
.bi-file-ruled::before { content: "\f3b3"; }
|
||
.bi-file-slides-fill::before { content: "\f3b4"; }
|
||
.bi-file-slides::before { content: "\f3b5"; }
|
||
.bi-file-spreadsheet-fill::before { content: "\f3b6"; }
|
||
.bi-file-spreadsheet::before { content: "\f3b7"; }
|
||
.bi-file-text-fill::before { content: "\f3b8"; }
|
||
.bi-file-text::before { content: "\f3b9"; }
|
||
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|
||
.bi-file-word::before { content: "\f3bb"; }
|
||
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|
||
.bi-file-x::before { content: "\f3bd"; }
|
||
.bi-file-zip-fill::before { content: "\f3be"; }
|
||
.bi-file-zip::before { content: "\f3bf"; }
|
||
.bi-file::before { content: "\f3c0"; }
|
||
.bi-files-alt::before { content: "\f3c1"; }
|
||
.bi-files::before { content: "\f3c2"; }
|
||
.bi-film::before { content: "\f3c3"; }
|
||
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|
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.bi-filter-circle::before { content: "\f3c5"; }
|
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.bi-filter-right::before { content: "\f3c7"; }
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.bi-filter-square-fill::before { content: "\f3c8"; }
|
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.bi-filter-square::before { content: "\f3c9"; }
|
||
.bi-filter::before { content: "\f3ca"; }
|
||
.bi-flag-fill::before { content: "\f3cb"; }
|
||
.bi-flag::before { content: "\f3cc"; }
|
||
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|
||
.bi-flower2::before { content: "\f3ce"; }
|
||
.bi-flower3::before { content: "\f3cf"; }
|
||
.bi-folder-check::before { content: "\f3d0"; }
|
||
.bi-folder-fill::before { content: "\f3d1"; }
|
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.bi-folder-minus::before { content: "\f3d2"; }
|
||
.bi-folder-plus::before { content: "\f3d3"; }
|
||
.bi-folder-symlink-fill::before { content: "\f3d4"; }
|
||
.bi-folder-symlink::before { content: "\f3d5"; }
|
||
.bi-folder-x::before { content: "\f3d6"; }
|
||
.bi-folder::before { content: "\f3d7"; }
|
||
.bi-folder2-open::before { content: "\f3d8"; }
|
||
.bi-folder2::before { content: "\f3d9"; }
|
||
.bi-fonts::before { content: "\f3da"; }
|
||
.bi-forward-fill::before { content: "\f3db"; }
|
||
.bi-forward::before { content: "\f3dc"; }
|
||
.bi-front::before { content: "\f3dd"; }
|
||
.bi-fullscreen-exit::before { content: "\f3de"; }
|
||
.bi-fullscreen::before { content: "\f3df"; }
|
||
.bi-funnel-fill::before { content: "\f3e0"; }
|
||
.bi-funnel::before { content: "\f3e1"; }
|
||
.bi-gear-fill::before { content: "\f3e2"; }
|
||
.bi-gear-wide-connected::before { content: "\f3e3"; }
|
||
.bi-gear-wide::before { content: "\f3e4"; }
|
||
.bi-gear::before { content: "\f3e5"; }
|
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.bi-gem::before { content: "\f3e6"; }
|
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.bi-geo-alt-fill::before { content: "\f3e7"; }
|
||
.bi-geo-alt::before { content: "\f3e8"; }
|
||
.bi-geo-fill::before { content: "\f3e9"; }
|
||
.bi-geo::before { content: "\f3ea"; }
|
||
.bi-gift-fill::before { content: "\f3eb"; }
|
||
.bi-gift::before { content: "\f3ec"; }
|
||
.bi-github::before { content: "\f3ed"; }
|
||
.bi-globe::before { content: "\f3ee"; }
|
||
.bi-globe2::before { content: "\f3ef"; }
|
||
.bi-google::before { content: "\f3f0"; }
|
||
.bi-graph-down::before { content: "\f3f1"; }
|
||
.bi-graph-up::before { content: "\f3f2"; }
|
||
.bi-grid-1x2-fill::before { content: "\f3f3"; }
|
||
.bi-grid-1x2::before { content: "\f3f4"; }
|
||
.bi-grid-3x2-gap-fill::before { content: "\f3f5"; }
|
||
.bi-grid-3x2-gap::before { content: "\f3f6"; }
|
||
.bi-grid-3x2::before { content: "\f3f7"; }
|
||
.bi-grid-3x3-gap-fill::before { content: "\f3f8"; }
|
||
.bi-grid-3x3-gap::before { content: "\f3f9"; }
|
||
.bi-grid-3x3::before { content: "\f3fa"; }
|
||
.bi-grid-fill::before { content: "\f3fb"; }
|
||
.bi-grid::before { content: "\f3fc"; }
|
||
.bi-grip-horizontal::before { content: "\f3fd"; }
|
||
.bi-grip-vertical::before { content: "\f3fe"; }
|
||
.bi-hammer::before { content: "\f3ff"; }
|
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.bi-hand-index-fill::before { content: "\f400"; }
|
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.bi-hand-index-thumb-fill::before { content: "\f401"; }
|
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.bi-hand-index-thumb::before { content: "\f402"; }
|
||
.bi-hand-index::before { content: "\f403"; }
|
||
.bi-hand-thumbs-down-fill::before { content: "\f404"; }
|
||
.bi-hand-thumbs-down::before { content: "\f405"; }
|
||
.bi-hand-thumbs-up-fill::before { content: "\f406"; }
|
||
.bi-hand-thumbs-up::before { content: "\f407"; }
|
||
.bi-handbag-fill::before { content: "\f408"; }
|
||
.bi-handbag::before { content: "\f409"; }
|
||
.bi-hash::before { content: "\f40a"; }
|
||
.bi-hdd-fill::before { content: "\f40b"; }
|
||
.bi-hdd-network-fill::before { content: "\f40c"; }
|
||
.bi-hdd-network::before { content: "\f40d"; }
|
||
.bi-hdd-rack-fill::before { content: "\f40e"; }
|
||
.bi-hdd-rack::before { content: "\f40f"; }
|
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.bi-hdd-stack-fill::before { content: "\f410"; }
|
||
.bi-hdd-stack::before { content: "\f411"; }
|
||
.bi-hdd::before { content: "\f412"; }
|
||
.bi-headphones::before { content: "\f413"; }
|
||
.bi-headset::before { content: "\f414"; }
|
||
.bi-heart-fill::before { content: "\f415"; }
|
||
.bi-heart-half::before { content: "\f416"; }
|
||
.bi-heart::before { content: "\f417"; }
|
||
.bi-heptagon-fill::before { content: "\f418"; }
|
||
.bi-heptagon-half::before { content: "\f419"; }
|
||
.bi-heptagon::before { content: "\f41a"; }
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||
.bi-hexagon-fill::before { content: "\f41b"; }
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.bi-hexagon-half::before { content: "\f41c"; }
|
||
.bi-hexagon::before { content: "\f41d"; }
|
||
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|
||
.bi-hourglass-split::before { content: "\f41f"; }
|
||
.bi-hourglass-top::before { content: "\f420"; }
|
||
.bi-hourglass::before { content: "\f421"; }
|
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|
||
.bi-house-door::before { content: "\f423"; }
|
||
.bi-house-fill::before { content: "\f424"; }
|
||
.bi-house::before { content: "\f425"; }
|
||
.bi-hr::before { content: "\f426"; }
|
||
.bi-hurricane::before { content: "\f427"; }
|
||
.bi-image-alt::before { content: "\f428"; }
|
||
.bi-image-fill::before { content: "\f429"; }
|
||
.bi-image::before { content: "\f42a"; }
|
||
.bi-images::before { content: "\f42b"; }
|
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.bi-inbox-fill::before { content: "\f42c"; }
|
||
.bi-inbox::before { content: "\f42d"; }
|
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.bi-inboxes-fill::before { content: "\f42e"; }
|
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.bi-inboxes::before { content: "\f42f"; }
|
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.bi-info-circle-fill::before { content: "\f430"; }
|
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.bi-info-circle::before { content: "\f431"; }
|
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.bi-info-square-fill::before { content: "\f432"; }
|
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.bi-info-square::before { content: "\f433"; }
|
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.bi-info::before { content: "\f434"; }
|
||
.bi-input-cursor-text::before { content: "\f435"; }
|
||
.bi-input-cursor::before { content: "\f436"; }
|
||
.bi-instagram::before { content: "\f437"; }
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||
.bi-intersect::before { content: "\f438"; }
|
||
.bi-journal-album::before { content: "\f439"; }
|
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.bi-journal-arrow-down::before { content: "\f43a"; }
|
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.bi-journal-arrow-up::before { content: "\f43b"; }
|
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.bi-journal-bookmark-fill::before { content: "\f43c"; }
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.bi-journal-bookmark::before { content: "\f43d"; }
|
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.bi-journal-check::before { content: "\f43e"; }
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.bi-journal-code::before { content: "\f43f"; }
|
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.bi-journal-medical::before { content: "\f440"; }
|
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.bi-journal-minus::before { content: "\f441"; }
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.bi-journal-plus::before { content: "\f442"; }
|
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.bi-journal-richtext::before { content: "\f443"; }
|
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.bi-journal-text::before { content: "\f444"; }
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.bi-journal-x::before { content: "\f445"; }
|
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.bi-journal::before { content: "\f446"; }
|
||
.bi-journals::before { content: "\f447"; }
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.bi-justify-right::before { content: "\f44a"; }
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.bi-kanban-fill::before { content: "\f44c"; }
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.bi-kanban::before { content: "\f44d"; }
|
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.bi-key-fill::before { content: "\f44e"; }
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.bi-key::before { content: "\f44f"; }
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.bi-keyboard-fill::before { content: "\f450"; }
|
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.bi-keyboard::before { content: "\f451"; }
|
||
.bi-ladder::before { content: "\f452"; }
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.bi-lamp-fill::before { content: "\f453"; }
|
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.bi-lamp::before { content: "\f454"; }
|
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.bi-laptop-fill::before { content: "\f455"; }
|
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.bi-laptop::before { content: "\f456"; }
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.bi-layer-backward::before { content: "\f457"; }
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.bi-layer-forward::before { content: "\f458"; }
|
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.bi-layers-fill::before { content: "\f459"; }
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.bi-layout-sidebar-inset-reverse::before { content: "\f45c"; }
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.bi-layout-sidebar-inset::before { content: "\f45d"; }
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.bi-layout-sidebar::before { content: "\f45f"; }
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.bi-layout-text-sidebar::before { content: "\f462"; }
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.bi-layout-text-window-reverse::before { content: "\f463"; }
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.bi-layout-text-window::before { content: "\f464"; }
|
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.bi-layout-three-columns::before { content: "\f465"; }
|
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.bi-life-preserver::before { content: "\f467"; }
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.bi-lightbulb-fill::before { content: "\f468"; }
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.bi-lightbulb-off-fill::before { content: "\f469"; }
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|
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.bi-lightbulb::before { content: "\f46b"; }
|
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.bi-lightning-charge-fill::before { content: "\f46c"; }
|
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.bi-lightning-charge::before { content: "\f46d"; }
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.bi-lightning-fill::before { content: "\f46e"; }
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.bi-lightning::before { content: "\f46f"; }
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|
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.bi-link::before { content: "\f471"; }
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.bi-linkedin::before { content: "\f472"; }
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.bi-list-check::before { content: "\f473"; }
|
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|
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.bi-list-stars::before { content: "\f476"; }
|
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.bi-list-task::before { content: "\f477"; }
|
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|
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.bi-list::before { content: "\f479"; }
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.bi-lock-fill::before { content: "\f47a"; }
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.bi-lock::before { content: "\f47b"; }
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.bi-mailbox2::before { content: "\f47d"; }
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.bi-map-fill::before { content: "\f47e"; }
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.bi-map::before { content: "\f47f"; }
|
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.bi-markdown-fill::before { content: "\f480"; }
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.bi-markdown::before { content: "\f481"; }
|
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.bi-megaphone-fill::before { content: "\f483"; }
|
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.bi-megaphone::before { content: "\f484"; }
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.bi-menu-app-fill::before { content: "\f485"; }
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.bi-menu-app::before { content: "\f486"; }
|
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.bi-menu-button-wide::before { content: "\f489"; }
|
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.bi-menu-button::before { content: "\f48a"; }
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.bi-menu-up::before { content: "\f48c"; }
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.bi-mic-fill::before { content: "\f48d"; }
|
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.bi-mic-mute-fill::before { content: "\f48e"; }
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.bi-mic-mute::before { content: "\f48f"; }
|
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.bi-mic::before { content: "\f490"; }
|
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|
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.bi-minecart::before { content: "\f492"; }
|
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|
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.bi-moon-fill::before { content: "\f494"; }
|
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.bi-moon-stars-fill::before { content: "\f495"; }
|
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.bi-moon-stars::before { content: "\f496"; }
|
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.bi-moon::before { content: "\f497"; }
|
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.bi-mouse-fill::before { content: "\f498"; }
|
||
.bi-mouse::before { content: "\f499"; }
|
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.bi-mouse2::before { content: "\f49b"; }
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.bi-mouse3-fill::before { content: "\f49c"; }
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.bi-music-note::before { content: "\f4a0"; }
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.bi-music-player-fill::before { content: "\f4a1"; }
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|
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.bi-node-plus-fill::before { content: "\f4a6"; }
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.bi-palette-fill::before { content: "\f4b0"; }
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.bi-palette::before { content: "\f4b1"; }
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.bi-patch-minus::before { content: "\f4ba"; }
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.bi-patch-plus-fill::before { content: "\f4bb"; }
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.bi-patch-plus::before { content: "\f4bc"; }
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.bi-patch-question::before { content: "\f4be"; }
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.bi-pause-btn::before { content: "\f4c0"; }
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.bi-pause-circle-fill::before { content: "\f4c1"; }
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.bi-pause-circle::before { content: "\f4c2"; }
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.bi-pause-fill::before { content: "\f4c3"; }
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.bi-pause::before { content: "\f4c4"; }
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.bi-pen-fill::before { content: "\f4c7"; }
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.bi-pen::before { content: "\f4c8"; }
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.bi-pencil::before { content: "\f4cb"; }
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.bi-pentagon-fill::before { content: "\f4cc"; }
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.bi-pentagon::before { content: "\f4ce"; }
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.bi-person-badge::before { content: "\f4d3"; }
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.bi-person-check-fill::before { content: "\f4d5"; }
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.bi-person-check::before { content: "\f4d6"; }
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.bi-person-circle::before { content: "\f4d7"; }
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.bi-person-dash-fill::before { content: "\f4d8"; }
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.bi-person-dash::before { content: "\f4d9"; }
|
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.bi-person-fill::before { content: "\f4da"; }
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.bi-person-lines-fill::before { content: "\f4db"; }
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.bi-person-plus-fill::before { content: "\f4dc"; }
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.bi-person-plus::before { content: "\f4dd"; }
|
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.bi-person-x-fill::before { content: "\f4df"; }
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.bi-person-x::before { content: "\f4e0"; }
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.bi-person::before { content: "\f4e1"; }
|
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||
.bi-phone-landscape-fill::before { content: "\f4e3"; }
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||
.bi-phone-landscape::before { content: "\f4e4"; }
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.bi-phone-vibrate::before { content: "\f4e6"; }
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.bi-phone::before { content: "\f4e7"; }
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.bi-pie-chart-fill::before { content: "\f4e8"; }
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.bi-pie-chart::before { content: "\f4e9"; }
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.bi-pin-angle-fill::before { content: "\f4ea"; }
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.bi-pin-angle::before { content: "\f4eb"; }
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.bi-pin-fill::before { content: "\f4ec"; }
|
||
.bi-pin::before { content: "\f4ed"; }
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.bi-pip-fill::before { content: "\f4ee"; }
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.bi-pip::before { content: "\f4ef"; }
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.bi-play-btn-fill::before { content: "\f4f0"; }
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.bi-play-btn::before { content: "\f4f1"; }
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.bi-play-circle-fill::before { content: "\f4f2"; }
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.bi-play-circle::before { content: "\f4f3"; }
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.bi-play-fill::before { content: "\f4f4"; }
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.bi-play::before { content: "\f4f5"; }
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.bi-plug-fill::before { content: "\f4f6"; }
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.bi-plug::before { content: "\f4f7"; }
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||
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.bi-plus-circle-fill::before { content: "\f4f9"; }
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.bi-plus-circle::before { content: "\f4fa"; }
|
||
.bi-plus-square-dotted::before { content: "\f4fb"; }
|
||
.bi-plus-square-fill::before { content: "\f4fc"; }
|
||
.bi-plus-square::before { content: "\f4fd"; }
|
||
.bi-plus::before { content: "\f4fe"; }
|
||
.bi-power::before { content: "\f4ff"; }
|
||
.bi-printer-fill::before { content: "\f500"; }
|
||
.bi-printer::before { content: "\f501"; }
|
||
.bi-puzzle-fill::before { content: "\f502"; }
|
||
.bi-puzzle::before { content: "\f503"; }
|
||
.bi-question-circle-fill::before { content: "\f504"; }
|
||
.bi-question-circle::before { content: "\f505"; }
|
||
.bi-question-diamond-fill::before { content: "\f506"; }
|
||
.bi-question-diamond::before { content: "\f507"; }
|
||
.bi-question-octagon-fill::before { content: "\f508"; }
|
||
.bi-question-octagon::before { content: "\f509"; }
|
||
.bi-question-square-fill::before { content: "\f50a"; }
|
||
.bi-question-square::before { content: "\f50b"; }
|
||
.bi-question::before { content: "\f50c"; }
|
||
.bi-rainbow::before { content: "\f50d"; }
|
||
.bi-receipt-cutoff::before { content: "\f50e"; }
|
||
.bi-receipt::before { content: "\f50f"; }
|
||
.bi-reception-0::before { content: "\f510"; }
|
||
.bi-reception-1::before { content: "\f511"; }
|
||
.bi-reception-2::before { content: "\f512"; }
|
||
.bi-reception-3::before { content: "\f513"; }
|
||
.bi-reception-4::before { content: "\f514"; }
|
||
.bi-record-btn-fill::before { content: "\f515"; }
|
||
.bi-record-btn::before { content: "\f516"; }
|
||
.bi-record-circle-fill::before { content: "\f517"; }
|
||
.bi-record-circle::before { content: "\f518"; }
|
||
.bi-record-fill::before { content: "\f519"; }
|
||
.bi-record::before { content: "\f51a"; }
|
||
.bi-record2-fill::before { content: "\f51b"; }
|
||
.bi-record2::before { content: "\f51c"; }
|
||
.bi-reply-all-fill::before { content: "\f51d"; }
|
||
.bi-reply-all::before { content: "\f51e"; }
|
||
.bi-reply-fill::before { content: "\f51f"; }
|
||
.bi-reply::before { content: "\f520"; }
|
||
.bi-rss-fill::before { content: "\f521"; }
|
||
.bi-rss::before { content: "\f522"; }
|
||
.bi-rulers::before { content: "\f523"; }
|
||
.bi-save-fill::before { content: "\f524"; }
|
||
.bi-save::before { content: "\f525"; }
|
||
.bi-save2-fill::before { content: "\f526"; }
|
||
.bi-save2::before { content: "\f527"; }
|
||
.bi-scissors::before { content: "\f528"; }
|
||
.bi-screwdriver::before { content: "\f529"; }
|
||
.bi-search::before { content: "\f52a"; }
|
||
.bi-segmented-nav::before { content: "\f52b"; }
|
||
.bi-server::before { content: "\f52c"; }
|
||
.bi-share-fill::before { content: "\f52d"; }
|
||
.bi-share::before { content: "\f52e"; }
|
||
.bi-shield-check::before { content: "\f52f"; }
|
||
.bi-shield-exclamation::before { content: "\f530"; }
|
||
.bi-shield-fill-check::before { content: "\f531"; }
|
||
.bi-shield-fill-exclamation::before { content: "\f532"; }
|
||
.bi-shield-fill-minus::before { content: "\f533"; }
|
||
.bi-shield-fill-plus::before { content: "\f534"; }
|
||
.bi-shield-fill-x::before { content: "\f535"; }
|
||
.bi-shield-fill::before { content: "\f536"; }
|
||
.bi-shield-lock-fill::before { content: "\f537"; }
|
||
.bi-shield-lock::before { content: "\f538"; }
|
||
.bi-shield-minus::before { content: "\f539"; }
|
||
.bi-shield-plus::before { content: "\f53a"; }
|
||
.bi-shield-shaded::before { content: "\f53b"; }
|
||
.bi-shield-slash-fill::before { content: "\f53c"; }
|
||
.bi-shield-slash::before { content: "\f53d"; }
|
||
.bi-shield-x::before { content: "\f53e"; }
|
||
.bi-shield::before { content: "\f53f"; }
|
||
.bi-shift-fill::before { content: "\f540"; }
|
||
.bi-shift::before { content: "\f541"; }
|
||
.bi-shop-window::before { content: "\f542"; }
|
||
.bi-shop::before { content: "\f543"; }
|
||
.bi-shuffle::before { content: "\f544"; }
|
||
.bi-signpost-2-fill::before { content: "\f545"; }
|
||
.bi-signpost-2::before { content: "\f546"; }
|
||
.bi-signpost-fill::before { content: "\f547"; }
|
||
.bi-signpost-split-fill::before { content: "\f548"; }
|
||
.bi-signpost-split::before { content: "\f549"; }
|
||
.bi-signpost::before { content: "\f54a"; }
|
||
.bi-sim-fill::before { content: "\f54b"; }
|
||
.bi-sim::before { content: "\f54c"; }
|
||
.bi-skip-backward-btn-fill::before { content: "\f54d"; }
|
||
.bi-skip-backward-btn::before { content: "\f54e"; }
|
||
.bi-skip-backward-circle-fill::before { content: "\f54f"; }
|
||
.bi-skip-backward-circle::before { content: "\f550"; }
|
||
.bi-skip-backward-fill::before { content: "\f551"; }
|
||
.bi-skip-backward::before { content: "\f552"; }
|
||
.bi-skip-end-btn-fill::before { content: "\f553"; }
|
||
.bi-skip-end-btn::before { content: "\f554"; }
|
||
.bi-skip-end-circle-fill::before { content: "\f555"; }
|
||
.bi-skip-end-circle::before { content: "\f556"; }
|
||
.bi-skip-end-fill::before { content: "\f557"; }
|
||
.bi-skip-end::before { content: "\f558"; }
|
||
.bi-skip-forward-btn-fill::before { content: "\f559"; }
|
||
.bi-skip-forward-btn::before { content: "\f55a"; }
|
||
.bi-skip-forward-circle-fill::before { content: "\f55b"; }
|
||
.bi-skip-forward-circle::before { content: "\f55c"; }
|
||
.bi-skip-forward-fill::before { content: "\f55d"; }
|
||
.bi-skip-forward::before { content: "\f55e"; }
|
||
.bi-skip-start-btn-fill::before { content: "\f55f"; }
|
||
.bi-skip-start-btn::before { content: "\f560"; }
|
||
.bi-skip-start-circle-fill::before { content: "\f561"; }
|
||
.bi-skip-start-circle::before { content: "\f562"; }
|
||
.bi-skip-start-fill::before { content: "\f563"; }
|
||
