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// 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>
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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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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 RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return 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(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${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 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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 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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 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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}}));
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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"; }
.bi-asterisk::before { content: "\f151"; }
.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"; }
.bi-badge-tm::before { content: "\f169"; }
.bi-badge-vo-fill::before { content: "\f16a"; }
.bi-badge-vo::before { content: "\f16b"; }
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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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<p>Marty Oehme </p>
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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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<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>
<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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<li><a href="#uganda" id="toc-uganda" class="nav-link" data-scroll-target="#uganda">Uganda</a>
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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>
<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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<li><a href="#benin" id="toc-benin" class="nav-link" data-scroll-target="#benin">Benin</a>
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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>
<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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<li><a href="#djibouti-ethiopia" id="toc-djibouti-ethiopia" class="nav-link" data-scroll-target="#djibouti-ethiopia">Djibouti-Ethiopia</a>
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<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>
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<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="co"># Set up the data extraction and figure drawing functions</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.io <span class="im">as</span> pio</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> gini_plot(country_df):</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> svg_render:</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> pio.renderers.default <span class="op">=</span> <span class="st">&quot;png&quot;</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> fig <span class="op">=</span> px.line(country_df, x<span class="op">=</span><span class="st">&quot;year&quot;</span>, y<span class="op">=</span><span class="st">&quot;gini&quot;</span>, markers<span class="op">=</span><span class="va">True</span>, labels<span class="op">=</span>{<span class="st">&quot;year&quot;</span>: <span class="st">&quot;Year&quot;</span>, <span class="st">&quot;gini&quot;</span>: <span class="st">&quot;Gini coefficient&quot;</span>}, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>, range_y<span class="op">=</span>[<span class="dv">0</span>,<span class="dv">100</span>])</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> fig.update_traces(marker_size<span class="op">=</span><span class="dv">10</span>)</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> fig.show()</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_consumption_gini_percapita(country_df):</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> country_df[country_df[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a> gini_plot(gni_cnsmpt_percapita)</span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_consumption_gini_percapita_ruralurban(country_df):</span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> country_df[country_df[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;source&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;World Bank&quot;</span>)]</span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a> gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;areacovr&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;All&quot;</span>)]</span>
<span id="cb1-23"><a href="#cb1-23" 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>
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</div>
<div class="cell" data-execution_count="5">
<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 functions to grab development aids by type of donating body</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co">## ODA donor type map, see DAC code sheet xlsx</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>donortypes <span class="op">=</span> {</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="dv">2</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="dv">3</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> <span class="dv">4</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a> <span class="dv">5</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a> <span class="dv">6</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a> <span class="dv">7</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="dv">8</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="dv">9</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a> <span class="dv">10</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a> <span class="dv">11</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a> <span class="dv">12</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a> <span class="dv">18</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a> <span class="dv">20</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a> <span class="dv">21</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a> <span class="dv">22</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a> <span class="dv">40</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a> <span class="dv">50</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a> <span class="dv">61</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a> <span class="dv">68</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a> <span class="dv">69</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a> <span class="dv">75</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a> <span class="dv">76</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a> <span class="dv">301</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a> <span class="dv">302</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a> <span class="dv">701</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a> <span class="dv">742</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a> <span class="dv">801</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-32"><a href="#cb2-32" aria-hidden="true" tabindex="-1"></a> <span class="dv">820</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-33"><a href="#cb2-33" aria-hidden="true" tabindex="-1"></a> <span class="dv">918</span>: <span class="st">&#39;dac&#39;</span>,</span>
<span id="cb2-34"><a href="#cb2-34" aria-hidden="true" tabindex="-1"></a> <span class="dv">104</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-35"><a href="#cb2-35" aria-hidden="true" tabindex="-1"></a> <span class="dv">807</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-36"><a href="#cb2-36" aria-hidden="true" tabindex="-1"></a> <span class="dv">811</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-37"><a href="#cb2-37" aria-hidden="true" tabindex="-1"></a> <span class="dv">812</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-38"><a href="#cb2-38" aria-hidden="true" tabindex="-1"></a> <span class="dv">901</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-39"><a href="#cb2-39" aria-hidden="true" tabindex="-1"></a> <span class="dv">902</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-40"><a href="#cb2-40" aria-hidden="true" tabindex="-1"></a> <span class="dv">903</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-41"><a href="#cb2-41" aria-hidden="true" tabindex="-1"></a> <span class="dv">905</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-42"><a href="#cb2-42" aria-hidden="true" tabindex="-1"></a> <span class="dv">906</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-43"><a href="#cb2-43" aria-hidden="true" tabindex="-1"></a> <span class="dv">907</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-44"><a href="#cb2-44" aria-hidden="true" tabindex="-1"></a> <span class="dv">909</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-45"><a href="#cb2-45" aria-hidden="true" tabindex="-1"></a> <span class="dv">913</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-46"><a href="#cb2-46" aria-hidden="true" tabindex="-1"></a> <span class="dv">914</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-47"><a