.bi-skip-start::before { content: "\f564"; }
|
||
.bi-slack::before { content: "\f565"; }
|
||
.bi-slash-circle-fill::before { content: "\f566"; }
|
||
.bi-slash-circle::before { content: "\f567"; }
|
||
.bi-slash-square-fill::before { content: "\f568"; }
|
||
.bi-slash-square::before { content: "\f569"; }
|
||
.bi-slash::before { content: "\f56a"; }
|
||
.bi-sliders::before { content: "\f56b"; }
|
||
.bi-smartwatch::before { content: "\f56c"; }
|
||
.bi-snow::before { content: "\f56d"; }
|
||
.bi-snow2::before { content: "\f56e"; }
|
||
.bi-snow3::before { content: "\f56f"; }
|
||
.bi-sort-alpha-down-alt::before { content: "\f570"; }
|
||
.bi-sort-alpha-down::before { content: "\f571"; }
|
||
.bi-sort-alpha-up-alt::before { content: "\f572"; }
|
||
.bi-sort-alpha-up::before { content: "\f573"; }
|
||
.bi-sort-down-alt::before { content: "\f574"; }
|
||
.bi-sort-down::before { content: "\f575"; }
|
||
.bi-sort-numeric-down-alt::before { content: "\f576"; }
|
||
.bi-sort-numeric-down::before { content: "\f577"; }
|
||
.bi-sort-numeric-up-alt::before { content: "\f578"; }
|
||
.bi-sort-numeric-up::before { content: "\f579"; }
|
||
.bi-sort-up-alt::before { content: "\f57a"; }
|
||
.bi-sort-up::before { content: "\f57b"; }
|
||
.bi-soundwave::before { content: "\f57c"; }
|
||
.bi-speaker-fill::before { content: "\f57d"; }
|
||
.bi-speaker::before { content: "\f57e"; }
|
||
.bi-speedometer::before { content: "\f57f"; }
|
||
.bi-speedometer2::before { content: "\f580"; }
|
||
.bi-spellcheck::before { content: "\f581"; }
|
||
.bi-square-fill::before { content: "\f582"; }
|
||
.bi-square-half::before { content: "\f583"; }
|
||
.bi-square::before { content: "\f584"; }
|
||
.bi-stack::before { content: "\f585"; }
|
||
.bi-star-fill::before { content: "\f586"; }
|
||
.bi-star-half::before { content: "\f587"; }
|
||
.bi-star::before { content: "\f588"; }
|
||
.bi-stars::before { content: "\f589"; }
|
||
.bi-stickies-fill::before { content: "\f58a"; }
|
||
.bi-stickies::before { content: "\f58b"; }
|
||
.bi-sticky-fill::before { content: "\f58c"; }
|
||
.bi-sticky::before { content: "\f58d"; }
|
||
.bi-stop-btn-fill::before { content: "\f58e"; }
|
||
.bi-stop-btn::before { content: "\f58f"; }
|
||
.bi-stop-circle-fill::before { content: "\f590"; }
|
||
.bi-stop-circle::before { content: "\f591"; }
|
||
.bi-stop-fill::before { content: "\f592"; }
|
||
.bi-stop::before { content: "\f593"; }
|
||
.bi-stoplights-fill::before { content: "\f594"; }
|
||
.bi-stoplights::before { content: "\f595"; }
|
||
.bi-stopwatch-fill::before { content: "\f596"; }
|
||
.bi-stopwatch::before { content: "\f597"; }
|
||
.bi-subtract::before { content: "\f598"; }
|
||
.bi-suit-club-fill::before { content: "\f599"; }
|
||
.bi-suit-club::before { content: "\f59a"; }
|
||
.bi-suit-diamond-fill::before { content: "\f59b"; }
|
||
.bi-suit-diamond::before { content: "\f59c"; }
|
||
.bi-suit-heart-fill::before { content: "\f59d"; }
|
||
.bi-suit-heart::before { content: "\f59e"; }
|
||
.bi-suit-spade-fill::before { content: "\f59f"; }
|
||
.bi-suit-spade::before { content: "\f5a0"; }
|
||
.bi-sun-fill::before { content: "\f5a1"; }
|
||
.bi-sun::before { content: "\f5a2"; }
|
||
.bi-sunglasses::before { content: "\f5a3"; }
|
||
.bi-sunrise-fill::before { content: "\f5a4"; }
|
||
.bi-sunrise::before { content: "\f5a5"; }
|
||
.bi-sunset-fill::before { content: "\f5a6"; }
|
||
.bi-sunset::before { content: "\f5a7"; }
|
||
.bi-symmetry-horizontal::before { content: "\f5a8"; }
|
||
.bi-symmetry-vertical::before { content: "\f5a9"; }
|
||
.bi-table::before { content: "\f5aa"; }
|
||
.bi-tablet-fill::before { content: "\f5ab"; }
|
||
.bi-tablet-landscape-fill::before { content: "\f5ac"; }
|
||
.bi-tablet-landscape::before { content: "\f5ad"; }
|
||
.bi-tablet::before { content: "\f5ae"; }
|
||
.bi-tag-fill::before { content: "\f5af"; }
|
||
.bi-tag::before { content: "\f5b0"; }
|
||
.bi-tags-fill::before { content: "\f5b1"; }
|
||
.bi-tags::before { content: "\f5b2"; }
|
||
.bi-telegram::before { content: "\f5b3"; }
|
||
.bi-telephone-fill::before { content: "\f5b4"; }
|
||
.bi-telephone-forward-fill::before { content: "\f5b5"; }
|
||
.bi-telephone-forward::before { content: "\f5b6"; }
|
||
.bi-telephone-inbound-fill::before { content: "\f5b7"; }
|
||
.bi-telephone-inbound::before { content: "\f5b8"; }
|
||
.bi-telephone-minus-fill::before { content: "\f5b9"; }
|
||
.bi-telephone-minus::before { content: "\f5ba"; }
|
||
.bi-telephone-outbound-fill::before { content: "\f5bb"; }
|
||
.bi-telephone-outbound::before { content: "\f5bc"; }
|
||
.bi-telephone-plus-fill::before { content: "\f5bd"; }
|
||
.bi-telephone-plus::before { content: "\f5be"; }
|
||
.bi-telephone-x-fill::before { content: "\f5bf"; }
|
||
.bi-telephone-x::before { content: "\f5c0"; }
|
||
.bi-telephone::before { content: "\f5c1"; }
|
||
.bi-terminal-fill::before { content: "\f5c2"; }
|
||
.bi-terminal::before { content: "\f5c3"; }
|
||
.bi-text-center::before { content: "\f5c4"; }
|
||
.bi-text-indent-left::before { content: "\f5c5"; }
|
||
.bi-text-indent-right::before { content: "\f5c6"; }
|
||
.bi-text-left::before { content: "\f5c7"; }
|
||
.bi-text-paragraph::before { content: "\f5c8"; }
|
||
.bi-text-right::before { content: "\f5c9"; }
|
||
.bi-textarea-resize::before { content: "\f5ca"; }
|
||
.bi-textarea-t::before { content: "\f5cb"; }
|
||
.bi-textarea::before { content: "\f5cc"; }
|
||
.bi-thermometer-half::before { content: "\f5cd"; }
|
||
.bi-thermometer-high::before { content: "\f5ce"; }
|
||
.bi-thermometer-low::before { content: "\f5cf"; }
|
||
.bi-thermometer-snow::before { content: "\f5d0"; }
|
||
.bi-thermometer-sun::before { content: "\f5d1"; }
|
||
.bi-thermometer::before { content: "\f5d2"; }
|
||
.bi-three-dots-vertical::before { content: "\f5d3"; }
|
||
.bi-three-dots::before { content: "\f5d4"; }
|
||
.bi-toggle-off::before { content: "\f5d5"; }
|
||
.bi-toggle-on::before { content: "\f5d6"; }
|
||
.bi-toggle2-off::before { content: "\f5d7"; }
|
||
.bi-toggle2-on::before { content: "\f5d8"; }
|
||
.bi-toggles::before { content: "\f5d9"; }
|
||
.bi-toggles2::before { content: "\f5da"; }
|
||
.bi-tools::before { content: "\f5db"; }
|
||
.bi-tornado::before { content: "\f5dc"; }
|
||
.bi-trash-fill::before { content: "\f5dd"; }
|
||
.bi-trash::before { content: "\f5de"; }
|
||
.bi-trash2-fill::before { content: "\f5df"; }
|
||
.bi-trash2::before { content: "\f5e0"; }
|
||
.bi-tree-fill::before { content: "\f5e1"; }
|
||
.bi-tree::before { content: "\f5e2"; }
|
||
.bi-triangle-fill::before { content: "\f5e3"; }
|
||
.bi-triangle-half::before { content: "\f5e4"; }
|
||
.bi-triangle::before { content: "\f5e5"; }
|
||
.bi-trophy-fill::before { content: "\f5e6"; }
|
||
.bi-trophy::before { content: "\f5e7"; }
|
||
.bi-tropical-storm::before { content: "\f5e8"; }
|
||
.bi-truck-flatbed::before { content: "\f5e9"; }
|
||
.bi-truck::before { content: "\f5ea"; }
|
||
.bi-tsunami::before { content: "\f5eb"; }
|
||
.bi-tv-fill::before { content: "\f5ec"; }
|
||
.bi-tv::before { content: "\f5ed"; }
|
||
.bi-twitch::before { content: "\f5ee"; }
|
||
.bi-twitter::before { content: "\f5ef"; }
|
||
.bi-type-bold::before { content: "\f5f0"; }
|
||
.bi-type-h1::before { content: "\f5f1"; }
|
||
.bi-type-h2::before { content: "\f5f2"; }
|
||
.bi-type-h3::before { content: "\f5f3"; }
|
||
.bi-type-italic::before { content: "\f5f4"; }
|
||
.bi-type-strikethrough::before { content: "\f5f5"; }
|
||
.bi-type-underline::before { content: "\f5f6"; }
|
||
.bi-type::before { content: "\f5f7"; }
|
||
.bi-ui-checks-grid::before { content: "\f5f8"; }
|
||
.bi-ui-checks::before { content: "\f5f9"; }
|
||
.bi-ui-radios-grid::before { content: "\f5fa"; }
|
||
.bi-ui-radios::before { content: "\f5fb"; }
|
||
.bi-umbrella-fill::before { content: "\f5fc"; }
|
||
.bi-umbrella::before { content: "\f5fd"; }
|
||
.bi-union::before { content: "\f5fe"; }
|
||
.bi-unlock-fill::before { content: "\f5ff"; }
|
||
.bi-unlock::before { content: "\f600"; }
|
||
.bi-upc-scan::before { content: "\f601"; }
|
||
.bi-upc::before { content: "\f602"; }
|
||
.bi-upload::before { content: "\f603"; }
|
||
.bi-vector-pen::before { content: "\f604"; }
|
||
.bi-view-list::before { content: "\f605"; }
|
||
.bi-view-stacked::before { content: "\f606"; }
|
||
.bi-vinyl-fill::before { content: "\f607"; }
|
||
.bi-vinyl::before { content: "\f608"; }
|
||
.bi-voicemail::before { content: "\f609"; }
|
||
.bi-volume-down-fill::before { content: "\f60a"; }
|
||
.bi-volume-down::before { content: "\f60b"; }
|
||
.bi-volume-mute-fill::before { content: "\f60c"; }
|
||
.bi-volume-mute::before { content: "\f60d"; }
|
||
.bi-volume-off-fill::before { content: "\f60e"; }
|
||
.bi-volume-off::before { content: "\f60f"; }
|
||
.bi-volume-up-fill::before { content: "\f610"; }
|
||
.bi-volume-up::before { content: "\f611"; }
|
||
.bi-vr::before { content: "\f612"; }
|
||
.bi-wallet-fill::before { content: "\f613"; }
|
||
.bi-wallet::before { content: "\f614"; }
|
||
.bi-wallet2::before { content: "\f615"; }
|
||
.bi-watch::before { content: "\f616"; }
|
||
.bi-water::before { content: "\f617"; }
|
||
.bi-whatsapp::before { content: "\f618"; }
|
||
.bi-wifi-1::before { content: "\f619"; }
|
||
.bi-wifi-2::before { content: "\f61a"; }
|
||
.bi-wifi-off::before { content: "\f61b"; }
|
||
.bi-wifi::before { content: "\f61c"; }
|
||
.bi-wind::before { content: "\f61d"; }
|
||
.bi-window-dock::before { content: "\f61e"; }
|
||
.bi-window-sidebar::before { content: "\f61f"; }
|
||
.bi-window::before { content: "\f620"; }
|
||
.bi-wrench::before { content: "\f621"; }
|
||
.bi-x-circle-fill::before { content: "\f622"; }
|
||
.bi-x-circle::before { content: "\f623"; }
|
||
.bi-x-diamond-fill::before { content: "\f624"; }
|
||
.bi-x-diamond::before { content: "\f625"; }
|
||
.bi-x-octagon-fill::before { content: "\f626"; }
|
||
.bi-x-octagon::before { content: "\f627"; }
|
||
.bi-x-square-fill::before { content: "\f628"; }
|
||
.bi-x-square::before { content: "\f629"; }
|
||
.bi-x::before { content: "\f62a"; }
|
||
.bi-youtube::before { content: "\f62b"; }
|
||
.bi-zoom-in::before { content: "\f62c"; }
|
||
.bi-zoom-out::before { content: "\f62d"; }
|
||
.bi-bank::before { content: "\f62e"; }
|
||
.bi-bank2::before { content: "\f62f"; }
|
||
.bi-bell-slash-fill::before { content: "\f630"; }
|
||
.bi-bell-slash::before { content: "\f631"; }
|
||
.bi-cash-coin::before { content: "\f632"; }
|
||
.bi-check-lg::before { content: "\f633"; }
|
||
.bi-coin::before { content: "\f634"; }
|
||
.bi-currency-bitcoin::before { content: "\f635"; }
|
||
.bi-currency-dollar::before { content: "\f636"; }
|
||
.bi-currency-euro::before { content: "\f637"; }
|
||
.bi-currency-exchange::before { content: "\f638"; }
|
||
.bi-currency-pound::before { content: "\f639"; }
|
||
.bi-currency-yen::before { content: "\f63a"; }
|
||
.bi-dash-lg::before { content: "\f63b"; }
|
||
.bi-exclamation-lg::before { content: "\f63c"; }
|
||
.bi-file-earmark-pdf-fill::before { content: "\f63d"; }
|
||
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||
.bi-phone-flip::before { content: "\f77c"; }
|
||
.bi-plugin::before { content: "\f77d"; }
|
||
.bi-postage-fill::before { content: "\f77e"; }
|
||
.bi-postage-heart-fill::before { content: "\f77f"; }
|
||
.bi-postage-heart::before { content: "\f780"; }
|
||
.bi-postage::before { content: "\f781"; }
|
||
.bi-postcard-fill::before { content: "\f782"; }
|
||
.bi-postcard-heart-fill::before { content: "\f783"; }
|
||
.bi-postcard-heart::before { content: "\f784"; }
|
||
.bi-postcard::before { content: "\f785"; }
|
||
.bi-search-heart-fill::before { content: "\f786"; }
|
||
.bi-search-heart::before { content: "\f787"; }
|
||
.bi-sliders2-vertical::before { content: "\f788"; }
|
||
.bi-sliders2::before { content: "\f789"; }
|
||
.bi-trash3-fill::before { content: "\f78a"; }
|
||
.bi-trash3::before { content: "\f78b"; }
|
||
.bi-valentine::before { content: "\f78c"; }
|
||
.bi-valentine2::before { content: "\f78d"; }
|
||
.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
|
||
.bi-wrench-adjustable-circle::before { content: "\f78f"; }
|
||
.bi-wrench-adjustable::before { content: "\f790"; }
|
||
.bi-filetype-json::before { content: "\f791"; }
|
||
.bi-filetype-pptx::before { content: "\f792"; }
|
||
.bi-filetype-xlsx::before { content: "\f793"; }
|
||
</style>
|
||
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bottom%3A0%7D%23title%2Dblock%2Dheader%2Equarto%2Dtitle%2Dblock%2Edefault%20%2Edescription%20%2Eabstract%2Dtitle%2C%23title%2Dblock%2Dheader%2Equarto%2Dtitle%2Dblock%2Edefault%20%2Eabstract%20%2Eabstract%2Dtitle%7Bmargin%2Dtop%3A1em%3Btext%2Dtransform%3Auppercase%3Bfont%2Dsize%3A%2E8em%3Bopacity%3A%2E8%3Bfont%2Dweight%3A400%7D%23title%2Dblock%2Dheader%2Equarto%2Dtitle%2Dblock%2Edefault%20%2Equarto%2Dtitle%2Dmeta%2Dauthor%7Bdisplay%3Agrid%3Bgrid%2Dtemplate%2Dcolumns%3A1fr%201fr%7D%0A" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
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</head>
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<body>
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<header id="title-block-header" class="quarto-title-block default page-columns page-full">
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<div class="quarto-title-banner page-columns page-full">
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<div class="quarto-title column-body">
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<div class="quarto-title-block"><div><h1 class="title">Drivers of Inequality</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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</div>
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<div class="quarto-title-meta">
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<div>
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<div class="quarto-title-meta-heading">Author</div>
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<div class="quarto-title-meta-contents">
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<p>Marty Oehme </p>
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</div>
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</div>
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<div>
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<div class="quarto-title-meta-heading">Published</div>
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<div class="quarto-title-meta-contents">
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<p class="date">Invalid Date</p>
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</div>
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</div>
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</div>
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</header><div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
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<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
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<nav id="TOC" role="doc-toc" class="toc-active">
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<h2 id="toc-title">Table of contents</h2>
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<ul>
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<li><a href="#vietnam" id="toc-vietnam" class="nav-link active" data-scroll-target="#vietnam">Vietnam</a>
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<ul class="collapse">
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<li><a href="#natural-disasters-and-inequalities" id="toc-natural-disasters-and-inequalities" class="nav-link" data-scroll-target="#natural-disasters-and-inequalities">Natural disasters and inequalities</a></li>
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<li><a href="#development-assistance-to-vietnam" id="toc-development-assistance-to-vietnam" class="nav-link" data-scroll-target="#development-assistance-to-vietnam">Development assistance to Vietnam</a></li>
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</ul></li>
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<li><a href="#uganda" id="toc-uganda" class="nav-link" data-scroll-target="#uganda">Uganda</a>
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<ul class="collapse">
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<li><a href="#inequalities-in-access-to-drinking-water" id="toc-inequalities-in-access-to-drinking-water" class="nav-link" data-scroll-target="#inequalities-in-access-to-drinking-water">Inequalities in access to drinking water</a></li>
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<li><a href="#development-assistance-to-uganda" id="toc-development-assistance-to-uganda" class="nav-link" data-scroll-target="#development-assistance-to-uganda">Development assistance to Uganda</a></li>
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</ul></li>
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<li><a href="#benin" id="toc-benin" class="nav-link" data-scroll-target="#benin">Benin</a>
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<ul class="collapse">
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||
<li><a href="#inequalities-in-access-to-electricity" id="toc-inequalities-in-access-to-electricity" class="nav-link" data-scroll-target="#inequalities-in-access-to-electricity">Inequalities in access to electricity</a></li>
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||
<li><a href="#development-assistance-to-benin" id="toc-development-assistance-to-benin" class="nav-link" data-scroll-target="#development-assistance-to-benin">Development assistance to Benin</a></li>
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||
</ul></li>
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||
<li><a href="#djibouti-ethiopia" id="toc-djibouti-ethiopia" class="nav-link" data-scroll-target="#djibouti-ethiopia">Djibouti-Ethiopia</a>
|
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<ul class="collapse">
|
||
<li><a href="#gender-inequalities-in-livelihood-opportunities" id="toc-gender-inequalities-in-livelihood-opportunities" class="nav-link" data-scroll-target="#gender-inequalities-in-livelihood-opportunities">Gender inequalities in livelihood opportunities</a></li>
|
||
<li><a href="#development-assistance-to-djibouti" id="toc-development-assistance-to-djibouti" class="nav-link" data-scroll-target="#development-assistance-to-djibouti">Development assistance to Djibouti</a></li>
|
||
</ul></li>
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||
<li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
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||
</ul>
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</nav>
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</div>
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<main class="content quarto-banner-title-block page-columns page-full" id="quarto-document-content">
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<div class="cell" data-execution_count="4">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> IPython.display <span class="im">import</span> Markdown</span>
|
||
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tabulate <span class="im">import</span> tabulate</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell" data-execution_count="5">
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<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up the data extraction and figure drawing functions</span></span>
|
||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
|
||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.io <span class="im">as</span> pio</span>
|
||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> gini_plot(country_df):</span>
|
||
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> svg_render:</span>
|
||
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> pio.renderers.default <span class="op">=</span> <span class="st">"png"</span></span>
|
||
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a> fig <span class="op">=</span> px.line(country_df, x<span class="op">=</span><span class="st">"year"</span>, y<span class="op">=</span><span class="st">"gini"</span>, markers<span class="op">=</span><span class="va">True</span>, labels<span class="op">=</span>{<span class="st">"year"</span>: <span class="st">"Year"</span>, <span class="st">"gini"</span>: <span class="st">"Gini coefficient"</span>}, template<span class="op">=</span><span class="st">"seaborn"</span>, range_y<span class="op">=</span>[<span class="dv">0</span>,<span class="dv">100</span>])</span>
|
||
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a> fig.update_traces(marker_size<span class="op">=</span><span class="dv">10</span>)</span>
|
||
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> fig.show()</span>
|
||
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_consumption_gini_percapita(country_df):</span>
|
||
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> country_df[country_df[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a> gini_plot(gni_cnsmpt_percapita)</span>
|
||
<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_consumption_gini_percapita_ruralurban(country_df):</span>
|
||
<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> country_df[country_df[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'source'</span>].<span class="bu">str</span>.contains(<span class="st">"World Bank"</span>)]</span>
|
||
<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'areacovr'</span>].<span class="bu">str</span>.contains(<span class="st">"All"</span>)]</span>
|
||
<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a> gini_plot(gni_cnsmpt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell" data-execution_count="6">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co">## Set up functions to grab development aids by type of donating body</span></span>
|
||
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co">## ODA donor type map, see DAC code sheet xlsx</span></span>
|
||
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>donortypes <span class="op">=</span> {</span>
|
||
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> <span class="dv">2</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> <span class="dv">3</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> <span class="dv">4</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> <span class="dv">5</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a> <span class="dv">6</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a> <span class="dv">7</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> <span class="dv">8</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> <span class="dv">9</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> <span class="dv">10</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> <span class="dv">11</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> <span class="dv">12</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a> <span class="dv">18</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> <span class="dv">20</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> <span class="dv">21</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a> <span class="dv">22</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a> <span class="dv">40</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a> <span class="dv">50</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a> <span class="dv">61</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a> <span class="dv">68</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a> <span class="dv">69</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a> <span class="dv">75</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a> <span class="dv">76</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a> <span class="dv">301</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a> <span class="dv">302</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a> <span class="dv">701</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a> <span class="dv">742</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a> <span class="dv">801</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a> <span class="dv">820</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a> <span class="dv">918</span>: <span class="st">'dac'</span>,</span>
|
||