href="#cb2-47" aria-hidden="true" tabindex="-1"></a> <span class="dv">915</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-48"><a href="#cb2-48" aria-hidden="true" tabindex="-1"></a> <span class="dv">921</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-49"><a href="#cb2-49" aria-hidden="true" tabindex="-1"></a> <span class="dv">923</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-50"><a href="#cb2-50" aria-hidden="true" tabindex="-1"></a> <span class="dv">926</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-51"><a href="#cb2-51" aria-hidden="true" tabindex="-1"></a> <span class="dv">928</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-52"><a href="#cb2-52" aria-hidden="true" tabindex="-1"></a> <span class="dv">932</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-53"><a href="#cb2-53" aria-hidden="true" tabindex="-1"></a> <span class="dv">940</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-54"><a href="#cb2-54" aria-hidden="true" tabindex="-1"></a> <span class="dv">944</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-55"><a href="#cb2-55" aria-hidden="true" tabindex="-1"></a> <span class="dv">948</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-56"><a href="#cb2-56" aria-hidden="true" tabindex="-1"></a> <span class="dv">951</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-57"><a href="#cb2-57" aria-hidden="true" tabindex="-1"></a> <span class="dv">952</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-58"><a href="#cb2-58" aria-hidden="true" tabindex="-1"></a> <span class="dv">953</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-59"><a href="#cb2-59" aria-hidden="true" tabindex="-1"></a> <span class="dv">954</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-60"><a href="#cb2-60" aria-hidden="true" tabindex="-1"></a> <span class="dv">956</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-61"><a href="#cb2-61" aria-hidden="true" tabindex="-1"></a> <span class="dv">958</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-62"><a href="#cb2-62" aria-hidden="true" tabindex="-1"></a> <span class="dv">959</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-63"><a href="#cb2-63" aria-hidden="true" tabindex="-1"></a> <span class="dv">960</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-64"><a href="#cb2-64" aria-hidden="true" tabindex="-1"></a> <span class="dv">963</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-65"><a href="#cb2-65" aria-hidden="true" tabindex="-1"></a> <span class="dv">964</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-66"><a href="#cb2-66" aria-hidden="true" tabindex="-1"></a> <span class="dv">966</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-67"><a href="#cb2-67" aria-hidden="true" tabindex="-1"></a> <span class="dv">967</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-68"><a href="#cb2-68" aria-hidden="true" tabindex="-1"></a> <span class="dv">971</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-69"><a href="#cb2-69" aria-hidden="true" tabindex="-1"></a> <span class="dv">974</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-70"><a href="#cb2-70" aria-hidden="true" tabindex="-1"></a> <span class="dv">976</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-71"><a href="#cb2-71" aria-hidden="true" tabindex="-1"></a> <span class="dv">978</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-72"><a href="#cb2-72" aria-hidden="true" tabindex="-1"></a> <span class="dv">979</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-73"><a href="#cb2-73" aria-hidden="true" tabindex="-1"></a> <span class="dv">980</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-74"><a href="#cb2-74" aria-hidden="true" tabindex="-1"></a> <span class="dv">981</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-75"><a href="#cb2-75" aria-hidden="true" tabindex="-1"></a> <span class="dv">982</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-76"><a href="#cb2-76" aria-hidden="true" tabindex="-1"></a> <span class="dv">983</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-77"><a href="#cb2-77" aria-hidden="true" tabindex="-1"></a> <span class="dv">988</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-78"><a href="#cb2-78" aria-hidden="true" tabindex="-1"></a> <span class="dv">990</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-79"><a href="#cb2-79" aria-hidden="true" tabindex="-1"></a> <span class="dv">992</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-80"><a href="#cb2-80" aria-hidden="true" tabindex="-1"></a> <span class="dv">997</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-81"><a href="#cb2-81" aria-hidden="true" tabindex="-1"></a> <span class="dv">1011</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-82"><a href="#cb2-82" aria-hidden="true" tabindex="-1"></a> <span class="dv">1012</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-83"><a href="#cb2-83" aria-hidden="true" tabindex="-1"></a> <span class="dv">1013</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-84"><a href="#cb2-84" aria-hidden="true" tabindex="-1"></a> <span class="dv">1014</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-85"><a href="#cb2-85" aria-hidden="true" tabindex="-1"></a> <span class="dv">1015</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-86"><a href="#cb2-86" aria-hidden="true" tabindex="-1"></a> <span class="dv">1016</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-87"><a href="#cb2-87" aria-hidden="true" tabindex="-1"></a> <span class="dv">1017</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-88"><a href="#cb2-88" aria-hidden="true" tabindex="-1"></a> <span class="dv">1018</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-89"><a href="#cb2-89" aria-hidden="true" tabindex="-1"></a> <span class="dv">1019</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-90"><a href="#cb2-90" aria-hidden="true" tabindex="-1"></a> <span class="dv">1020</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-91"><a href="#cb2-91" aria-hidden="true" tabindex="-1"></a> <span class="dv">1023</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-92"><a href="#cb2-92" aria-hidden="true" tabindex="-1"></a> <span class="dv">1024</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-93"><a href="#cb2-93" aria-hidden="true" tabindex="-1"></a> <span class="dv">1025</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-94"><a href="#cb2-94" aria-hidden="true" tabindex="-1"></a> <span class="dv">1037</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-95"><a href="#cb2-95" aria-hidden="true" tabindex="-1"></a> <span class="dv">1038</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-96"><a href="#cb2-96" aria-hidden="true" tabindex="-1"></a> <span class="dv">1311</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-97"><a href="#cb2-97" aria-hidden="true" tabindex="-1"></a> <span class="dv">1312</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-98"><a href="#cb2-98" aria-hidden="true" tabindex="-1"></a> <span class="dv">1313</span>: <span class="st">&#39;mlt&#39;</span>,</span>
<span id="cb2-99"><a href="#cb2-99" aria-hidden="true" tabindex="-1"></a> <span class="dv">30</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-100"><a href="#cb2-100" aria-hidden="true" tabindex="-1"></a> <span class="dv">45</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-101"><a href="#cb2-101" aria-hidden="true" tabindex="-1"></a> <span class="dv">55</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-102"><a href="#cb2-102" aria-hidden="true" tabindex="-1"></a> <span class="dv">62</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-103"><a href="#cb2-103" aria-hidden="true" tabindex="-1"></a> <span class="dv">70</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-104"><a href="#cb2-104" aria-hidden="true" tabindex="-1"></a> <span class="dv">72</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-105"><a href="#cb2-105" aria-hidden="true" tabindex="-1"></a> <span class="dv">77</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-106"><a href="#cb2-106" aria-hidden="true" tabindex="-1"></a> <span class="dv">82</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-107"><a href="#cb2-107" aria-hidden="true" tabindex="-1"></a> <span class="dv">83</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-108"><a href="#cb2-108" aria-hidden="true" tabindex="-1"></a> <span class="dv">84</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-109"><a href="#cb2-109" aria-hidden="true" tabindex="-1"></a> <span class="dv">87</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-110"><a href="#cb2-110" aria-hidden="true" tabindex="-1"></a> <span class="dv">130</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-111"><a href="#cb2-111" aria-hidden="true" tabindex="-1"></a> <span class="dv">133</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-112"><a href="#cb2-112" aria-hidden="true" tabindex="-1"></a> <span class="dv">358</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-113"><a href="#cb2-113" aria-hidden="true" tabindex="-1"></a> <span class="dv">543</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-114"><a href="#cb2-114" aria-hidden="true" tabindex="-1"></a> <span class="dv">546</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-115"><a href="#cb2-115" aria-hidden="true" tabindex="-1"></a> <span class="dv">552</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-116"><a href="#cb2-116" aria-hidden="true" tabindex="-1"></a> <span class="dv">561</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-117"><a href="#cb2-117" aria-hidden="true" tabindex="-1"></a> <span class="dv">566</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-118"><a href="#cb2-118" aria-hidden="true" tabindex="-1"></a> <span class="dv">576</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-119"><a href="#cb2-119" aria-hidden="true" tabindex="-1"></a> <span class="dv">611</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-120"><a href="#cb2-120" aria-hidden="true" tabindex="-1"></a> <span class="dv">613</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-121"><a href="#cb2-121" aria-hidden="true" tabindex="-1"></a> <span class="dv">732</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-122"><a href="#cb2-122" aria-hidden="true" tabindex="-1"></a> <span class="dv">764</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-123"><a href="#cb2-123" aria-hidden="true" tabindex="-1"></a> <span class="dv">765</span>: <span class="st">&#39;nondac&#39;</span>,</span>