<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a> <span class="dv">104</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a> <span class="dv">807</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a> <span class="dv">811</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a> <span class="dv">812</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a> <span class="dv">901</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a> <span class="dv">902</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a> <span class="dv">903</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a> <span class="dv">905</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a> <span class="dv">906</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a> <span class="dv">907</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a> <span class="dv">909</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a> <span class="dv">913</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-46"><a href="#cb3-46" aria-hidden="true" tabindex="-1"></a> <span class="dv">914</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-47"><a href="#cb3-47" aria-hidden="true" tabindex="-1"></a> <span class="dv">915</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-48"><a href="#cb3-48" aria-hidden="true" tabindex="-1"></a> <span class="dv">921</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-49"><a href="#cb3-49" aria-hidden="true" tabindex="-1"></a> <span class="dv">923</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-50"><a href="#cb3-50" aria-hidden="true" tabindex="-1"></a> <span class="dv">926</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-51"><a href="#cb3-51" aria-hidden="true" tabindex="-1"></a> <span class="dv">928</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-52"><a href="#cb3-52" aria-hidden="true" tabindex="-1"></a> <span class="dv">932</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-53"><a href="#cb3-53" aria-hidden="true" tabindex="-1"></a> <span class="dv">940</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-54"><a href="#cb3-54" aria-hidden="true" tabindex="-1"></a> <span class="dv">944</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-55"><a href="#cb3-55" aria-hidden="true" tabindex="-1"></a> <span class="dv">948</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-56"><a href="#cb3-56" aria-hidden="true" tabindex="-1"></a> <span class="dv">951</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-57"><a href="#cb3-57" aria-hidden="true" tabindex="-1"></a> <span class="dv">952</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-58"><a href="#cb3-58" aria-hidden="true" tabindex="-1"></a> <span class="dv">953</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-59"><a href="#cb3-59" aria-hidden="true" tabindex="-1"></a> <span class="dv">954</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-60"><a href="#cb3-60" aria-hidden="true" tabindex="-1"></a> <span class="dv">956</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-61"><a href="#cb3-61" aria-hidden="true" tabindex="-1"></a> <span class="dv">958</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-62"><a href="#cb3-62" aria-hidden="true" tabindex="-1"></a> <span class="dv">959</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-63"><a href="#cb3-63" aria-hidden="true" tabindex="-1"></a> <span class="dv">960</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-64"><a href="#cb3-64" aria-hidden="true" tabindex="-1"></a> <span class="dv">963</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-65"><a href="#cb3-65" aria-hidden="true" tabindex="-1"></a> <span class="dv">964</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-66"><a href="#cb3-66" aria-hidden="true" tabindex="-1"></a> <span class="dv">966</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-67"><a href="#cb3-67" aria-hidden="true" tabindex="-1"></a> <span class="dv">967</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-68"><a href="#cb3-68" aria-hidden="true" tabindex="-1"></a> <span class="dv">971</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-69"><a href="#cb3-69" aria-hidden="true" tabindex="-1"></a> <span class="dv">974</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-70"><a href="#cb3-70" aria-hidden="true" tabindex="-1"></a> <span class="dv">976</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-71"><a href="#cb3-71" aria-hidden="true" tabindex="-1"></a> <span class="dv">978</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-72"><a href="#cb3-72" aria-hidden="true" tabindex="-1"></a> <span class="dv">979</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-73"><a href="#cb3-73" aria-hidden="true" tabindex="-1"></a> <span class="dv">980</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-74"><a href="#cb3-74" aria-hidden="true" tabindex="-1"></a> <span class="dv">981</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-75"><a href="#cb3-75" aria-hidden="true" tabindex="-1"></a> <span class="dv">982</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-76"><a href="#cb3-76" aria-hidden="true" tabindex="-1"></a> <span class="dv">983</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-77"><a href="#cb3-77" aria-hidden="true" tabindex="-1"></a> <span class="dv">988</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-78"><a href="#cb3-78" aria-hidden="true" tabindex="-1"></a> <span class="dv">990</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-79"><a href="#cb3-79" aria-hidden="true" tabindex="-1"></a> <span class="dv">992</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-80"><a href="#cb3-80" aria-hidden="true" tabindex="-1"></a> <span class="dv">997</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-81"><a href="#cb3-81" aria-hidden="true" tabindex="-1"></a> <span class="dv">1011</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-82"><a href="#cb3-82" aria-hidden="true" tabindex="-1"></a> <span class="dv">1012</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-83"><a href="#cb3-83" aria-hidden="true" tabindex="-1"></a> <span class="dv">1013</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-84"><a href="#cb3-84" aria-hidden="true" tabindex="-1"></a> <span class="dv">1014</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-85"><a href="#cb3-85" aria-hidden="true" tabindex="-1"></a> <span class="dv">1015</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-86"><a href="#cb3-86" aria-hidden="true" tabindex="-1"></a> <span class="dv">1016</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-87"><a href="#cb3-87" aria-hidden="true" tabindex="-1"></a> <span class="dv">1017</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-88"><a href="#cb3-88" aria-hidden="true" tabindex="-1"></a> <span class="dv">1018</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-89"><a href="#cb3-89" aria-hidden="true" tabindex="-1"></a> <span class="dv">1019</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-90"><a href="#cb3-90" aria-hidden="true" tabindex="-1"></a> <span class="dv">1020</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-91"><a href="#cb3-91" aria-hidden="true" tabindex="-1"></a> <span class="dv">1023</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-92"><a href="#cb3-92" aria-hidden="true" tabindex="-1"></a> <span class="dv">1024</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-93"><a href="#cb3-93" aria-hidden="true" tabindex="-1"></a> <span class="dv">1025</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-94"><a href="#cb3-94" aria-hidden="true" tabindex="-1"></a> <span class="dv">1037</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-95"><a href="#cb3-95" aria-hidden="true" tabindex="-1"></a> <span class="dv">1038</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-96"><a href="#cb3-96" aria-hidden="true" tabindex="-1"></a> <span class="dv">1311</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-97"><a href="#cb3-97" aria-hidden="true" tabindex="-1"></a> <span class="dv">1312</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-98"><a href="#cb3-98" aria-hidden="true" tabindex="-1"></a> <span class="dv">1313</span>: <span class="st">'mlt'</span>,</span>
|
||
<span id="cb3-99"><a href="#cb3-99" aria-hidden="true" tabindex="-1"></a> <span class="dv">30</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-100"><a href="#cb3-100" aria-hidden="true" tabindex="-1"></a> <span class="dv">45</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-101"><a href="#cb3-101" aria-hidden="true" tabindex="-1"></a> <span class="dv">55</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-102"><a href="#cb3-102" aria-hidden="true" tabindex="-1"></a> <span class="dv">62</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-103"><a href="#cb3-103" aria-hidden="true" tabindex="-1"></a> <span class="dv">70</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-104"><a href="#cb3-104" aria-hidden="true" tabindex="-1"></a> <span class="dv">72</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-105"><a href="#cb3-105" aria-hidden="true" tabindex="-1"></a> <span class="dv">77</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-106"><a href="#cb3-106" aria-hidden="true" tabindex="-1"></a> <span class="dv">82</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-107"><a href="#cb3-107" aria-hidden="true" tabindex="-1"></a> <span class="dv">83</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-108"><a href="#cb3-108" aria-hidden="true" tabindex="-1"></a> <span class="dv">84</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-109"><a href="#cb3-109" aria-hidden="true" tabindex="-1"></a> <span class="dv">87</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-110"><a href="#cb3-110" aria-hidden="true" tabindex="-1"></a> <span class="dv">130</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-111"><a href="#cb3-111" aria-hidden="true" tabindex="-1"></a> <span class="dv">133</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-112"><a href="#cb3-112" aria-hidden="true" tabindex="-1"></a> <span class="dv">358</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-113"><a href="#cb3-113" aria-hidden="true" tabindex="-1"></a> <span class="dv">543</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-114"><a href="#cb3-114" aria-hidden="true" tabindex="-1"></a> <span class="dv">546</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-115"><a href="#cb3-115" aria-hidden="true" tabindex="-1"></a> <span class="dv">552</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-116"><a href="#cb3-116" aria-hidden="true" tabindex="-1"></a> <span class="dv">561</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-117"><a href="#cb3-117" aria-hidden="true" tabindex="-1"></a> <span class="dv">566</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-118"><a href="#cb3-118" aria-hidden="true" tabindex="-1"></a> <span class="dv">576</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-119"><a href="#cb3-119" aria-hidden="true" tabindex="-1"></a> <span class="dv">611</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-120"><a href="#cb3-120" aria-hidden="true" tabindex="-1"></a> <span class="dv">613</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-121"><a href="#cb3-121" aria-hidden="true" tabindex="-1"></a> <span class="dv">732</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-122"><a href="#cb3-122" aria-hidden="true" tabindex="-1"></a> <span class="dv">764</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-123"><a href="#cb3-123" aria-hidden="true" tabindex="-1"></a> <span class="dv">765</span>: <span class="st">'nondac'</span>,</span>
|
||
<span id="cb3-124"><a href="#cb3-124" aria-hidden="true" tabindex="-1"></a> <span class="dv">1601</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-125"><a href="#cb3-125" aria-hidden="true" tabindex="-1"></a> <span class="dv">1602</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-126"><a href="#cb3-126" aria-hidden="true" tabindex="-1"></a> <span class="dv">1603</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-127"><a href="#cb3-127" aria-hidden="true" tabindex="-1"></a> <span class="dv">1604</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-128"><a href="#cb3-128" aria-hidden="true" tabindex="-1"></a> <span class="dv">1605</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-129"><a href="#cb3-129" aria-hidden="true" tabindex="-1"></a> <span class="dv">1606</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-130"><a href="#cb3-130" aria-hidden="true" tabindex="-1"></a> <span class="dv">1607</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-131"><a href="#cb3-131" aria-hidden="true" tabindex="-1"></a> <span class="dv">1608</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-132"><a href="#cb3-132" aria-hidden="true" tabindex="-1"></a> <span class="dv">1609</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-133"><a href="#cb3-133" aria-hidden="true" tabindex="-1"></a> <span class="dv">1610</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-134"><a href="#cb3-134" aria-hidden="true" tabindex="-1"></a> <span class="dv">1611</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-135"><a href="#cb3-135" aria-hidden="true" tabindex="-1"></a> <span class="dv">1612</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-136"><a href="#cb3-136" aria-hidden="true" tabindex="-1"></a> <span class="dv">1613</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-137"><a href="#cb3-137" aria-hidden="true" tabindex="-1"></a> <span class="dv">1614</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-138"><a href="#cb3-138" aria-hidden="true" tabindex="-1"></a> <span class="dv">1615</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-139"><a href="#cb3-139" aria-hidden="true" tabindex="-1"></a> <span class="dv">1616</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-140"><a href="#cb3-140" aria-hidden="true" tabindex="-1"></a> <span class="dv">1617</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-141"><a href="#cb3-141" aria-hidden="true" tabindex="-1"></a> <span class="dv">1618</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-142"><a href="#cb3-142" aria-hidden="true" tabindex="-1"></a> <span class="dv">1619</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-143"><a href="#cb3-143" aria-hidden="true" tabindex="-1"></a> <span class="dv">1620</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-144"><a href="#cb3-144" aria-hidden="true" tabindex="-1"></a> <span class="dv">1621</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-145"><a href="#cb3-145" aria-hidden="true" tabindex="-1"></a> <span class="dv">1622</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-146"><a href="#cb3-146" aria-hidden="true" tabindex="-1"></a> <span class="dv">1623</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-147"><a href="#cb3-147" aria-hidden="true" tabindex="-1"></a> <span class="dv">1624</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-148"><a href="#cb3-148" aria-hidden="true" tabindex="-1"></a> <span class="dv">1625</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-149"><a href="#cb3-149" aria-hidden="true" tabindex="-1"></a> <span class="dv">1626</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-150"><a href="#cb3-150" aria-hidden="true" tabindex="-1"></a> <span class="dv">1627</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-151"><a href="#cb3-151" aria-hidden="true" tabindex="-1"></a> <span class="dv">1628</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-152"><a href="#cb3-152" aria-hidden="true" tabindex="-1"></a> <span class="dv">1629</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-153"><a href="#cb3-153" aria-hidden="true" tabindex="-1"></a> <span class="dv">1630</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-154"><a href="#cb3-154" aria-hidden="true" tabindex="-1"></a> <span class="dv">1631</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-155"><a href="#cb3-155" aria-hidden="true" tabindex="-1"></a> <span class="dv">1632</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-156"><a href="#cb3-156" aria-hidden="true" tabindex="-1"></a> <span class="dv">1633</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-157"><a href="#cb3-157" aria-hidden="true" tabindex="-1"></a> <span class="dv">1634</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-158"><a href="#cb3-158" aria-hidden="true" tabindex="-1"></a> <span class="dv">1635</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-159"><a href="#cb3-159" aria-hidden="true" tabindex="-1"></a> <span class="dv">1636</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-160"><a href="#cb3-160" aria-hidden="true" tabindex="-1"></a> <span class="dv">1637</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-161"><a href="#cb3-161" aria-hidden="true" tabindex="-1"></a> <span class="dv">1638</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-162"><a href="#cb3-162" aria-hidden="true" tabindex="-1"></a> <span class="dv">1639</span>: <span class="st">'private'</span>,</span>
|
||
<span id="cb3-163"><a href="#cb3-163" aria-hidden="true" tabindex="-1"></a>}</span>
|
||
<span id="cb3-164"><a href="#cb3-164" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb3-165"><a href="#cb3-165" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> totals_by_donortype(oda_frame):</span>
|
||
<span id="cb3-166"><a href="#cb3-166" aria-hidden="true" tabindex="-1"></a> totals <span class="op">=</span> oda_frame.loc[</span>
|
||
<span id="cb3-167"><a href="#cb3-167" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'RECIPIENT'</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&</span></span>
|
||
<span id="cb3-168"><a href="#cb3-168" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span></span>
|
||
<span id="cb3-169"><a href="#cb3-169" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb3-170"><a href="#cb3-170" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb3-171"><a href="#cb3-171" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb3-172"><a href="#cb3-172" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb3-173"><a href="#cb3-173" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb3-174"><a href="#cb3-174" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb3-175"><a href="#cb3-175" aria-hidden="true" tabindex="-1"></a> donortotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb3-176"><a href="#cb3-176" aria-hidden="true" tabindex="-1"></a> donortotals[<span class="st">"Donortype"</span>] <span class="op">=</span> donortotals[<span class="st">"DONOR"</span>].<span class="bu">map</span>(donortypes)</span>
|
||
<span id="cb3-177"><a href="#cb3-177" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> donortotals</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell" data-execution_count="7">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>svg_render <span class="op">=</span> <span class="va">True</span></span>
|
||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>pd.options.display.float_format <span class="op">=</span> <span class="st">"</span><span class="sc">{:.2f}</span><span class="st">"</span>.<span class="bu">format</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<section id="vietnam" class="level2 page-columns page-full">
|
||
<h2 class="anchored" data-anchor-id="vietnam">Vietnam</h2>
|
||
<hr>
|
||
<ul>
|
||
<li>Economic restructuring and trade liberalization further drives economy towards wage work, service work, the manufacturing sectors.</li>
|
||
<li>Structural changes drove poverty down in absolute terms, but leave those in vulnerable positions consistently at-risk of slipping into or worsening existing poverty.</li>
|
||
<li>Economic inequality in Vietnam intersectional between ethnic minorities, rural populations, regional and gendered dimensions.</li>
|
||
<li>Ethnic minorities increase in economic inequality, driven by worse returns on assets (human capital and land) and worse access to endowments (land and educational infrastructure).</li>
|
||
<li>Environmental degradation and environmental shocks consistently worsen within-sector inequalities for ethnic minority and female population.</li>
|
||
<li>Vietnam in vulnerable position to increasing exogenous shocks due to climate change, building capacity against which may require focus shift on risk management and preventative measures</li>
|
||
</ul>
|
||
<hr>
|
||
<p>Vietnam’s economy is now firmly in the third decade of ongoing economic reform (<em>Doi Moi</em>) as a market-based economy, which lead to remarkable growth phases through opening the economy to international trade while, seen over the bulk of its population, attempting to keep inequality rates managed through policies of controlling credit and reducing subsidies to state-owned enterprises <span class="citation" data-cites="Bui2019">(<a href="#ref-Bui2019" role="doc-biblioref">Bui & Imai, 2019</a>)</span>.</p>
|
||
<!-- poor/poverty <40%; mention low social mobility: different social insurances [@Bui2019] -->
|
||
<p>Poverty in Vietnam is marked by a drastic reduction in absolute terms over this time with some of the decline directly attributable to the liberalization of markets over the country’s growth more generally <span class="citation" data-cites="WorldBank2012 McCaig2011 Le2022">(<a href="#ref-Le2022" role="doc-biblioref">N. V. T. Le et al., 2022</a>; <a href="#ref-McCaig2011" role="doc-biblioref">McCaig, 2011</a>; <a href="#ref-WorldBank2012" role="doc-biblioref">World Bank, 2012</a>)</span>. While the rate of decline slowed since the mid-2000s <span class="citation" data-cites="VASS2006 VASS2011">(<a href="#ref-VASS2006" role="doc-biblioref">VASS, 2006</a>, <a href="#ref-VASS2011" role="doc-biblioref">2011</a>)</span>, it continued declining in tandem with small income inequality decreases. The overall income inequality decrease that Vietnam experienced from the early 2000s suggests that economic growth has been accompanied by equity extending beyond poverty reduction <span class="citation" data-cites="Benjamin2017">(<a href="#ref-Benjamin2017" role="doc-biblioref">Benjamin et al., 2017</a>)</span>. On the other hand, Le et al. <span class="citation" data-cites="Le2021">(<a href="#ref-Le2021" role="doc-biblioref">2021</a>)</span> suggest a slight increase in overall income distribution from 2010-2018. At the same time, the population groups most affected by poverty through welfare inequalities stay unaltered, as do largely the primary factors accompanying it: There is severe poverty persistence among ethnic minorities in Vietnam <span class="citation" data-cites="Baulch2012">(<a href="#ref-Baulch2012" role="doc-biblioref">Baulch et al., 2012</a>)</span>, concomitant with low education and skills, more prevalent dependency on subsistence agriculture, physical and social isolation, specific disadvantages which become linked to ethnic identities and a greater exposure to natural disasters and risks <span class="citation" data-cites="Kozel2014">(<a href="#ref-Kozel2014" role="doc-biblioref">Kozel, 2014</a>)</span>.</p>
|
||
<p>The country’s overall estimated Gini coefficient for income fluctuates between 0.42 and 0.44 between 2010 and 2018, with the highest levels of income inequality observed in the Central Highlands in 2016, though absolute income may be rising, with the top quintile having 9.2 times the income of the lowest quintile in 2010 and 9.8 times in 2016 <span class="citation" data-cites="Le2021">(<a href="#ref-Le2021" role="doc-biblioref">Q. H. Le et al., 2021</a>)</span>. For Gini coefficients estimated using consumption per capita, see <a href="#fig-vnm">Figure 1</a>, which shows similar trends of increasing inequality, with 2010 constituting a significant increase. Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional disparities, rural-urban divides and gendered lines, one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown <span class="citation" data-cites="Ebrahim2021">(<a href="#ref-Ebrahim2021" role="doc-biblioref">Ebrahim et al., 2021</a>)</span>.</p>
|
||
<div class="cell" data-execution_count="8">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> vnm[vnm[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'source'</span>].<span class="bu">str</span>.contains(<span class="st">"World Bank"</span>)]</span>
|
||
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'areacovr'</span>].<span class="bu">str</span>.contains(<span class="st">"All"</span>)]</span>
|
||
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>gini_plot(gni_cnsmpt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display">
|
||
<div id="fig-vnm" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 1: Gini index of consumption per capita for Vietnam</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||
</div>
|
||
<!--
|
||
* estimated Gini coeff, overall income distribution: [@Le2021]
|
||
* fluctuating 0.42-0.44 (2010-2018)
|
||
* highest in Central highlands (2016)
|
||
* absolute income inequality may be rising, top quintile 9.2 times income lowest quintile (2010) to 9.8 times (2016) (Hung 2019)
|
||
-->
|
||
<!-- rural inequality -->
|
||
<p>In the 1990s, as the initial stages of the Doi Moi reform bore fruit with economic growth, the first amplifications of inequalities along new rural-urban boundaries became visible. There are two complementary views on the primary dimensions of rural inequalities. On the one hand, the urban-rural divide may be driven by structural effects: the welfare returns to education and agricultural activities changed dramatically from, and with it the requirements on policy adaptations required for stemming inequality.</p>
|
||
<p>Nguyen et al. <span class="citation" data-cites="Nguyen2007">(<a href="#ref-Nguyen2007" role="doc-biblioref">2007</a>)</span> argue this for the period of 1993-1998, with their findings that income returns to education improved dramatically over this time and arguing through this that suggested development policies had a strictly urban bias — on the whole they would benefit both from better education and vastly benefit from the restructuring of Vietnam’s economy. This view was in turn confirmed when Theil Index decomposition found within-sector inequality remaining largely stable while between-sector inequality rose significantly <span class="citation" data-cites="Fesselmeyer2010">(<a href="#ref-Fesselmeyer2010" role="doc-biblioref">Fesselmeyer & Le, 2010</a>)</span>. On the other, Thu Le and Booth <span class="citation" data-cites="ThuLe2014">(<a href="#ref-ThuLe2014" role="doc-biblioref">2014</a>)</span> argue that the urban-rural inequality continued to increase over the years due to both covariate effects and the returns to those covariate effects, primarily education age structures and labor market activities, but also geographic location.</p>
|
||
<p>The gap between urban and rural sectors grew, a gap which would continue to widen until 2002, when within-sector rural inequalities started to become more important for inequalities than those between the sectors <span class="citation" data-cites="Fritzen2005 ThuLe2014">(<a href="#ref-Fritzen2005" role="doc-biblioref">Fritzen et al., 2005</a>; <a href="#ref-ThuLe2014" role="doc-biblioref">Thu Le & Booth, 2014</a>)</span>. In the time of within-sector inequality becoming more pronounced many studies, while important contributions to continued inequality research, had a tendency to mask those inequalities in favor of continued analysis of between-sector trends — often to the detriment of the high degree of heterogeneity depending on geographic characteristics such as remoteness or cultural factors, as Cao and Akita <span class="citation" data-cites="Cao2008">(<a href="#ref-Cao2008" role="doc-biblioref">2008</a>)</span> note.</p>
|
||
<p>In a recent study, Bui and Imai <span class="citation" data-cites="Bui2019">(<a href="#ref-Bui2019" role="doc-biblioref">2019</a>)</span> build on this earlier work, and also find access to basic education the linchpin of improving rural welfare while its lack combined with economic restructuring precluded many from equal opportunities toward human capital improvement. They find that, as within-sector became more pronounced again after 2010, the large proportion of uneducated heads of households in rural sectors and low social mobility of rural poor combine to increase within-sector inequality while the economy overall changing toward salaried work compounded within-rural and urban-rural disparities. Early income studies generally highlighted the important role of agricultural incomes in reducing, or at the very not exacerbating, income inequality <span class="citation" data-cites="Benjamin2004">(<a href="#ref-Benjamin2004" role="doc-biblioref">Benjamin & Brandt, 2004</a>)</span>. Benjamin et al. <span class="citation" data-cites="Benjamin2017">(<a href="#ref-Benjamin2017" role="doc-biblioref">2017</a>)</span> expand on this over a longer time-frame by decomposing different household income sources underlying Vietnam’s structural economic changes. They find that, while there is an overall decrease in income inequality throughout Vietnam between 2002 and 2014 and the urban-rural divide also continued its downward trend, rural inequality indeed increased over this time.</p>
|
||
<p>Wage income and family business income were the main drivers of overall inequality in 2002 (accounting for over 30% of income but 60% of inequality) and remittances add a small share on top, which, while decreased in effect (risen to 42% of total income), remain majorly correlated with income distributions and thus income inequality. <span class="citation" data-cites="Bui2019">Bui & Imai (<a href="#ref-Bui2019" role="doc-biblioref">2019</a>)</span> confirm this with a per capita income Gini coefficient of 0.36 to 0.39 between 2008 and 2010 which, decomposed into Theil indices for between rural and urban and within rural sectors show that rural-urban inequalities are smaller and decreasing, while within-rural inequalities are large and increasing. Thus, while the study points to both more prevalent and equally distributed labor markets and wage labor opportunities, these effects apply to the overall population and not just within-rural inequalities which are driven in large part by ethnicity, education and environmental factors.</p>
|
||
<!--
|
||
* estimated Gini coeff, rural and urban expenditure: [@Bui2019]
|
||
* 0.36-0.39 (2008-2010), 0.36 (2012)
|
||
* Theil rural-urban between: 0.22-0.19 (2008-2012)
|
||
* Theil rural-rural within: 0.74-0.83 (2008-2012)
|
||
-->
|
||
<!-- ethnicity inequality -->
|
||
<p>Ethnic minorities in Vietnam are distinctly over-represented in poverty in addition to often being left behind in the development process, not least due to being extreme representatives of the economic situation of Vietnam’s rural population. Ethnic minority households have a tenuous economic position - and it is deteriorating. In earlier studies on ethnic inequalities in Vietnam, a strong welfare gap between ethnic minorities and the majority was already visible. Van de Walle <span class="citation" data-cites="vandeWalle2001">(<a href="#ref-vandeWalle2001" role="doc-biblioref">2001</a>)</span> reports the situation of ethnic minorities inhabiting predominantly remote rural areas with lower living standards than the ethnic majority, a finding he suggests waws created due to environmental and structural differences (difficult terrain, poor infrastructure, less access to off-farm work and the market economy and inferior access to education) and compounded by social immobility and social isolation. Baulch et al. <span class="citation" data-cites="Baulch2012">(<a href="#ref-Baulch2012" role="doc-biblioref">2012</a>)</span> find that between 1993 and 2004, the welfare gap between ethnic minorities and the ethnic majority had increased by 14.6%, two-fifths of which were due to endowments such as demographic structure and education while geographic variables make up less than one-fifth. They additionally suggest some drivers of the inequality being the lack of ability speaking the Vietnamese language or the distance to a commune or district center amplifying isolating effects, though a large part of the change was linked to temporal changes of unobservable factors - which the study conjectures to be due to negative ethnic stereotyping, a poor understanding of ethnic customs and culture and further (unobserved) variations in household-level endowments.</p>
|
||
<p>While in 2002 the ethnic minority population living in rural areas was below 15%, it rose to over 18% in 2014 - both due to higher fertility among minorities and ethnic majority Kinh urbanizing at a higher rate - and the ratio of Kinh to minority incomes rose to more than 2.0 in 2014 <span class="citation" data-cites="Benjamin2017">(<a href="#ref-Benjamin2017" role="doc-biblioref">Benjamin et al., 2017</a>)</span>. The same study finds that income inequality rose even more sharply <em>within</em> ethnic minorities, while that of rural Kinh, though increasing from 2002 to 2014, fell back to 2002 levels around 2014. These findings suggest that the primary drivers of rural income inequality are a growing gap between Kinh and minorities while at the same time a similar rising inequality develops among minority rural populations themselves. <!-- TODO Find levels of population rural/urban in other sources --> In the same vein as the urban-rural divide, Nguyen et al. <span class="citation" data-cites="Nguyen2007">(<a href="#ref-Nguyen2007" role="doc-biblioref">2007</a>)</span> thus argue for structural policy failures which essentially lowered the returns on ethnicity along sectorial dividing lines of education and primary income types.</p>
|
||
<!--
|
||
* estimated Gini coeff: rural ethnicities [@Baulch2012]
|
||
* per capita HH expenditure between Kinh and Hoa majority 0.27 (1993, 2004)
|
||
* per capita HH expenditure within minority groups 0.24 (1993), 0.29 (2004) sg increase
|
||
-->
|
||
<section id="natural-disasters-and-inequalities" class="level3">
|
||
<h3 class="anchored" data-anchor-id="natural-disasters-and-inequalities">Natural disasters and inequalities</h3>
|
||
<!-- environmental inequality -->
|
||
<p>While the effect of agriculture on inequality outcomes is an equalizing one, its future growth, and that of agricultural livelihoods, is threatened by vulnerability to risks such as natural disasters and environmental degradation, exacerbated through climate change <span class="citation" data-cites="Kozel2014">(<a href="#ref-Kozel2014" role="doc-biblioref">Kozel, 2014</a>)</span>. Kozel <span class="citation" data-cites="Kozel2014">(<a href="#ref-Kozel2014" role="doc-biblioref">2014</a>)</span> goes on to argue the continuous precarity of poor households against economy-wide shocks (such as the effect of climate change on rainfall and temperatures) but also highlights the danger of vulnerable households <em>falling</em> into poverty through generated inequalities.</p>
|
||
<p>Looking at the particularities of flood risk management in the Ninh Binh province, Mottet and Roche <span class="citation" data-cites="Mottet2009">(<a href="#ref-Mottet2009" role="doc-biblioref">2009</a>)</span> find that most areas within the region are vulnerable. They find the strengths of current management lying in prevention with existing dykes designed to channel high waters, effective monitoring of weather conditions (rainfall or typhoons) and consolidation or elevation of existing residences, while the weaknesses are mainly centered around insufficient information given to inhabitants over flood risks, few compensation systems for flood victims and construction policies continuing to allow building in flood-endangered zones.</p>
|
||
<p>Sen et al. <span class="citation" data-cites="Sen2021">(<a href="#ref-Sen2021" role="doc-biblioref">2021</a>)</span> estimate that the main barriers to better information are farmers’ lack of trust toward formal climate-related services, their lack of perceived risk from climate change itself and difficulties in balancing both climate adaptation and economic benefits of interventions. They argue that, while ethnicity itself is not a barrier to information access with all farmers receiving information through informal channels — friends neighbors and market actors instead of agricultural departments or mass media — cultural issues such as language do come into play and act as a barrier. Reactionary economic mitigation efforts by households, such as reduced healthcare spending, selling of land or livestock assets, taking children out of school due to needing assistance at home can in turn lead to longer-term adverse consequences (thus, <em>mal-adaptation</em>) <span class="citation" data-cites="Kozel2014">(<a href="#ref-Kozel2014" role="doc-biblioref">Kozel, 2014</a>)</span>.</p>
|
||
<!--
|
||
* estimated Gini coeff: Ninh Binh province [@Kozel2014]
|
||
* 0.283 (2013)
|
||
* top 90th percentile 3.57 times income 10th percentile
|
||
* 13.63 percent in richest quintile
|
||
-->
|
||
<!-- extreme events / climate change -->
|
||
<p>The results are further intensification of inequality along existing social lines during extreme events such as flooding: The effects of inequalities mainly affecting ethnic minorities are illustrated by Son and Kingsbury <span class="citation" data-cites="Son2020">(<a href="#ref-Son2020" role="doc-biblioref">2020</a>)</span>, with droughts impacting yield losses between 50% and 100%, cold snaps leading to loss of livestock and floods damaging residential structures but even more importantly disrupting livelihoods through landslides, crop destruction and overflowing fish ponds. Locally employed coping strategies, they argue, are always conditional on the strength and foresight of institutions and implemented preventative policies along local but also regional and central levels.</p>
|
||
<p>Similarly, Ylipaa et al. <span class="citation" data-cites="Ylipaa2019">(<a href="#ref-Ylipaa2019" role="doc-biblioref">2019</a>)</span> analyze impacts mainly across the gender dimension to find that, resulting inequalities may be exacerbated with differentiated rights and responsibilities leading to unequal opportunities and, especially, decreased female mobility in turn increasing their vulnerability to climate impacts with a reduced capacity to adapt. Hudson et al. <span class="citation" data-cites="Hudson2021">(<a href="#ref-Hudson2021" role="doc-biblioref">2021</a>)</span> along the same dimension find that, while the set of relevant variables is largely similar with age, social capital, internal and external support after the flood and the perceived severity of previous flood impacts having major impacts, women tend to show longer recovery times and psychological variables can influence recovery rates more than some adverse flood impacts.</p>
|
||
<p>While the quantitative evidence for impacts of such shock events are relatively sparse, Jafino et al. <span class="citation" data-cites="Jafino2021">(<a href="#ref-Jafino2021" role="doc-biblioref">2021</a>)</span> lament the overuse of aggregate perspectives, instead disaggregating the local and inter-sectoral effects to find out that flood protection efforts in the Mekong Delta often predominantly support large-scale farming while small-scale farmers can be harmed through them. They find that measures decrease the aggregate total output and equity indicators by disaggregating profitability indicators into inundation, sedimentation, soil fertility, nutrient dynamics and behavioral land-use in an assessment which sees within-sector policy responses often having an effect on adjacent sectors, increasing the inter-district Gini coefficient.</p>
|
||
<p>Adaptation during these catastrophic events reinforces the asset and endowment drivers of non-shock event times, with impacts levels often depending on access to non-farm income sources, access to further arable land, knowledge of adaptive farming practices and mitigation of possible health risks such as water contamination <span class="citation" data-cites="Son2020">(<a href="#ref-Son2020" role="doc-biblioref">Son & Kingsbury, 2020</a>)</span>. Karpouzoglou et al. <span class="citation" data-cites="Karpouzoglou2019">(<a href="#ref-Karpouzoglou2019" role="doc-biblioref">2019</a>)</span> make the point that, ultimately, the pure coupling of flood resilience into infrastructural or institutional interventions needs to take care not to amplify existing inequalities through unforeseen consequences (‘ripple effects’) which can’t be escaped by vulnerable people due to their existing immobility.</p>
|
||
<!-- conclusion -->
|
||
<p>Inequality in Vietnam, then, is slowly rising across the whole population distribution, runs the danger of increasing due to schisms opening within individual sectors of vulnerable groups. Rural populations experience a trend towards increasing inequality within their sector, driven primarily by the social exclusion and geographic isolation of ethnic minorities, its most precarious population. Ethnic minorities’ inequality is slowly increasing due to receiving worse returns to their existing assets (especially human capital and land) and generally worse access to endowments in the first place (land and educational infrastructure). The restructuring of the economy, turning the labor force toward urban areas and within them wage work in manufacturing and service industries, leaves behind immobile rural populations whose ability to be employed for non-farm shrink further.</p>
|
||
<p>All these factors are at risk of experiencing large negative shocks as climate change exacerbates existing extreme environmental conditions, which in turn threaten to increase economic inequalities for both the rural population at large, ethnic minorities and women especially. Women in rural areas experience worse mobility and fewer economic opportunities and are thus less able to adapt to environmental degradation. While inequality as an aggregate is kept relatively low Vietnam’s growth rate, both ethnic minorities and the rural female population are thus at risk of being left behind economically.</p>
|
||
<!-- ## Script Issues
|
||
* Too wordy: on within-sector/between-sector rural dimensions; on non-quantitative environmental dimensions
|
||
* Too vague on 2 lowest quintile effects, especially as expressed through Gini impacts
|
||
* Wordings do not quite capture quintile poverty assessments for coming descriptive statistics
|
||
-->
|
||
<!-- development assistance -->
|
||
</section>
|
||
<section id="development-assistance-to-vietnam" class="level3 page-columns page-full">
|
||
<h3 class="anchored" data-anchor-id="development-assistance-to-vietnam">Development assistance to Vietnam</h3>
|
||
<div class="cell" data-execution_count="9">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load CRS data</span></span>
|
||
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Vietnam_11-13_05092022215007164.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Vietnam_14-16_05092022215226180.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Vietnam_17-20_05092022215427555.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.concat([dfsub1, dfsub2, dfsub3], ignore_index<span class="op">=</span><span class="va">True</span>)</span>
|
||
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">'</span><span class="ch">\ufeff</span><span class="st">"DONOR"'</span>: <span class="st">'DONOR'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell page-columns page-full" data-execution_count="10">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="co">## count amount of development aid financing instruments (grants/loans) by year and display</span></span>
|
||
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="co">## count USD amount of development aid financing instumrnets by year and display</span></span>
|
||
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">'Flow'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">'Flow'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">'Financetype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Financetype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid amount, in millions of USD"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-vnm-aid-financetype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 2: Total ODA for Vietnam per year, by financing type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>Official Development Assistance (ODA) to Vietnam reached its highest point in 2014 with almost 5bn USD but generally decreased in the intervening years, as can be seen in <a href="#fig-vnm-aid-financetype">Figure 2</a>. Decreasing continuously after 2014, development assistance reached its lowest point of the last ten years in 2019 when it fell to just under 2.5bn USD, before increasing slightly to just above 2.5bn USD in 2020. Development aid to Vietnam is primarily driven by ODA loans instead of ODA grants.</p>
|
||
<p>While grants were just under 1bn USD in 2011 and decreased slightly over the following years to 600m USD in 2019, decreasing loans were also the primary driver of the overall development aid contributions, with the overall monetary curve closely following that of loan contributions. Thus, while loans constituted almost triple the USD amount of grants to Vietnam in 2011, this number even climbed to almost 5 times the amount in 2014, before falling to just over 2.5 times the amount of USD in loans compared to grants in 2020. A large share of development aid contributions to Vietnam are also made up from other official flows<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>, a share which started equal to the absolute grant amount of USD contributed, rose steeply in 2013 to double the amount and fell equally steeply back to its original level in 2019.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="11">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">"Donortype"</span>] <span class="op">=</span> donortotals[<span class="st">"DONOR"</span>].<span class="bu">map</span>(donortypes)</span>
|
||
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">"Donortype"</span>] <span class="op">!=</span> <span class="st">"nondac"</span>)]</span>
|
||
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">'Donortype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Donortype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid, in millions"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-vnm-aid-donortype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 3: Total ODA for Vietnam per year, separated by donor type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in <a href="#fig-vnm-aid-donortype">Figure 3</a>. Both bilateral and multilateral contributions increase from 2011 to 2014 and subsequently begin decreasing. While bilateral contributions do not increase in absolute amounts afterwards until 2020, multilateral contributions increase again from 2019 to 2020. Nevertheless, bilateral contributions are consistently higher than multilateral, having around a 1.5 times higher share of absolute USD contribution, though growing to just over 2 times the share in 2017, before quickly shrinking down to just 1.3 times the share of multilateral contributions in 2018. This gap may close further in the future, with multilateral contributions being on an increase and bilateral contributions still decreasing.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="12">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 14020-22 - large scale potable water</span></span>
|
||
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 14030-32 - individual-level water and sanitation supply</span></span>
|
||
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 14081 - education and training in water supply</span></span>
|
||
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> (</span>
|
||
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14020</span>) <span class="op">|</span></span>
|
||
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span></span>
|
||
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14022</span>) <span class="op">|</span></span>
|
||
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14030</span>) <span class="op">|</span></span>
|
||
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span></span>
|
||
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14032</span>) <span class="op">|</span></span>
|
||
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43060</span>)</span>
|
||
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> ) <span class="op">&</span></span>
|
||
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a>floodaid <span class="op">=</span> totals[totals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of large scale water supply and sanitation</span></span>
|
||
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span> (floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14022</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">14020</span></span>
|
||
<span id="cb9-24"><a href="#cb9-24" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14020</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Large water supply and sanitation"</span></span>
|
||
<span id="cb9-25"><a href="#cb9-25" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of individual water supply and sanitation</span></span>
|
||
<span id="cb9-26"><a href="#cb9-26" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span> (floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14032</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">14030</span></span>
|
||
<span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14030</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Basic water supply and sanitation"</span></span>
|
||
<span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb9-29"><a href="#cb9-29" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by sector per year and sum all values</span></span>
|
||
<span id="cb9-30"><a href="#cb9-30" aria-hidden="true" tabindex="-1"></a>floodaid_grouped <span class="op">=</span> floodaid.groupby([<span class="st">'Year'</span>, <span class="st">'Sector'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb9-31"><a href="#cb9-31" aria-hidden="true" tabindex="-1"></a>floodaid_grouped <span class="op">=</span> floodaid_grouped.reset_index([<span class="st">'Year'</span>, <span class="st">'Sector'</span>])</span>
|
||
<span id="cb9-32"><a href="#cb9-32" aria-hidden="true" tabindex="-1"></a>floodaid_grouped.columns <span class="op">=</span> floodaid_grouped.columns.to_flat_index()</span>
|
||
<span id="cb9-33"><a href="#cb9-33" aria-hidden="true" tabindex="-1"></a>floodaid_grouped.columns <span class="op">=</span> [<span class="st">'Year'</span>, <span class="st">'Sector'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb9-34"><a href="#cb9-34" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb9-35"><a href="#cb9-35" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(floodaid_grouped[<span class="st">'Year'</span>], floodaid_grouped[<span class="st">'Sector'</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>floodaid_grouped[<span class="st">'Value'</span>], aggfunc<span class="op">=</span><span class="st">'sum'</span>)</span>
|
||
<span id="cb9-36"><a href="#cb9-36" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb9-37"><a href="#cb9-37" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename and reorder columns</span></span>
|
||
<span id="cb9-38"><a href="#cb9-38" aria-hidden="true" tabindex="-1"></a>crosstab.columns <span class="op">=</span> [<span class="st">'Basic water supply'</span>, <span class="st">'Disaster risk reduction'</span>, <span class="st">'Large water supply'</span>, <span class="st">'All'</span>]</span>
|
||
<span id="cb9-39"><a href="#cb9-39" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> crosstab[[<span class="st">'Basic water supply'</span>, <span class="st">'Large water supply'</span>, <span class="st">'Disaster risk reduction'</span>, <span class="st">'All'</span>]]</span>
|
||
<span id="cb9-40"><a href="#cb9-40" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb9-41"><a href="#cb9-41" aria-hidden="true" tabindex="-1"></a>Markdown(tabulate(crosstab.fillna(<span class="st">"0.00"</span>), headers<span class="op">=</span><span class="st">"keys"</span>, tablefmt<span class="op">=</span><span class="st">"github"</span>, floatfmt<span class="op">=</span><span class="st">".2f"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page" data-execution_count="12">
|
||
<div id="tbl-vnm-aid-water" class="anchored">
|
||
<table class="table table-sm table-striped">
|
||
<caption>Table 1: ODA projects of water supply and risk reduction in Vietnam per year, separated by project type</caption>
|
||
<colgroup>
|
||
<col style="width: 9%">
|
||
<col style="width: 25%">
|
||
<col style="width: 25%">
|
||
<col style="width: 30%">
|
||
<col style="width: 10%">
|
||
</colgroup>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Year</th>
|
||
<th>Basic water supply</th>
|
||
<th>Large water supply</th>
|
||
<th>Disaster risk reduction</th>
|
||
<th>All</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>2011</td>
|
||
<td>96.24</td>
|
||
<td>105.94</td>
|
||
<td>4.01</td>
|
||
<td>206.19</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2012</td>
|
||
<td>85.56</td>
|
||
<td>150.63</td>
|
||
<td>6.73</td>
|
||
<td>242.92</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2013</td>
|
||
<td>123.46</td>
|
||
<td>178.72</td>
|
||
<td>8.82</td>
|
||
<td>311.00</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2014</td>
|
||
<td>133.89</td>
|
||
<td>193.56</td>
|
||
<td>10.69</td>
|
||
<td>338.15</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2015</td>
|
||
<td>142.92</td>
|
||
<td>174.46</td>
|
||
<td>19.98</td>
|
||
<td>337.36</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2016</td>
|
||
<td>154.11</td>
|
||
<td>230.91</td>
|
||
<td>37.08</td>
|
||
<td>422.10</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2017</td>
|
||
<td>86.39</td>
|
||
<td>239.99</td>
|
||
<td>42.73</td>
|
||
<td>369.11</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2018</td>
|
||
<td>70.70</td>
|
||
<td>252.37</td>
|
||
<td>35.51</td>
|
||
<td>358.57</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2019</td>
|
||
<td>39.24</td>
|
||
<td>203.36</td>
|
||
<td>42.24</td>
|
||
<td>284.84</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2020</td>
|
||
<td>50.98</td>
|
||
<td>224.11</td>
|
||
<td>63.31</td>
|
||
<td>338.40</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>All</td>
|
||
<td>983.49</td>
|
||
<td>1954.03</td>
|
||
<td>271.11</td>
|
||
<td>3208.64</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. The categories under analysis are large- and small-scale water supply and sanitation infrastructure projects as well as disaster risk reduction which includes improved flooding prevention infrastructure. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The breakdown of project-based development aid for water supply infrastructure and disaster risk reduction in Vietnam can be seen in <a href="#tbl-vnm-aid-water">Table 1</a>. It shows the funds broken down into their use for three categories: First, contributions to provide access to basic water supply and sanitation, which subsumes building and maintaining handpumps, gravity wells, rainwater collection systems, storage tanks, and small, often shared, distribution systems. Second, contributions to large-scale water supply and sanitation, including potable water treatment plants, intake works, large pumping stations and storage, as well as the transmission and distribution through large-scale systems. And last, contributions towards disaster risk reduction which is a larger umbrella concept aimed at building local and national capacities, but includes infrastructure measures (e.g. flood protection systems), preparedness measures (such as early warning systems), and normative prevention measures (such as closer adherence to building and structural codes), as well as risk transfer systems (insurance schemes or risk funds). This constitutes the closest category to flood risk management itself, which is part of the overarching disaster risk management dimension.</p>