<span id="cb2-124"><a href="#cb2-124" aria-hidden="true" tabindex="-1"></a> <span class="dv">1601</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-125"><a href="#cb2-125" aria-hidden="true" tabindex="-1"></a> <span class="dv">1602</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-126"><a href="#cb2-126" aria-hidden="true" tabindex="-1"></a> <span class="dv">1603</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-127"><a href="#cb2-127" aria-hidden="true" tabindex="-1"></a> <span class="dv">1604</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-128"><a href="#cb2-128" aria-hidden="true" tabindex="-1"></a> <span class="dv">1605</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-129"><a href="#cb2-129" aria-hidden="true" tabindex="-1"></a> <span class="dv">1606</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-130"><a href="#cb2-130" aria-hidden="true" tabindex="-1"></a> <span class="dv">1607</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-131"><a href="#cb2-131" aria-hidden="true" tabindex="-1"></a> <span class="dv">1608</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-132"><a href="#cb2-132" aria-hidden="true" tabindex="-1"></a> <span class="dv">1609</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-133"><a href="#cb2-133" aria-hidden="true" tabindex="-1"></a> <span class="dv">1610</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-134"><a href="#cb2-134" aria-hidden="true" tabindex="-1"></a> <span class="dv">1611</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-135"><a href="#cb2-135" aria-hidden="true" tabindex="-1"></a> <span class="dv">1612</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-136"><a href="#cb2-136" aria-hidden="true" tabindex="-1"></a> <span class="dv">1613</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-137"><a href="#cb2-137" aria-hidden="true" tabindex="-1"></a> <span class="dv">1614</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-138"><a href="#cb2-138" aria-hidden="true" tabindex="-1"></a> <span class="dv">1615</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-139"><a href="#cb2-139" aria-hidden="true" tabindex="-1"></a> <span class="dv">1616</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-140"><a href="#cb2-140" aria-hidden="true" tabindex="-1"></a> <span class="dv">1617</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-141"><a href="#cb2-141" aria-hidden="true" tabindex="-1"></a> <span class="dv">1618</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-142"><a href="#cb2-142" aria-hidden="true" tabindex="-1"></a> <span class="dv">1619</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-143"><a href="#cb2-143" aria-hidden="true" tabindex="-1"></a> <span class="dv">1620</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-144"><a href="#cb2-144" aria-hidden="true" tabindex="-1"></a> <span class="dv">1621</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-145"><a href="#cb2-145" aria-hidden="true" tabindex="-1"></a> <span class="dv">1622</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-146"><a href="#cb2-146" aria-hidden="true" tabindex="-1"></a> <span class="dv">1623</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-147"><a href="#cb2-147" aria-hidden="true" tabindex="-1"></a> <span class="dv">1624</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-148"><a href="#cb2-148" aria-hidden="true" tabindex="-1"></a> <span class="dv">1625</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-149"><a href="#cb2-149" aria-hidden="true" tabindex="-1"></a> <span class="dv">1626</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-150"><a href="#cb2-150" aria-hidden="true" tabindex="-1"></a> <span class="dv">1627</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-151"><a href="#cb2-151" aria-hidden="true" tabindex="-1"></a> <span class="dv">1628</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-152"><a href="#cb2-152" aria-hidden="true" tabindex="-1"></a> <span class="dv">1629</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-153"><a href="#cb2-153" aria-hidden="true" tabindex="-1"></a> <span class="dv">1630</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-154"><a href="#cb2-154" aria-hidden="true" tabindex="-1"></a> <span class="dv">1631</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-155"><a href="#cb2-155" aria-hidden="true" tabindex="-1"></a> <span class="dv">1632</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-156"><a href="#cb2-156" aria-hidden="true" tabindex="-1"></a> <span class="dv">1633</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-157"><a href="#cb2-157" aria-hidden="true" tabindex="-1"></a> <span class="dv">1634</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-158"><a href="#cb2-158" aria-hidden="true" tabindex="-1"></a> <span class="dv">1635</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-159"><a href="#cb2-159" aria-hidden="true" tabindex="-1"></a> <span class="dv">1636</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-160"><a href="#cb2-160" aria-hidden="true" tabindex="-1"></a> <span class="dv">1637</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-161"><a href="#cb2-161" aria-hidden="true" tabindex="-1"></a> <span class="dv">1638</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-162"><a href="#cb2-162" aria-hidden="true" tabindex="-1"></a> <span class="dv">1639</span>: <span class="st">&#39;private&#39;</span>,</span>
<span id="cb2-163"><a href="#cb2-163" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb2-164"><a href="#cb2-164" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-165"><a href="#cb2-165" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> totals_by_donortype(oda_frame):</span>
<span id="cb2-166"><a href="#cb2-166" aria-hidden="true" tabindex="-1"></a> totals <span class="op">=</span> oda_frame.loc[</span>
<span id="cb2-167"><a href="#cb2-167" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;RECIPIENT&#39;</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&amp;</span></span>
<span id="cb2-168"><a href="#cb2-168" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</span></span>
<span id="cb2-169"><a href="#cb2-169" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb2-170"><a href="#cb2-170" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb2-171"><a href="#cb2-171" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb2-172"><a href="#cb2-172" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb2-173"><a href="#cb2-173" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb2-174"><a href="#cb2-174" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb2-175"><a href="#cb2-175" aria-hidden="true" tabindex="-1"></a> donortotals <span class="op">=</span> totals.copy()</span>
<span id="cb2-176"><a href="#cb2-176" aria-hidden="true" tabindex="-1"></a> donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">=</span> donortotals[<span class="st">&quot;DONOR&quot;</span>].<span class="bu">map</span>(donortypes)</span>
<span id="cb2-177"><a href="#cb2-177" 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="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>svg_render <span class="op">=</span> <span class="va">True</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>pd.options.display.float_format <span class="op">=</span> <span class="st">&quot;</span><span class="sc">{:.2f}</span><span class="st">&quot;</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 &amp; 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>. 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="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>gni_cnsmpt <span class="op">=</span> vnm[vnm[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;source&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;World Bank&quot;</span>)]</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;areacovr&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;All&quot;</span>)]</span>
<span id="cb4-5"><a href="#cb4-5" 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. Source: Author’s elaboration based on UNU-WIDER WIID (2022).</figcaption><p></p>
</figure>
</div>
</div>
</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. 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 &amp; 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 &amp; 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. 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 &amp; 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. 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 &amp; 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. 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. 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. 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. 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, 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. 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 &amp; 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. 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="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><span class="co"># Load CRS data</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Vietnam_11-13_05092022215007164.csv&#39;</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="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Vietnam_14-16_05092022215226180.csv&#39;</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="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Vietnam_17-20_05092022215427555.csv&#39;</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="cb5-5"><a href="#cb5-5" 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="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">&#39;</span><span class="ch">\ufeff</span><span class="st">&quot;DONOR&quot;&#39;</span>: <span class="st">&#39;DONOR&#39;</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="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>totals <span class="op">=</span> df.loc[</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-11"><a href="#cb6-11" 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="cb6-12"><a href="#cb6-12" 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="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Financetype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Financetype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid amount, in millions of USD&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb6-19"><a href="#cb6-19" 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-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>
<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>
<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. 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, non-export credits, 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="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">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</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">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">=</span> donortotals[<span class="st">&quot;DONOR&quot;</span>].<span class="bu">map</span>(donortypes)</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">!=</span> <span class="st">&quot;nondac&quot;</span>)]</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Donortype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid, in millions&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb7-18"><a href="#cb7-18" 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-vnm-aid-donortype" 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 3: Total ODA for Vietnam per year, separated by donor type</figcaption><p></p>
</figure>
</div>
</div>
</div>
<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>
<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="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><span class="co"># 14020-22 - large scale potable water</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 14030-32 - individual-level water and sanitation supply</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 14081 - education and training in water supply</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14020</span>) <span class="op">|</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14022</span>) <span class="op">|</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14030</span>) <span class="op">|</span></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14032</span>) <span class="op">|</span></span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">43060</span>)</span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> ) <span class="op">&amp;</span></span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>floodaid <span class="op">=</span> totals[totals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of large scale water supply and sanitation</span></span>
<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span> (floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14022</span>)].index, <span class="st">&#39;SECTOR&#39;</span>] <span class="op">=</span> <span class="dv">14020</span></span>
<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14020</span>)].index, <span class="st">&#39;Sector&#39;</span>] <span class="op">=</span> <span class="st">&quot;Large water supply and sanitation&quot;</span></span>
<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of individual water supply and sanitation</span></span>
<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span> (floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14032</span>)].index, <span class="st">&#39;SECTOR&#39;</span>] <span class="op">=</span> <span class="dv">14030</span></span>
<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a>floodaid.loc[floodaid[(floodaid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14030</span>)].index, <span class="st">&#39;Sector&#39;</span>] <span class="op">=</span> <span class="st">&quot;Basic water supply and sanitation&quot;</span></span>
<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by sector per year and sum all values</span></span>
<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a>floodaid_grouped <span class="op">=</span> floodaid.groupby([<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a>floodaid_grouped <span class="op">=</span> floodaid_grouped.reset_index([<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>])</span>
<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a>floodaid_grouped.columns <span class="op">=</span> floodaid_grouped.columns.to_flat_index()</span>
<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a>floodaid_grouped.columns <span class="op">=</span> [<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-35"><a href="#cb8-35" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(floodaid_grouped[<span class="st">&#39;Year&#39;</span>], floodaid_grouped[<span class="st">&#39;Sector&#39;</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>floodaid_grouped[<span class="st">&#39;Value&#39;</span>], aggfunc<span class="op">=</span><span class="st">&#39;sum&#39;</span>)</span>
<span id="cb8-36"><a href="#cb8-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-37"><a href="#cb8-37" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename and reorder columns</span></span>
<span id="cb8-38"><a href="#cb8-38" aria-hidden="true" tabindex="-1"></a>crosstab.columns <span class="op">=</span> [<span class="st">&#39;Basic water supply&#39;</span>, <span class="st">&#39;Disaster risk reduction&#39;</span>, <span class="st">&#39;Large water supply&#39;</span>, <span class="st">&#39;All&#39;</span>]</span>
<span id="cb8-39"><a href="#cb8-39" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> crosstab[[<span class="st">&#39;Basic water supply&#39;</span>, <span class="st">&#39;Large water supply&#39;</span>, <span class="st">&#39;Disaster risk reduction&#39;</span>, <span class="st">&#39;All&#39;</span>]]</span>
<span id="cb8-40"><a href="#cb8-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-41"><a href="#cb8-41" aria-hidden="true" tabindex="-1"></a><span class="co">#crosstab.style.format(escape=&quot;latex&quot;)</span></span>
<span id="cb8-42"><a href="#cb8-42" aria-hidden="true" tabindex="-1"></a>crosstab</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="11">
<div id="tbl-vnm-aid-water" class="anchored">
<div>
<table class="dataframe 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>
<thead>
<tr>
<th></th>
<th>Basic water supply</th>
<th>Large water supply</th>
<th>Disaster risk reduction</th>
<th>All</th>
</tr>
<tr>
<th>Year</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2011</th>
<td>96.24</td>
<td>105.94</td>
<td>4.01</td>
<td>206.19</td>
</tr>
<tr>
<th>2012</th>
<td>85.56</td>
<td>150.63</td>
<td>6.73</td>
<td>242.92</td>
</tr>
<tr>
<th>2013</th>
<td>123.46</td>
<td>178.72</td>
<td>8.82</td>
<td>311.00</td>
</tr>
<tr>
<th>2014</th>
<td>133.89</td>
<td>193.56</td>
<td>10.69</td>
<td>338.15</td>
</tr>
<tr>
<th>2015</th>
<td>142.92</td>
<td>174.46</td>
<td>19.98</td>
<td>337.36</td>
</tr>
<tr>
<th>2016</th>
<td>154.11</td>
<td>230.91</td>
<td>37.08</td>
<td>422.10</td>
</tr>
<tr>
<th>2017</th>
<td>86.39</td>
<td>239.99</td>
<td>42.73</td>
<td>369.11</td>
</tr>
<tr>
<th>2018</th>
<td>70.70</td>
<td>252.37</td>
<td>35.51</td>
<td>358.57</td>
</tr>
<tr>
<th>2019</th>
<td>39.24</td>
<td>203.36</td>
<td>42.24</td>
<td>284.84</td>
</tr>
<tr>
<th>2020</th>
<td>50.98</td>
<td>224.11</td>
<td>63.31</td>
<td>338.40</td>
</tr>
<tr>
<th>All</th>
<td>983.49</td>
<td>1954.03</td>
<td>271.11</td>
<td>3208.64</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<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>