|
||
<p>While overall aid contributions to Vietnam’s water supply and risk management sectors have slightly increased over time from 206m USD in 2011 to their peak of 422m USD in 2016, they have largely stagnated around the level of 300m to 350m USD per year since then. From the level of 96m USD in 2011, access to basic water supply saw significant increases to its contributions from 2013 to 2016, with 154m USD contributed at its peak in 2016 and shrinking drastically the following years to 39m USD in 2019, its lowest contribution year. Large water supply project contributions see a similar if less drastic curve, with contributions increasing from 105m USD in 2011 to 252m USD at their in 2018, before decreasing slightly over the next two years.</p>
|
||
<p>Thus, the contribution curves to basic and large-scale water supply projects somewhat follows the overall development aid contribution curve to Vietnam, with peaks between 2016 and 2018 before more or less drastic drops in aid contributions. Disaster risk reduction contributions, however, show the least similarity to the general trend, with contributions being only 4m USD in 2011 before increasing year-over-year (with the exception of 2018) to reach their peak with 63m USD in 2020. The most significant increases happened between the years 2014 and 2016, as well as again in 2020. While the other contribution sectors follow a shrinking contribution in the years following 2014, then, disaster risk reduction instead keeps on reaching an increase in its absolute contribution amounts, perhaps pointing to a continued necessity for development in the sector.</p>
|
||
<div style="page-break-after: always;"></div>
|
||
</section>
|
||
</section>
|
||
<section id="uganda" class="level2 page-columns page-full">
|
||
<h2 class="anchored" data-anchor-id="uganda">Uganda</h2>
|
||
<hr>
|
||
<ul>
|
||
<li>Poverty and inequality in Uganda are at a fluctuating level in Uganda, with poverty levels staying roughly stable and inequality slowly trending upward.</li>
|
||
<li>National poverty line set very low, potentially hiding additional households in states of deprivation and those in danger of reverting to poverty.</li>
|
||
<li>Inequality, poverty and informal economy in close circular relation in Uganda, presenting a vicious circle for those captive within.</li>
|
||
<li>Education levels of poor people are consistently low, with those of rural population more so.</li>
|
||
<li>Inadequate access to clean water can exacerbate these inequalities, directly influencing food security, rural child education and gender inequalities.</li>
|
||
<li>The district of Isingiro especially is dramatically below national average of clean water access, and in danger of exacerbation through climate change.</li>
|
||
</ul>
|
||
<hr>
|
||
<!-- intro/overall -->
|
||
<p>Uganda generally has a degree of inequality that fluctuates but over time seems largely unchanged, as does the share of people below its poverty line in recent years. The long-term level of income inequality in the country stayed relatively stagnant, with a Gini coefficient for the consumption per capita of 0.36 calculated for the 1992/93 census and a World Bank calculation of 0.43 for the year 2019, with the coefficient rising slighly in the years 2002/03 and 2009/10 during its fluctuation <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>, see also <a href="#fig-uga" role="doc-biblioref">Figure 4</a>)</span>, while <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finds a slight upward trend over time. However, the aggregation masks several important distinctions: Rural inequality overall is lower than urban inequality, with <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finding Gini coefficients of 0.35 and 0.41 for 2012/13 respectively. Additionally, he sees inequalities between income quintiles primarily driven by the highest (0.25) and lowest (0.14) quintiles, whereas middle-income show lower Gini coefficients (0.05-0.07). These inequality levels remained mostly unchanged between 2012/13 and 2019/20 but hide qualitative dimensions such as the shift out of a lower-income agricultural livelihood predominantly taking place among older men who have at least some level of formal education and are from already more well-off households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||
<div class="cell" data-execution_count="13">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> uga[uga[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>gini_plot(gni_cnsmpt_percapita)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display">
|
||
<div id="fig-uga" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img src="data:image/png;base64,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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 4: Gini index of consumption per capita for Uganda</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||
</div>
|
||
<!-- poverty -->
|
||
<p>The World Bank <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">2022f</a>)</span> report goes on to examine the share of people below the poverty line in Uganda: around 30% of households are in a state of poverty in 2019/20, which once again fluctuated but roughly reflects the share of 30.7% households in poverty in 2012/13. Two surges in rural household poverty in 2012/2013 and 2016/17 can be linked to droughts in the country, with an improvement in 2019/20 conversely being linked to favorable weather conditions. <!-- TODO find citation or put Atamanov --> <span class="citation" data-cites="Ssewanyana2012">Ssewanyana & Kasirye (<a href="#ref-Ssewanyana2012" role="doc-biblioref">2012</a>)</span> find that in absolute terms poverty fell significantly (from 28.5% in 2005/06 to 23.9% in 2009/10) but there are clear relative regional differences emerging, with Western Ugandan households increasing in poverty while Northern and Eastern households reduced their share of households below the poverty line. Additionally, they find that while transient poverty is more common than chronic poverty in Uganda, nearly 10% of households continue to live in persistent material deprivation.</p>
|
||
<p>Lastly, for a long time it has been seen as an issue that Uganda puts its national poverty line too low with the line being put between 0.94 USD PPP and 1.07 USD PPP depending on the province (lower than the international live of 1.90 USD PPP), while <span class="citation" data-cites="vandeVen2021">van de Ven et al. (<a href="#ref-vandeVen2021" role="doc-biblioref">2021</a>)</span> estimate a living income of around 3.82 USD PPP would be required for a national poverty line that meets basic human rights for a decent living. <!-- TODO find a source for the national poverty line being too low (quant data is already in vandeVen2021) --></p>
|
||
<!-- endowment/assets: education, ..? -->
|
||
<p>Esaku <span class="citation" data-cites="Esaku2021 Esaku2021a">(<a href="#ref-Esaku2021" role="doc-biblioref">2021b</a>, <a href="#ref-Esaku2021a" role="doc-biblioref">2021a</a>)</span> finds a somewhat circular driving relationship between Ugandan inequality, poverty and working in what calls the shadow economy: inequality increases the size of the informal economy, as a large subsistence sector creates revenue tax shortfalls, undermines the governments efforts to attain equitable income distributions in the economy and the creation of social safety nets for the poort, who, in turn, have to turn to the informal economy to secure their livelihoods, increasing its size both short- and long-term and feeding back into the cycle.</p>
|
||
<p><span class="citation" data-cites="Cali2014">Cali (<a href="#ref-Cali2014" role="doc-biblioref">2014</a>)</span> finds that, already, one of the primary determinants of income disparity in more trade-exposed markets of Uganda in the 1990s were the increasing education differences leading to more disparate wage premiums. Additionally, slow structural change — further impeded by the onset of the COVID-19 pandemic, which pushed both urban and rural residents back into poverty — leaves a low-productivity agricultural sector which becomes, in combination with a lack of education, the strongest predictor of poverty: the poverty rate in households with an uneducated household head (17% of all households) is 48% (2019/20), while already households with a household head possessing primary education (also 17% of all) nearly cuts this in half with 25% poverty rate (2019/20) <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||
<p>The World Bank <span class="citation" data-cites="WorldBank2022">(<a href="#ref-WorldBank2022" role="doc-biblioref">2022e</a>)</span> calculated a Learning Poverty Indicator for Uganda which finds that 82% of children at late primary age are not proficient in reading, 81% of children do not achieve minimum proficiency level in reading at the end of primary schooling, and 4% of primary school-aged children are not enrolled in school at all. <span class="citation" data-cites="Datzberger2018">Datzberger (<a href="#ref-Datzberger2018" role="doc-biblioref">2018</a>)</span> argues these problems primarily exist in Uganda due to choosing an approach to education that is primarily assimilation-based, that is, intended to effect change at the individual-level through fostering grassroots education throughout society at large, instead of looking into more transformative policy approaches which would operate on a more systemic level, removing oppressive structures of inequality in tandem with government institutions at multiple levels.</p>
|
||
<!-- water access -->
|
||
<section id="inequalities-in-access-to-drinking-water" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inequalities-in-access-to-drinking-water">Inequalities in access to drinking water</h3>
|
||
<p>Such personal circumstances as access to a timely education play decisive role in life and human capital development — circumstances to which decent housing as well as access to clean water are equally fundamental building blocks <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>. In 1990 a policy initiative to shift from a supply-driven to a demand-driven model for rural drinking water provision was enacted which, over time, improved rural safe water coverage slightly but also made operation and maintenance of improved water sources pose a challenge that could impede long-term access to safe water.</p>
|
||
<p>In the country, access to improved water sources rose from 44% in 1990 to 60% in 2004 and 66% in 2010 <span class="citation" data-cites="Naiga2015">(<a href="#ref-Naiga2015" role="doc-biblioref">Naiga et al., 2015</a>)</span>. In 2019, access to improved sources of drinking water in the country is at a level of 87% in urban areas and 74% in rural areas, with relatively little inequality in rural regions between poor and non-poor households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||
<p>Health care facilities in rural areas are generally well connected to improved sources with 94% of facilities having access to public stand posts, protected spring technology, deep boreholes and some to rain harvesting tanks, gravity flow schemes or groundwater-based pumped piped water supplies <span class="citation" data-cites="Mulogo2018">(<a href="#ref-Mulogo2018" role="doc-biblioref">Mulogo et al., 2018</a>)</span>. Thus, individual households are generally less well connected than health care facilities, and rural households in turn less well than urban households.</p>
|
||
<!-- Isingiro district -->
|
||
<p>The same study found for the Isingiro district in Western Uganda on the other hand, in 2010, only 28% of households had access to improved water <span class="citation" data-cites="Mulogo2018">(<a href="#ref-Mulogo2018" role="doc-biblioref">Mulogo et al., 2018</a>)</span>. <!-- TODO check validity --> <span class="citation" data-cites="Naiga2015">Naiga et al. (<a href="#ref-Naiga2015" role="doc-biblioref">2015</a>)</span> investigated the characteristics of improved water access in the Isingiro district, finding that whereas the national average distance to travel for a water source is 0.2km in urban and 0.8km in rural locations, in Isingiro it is 1.5km, and of the fewer existing improved water sources, only 53% were fully functional, with 24% being only partly functional (having only low or intermittent yield) and 18% not being functional at all. Additionally, they found blocked drainage channels in some of the sources which could in turn lead to a possible health risk due to contamination of the source.</p>
|
||
<p><span class="citation" data-cites="Naiga2018">Naiga (<a href="#ref-Naiga2018" role="doc-biblioref">2018</a>)</span> argues that some reasons for the low access to working improved water sources is the absence of many of the organizational characteristics prescribed by the design principles of community-managed water infrastructure management — unclear social boundaries, missing collective-choice arrangements and a lack of sanctions or conflict resolution mechanisms — in other words, a policy failure resulting in lack of sufficient self-governance arrangements.</p>
|
||
<p>Such inequalities in water access often stand in direct relationship with other inequalities such as along gender, geographic or income dimensions, with fetching water traditionally being a female care role, the cost of user fees to gain access to improved water being prohibitive to poorer households, while the remoteness of many households’ location makes the trekk to the source more time-consuming and replacement parts for repairs difficult to source in an adequate time <span class="citation" data-cites="Naiga2015">(<a href="#ref-Naiga2015" role="doc-biblioref">Naiga et al., 2015</a>)</span>.</p>
|
||
<!-- water access during extreme events -->
|
||
<p>Looking into the effects of climate change and its accompanying increase in climate shock events, especially droughts, on such gender roles, <span class="citation" data-cites="Nagasha2019">Nagasha et al. (<a href="#ref-Nagasha2019" role="doc-biblioref">2019</a>)</span> find that it gender roles adapt while gender inequalities tend to increase, with men participating more in firewood collection and water fetching but generally focused on assuming a single reproductive role while women played multiple roles simultaneously. Two effects they found of this exacerbation were the women often being forced to engage their children in work activities to manage the simultaneous workload, and women, due to their exclusion from landownership in the region, being brought further into a state of dependence and thus made even more vulnerable to future climate change effects.</p>
|
||
<p>Water supply use seems to experience little change during emergency situations, and people’s willingness (or ability) to pay for water is also too small to maintain water revenue without addressing the disparity in socio-economic attributes of households <span class="citation" data-cites="Sempewo2021 Sempewo2021a">(<a href="#ref-Sempewo2021" role="doc-biblioref">Sempewo, Kisaakye, et al., 2021</a>; <a href="#ref-Sempewo2021a" role="doc-biblioref">Sempewo, Mushomi, et al., 2021</a>)</span>. Taken together, this hints at one possibility of subsequent health disparity increases due to prior income inequalities and poverty during emergency situations such as climate shocks.</p>
|
||
<p>Access to water is also one of the primary reasons for both real and perceived food insecurity vulnerabilities, even more so during climate shocks. In Uganda, <span class="citation" data-cites="Cooper2016">Cooper & Wheeler (<a href="#ref-Cooper2016" role="doc-biblioref">2016</a>)</span> investigate the vulnerability of rural farmers to climate events and find that, while most farmers implement anticipatory and livelihood coping responses (54.7%), many responses only protect against very specific events (45.4%) and most had no response at all to coping with rainfall variability: while farmers with more land, education, access to government extensions and non-farm livelihoods have more capacity to buffer the shock, both wealthier farmers (droughts as highest perceived risk) and poor farmers (extreme rainfall as highest) perceive themselves most vulnerable to rainfall-based events.</p>
|
||
<p>In the Isingiro district, <span class="citation" data-cites="Twongyirwe2019">Twongyirwe et al. (<a href="#ref-Twongyirwe2019" role="doc-biblioref">2019</a>)</span> find that most farmers (68.6%) perceive food insecurity as a problem with the overwhelming majority seeing droughts as the major contributory issue to this food insecurity (95.6%). They also find that mainly higher-income and larger farms see it as less of a problem, while 13% of all farmers report that they did not, or could not, do anything to respond to the drought effects. Lastly, even for inhabitants of wetland areas, droughts can pose problems. <span class="citation" data-cites="Yikii2017">Yikii et al. (<a href="#ref-Yikii2017" role="doc-biblioref">2017</a>)</span>, looking at the prevalence and determining factors of food insecurity in wetland adjacent areas in the district, find that 93% of households within wetlands are already food insecure due to poverty, low levels of labor productivity and low levels of education, which they argue would worsen in droughts unless the government finds ways of promoting food and nutrition education, alternative income generating activities, drought resistant crop varieties and ways of water conservation.</p>
|
||
<p>Uganda houses around 1.3 million refugees in 13 refugee camps located in 11 districts across the country, including Nakivale refugee camp in the Isingiro district. In refugee camps, water continues to be a scarce resource: While concrete reports on refugee camps in the Isingiro district are scarce, the circumstances in neighboring refugee camps have more received more quantification, with only 67% of the Kyangwali refugee camp having access to improved water sources and only 46% access to sanitation service facilities <span class="citation" data-cites="Calderon-Villarreal2022">(<a href="#ref-Calderon-Villarreal2022" role="doc-biblioref">Calderón-Villarreal et al., 2022</a>)</span>. Little access to sanitation sites can in turn negatively affect access to clean water if no improved water sources are nearby, as was the case with a prolonged cholera outbreak in Kyangwali due to a contaminated stream in 2018 <span class="citation" data-cites="Monje2020">(<a href="#ref-Monje2020" role="doc-biblioref">Monje et al., 2020</a>)</span>.</p>
|
||
<p>Such resource scarcity can also be a gendered problem, with predominantly girls and young women experiencing an increased amount of sexual and gender based violence as access to resources (especially water, food and firewood) becomes more scarce <span class="citation" data-cites="Logie2021">(<a href="#ref-Logie2021" role="doc-biblioref">Logie et al., 2021</a>)</span>. In Nakivale refugee camp growing numbers of refugees have arrived throughout 2022, many re-situated from other Ugandan refugee camps <span class="citation" data-cites="UNHCR2022">(<a href="#ref-UNHCR2022" role="doc-biblioref">UNHCR, 2022</a>)</span>. Here, water scarcity is an increasingly urgent issue, with its primary reasons being a limited waste management system exacerbating Lake Nakivale’s water quality degradation and a poor state of water reticulation: a large number of non-functioning tap stands, underfunded and non-functioning water treatment plants, while peripheries of the settlement are not covered by water supply at all <span class="citation" data-cites="UNHCR2020">(<a href="#ref-UNHCR2020" role="doc-biblioref">UNHCR, 2020</a>)</span>.</p>
|
||
<!-- conclusion -->
|
||
<p>Thus, while Uganda’s poverty and inequality are not trending towards drastically worsening over the last years, hidden disparities bring its issues in focus once disaggregated: Nationally, poverty is a looming transient affair for many households, more if increasing the country’s very low national line of poverty. Inequality derives itself partly from this poverty, making it necessary for many to accept informal work which, taken at large, in turn fosters further national inequality.</p>
|
||
<p>The role education plays in Uganda’s allocation of poverty cannot be overstated, with especially many rural children not having adequate opportunity to access timely education. This disparity could be exacerbated by poor quality access to clean water through improved water sources, which in turn worsens food securities, retrenches gender role inequalities and precludes more children from their education. In the district of Isingiro in West Uganda access to water is considerably below the national average, with policy failures during implementation now leading to partly or non-functional water sources. The problem runs danger of deteriorating with an increased amount of climate shocks such as droughts threatening to exacerbate existing inequalities and drive further households into poverty.</p>
|
||
<!-- development assistance -->
|
||
</section>
|
||
<section id="development-assistance-to-uganda" class="level3 page-columns page-full">
|
||
<h3 class="anchored" data-anchor-id="development-assistance-to-uganda">Development assistance to Uganda</h3>
|
||
<div class="cell" data-execution_count="14">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load CRS data</span></span>
|
||
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Uganda_11-13_05092022214241555.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Uganda_14-16_05092022213749491.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Uganda_17-20_05092022213356210.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.concat([dfsub1, dfsub2, dfsub3], ignore_index<span class="op">=</span><span class="va">True</span>)</span>
|
||
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">'</span><span class="ch">\ufeff</span><span class="st">"DONOR"'</span>: <span class="st">'DONOR'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell page-columns page-full" data-execution_count="15">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a><span class="co">## count amount of development aid financing instruments (grants/loans) by year and display</span></span>
|
||
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a><span class="co">## count USD amount of development aid financing instumrnets by year and display</span></span>
|
||
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">'Flow'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">'Flow'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">'Financetype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Financetype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid amount, in millions of USD"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-uga-aid-financetype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 5: Total ODA for Uganda per year, by financing type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in <a href="#fig-uga-aid-financetype">Figure 5</a>. The Official Development Assistance overall further increased to over 2.2bn USD in 2019, before rapidly increasing in 2020 to over 3.0bn USD. The overall trend of increasing aid money is largely due to increases in development grants which especially increased from 2015 to 2017. In general, development loans play a smaller role in absolute terms: Whereas in 2011 around 1.2bn USD funds came in the form of grants, only around 0.3bn USD were in the form of loans. The absolute portion of loans slowly increased until 2019 to just over 500m USD, before significantly increasing in 2020, tripling to almost 1.5bn USD.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="16">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">"Donortype"</span>] <span class="op">=</span> donortotals[<span class="st">"DONOR"</span>].<span class="bu">map</span>(donortypes)</span>
|
||
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">"Donortype"</span>] <span class="op">!=</span> <span class="st">"nondac"</span>)]</span>
|
||
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">'Donortype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Donortype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid amount, in millions of USD"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-uga-aid-donortype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 6: Total ODA for Uganda per year, separated by donor type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>In terms of predominant donor types, bilateral aid to Uganda was much higher than multilateral aid to the country until 2019. In 2011 only about 400m USD were provided through multilateral donors while almost 1.2bn USD were provided via bilateral donors, though the multilateral contributions quickly grew to over 600m USD in 2013. Despite a significant decrease of multilateral aid in 2014, the amount of multilateral aid kept generally stagnant until 2018, when the amount first increased to 800m USD in 2019 and subsequently to over 1.7bn in 2020.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="17">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 14020-22 - large scale potable water</span></span>
|
||
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 14030-32 - individual-level water and sanitation supply</span></span>
|
||
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 14081 - education and training in water supply</span></span>
|
||
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> ( (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14020</span>) <span class="op">|</span></span>
|
||
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span></span>
|
||
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14022</span>) <span class="op">|</span></span>
|
||