<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. 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 relative poverty 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="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>gni_cnsmpt <span class="op">=</span> uga[uga[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb9-3"><a href="#cb9-3" 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 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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. Source: Author’s elaboration based on UNU-WIDER WIID (2022).</figcaption><p></p>
</figure>
</div>
</div>
</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 &amp; 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. 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. <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>. 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. 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>. 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. <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. 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 &amp; 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. 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>. 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. 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="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><span class="co"># Load CRS data</span></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Uganda_11-13_05092022214241555.csv&#39;</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="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Uganda_14-16_05092022213749491.csv&#39;</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="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Uganda_17-20_05092022213356210.csv&#39;</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="cb10-5"><a href="#cb10-5" 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="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">&#39;</span><span class="ch">\ufeff</span><span class="st">&quot;DONOR&quot;&#39;</span>: <span class="st">&#39;DONOR&#39;</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="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>totals <span class="op">=</span> df.loc[</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-11"><a href="#cb11-11" 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="cb11-12"><a href="#cb11-12" 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="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Financetype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Financetype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid amount, in millions of USD&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb11-19"><a href="#cb11-19" 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-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>
<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>
<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="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">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</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">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">=</span> donortotals[<span class="st">&quot;DONOR&quot;</span>].<span class="bu">map</span>(donortypes)</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">!=</span> <span class="st">&quot;nondac&quot;</span>)]</span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Donortype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid amount, in millions of USD&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb12-18"><a href="#cb12-18" 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-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>
<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>
<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>
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<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><span class="co"># 14020-22 - large scale potable water</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 14030-32 - individual-level water and sanitation supply</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 14081 - education and training in water supply</span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>totals <span class="op">=</span> df.loc[</span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> ( (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14020</span>) <span class="op">|</span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span></span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14022</span>) <span class="op">|</span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14030</span>) <span class="op">|</span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span></span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14032</span>) <span class="op">|</span></span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14081</span>)</span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a> ) <span class="op">&amp;</span></span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a>wateraid <span class="op">=</span> totals[totals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of large scale water supply and sanitation</span></span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14021</span>) <span class="op">|</span> (wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14022</span>)].index, <span class="st">&#39;SECTOR&#39;</span>] <span class="op">=</span> <span class="dv">14020</span></span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14020</span>)].index, <span class="st">&#39;Sector&#39;</span>] <span class="op">=</span> <span class="st">&quot;Water supply and sanitation - large systems&quot;</span></span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate different measures of individual water supply and sanitation</span></span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14031</span>) <span class="op">|</span> (wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14032</span>)].index, <span class="st">&#39;SECTOR&#39;</span>] <span class="op">=</span> <span class="dv">14030</span></span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a>wateraid.loc[wateraid[(wateraid[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">14030</span>)].index, <span class="st">&#39;Sector&#39;</span>] <span class="op">=</span> <span class="st">&quot;Basic drinking water supply and basic sanitation&quot;</span></span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by sector per year and sum all values</span></span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a>wateraid_grouped <span class="op">=</span> wateraid.groupby([<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a>wateraid_grouped <span class="op">=</span> wateraid_grouped.reset_index([<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>])</span>
<span id="cb13-30"><a href="#cb13-30" aria-hidden="true" tabindex="-1"></a>wateraid_grouped.columns <span class="op">=</span> wateraid_grouped.columns.to_flat_index()</span>
<span id="cb13-31"><a href="#cb13-31" aria-hidden="true" tabindex="-1"></a>wateraid_grouped.columns <span class="op">=</span> [<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Sector&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb13-32"><a href="#cb13-32" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> pd.crosstab(wateraid_grouped[<span class="st">&#39;Year&#39;</span>], wateraid_grouped[<span class="st">&#39;Sector&#39;</span>], margins<span class="op">=</span><span class="va">True</span>, values<span class="op">=</span>wateraid_grouped[<span class="st">&#39;Value&#39;</span>], aggfunc<span class="op">=</span><span class="st">&#39;sum&#39;</span>)</span>
<span id="cb13-33"><a href="#cb13-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-34"><a href="#cb13-34" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename and reorder columns</span></span>
<span id="cb13-35"><a href="#cb13-35" aria-hidden="true" tabindex="-1"></a>crosstab.columns <span class="op">=</span> [<span class="st">&#39;Basic water supply&#39;</span>, <span class="st">&#39;Education and training&#39;</span>, <span class="st">&#39;Large water supply&#39;</span>, <span class="st">&#39;All&#39;</span>]</span>
<span id="cb13-36"><a href="#cb13-36" aria-hidden="true" tabindex="-1"></a>crosstab <span class="op">=</span> crosstab[[<span class="st">&#39;Basic water supply&#39;</span>, <span class="st">&#39;Large water supply&#39;</span>, <span class="st">&#39;Education and training&#39;</span>, <span class="st">&#39;All&#39;</span>]]</span>
<span id="cb13-37"><a href="#cb13-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-38"><a href="#cb13-38" aria-hidden="true" tabindex="-1"></a>crosstab</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="16">
<div id="tbl-uga-aid-watersupply" class="anchored">
<div>
<table class="dataframe table table-sm table-striped"><caption>Table 2: ODA projects of water and sanitation supply in Uganda per year, separated by project type </caption>
<thead>
<tr>
<th></th>
<th>Basic water supply</th>
<th>Large water supply</th>
<th>Education and training</th>
<th>All</th>
</tr>
<tr>
<th>Year</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2011</th>
<td>10.74</td>
<td>17.00</td>
<td>14.53</td>
<td>42.27</td>
</tr>
<tr>
<th>2012</th>
<td>26.08</td>
<td>30.15</td>
<td>12.40</td>
<td>68.63</td>
</tr>
<tr>
<th>2013</th>
<td>10.18</td>
<td>49.49</td>
<td>4.54</td>
<td>64.21</td>
</tr>