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14030</span>) <span class="op">|</span></span>
|
||
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span></span>
|
||
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14032</span>) <span class="op">|</span></span>
|
||
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14081</span>)</span>
|
||
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a> ) <span class="op">&</span></span>
|
||
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a>wateraid <span class="op">=</span> totals[totals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of large scale water supply and sanitation</span></span>
|
||
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span> (wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14022</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">14020</span></span>
|
||
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14020</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Water supply and sanitation - large systems"</span></span>
|
||
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of individual water supply and sanitation</span></span>
|
||
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span> (wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14032</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">14030</span></span>
|
||
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">14030</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Basic drinking water supply and basic sanitation"</span></span>
|
||
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by sector per year and sum all values</span></span>
|
||
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a>wateraid_grouped <span class="op">=</span> wateraid.groupby([<span class="st">'Year'</span>, <span class="st">'Sector'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a>wateraid_grouped <span class="op">=</span> wateraid_grouped.reset_index([<span class="st">'Year'</span>, <span class="st">'Sector'</span>])</span>
|
||
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a>wateraid_grouped.columns <span class="op">=</span> wateraid_grouped.columns.to_flat_index()</span>
|
||
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a>wateraid_grouped.columns <span class="op">=</span> [<span class="st">'Year'</span>, <span class="st">'Sector'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(wateraid_grouped[<span class="st">'Year'</span>], wateraid_grouped[<span class="st">'Sector'</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>wateraid_grouped[<span class="st">'Value'</span>], aggfunc<span class="op">=</span><span class="st">'sum'</span>)</span>
|
||
<span id="cb14-33"><a href="#cb14-33" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb14-34"><a href="#cb14-34" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename and reorder columns</span></span>
|
||
<span id="cb14-35"><a href="#cb14-35" aria-hidden="true" tabindex="-1"></a>crosstab.columns <span class="op">=</span> [<span class="st">'Basic water supply'</span>, <span class="st">'Education and training'</span>, <span class="st">'Large water supply'</span>, <span class="st">'All'</span>]</span>
|
||
<span id="cb14-36"><a href="#cb14-36" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> crosstab[[<span class="st">'Basic water supply'</span>, <span class="st">'Large water supply'</span>, <span class="st">'Education and training'</span>, <span class="st">'All'</span>]]</span>
|
||
<span id="cb14-37"><a href="#cb14-37" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb14-38"><a href="#cb14-38" aria-hidden="true" tabindex="-1"></a>Markdown(tabulate(crosstab.fillna(<span class="st">"0.00"</span>), headers<span class="op">=</span><span class="st">"keys"</span>, tablefmt<span class="op">=</span><span class="st">"github"</span>, floatfmt<span class="op">=</span><span class="st">".2f"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page" data-execution_count="17">
|
||
<div id="tbl-uga-aid-watersupply" class="anchored">
|
||
<table class="table table-sm table-striped">
|
||
<caption>Table 2: ODA projects of water and sanitation supply in Uganda per year, separated by project type</caption>
|
||
<colgroup>
|
||
<col style="width: 9%">
|
||
<col style="width: 25%">
|
||
<col style="width: 25%">
|
||
<col style="width: 30%">
|
||
<col style="width: 9%">
|
||
</colgroup>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Year</th>
|
||
<th>Basic water supply</th>
|
||
<th>Large water supply</th>
|
||
<th>Education and training</th>
|
||
<th>All</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>2011</td>
|
||
<td>10.74</td>
|
||
<td>17.00</td>
|
||
<td>14.53</td>
|
||
<td>42.27</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2012</td>
|
||
<td>26.08</td>
|
||
<td>30.15</td>
|
||
<td>12.40</td>
|
||
<td>68.63</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2013</td>
|
||
<td>10.18</td>
|
||
<td>49.49</td>
|
||
<td>4.54</td>
|
||
<td>64.21</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2014</td>
|
||
<td>21.06</td>
|
||
<td>37.09</td>
|
||
<td>0.42</td>
|
||
<td>58.58</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2015</td>
|
||
<td>22.28</td>
|
||
<td>51.68</td>
|
||
<td>0.20</td>
|
||
<td>74.16</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2016</td>
|
||
<td>12.18</td>
|
||
<td>72.62</td>
|
||
<td>0.00</td>
|
||
<td>84.80</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2017</td>
|
||
<td>14.17</td>
|
||
<td>94.10</td>
|
||
<td>0.10</td>
|
||
<td>108.37</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2018</td>
|
||
<td>10.77</td>
|
||
<td>110.02</td>
|
||
<td>0.06</td>
|
||
<td>120.85</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2019</td>
|
||
<td>16.03</td>
|
||
<td>89.60</td>
|
||
<td>0.49</td>
|
||
<td>106.12</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2020</td>
|
||
<td>21.08</td>
|
||
<td>125.15</td>
|
||
<td>0.20</td>
|
||
<td>146.43</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>All</td>
|
||
<td>164.57</td>
|
||
<td>676.89</td>
|
||
<td>32.96</td>
|
||
<td>874.42</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. The categories under analysis are large- and small-scale water supply and sanitation infrastructure projects as well as education and training for the management of water supply infrastructure. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The breakdown of development aid to water supply infrastructure and education projects can be seen in <a href="#tbl-uga-aid-watersupply">Table 2</a>. It shows that overall the contributions to improve water access have been increasing, starting at 42.27m USD in 2011 and climbing to 146.43m USD by 2020. The development funds are broken down into three categories: Basic and large water supply improvement and education and training.</p>
|
||
<p>Education and training encompasses training for both professionals in the field itself and service providers. Water supply improvement is broken down into funds for large systems — potable water treatment plants, intake works, large pumping stations and storage, as well as large-scale transmission and distribution systems — and more individual-level basic water supply, such as handpumps, gravity wells, rainwater collection systems, storage tanks, and smaller, often shared, distributions systems. The basic water supply encompasses a more endpoint-oriented collection of measures, often situated in rural locations. Both the large and small scale categories encompass sanitation, with larger-scale sewage pumping stations and trunk sewers, as well as smaller on-site disposal and sanitation systems, latrines and alternative systems. This is due to most infrastructure projects missing the concrete dimensions to separate water supply from sanitation in the data, either due to infrastructural overlap or missing data points.</p>
|
||
<p>The split shows that while basic water supply infrastructure projects have tended to be contributed to between 10m USD and 20m USD, with little overall increase from 2011 to 2020. Large-scale water supply and sanitation projects have, however, seen a significant increase over time, starting at a contribution of 17m USD in 2011 and receiving a 125.15m USD contribution in 2020. This may speak to the necessity of larger infrastructure in place before more basic water supply infrastructure can make use of it, or the provision of large infrastructure at the cost of implementations at smaller scales.</p>
|
||
<p>Education and training for water infrastructure management and service provision, while still receiving contributions of 14.53m USD and 12.40m USD in 2011 and 2012 respectively, significantly decrease over the next years to amounts continuously under one million. The monetary focus for aid provision thus lies on large-scale water supply and sanitation projects for these years.</p>
|
||
<div style="page-break-after: always;"></div>
|
||
</section>
|
||
</section>
|
||
<section id="benin" class="level2 page-columns page-full">
|
||
<h2 class="anchored" data-anchor-id="benin">Benin</h2>
|
||
<hr>
|
||
<ul>
|
||
<li>A stable and increasing real GDP growth rates but slow decrease in poverty levels.</li>
|
||
<li>Poverty affects households in poorly educated households in rural areas to much higher levels than urban areas.</li>
|
||
<li>Education disparities happen mainly along community-level dimensions through high socio-economic segregation of schools and different access to resources.</li>
|
||
<li>Large disparity of access to electricity between urban and rural households, which directly negatively affects the environmental conditions of individual rural households.</li>
|
||
<li>No access to electricity due to both lacking rural infrastructure and electrical grid connection costs being too high.</li>
|
||
<li>Rapid electrification will require both infrastructure expansion and policy commitment to finding ways of lowering grid connection costs.</li>
|
||
</ul>
|
||
<hr>
|
||
<!-- intro/overall -->
|
||
<p>Benin in recent years has seen fairly stable real GDP growth rates and downward trending poverty levels in absolute terms. Its growth rate averaged 6.4% for the years 2017 to 2019 and, with a decrease during the intermittent years due to the Covid-19 pandemic, has recovered to a rate of 6.6% in 2021 <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>. There only exists sporadic and fluctuating data on the country’s overall inequality, with the World Bank Development Index noting a Gini coefficient of 38.6 for the year (2003) before rising to 43.4 (2011) and up to 47.8 (2015), though decreasing below the 2003 level to 37.8 (2018) in its most recent calculation, see <a href="#fig-ben">Figure 7</a>. At the same time, the country’s poverty rate, even measured based on the international line, only decreased at a very slow rate in its most recent years, from a share of households in poverty at 18.8% in 2019, to 18.7% in 2020 and 18.3% at the end of 2021, with the reduction threatened to be slowed further through increased prices on food and energy <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>.</p>
|
||
<div class="cell" data-execution_count="18">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> ben[ben[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>gini_plot(gni_cnsmpt_percapita)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display">
|
||
<div id="fig-ben" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 7: Gini index of consumption per capita for Benin</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||
</div>
|
||
<!-- poverty -->
|
||
<p>Based on its national poverty line, Benin’s overall poverty rate is 38.5%, though it hides a strong spatial disparity in the incidence of poverty between rural (44.2%) and urban (31.4) areas <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>. Looking at the effect of income growth on the time to exit poverty, <span class="citation" data-cites="Alia2017">Alia (<a href="#ref-Alia2017" role="doc-biblioref">2017</a>)</span> finds a general negative correlation with stronger growth indeed leading to shorter average exit times (7-10 years for a household at a per capita growth rate of 4.2%), though this aggregate also hides a large heterogeneity primarily determined by a households size, its available human capital and whether it is located rurally. So while the study does conclude for an overall equitable pro-poor growth in Benin, rural households, beside already being relatively more poverty stricken, are in danger of being left further behind during periods of overall growth. <span class="citation" data-cites="Djossou2017">Djossou et al. (<a href="#ref-Djossou2017" role="doc-biblioref">2017</a>)</span> find similar pro-poor growth with spatial disparities but surprisingly see urban households potentially benefiting less than rural households from additional growth, with efforts to open up communities to harness the benefits of growth often primarily targeted at rural communities.</p>
|
||
<!-- drivers: endowment/assets: education, ..? -->
|
||
<p>Using the Learning Poverty index, which combines the share of school deprivation (the share of primary-aged children out-of-school) and learning deprivation (share of pupils below a minimum proficiency in reading), a <span class="citation" data-cites="WorldBank2022a">World Bank (<a href="#ref-WorldBank2022a" role="doc-biblioref">2022a</a>)</span> report shows that 56% of children at late primary age in Benin are not proficient in reading, 55% do not achieve minimum proficiency levels at the end of primary school and 3% of primary school-aged children are not enrolled in school at all. <!-- TODO These levels are higher than in Uganda, though, since ... gender dimension? --> Looking purely at attendance rates, <span class="citation" data-cites="McNabb2018">McNabb (<a href="#ref-McNabb2018" role="doc-biblioref">2018</a>)</span> finds that the primary household-level determinants of attendance are the wealth of a household, its religion, as well as the education level of its household head. Here, gender disparities persist, however, with girls continuously less likely to attend and adopted girls being at the greatest disadvantage, while boys tend to face higher opportunity costs than girls due to often working in the fields in which case the distance to a school begins to play an important role. While the household-level variables do play a role — through the availability of educational resources at home, differences in schooling quality and overall health and well-being — <span class="citation" data-cites="Gruijters2020">Gruijters & Behrman (<a href="#ref-Gruijters2020" role="doc-biblioref">2020</a>)</span> find that most of the disparity stems from the community-level: the difference in school quality is large, marked by high socio-economic segregation between schools, and primarily determined through an unequal distribution of teaching resources including teachers and textbooks.</p>
|
||
<p>Thus, while growth is generally pro-poor in Benin, its primary determinants do not cluster only at the household level, but are comprised of partly household-level but especially community-level differences.</p>
|
||
<section id="inequalities-in-access-to-electricity" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inequalities-in-access-to-electricity">Inequalities in access to electricity</h3>
|
||
<!-- electricity access -->
|
||
<p>One of the foremost examples of the effects of inequal endowments can have is brought by <span class="citation" data-cites="VanDePoel2009">Van De Poel et al. (<a href="#ref-VanDePoel2009" role="doc-biblioref">2009</a>)</span> when they look at the determinants of rural infant death rates in Benin among others and find that environmental factors — such as access to a safe water source, quality housing materials and electricity — are the primary determinants, ahead even of access to a health facility in the community. Access to electricity in the country especially underlies a large heterogeneity based on location. The overall level of electrification of Benin has been rising slowly — though outpacing population growth — from 22% in 2000 to 26% in 2005, 34% in 2010, a decline to 30% in 2015 and then a faster increase to 40% in 2019, although a broad difference in electrification levels between urban (65%) and rural (17%) regions remain <span class="citation" data-cites="WorldBank2021">(<a href="#ref-WorldBank2021" role="doc-biblioref">World Bank, 2021</a>)</span>.</p>
|
||
<p>In rural areas there are generally three approaches to electrification that work outside of a connection to the main grid, individual installation of solar panels or generators for smaller electric appliances, collective solutions like kiosks offering electric charging for some cost, or autonomous mini-grids powering a portion of a more densely populated rural area (though often requiring permits or licenses if above certain sizes) <span class="citation" data-cites="Jaglin2019">(<a href="#ref-Jaglin2019" role="doc-biblioref">Jaglin, 2019</a>)</span>.</p>
|
||
<p><span class="citation" data-cites="Rateau2022">Rateau & Choplin (<a href="#ref-Rateau2022" role="doc-biblioref">2022</a>)</span> see one of the primary reasons for off-grid electrification in either physical unavailability in rural areas or a prohibitively high cost for connection to the grid. However, these more individualized solutions are often only targeted at credit-worthy customers and can lead to a further increase in inequalities between income percentiles, leaving behind households which are already neglected within the field of energy access <span class="citation" data-cites="Barry2020">(<a href="#ref-Barry2020" role="doc-biblioref">Barry & Creti, 2020</a>)</span>. The former, physical access, is argued by <span class="citation" data-cites="Djossou2017">Djossou et al. (<a href="#ref-Djossou2017" role="doc-biblioref">2017</a>)</span> as well, emphasizing the need for continued infrastructure expansion to more households, in order to provide access to more durable goods (fridges, mobile phones and internet) which can help decrease the inequality gap. The latter, prohibitively high costs, should not be disregarded in such an infrastructure expansion as well, however.</p>
|
||
<p>One of the major obstacles to main grid connection remains the high charge a customer is expected to pay with solutions requiring continued political commitment to identify, examine and implement more low-cost electrification processes as well as financing solutions. <span class="citation" data-cites="Golumbeanu2013">Golumbeanu & Barnes (<a href="#ref-Golumbeanu2013" role="doc-biblioref">2013</a>)</span> point out the main obstacles that need to be addressed here: the lack of incentives to increase electrical affordability, a weak utilities commitment toward providing broad electricity access with focus often lying more on high-consumption urban markets, often overrated technical specifications for low loads, too great distances between households and distribution poles in an area, and an overall lack of affordable financing solutions.</p>
|
||
<!-- conclusion -->
|
||
<p>Thus, though having a relatively stable and growing real GDP, Benin suffers from slow decreases in its poverty rates coupled with a relative unchanged income inequality. Additionally, the country’s poverty rates have a high heterogeneity with relatively more rural households and households with poor education in poverty. A large part of education disparities happens at the community-level, with schools marked by high socio-economic segregation, but household-level disparities, especially environmental ones, playing a role. One of those determinants is a household’s access to electricity, of which there is an enormous disparity between urban and rural households. The primary reasons for not having access to electricity are the lack of physical infrastructure available in rural areas, as well as connection costs to the main electrical grid being too high. To decrease the effects of this driving force of inequality, both infrastructural expansion as well as policy commitments toward affordable connections to electrical grids are thus of vital importance.</p>
|
||
<!-- development assistance -->
|
||
</section>
|
||
<section id="development-assistance-to-benin" class="level3 page-columns page-full">
|
||
<h3 class="anchored" data-anchor-id="development-assistance-to-benin">Development assistance to Benin</h3>
|
||
<div class="cell" data-execution_count="19">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load CRS data</span></span>
|
||
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Benin_11-13_05092022185506030.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Benin_14-16_05092022192438936.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Benin_17-20_05092022192856890.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.concat([dfsub1, dfsub2, dfsub3], ignore_index<span class="op">=</span><span class="va">True</span>)</span>
|
||
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">'</span><span class="ch">\ufeff</span><span class="st">"DONOR"'</span>: <span class="st">'DONOR'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell page-columns page-full" data-execution_count="20">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'RECIPIENT'</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&</span> <span class="co"># Benin</span></span>
|
||
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a><span class="co">## count amount of development aid financing instruments (grants/loans) by year and display</span></span>
|
||
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a><span class="co">## count USD amount of development aid financing instumrnets by year and display</span></span>
|
||
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">'Flow'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">'Flow'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">'Financetype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Financetype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid amount, in millions of USD"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-ben-aid-financetype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img src="data:image/png;base64,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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 8: Total ODA for Benin per year, by financing type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The total amount of development aid for Benin registered by the OECD Creditor Reporting System has been fluctuating, with an overall upward trend since 2011: The aid broken down by financing type can be seen in <a href="#fig-ben-aid-financetype">Figure 8</a> and shows that money has predominantly been given by way of ODA grants, with roughly double the absolute monetary amount of ODA loans per year. There was an increase in both ODA grants and ODA loans which lead to a significant increase in total development assistance in 2017, and while loans decreased until 2019, grants steadily increased from 2018 to 2020. With loans also beginning to increase from 2019, the overall amount of development assistance saw a large increase in 2020, most likely predominantly due to Covid-19 pandemic related aid packages.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="21">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'RECIPIENT'</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&</span> <span class="co"># Benin</span></span>
|
||
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">"Donortype"</span>] <span class="op">=</span> donortotals[<span class="st">"DONOR"</span>].<span class="bu">map</span>(donortypes)</span>
|
||
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">"Donortype"</span>] <span class="op">!=</span> <span class="st">"nondac"</span>)]</span>
|
||
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">'Donortype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Donortype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid amount, in millions of USD"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a><span class="co"># 0-figure</span></span>
|
||
<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a>fig.update_yaxes(rangemode<span class="op">=</span><span class="st">"tozero"</span>)</span>
|
||
<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-ben-aid-donortype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 9: Total ODA for Benin per year, separated by donor type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The total amount of development aid for Benin registered by the OECD Creditor Reporting System, broken down into individual donor types can be seen in <a href="#fig-ben-aid-donortype">Figure 9</a>. It shows that bilateral development aid by individual member countries tended to be higher than that provided through multilateral donors until 2019. Beginning in 2020 this split reversed to higher development aid amounts donated through multilateral donors than individual bilateral aid.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="22">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'RECIPIENT'</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&</span> <span class="co"># Benin</span></span>
|
||
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">23630</span>) <span class="op">|</span> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">23631</span>)) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>electricityaid <span class="op">=</span> totals[totals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a>el_grouped <span class="op">=</span> electricityaid.groupby([<span class="st">'Year'</span>, <span class="st">'Flow'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a>el_grouped <span class="op">=</span> el_grouped.reset_index([<span class="st">'Year'</span>, <span class="st">'Flow'</span>])</span>
|
||
<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>el_grouped.columns <span class="op">=</span> el_grouped.columns.to_flat_index()</span>
|
||
<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>el_grouped.columns <span class="op">=</span> [<span class="st">'Year'</span>, <span class="st">'Flow'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(el_grouped[<span class="st">'Year'</span>], el_grouped[<span class="st">'Flow'</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>el_grouped[<span class="st">'Value'</span>], aggfunc<span class="op">=</span><span class="st">'sum'</span>)</span>
|
||
<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>Markdown(tabulate(crosstab.fillna(<span class="st">"0.00"</span>), headers<span class="op">=</span><span class="st">"keys"</span>, tablefmt<span class="op">=</span><span class="st">"github"</span>, floatfmt<span class="op">=</span><span class="st">".2f"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page" data-execution_count="22">
|
||
<div id="tbl-ben-aid-electricity" class="anchored">
|
||
<table class="table table-sm table-striped">
|
||
<caption>Table 3: ODA for transmission and distribution of electric power in Benin per year, separated by financing type</caption>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Year</th>
|
||
<th>ODA Grants</th>
|
||
<th>ODA Loans</th>
|
||
<th>All</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>2011</td>
|
||
<td>0.17</td>
|
||
<td>5.29</td>
|
||
<td>5.46</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2012</td>
|
||
<td>4.81</td>
|
||
<td>3.27</td>
|
||
<td>8.08</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2013</td>
|
||
<td>3.82</td>
|
||
<td>18.90</td>
|
||
<td>22.72</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2014</td>
|
||
<td>6.80</td>
|
||
<td>8.60</td>
|
||
<td>15.41</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2015</td>
|
||
<td>6.00</td>
|
||
<td>24.73</td>
|
||
<td>30.72</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2016</td>
|
||
<td>3.01</td>
|
||
<td>16.50</td>
|
||
<td>19.51</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2017</td>
|
||
<td>2.37</td>
|
||
<td>19.73</td>
|
||
<td>22.10</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2018</td>
|
||
<td>7.78</td>
|
||
<td>22.83</td>
|
||
<td>30.61</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2019</td>
|
||
<td>14.15</td>
|
||
<td>27.92</td>
|
||
<td>42.07</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2020</td>
|
||
<td>55.19</td>
|
||
<td>45.59</td>
|
||
<td>100.78</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>All</td>
|
||
<td>104.10</td>
|
||
<td>193.36</td>
|
||
<td>297.46</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. The category under analysis is Electric Power transmission and distribution (centralized grids) within the data. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p><a href="#tbl-ben-aid-electricity">Table 3</a> shows the amounts of project-bound development aid to Benin for the transmission and distribution of electric power within its centralized grid. The category subsumes grid distribution from the power source to end users and transmission lines. It also includes storage of energy to generate power (e.g. batteries) and projects to extend grid access, especially in rural areas. For development aid to the electrification of Benin, the monetary contributions are smaller but increasing and show trends quite different to that of overall development aid to the country.</p>
|
||
<p>The amount of overall development contributions to electrification increases from 2011 to 2020, with significant increases in 2013 and 2015 for loans and 2019, 2020 for grants. While there is a steady increase to the overall development aid toward electrification, increases in grants tend to lag behind increases in loans for Benin, with grants exceeding 10mil. USD for the first time in 2019 while loans already reached 18.90mil. USD in 2013. Over the complete period of 2011 to 2020, however, grants for the transmission and distribution of electric power in Benin have consistently been lower than loans.</p>
|
||
<div style="page-break-after: always;"></div>
|
||
</section>
|
||
</section>
|
||
<section id="djibouti-ethiopia" class="level2 page-columns page-full">
|
||
<h2 class="anchored" data-anchor-id="djibouti-ethiopia">Djibouti-Ethiopia</h2>
|
||
<hr>
|
||
<ul>
|
||
<li>Stable average GDP growth rates recently greatly reduced through local and global economic instabilities.</li>
|
||
<li>Enormous poverty rate with much disparity between Djibouti city and other country regions and country set to miss many future poverty rate goals.</li>
|
||
<li>High levels of deprivation for rural poor, little participation in labor force and lacking rural infrastructure.</li>
|
||
<li>Labor market highly dependent on trade and internal dichotomy of public administration sector and large informal sector comprised of predominantly unskilled workers.</li>
|
||
<li>Women facing fewer opportunities for upward educational mobility, less labor market participation, higher unemployment rates, a persisting (but closing) literacy gap.</li>
|
||
<li>Rural nomadic and pastoralist people highly vulnerable after droughts and regional instability, leading many to flee country or become sedentary and greatly reducing their numbers.</li>
|
||
</ul>
|
||
<hr>
|
||
<!-- intro -->
|
||
<p>Djibouti occupies a somewhat singular position, being a tiny country with an economy focused primarily around its deep-water port, trying to establish itself as a regional hub for trade and commerce. The country’s GDP has averaged roughly 6% per year before the Covid-19 pandemic greatly reduced those growth rates <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. However, the country’s inequality levels are high (Gini coefficient 41.6) and its poverty rates are extreme <span class="citation" data-cites="WorldBank2022c">(21.1%, <a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. Additionally in many cases there is a lack of data or the data itself are lacking in several socio-economic dimensions which hinders analysis and policy design.</p>
|
||
<div class="cell" data-execution_count="23">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> dji[dji[<span class="st">'resource'</span>].<span class="bu">str</span>.contains(<span class="st">"Consumption"</span>)]</span>
|
||
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">'scale'</span>].<span class="bu">str</span>.contains(<span class="st">"Per capita"</span>)]</span>
|
||
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>gini_plot(gni_cnsmpt_percapita)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display">
|
||
<div id="fig-dji" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img src="data:image/png;base64,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" class="img-fluid figure-img"></p>
|
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<p></p><figcaption class="figure-caption">Figure 10: Gini index of consumption per capita for Djibouti</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
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<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||
</div>
|
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<!-- poverty -->
|
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<p>Poverty in Djibouti is high and marked by high deprivation: Using the national poverty line of around 2.18USD (2011 PPP) the poverty rate for the overall country by consumption is estimated at 21.1% in 2017, while 17% live in extreme poverty under the international poverty line of 1.90USD (2011 PPP) and 32% of the population are still under the international lower middle income poverty line of 3.20USD (2011 PPP) <span class="citation" data-cites="Mendiratta2019 WorldBank2022c">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-WorldBank2022c" role="doc-biblioref">2022d</a>)</span>. Furthermore, there is a significant spatial disparity between poverty rates. <span class="citation" data-cites="Ibarra2020">World Bank (<a href="#ref-Ibarra2020" role="doc-biblioref">2020a</a>)</span> estimate only 15% of Djibouti’s overall population living in rural areas, with 45% of the country’s poor residing in rural areas while 37% reside in the Balbala<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a> area <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>. The study goes on to describe the high levels of deprivation for the rural poor, with the country’s highest dependency ratios, lowest participation in the labor force, very low levels of employment in the households’ heads and very low school enrollment, and while urban poor face similar restrictions they have better access to public services and higher school attendance rates. Access to basic amenities and services in Djibouti is low (42.1%) and 15.5% of the population have no access to both electricity and sanitation, and all people in monetary poverty are also deprived along multiple dimensions <span class="citation" data-cites="Mendiratta2020">(<a href="#ref-Mendiratta2020" role="doc-biblioref">World Bank, 2020b</a>)</span>.</p>
|
||
<p>Over half the working-age population does not participate in the labor force with employment being estimated at 45% in 2017, lower than the 46.3% estimated for 1996, despite the country’s economic growth <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <span class="citation" data-cites="Emara2020">Emara & Mohieldin (<a href="#ref-Emara2020" role="doc-biblioref">2020</a>)</span> look at the overall impact of financial inclusion on poverty levels but find that, first, Djibouti is way above its targeted poverty levels, second, it is not only one of the only countries in the region (together with Yemen) to not achieve a 5% poverty level target yet, but not even on track to achieve this target by 2030 solely through improvements in financial inclusion.</p>
|
||
<!-- inequality -->
|
||
<p>Inequality in Djibouti is high, with the lowest decile only making up 1.9% of total consumption while the richest decile enjoy 32% of the total consumption, 16 times as much as those at the lowest decile <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. The country has an estimated Gini coefficient for consumption per capita of 41.6 in 2017, making it one of the most unequal countries in the region <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>, see also <a href="#fig-dji" role="doc-biblioref">Figure 10</a>)</span>. More of its inequality hides in a large spatial and gendered heterogeneity. Urban poor face high deprivation but higher access to public services and schooling compared to the rural poor, who have only 41% access to improved water sources, 10% access to sanitation, 3% access to electricity, and with only one third living close (under 1km) to a primary school <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>.</p>
|
||
<p>While in general over half the working-age population does not participate in the labor force, the makeup is 59% of men and only 32% of women who participate, mirroring unemployment rates with an estimated third of men and two thirds of women being unemployed <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <span class="citation" data-cites="Mendiratta2019">World Bank (<a href="#ref-Mendiratta2019" role="doc-biblioref">2019</a>)</span> also find the labor market itself highly unequal, with its dichotomy of a public administrative sector (drawing mainly highly skilled workers) and informal private sector making up 90% of the overall labor market, the majority of women working in the informal sector and almost half of the jobs for women in this sector consisting of one-person ‘self-employed’ enterprises. Nearly 41% of working-age women find themselves in positions of vulnerable employment <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>.</p>
|
||
<!-- drivers -->
|
||
<p>Djibouti’s economy is primarily, and within its formal sector almost exclusively, driven by its strategic location and possession of a deep-water port so it can act as a regional refueling, trading and transport shipment center <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. At the same time, this interconnected economic nature and the country’s heavy reliance on food and energy imports marks a key vulnerability and makes it immediately dependent on the stability of global trade and export markets, a stability which was recently disrupted through a global pandemic <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>.</p>
|
||
<p>Likewise, Djibouti depends on regional stability, since its economic growth is tightly coupled with the Ethiopian economy, sourcing around 70% of its port trade from this landlocked neighbor <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. A series of droughts in the country threatened the livelihood of its nomadic and pastoralist population, with many fleeing to neighboring countries, some becoming sedentary in village or city outskirts, and the overall nomadic population decreasing by nearly three quarters from 2009 to 2017 <span class="citation" data-cites="Ibarra2020 Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-Ibarra2020" role="doc-biblioref">2020a</a>)</span>.</p>
|
||
<p>Additionally, during the early waves of Covid-19 Djibouti had one of the highest infection rates in the region, and though it had a high recovery rate, it also had one of the highest fatality rates, possibly due to deficiencies in its healthcare system <span class="citation" data-cites="ElKhamlichi2022">(<a href="#ref-ElKhamlichi2022" role="doc-biblioref">El Khamlichi et al., 2022</a>)</span>. The country’s rising costs of now fast-maturing debts made the government leave social spending behind, leaving a budget of 5% for health and 3% for social expenditures, spendings which looks diminutive compared to its over 30% expenditures on public infrastructure <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. Only 10% of rural poor inhabitants live close (under 1km) to a health facility <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>.</p>
|
||
<p>While still facing reduced rates of labor market participation, the country has expended effort on increasing women’s opportunity for education: Having overall lower literacy rates for women still, the overall literacy rates in younger cohorts (10-24 years old) is significantly higher compared to older ones, and the gaps have decreased from 24% difference between the genders (40-60 years old) to 10% (15-24 years old) and 2% (10-14 years old) <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>.</p>
|
||
<section id="gender-inequalities-in-livelihood-opportunities" class="level3">
|
||
<h3 class="anchored" data-anchor-id="gender-inequalities-in-livelihood-opportunities">Gender inequalities in livelihood opportunities</h3>
|
||
<p>Women’s lower secondary completion rate grew from 28.6% in 2009 (compared to 35.2% men) to 56.3% in 2021 (54.0% for men) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>. However, for 2017, women’s upward educational mobility was still significantly worse than men’s, with non-poor men having an upward mobility of 53%, non-poor women 29%, poor men 19% and poor women only 10% against the national average of 36% <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <!-- trade and inclusion --> Such differences reflect themselves in firm ownership structures and on the labor market, where 22.3% of all firms have female participation in ownership and only 14.2% a female top manager, and both salaried employment and agricultural employment are male-dominated (though agricultural work only with a slight and shrinking difference of 4%) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>.</p>
|
||
<p>Overall it seems, however, that past growth in the country’s GDP is likely not favorable for an inclusive growth path, with its large-scale infrastructure investments mostly creating demand for skilled workers and neglect of social spending not allowing the buffers and social safety nets that prevent further drift into inequality. <span class="citation" data-cites="Brass2008">Brass (<a href="#ref-Brass2008" role="doc-biblioref">2008</a>)</span> argues even that the country leadership’s policy decisions carry increased weight in this, towards a path of ever increasing economic dependence and into a predicament of economic diversification requiring a more educated population, but a more educated population without already accompanying diversified economy likely enacting a successful policy or governmental opposition.</p>
|
||
<!-- conclusion -->
|
||
<p>Thus, Djibouti represents a country with an overall solid growth rate but accompanying high inequalities and poverty rates, from which path it does not seem to detach without more policy intervention. It is a country with one of the highest poverty rates in the region and an enormous spatial disparity in poverty between the prime sectors of Djibouti city and the rest of the country. The rural sectors face high levels of deprivation, economic disparity and largely lacking infrastructure, and the majority of its population not participating in the labor force. The country’s labor market is to the largest degree dichotomized in the public administrative sector, comprised of mostly skilled workers, and a large private informal sector comprised mostly of unskilled workers, many of which are women. The overall economy is dependent on high levels of regional and global stability which was recently undermined by droughts, Ethiopian conflict and the Covid-19 pandemic. Nomadic and pastoralist people in the country’s rural regions were hit especially hard, with the nomadic population decreasing by nearly three quarters and many fleeing or becoming sedentary. Women face less opportunity in the country with worse upward educational mobility, less participation in the labor force, higher unemployment rates, and a continuing, if closing, gender literacy gap. Djibouti is set to miss most of its poverty target levels and move along a growth pathway that does not lend itself to inclusion unless active policy measures changing its economic investment and growth strategies are examined.</p>
|
||
<!-- development assistance -->
|
||
</section>
|
||
<section id="development-assistance-to-djibouti" class="level3 page-columns page-full">
|
||
<h3 class="anchored" data-anchor-id="development-assistance-to-djibouti">Development assistance to Djibouti</h3>
|
||
<div class="cell" data-execution_count="24">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load CRS data</span></span>
|
||
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Djibouti_11-13_05092022210301944.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Djibouti_14-16_05092022210632022.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">'data/raw/OECD_CRS/CRS1_Djibouti_17-20_05092022210913679.csv'</span>, parse_dates<span class="op">=</span><span class="va">True</span>, low_memory<span class="op">=</span><span class="va">False</span>)</span>
|
||
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.concat([dfsub1, dfsub2, dfsub3], ignore_index<span class="op">=</span><span class="va">True</span>)</span>
|
||
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">'</span><span class="ch">\ufeff</span><span class="st">"DONOR"'</span>: <span class="st">'DONOR'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
</div>
|
||
<div class="cell page-columns page-full" data-execution_count="25">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a><span class="co">## count amount of development aid financing instruments (grants/loans) by year and display</span></span>
|
||
<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a><span class="co">## count USD amount of development aid financing instumrnets by year and display</span></span>
|
||
<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">'Flow'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">'Flow'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">'Financetype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb22-18"><a href="#cb22-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Financetype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid, in millions"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb22-19"><a href="#cb22-19" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-dji-aid-financetype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img src="data:image/png;base64,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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 11: Total ODA for Djibouti per year, by financing type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The amount of Official Development Assistance to Djibouti has generally been increasing since 2011, first steadily and, since 2017, more rapidly, as can be seen in <a href="#fig-dji-aid-financetype">Figure 11</a>. With just under 150m USD in assistance contributions 2011 and just over 320m USD at its peak in 2020, Djibouti has received less overall ODA funds than the other countries surveyed in this study.</p>
|
||
<p>The primary type of development assistance provided are grants, with loans making up between half and one third of the absolute grant amount in USD between 2011 and 2020. Grants have trended slowly upwards from just over 100m USD in 2011 to 135m in 2014, before fluctuating around this level until 2017, and finally increasing more significantly to over 200m USD in 2020. Loans had a more significant jump earlier, from there relatively stagnant level of under 40m USD in 2014 to 80m USD in 2015, with a similarly significant jump from 2018 to 2019, before decreasing slightly again to just over 110m in 2020. While largely comprising less than 10m USD until 2018, other official flows (non-export credits) had a large increase to over 75m USD in 2019, be decreasing almost as significantly again the following year.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="26">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
|
||
<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">"Donortype"</span>] <span class="op">=</span> donortotals[<span class="st">"DONOR"</span>].<span class="bu">map</span>(donortypes)</span>
|
||
<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">"Donortype"</span>] <span class="op">!=</span> <span class="st">"nondac"</span>)]</span>
|
||
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">'Donortype'</span>, <span class="st">'Year'</span>])</span>
|
||
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
|
||
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">'Donortype'</span>, <span class="st">'Year'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb23-17"><a href="#cb23-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">'Year'</span>, y<span class="op">=</span><span class="st">'Value'</span>, color<span class="op">=</span><span class="st">'Donortype'</span>, labels<span class="op">=</span>{<span class="st">"Value"</span>: <span class="st">"Development aid, in millions"</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">"seaborn"</span>)</span>
|
||
<span id="cb23-18"><a href="#cb23-18" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page">
|
||
<div id="fig-dji-aid-donortype" class="quarto-figure quarto-figure-center anchored">
|
||
<figure class="figure">
|
||
<p><img 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" class="img-fluid figure-img"></p>
|
||
<p></p><figcaption class="figure-caption">Figure 12: Total ODA for Djibouti per year, separated by donor type</figcaption><p></p>
|
||
</figure>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The primary donor type of development assistance to Djibouti has been through bilateral donors for the majority of time between 2011 and 2020, see <a href="#fig-dji-aid-donortype">Figure 12</a>. While bilateral contributions have been consistently around 100m USD, multilateral contributions slowly increased from 45m USD in 2011 to just under 70m USD 2016. This situation changed, however, with both types of contributions increasing more significantly in 2018 and 2019. The multilateral contribution of 108m USD in 2019 is larger than the previous year’s bilateral contributions (104m USD), though those equally rose significantly to almost 160m USD in 2019. While the bilateral contributions spiked in 2019 before falling to 130m USD the following year, multilateral contributions kept increasing significantly to over 170m USD in 2020. For the first time in 2020, then, multilateral contributions provided a significantly larger share of development assistance to Djibouti than bilateral contributions, a trend which may move even further apart if bilateral contributions keep decreasing while multilateral ones increase.</p>
|
||
<div class="cell page-columns page-full" data-execution_count="27">
|
||
<details>
|
||
<summary>Code</summary>
|
||
<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
|
||
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a> (</span>
|
||
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">15170</span>) <span class="op">|</span></span>
|
||
<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">15180</span>) <span class="op">|</span></span>
|
||
<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25010</span>) <span class="op">|</span></span>
|
||
<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25020</span>) <span class="op">|</span></span>
|
||
<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25030</span>) <span class="op">|</span></span>
|
||
<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25040</span>) <span class="op">|</span></span>
|
||
<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33110</span>) <span class="op">|</span></span>
|
||
<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33120</span>) <span class="op">|</span></span>
|
||
<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33130</span>) <span class="op">|</span></span>
|
||
<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33140</span>) <span class="op">|</span></span>
|
||
<span id="cb24-13"><a href="#cb24-13" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33150</span>) <span class="op">|</span></span>
|
||
<span id="cb24-14"><a href="#cb24-14" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33181</span>) <span class="op">|</span></span>
|
||
<span id="cb24-15"><a href="#cb24-15" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43071</span>) <span class="op">|</span></span>
|
||
<span id="cb24-16"><a href="#cb24-16" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43072</span>)</span>
|
||
<span id="cb24-17"><a href="#cb24-17" aria-hidden="true" tabindex="-1"></a> ) <span class="op">&</span></span>
|
||
<span id="cb24-18"><a href="#cb24-18" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'CHANNEL'</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&</span></span>
|
||
<span id="cb24-19"><a href="#cb24-19" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AMOUNTTYPE'</span>] <span class="op">==</span> <span class="st">'D'</span>) <span class="op">&</span></span>
|
||
<span id="cb24-20"><a href="#cb24-20" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">'FLOW'</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&</span> <span class="co"># Total</span></span>