<tr>
<th>2014</th>
<td>21.06</td>
<td>37.09</td>
<td>0.42</td>
<td>58.58</td>
</tr>
<tr>
<th>2015</th>
<td>22.28</td>
<td>51.68</td>
<td>0.20</td>
<td>74.16</td>
</tr>
<tr>
<th>2016</th>
<td>12.18</td>
<td>72.62</td>
<td>0.00</td>
<td>84.80</td>
</tr>
<tr>
<th>2017</th>
<td>14.17</td>
<td>94.10</td>
<td>0.10</td>
<td>108.37</td>
</tr>
<tr>
<th>2018</th>
<td>10.77</td>
<td>110.02</td>
<td>0.06</td>
<td>120.85</td>
</tr>
<tr>
<th>2019</th>
<td>16.03</td>
<td>89.60</td>
<td>0.49</td>
<td>106.12</td>
</tr>
<tr>
<th>2020</th>
<td>21.08</td>
<td>125.15</td>
<td>0.20</td>
<td>146.43</td>
</tr>
<tr>
<th>All</th>
<td>164.57</td>
<td>676.89</td>
<td>32.96</td>
<td>874.42</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<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>
<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. 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. 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 relative 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 relative rate 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="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>gni_cnsmpt <span class="op">=</span> ben[ben[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb14-3"><a href="#cb14-3" 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. Source: Author’s elaboration based on UNU-WIDER WIID (2022).</figcaption><p></p>
</figure>
</div>
</div>
</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 boy 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 &amp; 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>
<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>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. 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, altough 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>. 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 &amp; 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 &amp; 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. 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 &amp; 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="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><span class="co"># Load CRS data</span></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Benin_11-13_05092022185506030.csv&#39;</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="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Benin_14-16_05092022192438936.csv&#39;</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="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Benin_17-20_05092022192856890.csv&#39;</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="cb15-5"><a href="#cb15-5" 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="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">&#39;</span><span class="ch">\ufeff</span><span class="st">&quot;DONOR&quot;&#39;</span>: <span class="st">&#39;DONOR&#39;</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="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>totals <span class="op">=</span> df.loc[</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;RECIPIENT&#39;</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&amp;</span> <span class="co"># Benin</span></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-12"><a href="#cb16-12" 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="cb16-13"><a href="#cb16-13" 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="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Financetype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Financetype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid amount, in millions of USD&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb16-20"><a href="#cb16-20" 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-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>
<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>
<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="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">&#39;RECIPIENT&#39;</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&amp;</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">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</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">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">=</span> donortotals[<span class="st">&quot;DONOR&quot;</span>].<span class="bu">map</span>(donortypes)</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">!=</span> <span class="st">&quot;nondac&quot;</span>)]</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Donortype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid amount, in millions of USD&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb17-19"><a href="#cb17-19" 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-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>
<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>
<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="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">&#39;RECIPIENT&#39;</span>] <span class="op">==</span> <span class="dv">236</span>) <span class="op">&amp;</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">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">23630</span>) <span class="op">|</span> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">23631</span>)) <span class="op">&amp;</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">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a> ((df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">11</span>) <span class="op">|</span> (df[<span class="st">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">13</span>)) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</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>electricityaid <span class="op">=</span> totals[totals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>el_grouped <span class="op">=</span> electricityaid.groupby([<span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Flow&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>el_grouped.style.<span class="bu">format</span>(escape<span class="op">=</span><span class="st">&quot;latex&quot;</span>)</span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>el_grouped</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="21">
<div id="tbl-ben-aid-electricity" class="anchored">
<div>
<table class="dataframe 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>
<th></th>
<th></th>
<th>Value</th>
</tr>
<tr>
<th></th>
<th></th>
<th>sum</th>
</tr>
<tr>
<th>Year</th>
<th>Flow</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="2" valign="top">2011</th>
<th>ODA Grants</th>
<td>0.17</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>5.29</td>
</tr>
<tr>
<th rowspan="2" valign="top">2012</th>
<th>ODA Grants</th>
<td>4.81</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>3.27</td>
</tr>
<tr>
<th rowspan="2" valign="top">2013</th>
<th>ODA Grants</th>
<td>3.82</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>18.90</td>
</tr>
<tr>
<th rowspan="2" valign="top">2014</th>
<th>ODA Grants</th>
<td>6.80</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>8.60</td>
</tr>
<tr>
<th rowspan="2" valign="top">2015</th>
<th>ODA Grants</th>
<td>6.00</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>24.73</td>
</tr>
<tr>
<th rowspan="2" valign="top">2016</th>
<th>ODA Grants</th>
<td>3.01</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>16.50</td>
</tr>
<tr>
<th rowspan="2" valign="top">2017</th>
<th>ODA Grants</th>
<td>2.37</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>19.73</td>
</tr>
<tr>
<th rowspan="2" valign="top">2018</th>
<th>ODA Grants</th>
<td>7.78</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>22.83</td>
</tr>
<tr>
<th rowspan="2" valign="top">2019</th>
<th>ODA Grants</th>
<td>14.15</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>27.92</td>
</tr>
<tr>
<th rowspan="2" valign="top">2020</th>
<th>ODA Grants</th>
<td>55.19</td>
</tr>
<tr>
<th>ODA Loans</th>
<td>45.59</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<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>
<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. 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="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>gni_cnsmpt <span class="op">=</span> dji[dji[<span class="st">&#39;resource&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Consumption&quot;</span>)]</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>gni_cnsmpt_percapita <span class="op">=</span> gni_cnsmpt[gni_cnsmpt[<span class="st">&#39;scale&#39;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;Per capita&quot;</span>)]</span>
<span id="cb19-3"><a href="#cb19-3" 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>
<p></p><figcaption class="figure-caption">Figure 10: Gini index of consumption per capita for Djibouti. Source: Author’s elaboration based on UNU-WIDER WIID (2022).</figcaption><p></p>
</figure>
</div>
</div>
</div>
<!-- poverty -->
<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="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</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. Acess 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>. 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 &amp; 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>
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<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>. 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>. 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>. 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>
<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>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>. 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>. 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="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><span class="co"># Load CRS data</span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>dfsub1 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Djibouti_11-13_05092022210301944.csv&#39;</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="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>dfsub2 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Djibouti_14-16_05092022210632022.csv&#39;</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="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>dfsub3 <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/raw/OECD_CRS/CRS1_Djibouti_17-20_05092022210913679.csv&#39;</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="cb20-5"><a href="#cb20-5" 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="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.rename(columns<span class="op">=</span>{<span class="st">&#39;</span><span class="ch">\ufeff</span><span class="st">&quot;DONOR&quot;&#39;</span>: <span class="st">&#39;DONOR&#39;</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="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>totals <span class="op">=</span> df.loc[</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</span> <span class="co"># Total</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> totals.copy()</span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a>financetotals <span class="op">=</span> financetotals[financetotals[<span class="st">&#39;DONOR&#39;</span>] <span class="op">&lt;</span> <span class="dv">20000</span>] <span class="co"># drop all &#39;total&#39; aggregations</span></span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-11"><a href="#cb21-11" 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="cb21-12"><a href="#cb21-12" 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="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals.groupby([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a>financetotals_grouped <span class="op">=</span> financetotals_grouped.reset_index([<span class="st">&#39;Flow&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> financetotals_grouped.columns.to_flat_index()</span>
<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a>financetotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Financetype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb21-17"><a href="#cb21-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-18"><a href="#cb21-18" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(financetotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Financetype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid, in millions&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb21-19"><a href="#cb21-19" 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 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qYo3rSZK9XtXivA6zYppSFRoEgrIMCr0f0CvBoFNEB1AV4BXl8E3tili8E/liWm2YMIr1UrN0KrIDYGwaVL6z7T9JonAry6u05uIAoUCQUEeDW6WYBXo4AGqK7Xg9wAXS9gomxLll8Sb+7SkHxgP86M+zCfQcydrTZkGAIjIj0+vPSaJwK8Hnel3FAU8EsFBHg1ulWAV6OABqiu14PcAF0X4HXgJG8CL007O/ljJP39N4LLlEFEnboqbzakzE1eGVp6zRMBXq+4U24qCvidAgK8Gl0qwKtRQANU1+tBboCuC/D6MPDm5OTgaK9uyE5KQtUhIxBWpYpXh5Re88TIwJuZmYl69eqhePHi+fbPpaN4ZHCjRo0K5bM9e/agV69eWLt2rar//PPP48cff3S6rWbNmmHixIm4++67na6j9UJXbdRyP0/eS4udUtezCgjwatRbgFejgAaorteD3ABdF+D1YeBNOXIYp8eMQnCp0rh57HivDye95okngDcpJQPHz+Vux1ajYnFEhoe4RU8T8P7+++8oX768W9o0NZKVlYWgoCD1z8aNG2Pr1q1Ot+8N4HXVRqc7Y+VCT95Li51S17MKCPBq1FuAV6OABqiu14PcAF0X4PVh4L38/Xe4tmolSj7eEje1/5fXh5Ne80Rv4N289zwmfrsTSakZSsOyJSPwXqd7cHOlGM2aOgO806dPx8KFC1G6dGk8/vjj+Oqrr/Dbb79h165deOedd7Bq1Splh/m/zSO8ffv2xZIlS9CwYUMkJyejW7dueOKJJ1SddevWgSe18XPzYgLeyMhI9O/fHy1btsTPP/8Mntw2cuRIPPDAA2jbti3eeOMNZRPL6tWr8dlnn6lI8vr16zF58mQVtY6JicGoUaNQoUIFpKenY+DAgTh8+DAyMjJQq1YtjBkzBoMHD86zkfZ99NFHaN68OX755RfVBv89YcIEVe/hhx/G0KFD1T1t3Wf//v027TbXg3139xcNzYNCGvCaAgK8GqUX4NUooAGq6/UgN0DXBXh9GHiPvfM2Mq/Gokr/gQi/pbbXh5Ne88RV4P3rnwsuaTF2zjakpWflq1O7akm82MJ5TUODg3BX7YK5046A98iRI+jQoYOC2hIlSiiI27Ztm0pVcBZ4r169qgB13759Cpa3b9+uYJTlvffeQ/Xq1fHaa69ZBV6mWhBsx48fr6B3wYIFmD9/Pr7//nsFt8ePH1fAykL4rlu3Llq1aoWnnnpKXVu1alXMnTtXgemsWbMUNBOIeRQyC3/XpEkTVKpUKc/GQ4cOqXvymnvvvVcB+okTJ9R9maZz3333qf4HBATYvI+pDWt2m+vh0kCQi/1eAQFejS4W4NUooAGq6/UgN0DXBXh9FHjTzpzByaHvIzAyEjUnTlVw4O2i1zxxBXivxqfi5WG/eFyKUsXD8NWQ3KiqeTEBb6lSpfL5qFy5ciriOW/ePDDdgVFeFv7M6GZhgTc2NhaPPfYYNm3ahLCwMDCS+8MPP6BixYo2gbd9+/YKrlkOHDiA119/HRs2bMCpU6fwwgsv4M8//1Qgev/992Px4sXYvHmzAtsZM2aoOklJSSq6vHfvXvXnrbfewvDhw9G0aVOEh4era8whlLDasWPHvBQMRnb5+YgRI9S1jz76KKZMmYKDBw/avA9B3JbdArweH/6GuaEAr0ZXCfBqFNAA1fV6kBug6wK8Pgq8sT8tQeySRYh54EGUe7mzTwwlveaJK8CbmJKBCfN2uKSHtYhwdEQo6lUv5XQ7UREh6P3vhjaB11YOL0GX8DZ27FhVd/fu3ejdu3ehgZdtdOnSBe3atVNR1dGjR+O7774rYJcppYER3s6dO2Pjxo3qGsKo+b9bt26t4JWpCuPGjVORX0ZtabclRM+ePRtlypRRkMqoL+GXUeMhQ4YgJSUlX4SXEWembbAwGk1ofvfdd9W/mULByO2WLVts3odQa8tuAV6nh22Ru1CAV6PLBXg1CmiA6no9yA3QdQFeHwXek8OHIO3USVTq0QeRd9zhE0NJr3niCvAWRogJ3+7Aum2n81Xt8uxtePbBmoVpLl8dRykNBEPCpinCy5xb5sMywkv47dOnD9asWaPaJDQzCsr0B/McXkvAW7ZsmapfpUoVMJLMaKplcRZ4p02bpmA1LS1NAe4rr7yiItO8x8yZM+3qc/36dQXvvBdTGExpF4RqZ4D32LFjNu9jCebm/xbg1Txs/bYBAV6NrhXg1SigAarr9SA3QNcFeH0QeDOvX8exvr0QEBKCWlOmIyA42CeGkl7zRG/gpXhLNhzFsbNxSsf7bq+Ae2+r4BZNHQEvF1/93//9X14OL7caY2SUwHrx4kWVnsBIaMmSJVV+L1MPLIE3ISFBbS9GCA4NDUVqaipatGiB6OhofPPNN2A6RWGBl9FnQjcjsF9//bVaAHblyhU8+eSTKle3WrVqKhLLqC8BmHm93D2C6QYsgwYNUte8+OKLeTYyX9cZ4OUiOFv3sQe8lnq4xZHSiF8oIMCr0Y0CvBoFNEB1vR7kBui6AK8PAu+1Natx+bu5iL77HlT475s+M4z0mieeAF69RLS3Dy93QOBreeawMmrKRWtMAWDOrWl/XaYkrFixQoEmF4txURo/s9yHl5FXguQnn3yCW2+9VcHx5cuX8cUXX1jtmrMRXlbmfaOiovDtt9/mtcVFatzHlyUkJATDhg1T971w4QIGDBigcnKZV84Fc+xDRESEig7TRi5++/DDDx2mNNx+++1qMZy1+9gDXtpkqYde/pV2jaVAkQJergLlt1DmI1WuXFm9OqpRo4byGL9F83URX99wKxXmEDEfydFnArzGGvCFsVavB3lhbPF2HTlaOL8HvHHS2pmPxiD54AGUf60rije5z9tDIu/+es0TIwOvq875559/0L179zzgdbU+r2c0llFjUzpBYdqQOqKAPypQZICX/5HwNQr3O2ReE1eB/vXXX5gzZ47ae5CvjrhNCr9VTp06VSXvM5ne3mccEAK8/jgt8vdJrwe5EZUT4PUu8GalpOBoj9yobs3J0xD0v1XwvjCW9JonArzOe5fPrkWLFqmtzl599VXnK8qVokARUKDIAC/Bla9Z+IqFhblQPXv2VJFdvjIiCDMPiSUxMVHtBcj9DLlgwNZnzJcS4PX/WaLXg9yIygnwehd44//YiAuzZyHitttRudfbPjWE9JonRQl4fcqhYowo4GcKFBngNffbtWvXVDoDk/l5KgxXojIRn6fBmAqBlzlLhGFbnxGeBXj9bEZY6Y5eD3IjKifA613gPTd1EhJ37UTZl15GiYcf8akhpNc8EeD1KTeLMaKAYRUocsD78ccfq9SF2267TYEuj3Nkvi5XljKZ3lR4vCE/556Ctj6rX78+0tIyDet8e4aHhgYhIyMLOTl+2T2XOkXI477+mZnZLtXzx4upQ0hIENItTqbyx7460ydPzpPs9HTsfPkV5GRl4a7PZyE4OtoZEz12jV7zpFgx39iFwmNCyo1EAVFAFwWKHPBSRZ4aw824uVH2ypUr1RGKXNFqHuHlNi/cdoXAa+szRnjj41N0cYy3G42KCkNycjqyswXych+4AUhLy/C2W7x+f557z0heYmKq123xBQM8OU/itm7DqckTEVGrFmoOGeoL3c9ng17zpHjx3NO6pIgCooAooEWBIgO8PCaRubmMyrIQ5OrVq6c2/WauLk+QMW3hwuMZH3nkEfV7bgFj6zNuxyIpDVqGnzHq6vWq1hi9z2+lpDTk18OTuzRc+HwG4v/chJueb4+SLZ/yueGj1zyRlAafc7UYJAoYUoEiA7wE1w8++EDtccjcXUZueWQizxznNi48v5uL1rhLA49Q5KbfH330kYJkW5/R4wK8hhz3Lhmt14PcJSN85GIBXu8Ab052ttqdITs1FTU+HIeQ0rlbJvpS0WueCPD6kpfFFlHAuAoUGeCliz799FMVrWVOLnN333//fTRsmHv+OSO9Q4cOVfDLDbQJvdwI3NFnArzGHfzOWq7Xg9zZ+/vSdQK83gHe5P3/4Mz4sShWsRKqDR/lS0Mizxa95okAr0+6W4wSBQynQJECXj28I8Crh6q+1aZeD3Lf6qVz1gjwegd4L837BtfXrUHpZ1qjdJt2zjnLw1fpNU8EeD3sSLmdKOCnCgjwanSsAK9GAQ1QXa8HuQG6XsBEAV7vAO/Rt3siKy4OVd8fgrDquadD+lrRa54YHXi5XoQ7A/HtIre4DA8PV2tE3n77bZVeZzp+mCd7clFoRkYG7rzzTnTr1k39bVnatGmD5ORkrFq1yuYQ4MLsL7/8Ui3OvnTpErhnPNev9OnTR6XtubM8//zzaoG3FFHA1xUQ4NXoIQFejQIaoLpeD3IDdF2A14GTPLFoLfXEMZwaORxBMTGoOX6Szw4bveaJJ4A3OzUJ6ZdOKG1Dy1ZHYFik23QeNmwYtm3bhrFjx6Ju3brgPvBcH7Jz504sWbIEQUFBagH177//jvLlyyMtLQ1Lly7F6NGjMXPmTDRq1CjPlsOHD6Nv374ICwvDgAED0KBBA6t2jhkzRrXH+7BtQjTvxXUqCxYsQFRUlNv617hxY2zdutVt7UlDooBeCgjwalRWgFejgAaorteD3ABdF+D1AeC9smgBri7/CSWaP4ayHV7y2WGj1zzRG3iTDm7B5Z+mIjstWWkbHHMTyr/QH6HltEfSz507p46tX758OWrUuNEeo76tWrVCx44d0b59+3zAa3Iw15z8+uuvKkprKgTZcuXKKeDdv3+/WnhtWbjL0IMPPojFixfjlltusTpe9u3bp4C5Tp06OH36NObPn68i0F9//bUC8OjoaLWOhQDO+/Tv3x8tW7ZUi715aunIkSPxwAMPKPgmSHMtDOF68uTJIJQTsGvVqgXaGxER4bNjVgwrWgoI8Gr0twCvRgENUF2vB7kBui7A6wPAe3Lwe0g7dxaV+/ZHRN16Pjts9JonrgJv8uFtLml0adHHyM5Iy1enWKVbULLp8063ExAcivAadxS4njDIfd55YqdlmTRpEo4ePQoehmQe4TVdd+TIETz99NPYs2ePSkngYuvmzZsrkOWWmATQdevWqc/My5o1a1R0mDsT2SoHDx4EUxG49/wLL7yAuLg4tRsR0ySYZsGTSAmtXMh96NAhtG3bVh3QxHsyQmwC5KtXryrwJUAThpnawPQNFkaTmzRp4vYUCqedIheKAhYKCPBqHBICvBoFNEB1vR7kBui6AK+XgTcj9gqO9++LwLAw1Jw8DQGBgT47bPSaJ64Ab1biNZyc1EW7RjxikscKOlmCokqiWs9ZBa7+5ptvFEQycmpZ5s2bpyCRByBZA96EhAQVOWU6RExMjIr2Lly4UEVRWfr166cglRBqXngNgZR/WNgOI74sTJdgvaZNmyqI3b17t4roWpZly5YpsKVtBF5GoXft2qUuO3DgAF5//XVs2LAB5sDLFI233npLRZ3ZPnOVpYgCvqSAAK9GbwjwahTQANX1epAboOsCvF4G3msrV+Dyj9+j+P1NUb7zaz49ZPSaJ64Ab3ZqIi4tneKSTtYiwoHhUQirXNfpdnh92VbdC1xPoGUklyd6Whb+/vjx4yp1wBrwMvrbunVr7N27FwEBAejRo4fK++XCNhamRXAhGiPI5oVgzMgs/7YsgwYNws0336yAtFOnTvjjjz/UJYweM/2AAMyIMUGWW3cS1Am8nTt3Vlt3spj/2xx4+Rn7O3fuXGUzQXzIkCECvk6PIrlQbwUEeDUqLMCrUUADVNfrQW6Argvwehl4T48egZSjR1Hxze6Ianhj8ZIvjh295okrwFsYXS79NAWJu/PDYekWnRBzzzOFaS5fHebTNmvWTOW51q5dO+8z7qLA3RZeeuklFWm1BrxTpkxR6QwzZsxQebNPPPGEWogWHMyjznOBl5FbLnBjGoKpMKJ777334ttvv8Udd+RPs+De8zVr1lTAaw6xtI+7OjAqTOBllJipE64Cr8mG69evo3fv3qrvr776qmYdpQFRwB0KCDfH+NcAACAASURBVPBqVFGAV6OABqiu14PcAF0X4PUi8GYlJuJor24ICAlBrSnTEfA/0PHVcaPXPNEbeKln3F/LkHbxuJI2svY9iKzTxG0yczHX+vXrVa4ud2kgvPLnHTt2qLQBRm/NgZcgy5QC7u7AlAh+xqgpj7