|
||
<span id="cb24-21"><a href="#cb24-21" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'FLOWTYPE'</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&</span></span>
|
||
<span id="cb24-22"><a href="#cb24-22" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">'AIDTYPE'</span>] <span class="op">==</span> <span class="st">"100"</span>) <span class="co"># contains mixed int and string representations</span></span>
|
||
<span id="cb24-23"><a href="#cb24-23" aria-hidden="true" tabindex="-1"></a> ]</span>
|
||
<span id="cb24-24"><a href="#cb24-24" aria-hidden="true" tabindex="-1"></a>aid <span class="op">=</span> totals[totals[<span class="st">'DONOR'</span>] <span class="op"><</span> <span class="dv">20000</span>] <span class="co"># drop all 'total' aggregations</span></span>
|
||
<span id="cb24-25"><a href="#cb24-25" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-26"><a href="#cb24-26" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of topics</span></span>
|
||
<span id="cb24-27"><a href="#cb24-27" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">15170</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">15180</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">15170</span></span>
|
||
<span id="cb24-28"><a href="#cb24-28" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">15170</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Women's rights support"</span></span>
|
||
<span id="cb24-29"><a href="#cb24-29" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-30"><a href="#cb24-30" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25010</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25020</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25030</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25040</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">25010</span></span>
|
||
<span id="cb24-31"><a href="#cb24-31" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">25010</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Business growth"</span></span>
|
||
<span id="cb24-32"><a href="#cb24-32" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-33"><a href="#cb24-33" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33110</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33120</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33130</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33140</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33150</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33181</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">33110</span></span>
|
||
<span id="cb24-34"><a href="#cb24-34" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">33110</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Trade development"</span></span>
|
||
<span id="cb24-35"><a href="#cb24-35" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-36"><a href="#cb24-36" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43071</span>) <span class="op">|</span> (aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43072</span>)].index, <span class="st">'SECTOR'</span>] <span class="op">=</span> <span class="dv">43071</span></span>
|
||
<span id="cb24-37"><a href="#cb24-37" aria-hidden="true" tabindex="-1"></a>aid.loc[aid[(aid[<span class="st">'SECTOR'</span>] <span class="op">==</span> <span class="dv">43071</span>)].index, <span class="st">'Sector'</span>] <span class="op">=</span> <span class="st">"Food security"</span></span>
|
||
<span id="cb24-38"><a href="#cb24-38" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-39"><a href="#cb24-39" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by sector per year and sum all values</span></span>
|
||
<span id="cb24-40"><a href="#cb24-40" aria-hidden="true" tabindex="-1"></a>aid_grouped <span class="op">=</span> aid.groupby([<span class="st">'Year'</span>, <span class="st">'Sector'</span>]).agg({<span class="st">'Value'</span>: [<span class="st">'sum'</span>]})</span>
|
||
<span id="cb24-41"><a href="#cb24-41" aria-hidden="true" tabindex="-1"></a>aid_grouped <span class="op">=</span> aid_grouped.reset_index([<span class="st">'Year'</span>, <span class="st">'Sector'</span>])</span>
|
||
<span id="cb24-42"><a href="#cb24-42" aria-hidden="true" tabindex="-1"></a>aid_grouped.columns <span class="op">=</span> aid_grouped.columns.to_flat_index()</span>
|
||
<span id="cb24-43"><a href="#cb24-43" aria-hidden="true" tabindex="-1"></a>aid_grouped.columns <span class="op">=</span> [<span class="st">'Year'</span>, <span class="st">'Sector'</span>, <span class="st">'Value'</span>]</span>
|
||
<span id="cb24-44"><a href="#cb24-44" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-45"><a href="#cb24-45" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(aid_grouped[<span class="st">'Year'</span>], aid_grouped[<span class="st">'Sector'</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>aid_grouped[<span class="st">'Value'</span>], aggfunc<span class="op">=</span><span class="st">'sum'</span>)</span>
|
||
<span id="cb24-46"><a href="#cb24-46" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-47"><a href="#cb24-47" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename and reorder columns</span></span>
|
||
<span id="cb24-48"><a href="#cb24-48" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> crosstab[[<span class="st">'Trade development'</span>, <span class="st">'Business growth'</span>, <span class="st">"Women's rights support"</span>, <span class="st">'Food security'</span>, <span class="st">'All'</span>]]</span>
|
||
<span id="cb24-49"><a href="#cb24-49" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb24-50"><a href="#cb24-50" aria-hidden="true" tabindex="-1"></a>Markdown(tabulate(crosstab.fillna(<span class="st">"0.00"</span>), headers<span class="op">=</span><span class="st">"keys"</span>, tablefmt<span class="op">=</span><span class="st">"github"</span>, floatfmt<span class="op">=</span><span class="st">".2f"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
</details>
|
||
<div class="cell-output cell-output-display column-page" data-execution_count="27">
|
||
<div id="tbl-dji-aid-projects" class="anchored">
|
||
<table class="table table-sm table-striped">
|
||
<caption>Table 4: ODA projects of advancing inclusive growth, separated by project type</caption>
|
||
<colgroup>
|
||
<col style="width: 8%">
|
||
<col style="width: 21%">
|
||
<col style="width: 19%">
|
||
<col style="width: 26%">
|
||
<col style="width: 17%">
|
||
<col style="width: 7%">
|
||
</colgroup>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Year</th>
|
||
<th>Trade development</th>
|
||
<th>Business growth</th>
|
||
<th>Women’s rights support</th>
|
||
<th>Food security</th>
|
||
<th>All</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>2011</td>
|
||
<td>0.12</td>
|
||
<td>0.30</td>
|
||
<td>0.08</td>
|
||
<td>0.00</td>
|
||
<td>0.49</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2012</td>
|
||
<td>0.02</td>
|
||
<td>0.54</td>
|
||
<td>0.09</td>
|
||
<td>0.00</td>
|
||
<td>0.65</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2013</td>
|
||
<td>0.06</td>
|
||
<td>0.41</td>
|
||
<td>0.09</td>
|
||
<td>0.00</td>
|
||
<td>0.56</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2014</td>
|
||
<td>0.13</td>
|
||
<td>0.42</td>
|
||
<td>0.06</td>
|
||
<td>0.00</td>
|
||
<td>0.61</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2015</td>
|
||
<td>0.07</td>
|
||
<td>0.58</td>
|
||
<td>0.13</td>
|
||
<td>0.00</td>
|
||
<td>0.78</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2016</td>
|
||
<td>0.00</td>
|
||
<td>0.82</td>
|
||
<td>0.77</td>
|
||
<td>0.00</td>
|
||
<td>1.59</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2017</td>
|
||
<td>0.30</td>
|
||
<td>1.26</td>
|
||
<td>0.22</td>
|
||
<td>0.00</td>
|
||
<td>1.78</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2018</td>
|
||
<td>0.28</td>
|
||
<td>1.14</td>
|
||
<td>0.22</td>
|
||
<td>0.03</td>
|
||
<td>1.67</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>2019</td>
|
||
<td>0.52</td>
|
||
<td>1.92</td>
|
||
<td>0.28</td>
|
||
<td>0.06</td>
|
||
<td>2.78</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2020</td>
|
||
<td>7.72</td>
|
||
<td>1.73</td>
|
||
<td>0.26</td>
|
||
<td>0.00</td>
|
||
<td>9.71</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>All</td>
|
||
<td>9.22</td>
|
||
<td>9.13</td>
|
||
<td>2.19</td>
|
||
<td>0.08</td>
|
||
<td>20.62</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div data-custom-style="caption">
|
||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||
</div>
|
||
<p>The sector-based breakdown of aid contributions for inclusive business growth in Djibouti can be seen in <a href="#tbl-dji-aid-projects">Table 4</a>. It shows that overall development assistance to the necessary inclusive growth sectors in Djibouti is still small in absolute terms, especially for those in vulnerable positions. The table is broken down into four sectors of development aid which drive the potential for inclusive growth in trade and business:</p>
|
||
<p>First, trade development encompasses trade policy and administrative management, trade facilitation, regional trade agreements, multilateral trade negotiations, trade-related adjustments and trade education and training. Second, business growth is the combination of business policy and administrative management, privatization, business development services as well responsible business conduct — meaning the establishing of policy reform, implementation and enforcement of responsible business conduct, including, among others, implementation of guidelines for human rights support. Third, and specifically aimed at the inclusion of women in economic activities, is the support for women’s rights which includes the establishment of, and assistance for, women’s rights organizations and institutions to enhance their effectiveness, influence and sustainability. And last, the provision for and protection of food security for those in vulnerable positions, through capacity strengthening and household-level food security programmes, short- or long-term, excluding emergency food assistance measures (such as for disaster crisis affected households).</p>
|
||
<p>The amount of aid contributions into these sectors of inclusive growth in Djibouti is small in comparison with development assistance to the other countries analyzed. The absolute amount of contributions has consistently stayed under 10m USD per year for all four sectors combined, though an overall growth trend is visible from 0.5m USD in 2011 to 1.6m USD in 2016 and more rapid growth in 2020 to just under 10m USD. Most of this recent growth in 2020 is driven by contributions to trade development with 7.7m USD, while business growth and women’s rights support are seeing much smaller contributions yet. The business growth sector, though seeing small contributions in absolute terms, has seen a continued increase in contributions from 0.3m USD in 2011 to 1.7m USD in 2020, with almost 2m USD at its peak in 2019. Women’s rights support, on the other hand, has seen some increase from its small contributions of not even 0.1m USD in 2011 to almost 0.8m USD in 2016, but overall assistance to the sector stays stagnant at only around 0.25m USD in recent years. Lastly, food security remains almost completely without Official Development Assistance contributions, with barely 0.05m USD being contributed at its peak in 2019. Thus, development contributions to Djibouti’s trade sector itself are increasing, though at the same time contributions to inclusive growth specifically, aimed at vulnerable populations and an inclusive business environment, are growing slowly at best and stagnant for protection measures for those in vulnerable groups.</p>
|
||
<div style="page-break-after: always;"></div>
|
||
</section>
|
||
</section>
|
||
<section id="references" class="level2">
|
||
|
||
<!-- -->
|
||
|
||
|
||
</section>
|
||
|
||
|
||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-line-spacing="2" role="doc-bibliography">
|
||
<div id="ref-Alia2017" class="csl-entry" role="doc-biblioentry">
|
||
Alia, D. Y. (2017). Progress <span>Toward The Sustainable Development Goal</span> on <span>Poverty</span> : <span>Assessing The Effect</span> of <span>Income Growth</span> on <span>The Exit Time</span> from <span>Poverty</span> in <span>Benin</span>: <span>Exit Time Out</span> of <span>Poverty</span> in <span>Benin</span>. <em>Sustainable Development</em>, <em>25</em>(6), 495–503. <a href="https://doi.org/10.1002/sd.1674">https://doi.org/10.1002/sd.1674</a>
|
||
</div>
|
||
<div id="ref-Barry2020" class="csl-entry" role="doc-biblioentry">
|
||
Barry, M. S., & Creti, A. (2020). Pay-as-you-go contracts for electricity access: <span>Bridging</span> the <span>“last mile”</span> gap? <span>A</span> case study in <span>Benin</span>. <em>Energy Economics</em>, <em>90</em>, 104843. <a href="https://doi.org/10.1016/j.eneco.2020.104843">https://doi.org/10.1016/j.eneco.2020.104843</a>
|
||
</div>
|
||
<div id="ref-Baulch2012" class="csl-entry" role="doc-biblioentry">
|
||
Baulch, B., Pham, H. T., & Reilly, B. (2012). Decomposing the <span>Ethnic Gap</span> in <span>Rural Vietnam</span>, 1993–2004. <em>Oxford Development Studies</em>, <em>40</em>(1), 87–117. <a href="https://doi.org/10.1080/13600818.2011.646441">https://doi.org/10.1080/13600818.2011.646441</a>
|
||
</div>
|
||
<div id="ref-Benjamin2004" class="csl-entry" role="doc-biblioentry">
|
||
Benjamin, D., & Brandt, L. (2004). Agriculture and income distribution in rural <span>Vietnam</span> under economic reforms: A tale of two regions. In P. Glewwe, N. Agrawal, & D. Dollar (Eds.), <em>Economic <span>Growth</span>, <span>Poverty</span> and <span>Household Welfare</span> in <span>Vietnam</span></em> (pp. 133–186). <span>World Bank</span>.
|
||
</div>
|
||
<div id="ref-Benjamin2017" class="csl-entry" role="doc-biblioentry">
|
||
Benjamin, D., Brandt, L., & McCaig, B. (2017). Growth with equity: Income inequality in <span>Vietnam</span>, 2002–14. <em>The Journal of Economic Inequality</em>, <em>15</em>(1), 25–46. <a href="https://doi.org/10.1007/s10888-016-9341-7">https://doi.org/10.1007/s10888-016-9341-7</a>
|
||
</div>
|
||
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||
Brass, J. N. (2008). Djibouti’s unusual resource curse. <em>The Journal of Modern African Studies</em>, <em>46</em>(4), 523–545. <a href="https://doi.org/10.1017/S0022278X08003479">https://doi.org/10.1017/S0022278X08003479</a>
|
||
</div>
|
||
<div id="ref-Bui2019" class="csl-entry" role="doc-biblioentry">
|
||
Bui, T. P., & Imai, K. S. (2019). Determinants of <span>Rural-Urban Inequality</span> in <span>Vietnam</span>: <span> Detailed Decomposition Analyses Based</span> on <span>Unconditional Quantile Regressions</span>. <em>The Journal of Development Studies</em>, <em>55</em>(12), 2610–2625. <a href="https://doi.org/10.1080/00220388.2018.1536265">https://doi.org/10.1080/00220388.2018.1536265</a>
|
||
</div>
|
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<div id="ref-Calderon-Villarreal2022" class="csl-entry" role="doc-biblioentry">
|
||
Calderón-Villarreal, A., Schweitzer, R., & Kayser, G. (2022). Social and geographic inequalities in water, sanitation and hygiene access in 21 refugee camps and settlements in <span>Bangladesh</span>, <span> Kenya</span>, <span>Uganda</span>, <span>South Sudan</span>, and <span>Zimbabwe</span>. <em>International Journal for Equity in Health</em>, <em>21</em>(1), 27. <a href="https://doi.org/10.1186/s12939-022-01626-3">https://doi.org/10.1186/s12939-022-01626-3</a>
|
||
</div>
|
||
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|
||
Cali, M. (2014). Trade boom and wage inequality: Evidence from <span>Ugandan</span> districts. <em>Journal of Economic Geography</em>, <em>14</em>(6), 1141–1174. <a href="https://doi.org/10.1093/jeg/lbu001">https://doi.org/10.1093/jeg/lbu001</a>
|
||
</div>
|
||
<div id="ref-Cao2008" class="csl-entry" role="doc-biblioentry">
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VASS. (2011). <em>Poverty <span>Reduction</span> in <span>Vietnam</span>: <span>Achievements</span> and <span> Challenges</span></em>. <span>Vietnam Academy of Social Sciences</span>.
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||
</div>
|
||
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|
||
World Bank. (2019). <em>Challenges to <span>Inclusive Growth</span>: <span>A Poverty</span> and <span>Equity Assessment</span> of <span>Djibouti</span></em> (No. 18; Poverty and <span>Equity Note</span>). <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/449741576097502078/Challenges-to-Inclusive-Growth-A-Poverty-and-Equity-Assessment-of-Djibouti
|
||
"> http://documents.worldbank.org/curated/en/449741576097502078/Challenges-to-Inclusive-Growth-A-Poverty-and-Equity-Assessment-of-Djibouti </a>
|
||
</div>
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||
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World Bank. (2012). <em>Vietnam poverty assessment: Well begun, not yet done - <span>Vietnam</span> ’s remarkable progress on poverty reduction and the emerging challenges</em>. <span>World Bank</span>.<a href="
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http://documents.worldbank.org/curated/en/563561468329654096/2012-Vietnam-poverty-assessment-well-begun-not-yet-done-Vietnams-remarkable-progress-on-poverty-reduction-and-the-emerging-challenges
|
||
"> http://documents.worldbank.org/curated/en/563561468329654096/2012-Vietnam-poverty-assessment-well-begun-not-yet-done-Vietnams-remarkable-progress-on-poverty-reduction-and-the-emerging-challenges </a>
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||
World Bank. (2020a). <em>Location <span>Matters</span>: <span>Welfare Among Urban</span> and <span>Rural Poor</span> in <span>Djibouti</span></em> (No. 18; Poverty and <span>Equity Note</span>). <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/203361579888116251/Location-Matters-Welfare-Among-Urban-and-Rural-Poor-in-Djibouti
|
||
"> http://documents.worldbank.org/curated/en/203361579888116251/Location-Matters-Welfare-Among-Urban-and-Rural-Poor-in-Djibouti </a>
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</div>
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||
<div id="ref-Mendiratta2020" class="csl-entry" role="doc-biblioentry">
|
||
World Bank. (2020b). <em>The <span>Multi-Dimensional Nature</span> of <span>Poverty</span> in <span>Djibouti</span></em> (No. 30; Poverty and <span>Equity Note</span>). <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/272691596006234817/The-Multi-Dimensional-Nature-of-Poverty-in-Djibouti
|
||
"> http://documents.worldbank.org/curated/en/272691596006234817/The-Multi-Dimensional-Nature-of-Poverty-in-Djibouti </a>
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||
</div>
|
||
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||
World Bank. (2021). <em>Tracking <span>SDG</span> 7: <span>The Energy Progress</span> <span>Report</span></em>. <span>World Bank</span>.
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||
</div>
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||
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|
||
World Bank. (2022a). <em>Benin - <span>Learning Poverty Brief</span></em>. <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588
|
||
"> http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588 </a>
|
||
</div>
|
||
<div id="ref-WorldBank2022d" class="csl-entry" role="doc-biblioentry">
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||
World Bank. (2022b). <em>Djibouti <span>Gender Landscape</span></em> (Country <span>Gender Landscape</span>). <span>World Bank</span>.<a href="
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||
http://documents.worldbank.org/curated/en/099929206302212659/IDU068dce0c7003280435b099f8040232925d37f
|
||
"> http://documents.worldbank.org/curated/en/099929206302212659/IDU068dce0c7003280435b099f8040232925d37f </a>
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||
</div>
|
||
<div id="ref-WorldBank2022b" class="csl-entry" role="doc-biblioentry">
|
||
World Bank. (2022c). <em>Macro <span>Poverty Outlook</span> for <span>Benin</span> : <span>April</span> 2022</em>. <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/099930404182210208/IDU0ef8057e509b5f0432c0b50d00f85b54deb33
|
||
"> http://documents.worldbank.org/curated/en/099930404182210208/IDU0ef8057e509b5f0432c0b50d00f85b54deb33 </a>
|
||
</div>
|
||
<div id="ref-WorldBank2022c" class="csl-entry" role="doc-biblioentry">
|
||
World Bank. (2022d). <em>Macro <span>Poverty Outlook</span> for <span>Djibouti</span> : <span>April</span> 2022</em>. <span>World Bank</span>.<a href="
|
||
https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099310104232265208/idu08979c8f809e1604dc70be93050dce6a02a23
|
||
"> https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099310104232265208/idu08979c8f809e1604dc70be93050dce6a02a23 </a>
|
||
</div>
|
||
<div id="ref-WorldBank2022" class="csl-entry" role="doc-biblioentry">
|
||
World Bank. (2022e). <em>Uganda - <span>Learning Poverty Brief</span></em>. <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588
|
||
"> http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588 </a>
|
||
</div>
|
||
<div id="ref-Atamanov2022" class="csl-entry" role="doc-biblioentry">
|
||
World Bank. (2022f). <em>Uganda <span>Poverty Assessment</span>: <span>Strengthening Resilience</span> to <span> Accelerate Poverty Reduction</span></em>. <span>World Bank</span>.<a href="
|
||
http://documents.worldbank.org/curated/en/099135006292235162/P17761605286900b10899b0798dcd703d85
|
||
"> http://documents.worldbank.org/curated/en/099135006292235162/P17761605286900b10899b0798dcd703d85 </a>
|
||
</div>
|
||
<div id="ref-Yikii2017" class="csl-entry" role="doc-biblioentry">
|
||
Yikii, F., Turyahabwe, N., & Bashaasha, B. (2017). Prevalence of household food insecurity in wetland adjacent areas of <span>Uganda</span>. <em>Agriculture & Food Security</em>, <em>6</em>(1), 1–12.
|
||
</div>
|
||
<div id="ref-Ylipaa2019" class="csl-entry" role="doc-biblioentry">
|
||
Ylipaa, J., Gabrielsson, S., & Jerneck, A. (2019). Climate change adaptation and gender inequality: <span>Insights</span> from rural vietnam. <em>Sustainability (Basel, Switzerland)</em>, <em>11</em>(10), 2805–2805. <a href="https://doi.org/10.3390/su11102805">https://doi.org/10.3390/su11102805</a>
|
||
</div>
|
||
</div></section><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
|
||
|
||
<ol>
|
||
<li id="fn1"><p>Other official flows, per OECD CRS definition, describe contributions that do not meet the ODA criteria. These can include grants for primarily representational or commercial purposes, contributions having a grant element under the required share of 25% or primarily export-facilitating contributions. See https://doi.org/10.1787/6afef3df-en for a full definition.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
|
||
<li id="fn2"><p>The Balbala area comprises the 4th and 5th district out of the five districts of Djibouti city.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
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</ol>
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<div class="sourceCode" id="cb25" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
|
||
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a><span class="an">nocite:</span><span class="co"> |</span></span>
|
||
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="co"> | @UNU-WIDER2022a</span></span>
|
||
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a><span class="co"> | @UNU-WIDER2022</span></span>
|
||
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a><span class="co"> | @OECD2022</span></span>
|
||
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
|
||
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>{{< include data-prep.qmd >}}</span>
|
||
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-12"><a href="#cb25-12" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
|
||
<span id="cb25-13"><a href="#cb25-13" aria-hidden="true" tabindex="-1"></a>svg_render <span class="op">=</span> <span class="va">True</span></span>
|
||
<span id="cb25-14"><a href="#cb25-14" aria-hidden="true" tabindex="-1"></a>pd.options.display.float_format <span class="op">=</span> <span class="st">"</span><span class="sc">{:.2f}</span><span class="st">"</span>.<span class="bu">format</span></span>
|
||
<span id="cb25-15"><a href="#cb25-15" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
|
||
<span id="cb25-16"><a href="#cb25-16" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-17"><a href="#cb25-17" aria-hidden="true" tabindex="-1"></a>{{< include _drivers-of-inequality-vietnam.qmd >}}</span>
|
||
<span id="cb25-18"><a href="#cb25-18" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-19"><a href="#cb25-19" aria-hidden="true" tabindex="-1"></a>{{< pagebreak >}}</span>
|
||
<span id="cb25-20"><a href="#cb25-20" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-21"><a href="#cb25-21" aria-hidden="true" tabindex="-1"></a>{{< include _drivers-of-inequality-uganda.qmd >}}</span>
|
||
<span id="cb25-22"><a href="#cb25-22" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-23"><a href="#cb25-23" aria-hidden="true" tabindex="-1"></a>{{< pagebreak >}}</span>
|
||
<span id="cb25-24"><a href="#cb25-24" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-25"><a href="#cb25-25" aria-hidden="true" tabindex="-1"></a>{{< include _drivers-of-inequality-benin.qmd >}}</span>
|
||
<span id="cb25-26"><a href="#cb25-26" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-27"><a href="#cb25-27" aria-hidden="true" tabindex="-1"></a>{{< pagebreak >}}</span>
|
||
<span id="cb25-28"><a href="#cb25-28" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-29"><a href="#cb25-29" aria-hidden="true" tabindex="-1"></a>{{< include _drivers-of-inequality-djibouti.qmd >}}</span>
|
||
<span id="cb25-30"><a href="#cb25-30" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-31"><a href="#cb25-31" aria-hidden="true" tabindex="-1"></a>{{< pagebreak >}}</span>
|
||
<span id="cb25-32"><a href="#cb25-32" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb25-33"><a href="#cb25-33" aria-hidden="true" tabindex="-1"></a><span class="fu">## References</span></span>
|
||
<span id="cb25-34"><a href="#cb25-34" aria-hidden="true" tabindex="-1"></a></span>
|
||
</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div></div></div></div></div>
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