pnPfPChWf8/OWXX873+2nTpiloZfSYW5sxH5dt8rRRfsZDlcyBl/chTDNazHxeRpO59RlPJrUX4SVc33333QrMf/rpJxUpZvoDC6PJ1apVQ5cubkgxcZs3pKGirIAAr0bvC/BqFNAA1fV6kBug6wK8XgTe67+ux6VvvkJkg0ao9FbB1+W+Nn70mieeAF49tWQ0l5FS7rbAfXgZQeXODdyHt3jx4vn24WU+Lffl5e4J3AHBtGcuF5gxBYGL2MwLF6hNnTpVRWMtC2F1zpw5OH/+vIoKMx+YObZMg7CEWKYmMC83NTUVlSpVQs+ePdG1a1cVgeaewbZSGnjPV155BSdOnFCAzpxftkWIr169ulo8J7s06Dm6pG1XFBDgdUUtK9cK8GoU0ADV9XqQG6DrArxeBN4zE8cjee8elbvLHF5fL3rNE6MDr6/7TewTBYqKAgK8Gj0twKtRQANU1+tBboCuC/B6CXizUlJUOgOys9XuDEEGWPGu1zwR4DXi/xRisyjgewoI8Gr0iQCvRgENUF2vB7kBui7A6yXgTfhrM87P+FTtu8v9d41Q9JonArxG8L7YKAr4vgICvBp9JMCrUUADVNfrQW6Argvwegl4z382HQlbt6Bsh44o0byFIYaKXvNEgNcQ7hcjRQGfV0CAV6OLBHg1CmiA6no9yA3QdQFeLwBvTmYmjnR/AzkZGbh53EQElyhhiKGi1zwR4DWE+8VIUcDnFRDg1egiAV6NAhqgul4PcgN0XYDXC8CbtOdvnJ00AWHVa6Dq+0MMM0z0micCvIYZAmKoKODTCgjwanSPAK9GAQ1QXa8HuQG6LsDrBeC9+PVsxG34DaXbtEPpZ1obZpjoNU8EeA0zBMRQUcCnFRDg1egeAV6NAhqgul4PcgN0XYDXw8DLPVu5O0N2UhKqDR+FYhUrGWaY6DVPBHgNMwTEUFHApxUQ4NXoHgFejQIaoLpeD3IDdF2A18PAm3LkME6PGYXgUqVx89jxhhoies0TAV5DDQMxVhTwWQUEeDW6RoBXo4AGqK7Xg9wAXRfg9TDwXv7+O1xbtRIlWz6Fm57PPaLVKEWveSLAa5QRIHaKAr6tgACvRv8I8GoU0ADV9XqQG6DrArweBt5j77yNzKuxqPLuIITXrGmoIaLXPDE68GZnZ+Pzzz/H999/r44WDg8PV8f18mjhUqVK5TtaODAwEBkZGbjzzjvRrVs39bdladOmDZKTk7Fq1Sqr44NHE9erVw+///47ypcvb6gxJMaKAnoqIMCrUV0BXo0CGqC6Xg9yA3RdgNeDwJt25gxODn0fQTExajuygIAAQw0RveaJJ4A3KSMZJ6+fVXpXK1EJkSERbtN+2LBh2LZtG8aOHYu6devi2rVr+Oijj7Bz504sWbIEQUFB+QA1LS0NS5cuxejRozFz5kw0atQoz5bDhw+jb9++CAsLw4ABA9CgQYMCdgrwus110pCfKSDAq9GhArwaBTRAdb0e5AbougCvB4E3dtlSxC5eiJiHHkG5/7xsuOGh1zzRG3j/OrsL07Z8jeSMFKX5TZGl0a9ZV1QvUUWzD86dO4fHHnsMy5cvR40aNfLaY9S3VatW6NixI9q3b281Ivvpp5/i119/xXfffZdXb8yYMShXrpwC3v3792P48OEuA+/PP/8Mtk3QjoiIAIG85v/eJjAK/fXXX6vPoqOjMW7cOBUl5r369++Pli1bgvXj4+MxcuRIPPDAA0hPT8fAgQNBGGd0ulatWqCdbFuKKOBLCgjwavSGAK9GAQ1QXa8HuQG6LsDrQeA9OXwI0k6dROVebyPittsNNzz0mieuAu/2c3tc0m7in7OQlpmer06t0tXxXP2nnG4nJCgEd5SrW+B6RnA/++wzrFixosBnkyZNwtGjR/Hxxx9bBd4jR47g6aefxp49exAaGoqsrCw0b94cixcvRkhIiILPdevWqc/Mi70ILwG8devWWLZsmQLZBQsWYN68eervuLg4PProoypVgqkWo0aNUgA7dOhQHDp0CG3btsX48ePVfXn9/PnzVZoGAfjHH39UaRsss2bNQpMmTXD77cYbw047XC40pAICvBrdJsCrUUADVNfrQW6Argvwegh4M69fx7G+vRAYFoaak6chIDDQcMNDr3niCvBeS41D1yUDNGuXgxwEwPmUkpJhMfjs2TEF7vvNN98ogGTU1LIQNAmLs2fPtgq8CQkJaNiwoUqHiImJUdHehQsXYvLkyaqpfv36KUAlgDoLvD/88ANWr16NGTNmqCqMzt56663YsWOHiuiaF0IxwZb2EXgZid61a5e65MCBA3j99dexYcMGlZrx1ltvqWhz06ZNVY6yFFHAFxUQ4NXoFQFejQIaoLpeD3IDdF2A10PAe23Nalz+bi6i77kXFV7/rxGHBvSaJ64Ab2J6Mj7Z8qVL+lmLCEeHRqJ2mZudbicqNBJvNSmYhkKgZSR35cqVBdri748fP67SBqwtMmP0l9HYvXv3qnzuHj16KLjkwjYWpkXUr19fRZCdBV6mMrBd5hCbCiOxhNvKlSurVITdu3erqPHVq1dRunRpBesE3s6dO2Pjxo2qmuW/2c+5c+cqWwngQ4YMEfB1evTIhZ5SQIBXo9ICvBoFNEB1vR7kBui6AK+HgPfMuA+RfGA/Kvz3TUTffY8Rh4ZPAG9hhPtky1f47cTmfFVfbvA8nq79aGGay1cnNjYWzZo1U4vTateunfcZDxjhbgsvvfSSShWwBrxTpkxR6QyMxjJn9oknnlA7LwQHB+cB74MPPqgWuDEFwVTspTQwwsuIMxfDsaSmpqrUA4L02rVr8eWXX6pUBQIvo8lMn3AGeE33vn79Onr37q36/Oqrr2rWTxoQBdypgACvRjUFeDUKaIDqArw3nBQUFIiYmHBcvZpkAM/pb2KpUpGIi0tBVlZ2oW+WlZKCoz3eBAIDUWvKdARa5GQWumEPV9RrnrgS4S1sl5cfWosT186o6o0r34l7Kt1V2KYK1GM0df369SpXl7s0EF75M9MImDLA6K058DJyy4grF5MxJYKfMXq6fft2Vc+8cKcGfv7yyzeiy/aA98KFCyovmO1XqFBB5eAuWrQI3377rboXgZoRY+bzMqLM7c8IyfYivOwD84uZ8sAyaNAgVKtWDV26dHGbhtKQKOAOBQR4NaoowKtRQANU1+tBboCuFzBRgDe/JO4A3vhNG3Hhi1mIvOMOVOrRx4jDQtms1zzxBPDqKTqjuYyScrcF7sPL6Cl3buA+vMWLF8+3Dy93RyCw1qlTR20/Zlr49fzzz6NTp04KVs3LmjVrMHXqVBWJNRUT8LJtU/oDP2Pu73333afyhk27NPAa5t5WrVpVpTAwL5dR30qVKqFnz57o2rWrikJz32BbKQ2EaII36xPeq1evrrZUk10a9BxV0nZhFBDgLYxqZnUEeDUKaIDqej3IDdB1AV4HTnIH8J6bOgmJu3ai3MudEfPAg0YcFgK8hvWaGC4KFB0FBHg1+lqAV6OABqguwHvDSRLhdW+ENzs9HUe6vwFkZaHmxKkIiooywIywbqJe88ToEV7DOlQMFwX8TAEBXo0OFeDVKKABquv1IDdA1yXCq3OEN3HHdpybNgXht9RGlf4DjTgk8mzWa54I8Bp6WIjxooDPKCDAq9EVArwaBTRAdb0e5AbougCvzsB74fMZiP9zE25q/y+UfDz/fqpGGx96zRMBXqONBLFXFPBNBQR4NfpFgFejgAaorteD3ABdF+DVEXhzsrPV7gzZqamo8eE4hJQuY8QhIRFeQ3tNjBcFio4CArwafS3Aq1FAA1QX4L3hJMnhzT9gtSxaS97/D86MH4tilaug2tARBpgJ9k3Ua55IhNfwQ0M6IAr4hAICvBrdIMCrUUADVNfrQW6ArkuEV8cI76V53+D6ujUo3bqN+mP0otc8EeA1+sgQ+0UB31BAgFejHwR4NQpogOp6PcgN0HUBXh2B9+jbPZEVF4dqQ4ajWJWqRhwO+WzWa54I8Bp+aEgHRAGfUECAV6MbBHg1CmiA6no9yA3QdQFenYA39cRxnBo5DMGlSuPmseONOBQK2KzXPBHg9YvhIZ0QBbyugACvRhcI8GoU0ADV9XqQG6DrArw6Ae+VRQtwdflPKNnicdz0YgcjDgUBXr/wmnRCFCg6CgjwavS1AK9GAQ1QXYD3hpNk0Vr+AVvYRWsnB7+HtHNnUeWddxFeu44BZoFjE/WaJ0aP8GZnZ+Pzzz/H999/r44WDg8PV0f18mjhUqVKKWGHDBkCHhP8wQcfYN26depnHvPLI4nXrl1rU/w9e/agV69edq9h5X/++Qfdu3cvcJ3pGOIyZcqoY4gzMjJw5513olu3bupvPYotW/S4l5Y2eZzzjz/+aLOJNm3aIDk5GatWrSr0bZz1nyNbCm1AEatYpID3p59+UueO86zwChUqYNSoUahZsybS0tJw2223oVy5cnnuf/DBB9V/Piy//fYbRowYgZSUFNSqVQvjx48H/4NgEeD1/xmj14PciMoJ8GoH3ozYKzjevy8CIyPV6WoBAQFGHAoFbNZrnngCeLOSkpB0/ITqU2SN6giKjHSbT4YNG4Zt27Zh7NixqFu3Lq5du4aPPvoIO3fuxJIlS1CsWDHcfffdWLBgAapVq5bv56ysLAQFBdm1xZlrHAHv77//jvLly6tn4dKlSzF69GjMnDkTjRo1cpsOpoaMAryNGzfG1q1brfb/8OHD6Nu3L8LCwjBgwAA0aNCg0Do54z97thT6xkWwYpEB3pMnT6Jdu3bqP5Xq1avj008/xR9//IE5c+bg0qVLaNWqFbZs2VJgCMTHx+Oxxx5T39Bvv/12BcyHDh3C5MmTBXiLyITR60FuRPkEeLUD77WVK3D5x+9RvNkDKP/Kq0YcBlZt1mue6A28VzdvwaHJU5GVlKz6VazsTaj3bn9E3lxDs2/OnTunnh/Lly9HjRo32mPUl8+cjh07YseOHWAw5tZbb0VUVJR6DvFnRhC/+uqrvKgsI4lTpkxBenq6AucxY8bgyJEj+SK8jCIzKkxIjo6Oxrhx4xTIOgu8pg7z+fjrr7/iu+++U79av369euYxChwTE6OCRQwatW3bFm+88QYef/xxdd3q1avx2WefqciorTqWtvz888/qeUybIyIiwC8IDETt3bsX/fr1Q/PmzUEgJ4wPHz4cTZo0wf79+9G/f3/12S+//KLs4peICRMmgDD68MMPY+jQoXZtN7XRsmVL0AY+60eOHIkHHnhAwSy/jDRs2BCTJk1SGpoXas8AGYGX7dAuFvpm4MCBygZGyxkg47XBwcFWf3/06NE8/9mqO3jw4Hy2bNiwwaqP7fWHtlkbP3zbYMtPmge/DzZQZIA3MTER58+fxy233KLcwEnXtWtXNZH4nwZ/tvbqaMWKFVi4cCFmzZql6rGd++67D9u3b0doaKhEeH1wULvbJL0e5O620xPtCfBqB95To0ci9egRVOzeC1F33uUJt3nkHnrNE1eB9+rWbS719+BHHyM7LS1fnejat6By++edbicwNBQl7ryjwPWEJgIgnyOWhSBF4CFIMkJI6CJYmX6+fPlyHgwxKPPss8+q6CvfLvbp00dB7/333593TVxcHB599FEFNkyVIJQSugh+rgIvn4lPP/00+Mqd7T711FMqWFS1alXMnTtXQRKfiezb8ePHFdSxvPPOO8ouwrytOua28AtB69atsWzZMtV33mPevHnqb17HIBUhv0WLFupLA4GW6R4MOhG2GYi69957VQrGiRMnMH/+fOTk5KhnNJ/nfHtiyw5TG3xjS+jlPVmfXxquXr2qwHffvn0F/MaILEF78eLFCAkJUXWZhkIeoA8J+7SLhRoR0M+cOWP197zGlJJiq26lSpXybLHnY3v9sTV+qK8tfZwe/Aa6sMgAr6VPOBA5mfmfDl8t/fe//1WvkzgwypYtq/6zIBxPmzZN5V3xW5apcDJ9++23KlIsKQ0GGu2FNFWvB3khzfFqNQFebcCblZiIo726ISAkBLWmTEdAcLBX/enOm+s1T1wB3vSr17C1Uxft3crJAVxINQktVRKNZ+cGRczLN998owCUUVfLQrAj5PAtoyPgZdCF7TASal7s5YASIglxs2fPdhl4ExISVHSTqRiEOdo5Y8YMdeukpCT1GSOwDCK98MIL+PPPPxVoEsAJgps3b7ZZh9FPUz7xDz/8oKLCprYZ5WR0m1Hv06dPo0OHDur5THAlvNevX19FwPlMZnTclHJAECakMvWQheBPUD548KBNOwjq7du3x65du1SdAwcOqLxpRlDtAS8j3/SH6S0vo9C8H8GXtr711lsq4tu0aVOVr81i6/fm/rN1jT1bzH1M4LXVH1vjZ9GiRTb1Icz7WymSwMvJyNchhNaKFSvi7NmzatLxFVKJEiXw5Zdfqlc5K1euVPm6/EbHb66mwtclBGFOvri4FH8bE6o/0dFhSE5OR1ZWtl/2z5VOhYURSgKQmprhSjW/vJbAS7BJSEj1y/652ilX58nV9WtxbvZsFG98D6p27+Hq7Xz6er3mSUxMLjQ4UzITE3F44hRnLs27xlpEODgqCsXr1XW6neDoKNzSs3uB6wmKDKrwWWJZ+HtC18SJEx0CL/NpCTR8bW9ezIGJzylGWnfv3q2ijQSl0qVLK9h2NcLLyDMjr4RaRiunT5+unpXmhSDNaDOvI+ARVplCwQgpA0q26jCoZAJeAjzvZd4vpg4S5AjWb775pkqtMBV+xvQP3uu1115T62tYCJ+8/t1331X/ZooFn92EY1t2UJ/OnTtj48aNqg71Nf3bHmT26NFDASzTKFiYnkIWYLSbhT5nFJzaEYK5IJHga+33likp1q7h2iFTtNmej83tt+yPrfFjz0+mdUpOTwIDXFjkgJffkvmfAida7dq1rbqIA5gTi69E+A2Ir5bMI7xcYMDXFozwpqdnGsDNrpsYEhKEjIws1yv6YQ3+x8Zgj8B/rnNlbNwY5K5qceiDDxC382/c3KM7Sj/QzK9mi17zJDRU3yj44UlTcGndDaiiU2q82gkVWz+j2T+xsbFo1qyZysE0f94wGsoAy0svvaQipM5EeJkWYUqtMxlmDry8B4M1fC1P4GVUj9HWwgAvo6Nsm5FXtksAJTRZKwz+EMqYY0sofuWVV+zWMYdvRnj5TDa1zQXlfPYSKE+dOqWiuPyZxRThZVSX0OwM8B47dsym7fYA0RbwMs/3iSeeUKmQzMs1AS8XuTPdxLTrBn9//fp19O7dW/n/1Vdv5Oqb//6ee+6xusuG+TVM3TABrz0f2+sPx4K18ePIt5ongI814FHg5asMpgkQKCk0/+akd7QK1V2a8VUFX3nwm2nlypXzNcvJytWyponFXRsYCeY3RH5j/eKLL9Rn/A+MW8owh5chf0lpcJd3fLcdvV7V+m6PbVsmKQ35tXFlW7Ls9HQc6f4GQ0KoOXkagv73utOI48CazXrNE1dSGgqr5bmly5B0/LiqXrrJPSh1b5PCNlWgHqOXzHn9+OOPVX4roYk/87U9Uw74HHEEvAQ85tTyuUmoZACGcMVX6aYcUKZPEMQYaWSuJyOR3DaLUOlshJfPZMItF46xvXr16qn0gSeffFIFeZj2x2ciwdsEqYxSM6eYEVbCNXNx7dUxt+XChQuqX7wnF8HxWcsgE9++8jryAe/z0EMPqcVpjIYzCkq4cwZ42aYt2+0BIlM6GNgi9PPLg6kwcstnP/1nXrhTA7XiokNGYZlawDJo0CClWcmSJa3+nvm9Jv9xLFir++KLL+bZQn1s+dhef2yNH37hsudbt00CH2nIY8DLiCodytQBfnvkgOaKTL56ME8X0EsXLjbj6wV+A+bKSfPCCcRXIvxmXLx4cfWtlnbyPwrW438qnOD85slXNhcvXsx7BSPAq5fHfKddvR7kvtND5y0R4C088Cb8tRnnZ3yKiPq3oXKfvs6LbpAr9ZonngBePSVmNJcgyDQ5giABijs3cB9ePm9YHAEvr2FaBJ+d3DuXOz4QpLlQywRMjEoyB5VRUi506tmzp1qMTahhhNDRPrwMPLHtOnXqqF0K+LwzFQI7YZOFgE4gZq6tqXCRGmGPz3VHdezt0kA9mB7BxXGm66gVo7qMInMXBW6V5izwsg+2bLcHiOwDI9XU95NPPsnrK/fD7dSpk4J088KFdNzBiZxD+KUvmHfMt8Dc4o1fcqz93nyXBsK/tWvISSZbuFUqYduajxmIs5WiYWv8cCcPR77Vc254um2PAS9D/oyscrsRhvj5LZTfBPkNjt9Y9C4M6b/33nsqp8m8cOUnJxknM19JmAYpJ50pCswcH6505TdYTnJCL3N9WQR49fac99vX60Hu/Z65boEAb+GB9/xn05GwdQvKvfQfxDz8qOvi+3gNveaJ0YHXx93ms+YZZb9enxVQDCuggMeAl6s6+QqHOTX8xrlp0yZlDL+t8RWBUYsAr1E957zdej3InbfAd64U4C0c8OZkZqp0hpyMDNw8biKC//eF2Xc8q90SveaJAK923xixBQFeI3rNt232GPDylQpzgPiHh0Dw1BmmBnAfOB4AYdQiwGtUzzlvt14Pcuct8J0rBXgLB7xJe/7G2UkTEHbzzag68MYWh77jWe2W6DVPBHi1+0ZaEAVEAcBjwMvcI+bBMkeIiddcvMbVqVylyFQDoxYBXqN6znm79XqQO2+B71wpwFs44L349ZeI2/Aryjz3Ako9mT//z3e8q80SveaJAK82v0htUUAUyFXAY8DLm3GDZ+7tZsqN5YpHrmY08gbHArz+P5X0epAbUTkBXteBl4uWeNhEdlISanw4DiGlyxjR9Q5t1mueCPA6lF4uEAVEAScU8CjwOmGP4S4R4DWcy1w2WK8HucuG+EAFAV7XgTfl8GGc/nAUQitUQPURo33Ai/qYoNc8EeDVx1/SqihQ1BTwGPAyusvtNHimNPeaMy+m01KMKL4ArxG95prNej3IXbPCN64W4HUdeC9//x2urVqJUk89gzLtnvcNR+pghV7zRIBXB2dJk6JAEVTAY8DLzY2Zt8tjeS0Pmnj22WcNK70Ar2Fd57Thej3InTbAhy4U4HUdeI+98zYyr8ai6nuDEVbjZh/ypntN0WueCPC610/SmihQVBXwGPDecccdalsy03F8/iK4AK+/eNJ2P/R6kBtROQFe14A37cwZnBz6PoJiYlBz/CQjutxpm/WaJwK8TrtALhQFRAE7CngMeJ955hl1yhkXrflTEeD1J29a74teD3IjKifA6xrwxi5bitjFC1HikUdRtuN/jOhyp23Wa54I8DrtArlQFBAFfAF4eSwij1bkEXmWp52ZH2FoNG8J8BrNY67bq9eD3HVLvF9DgNc14D05YijSTp5A5T79EFH/xlGs3vek+y3Qa54YHXizs7Px+eefq+04ebRweHg4eAwsjxYuVaqUcgSPrOUe9ZbH3brLS87YMGTIEPCIXB5fu27dOvUzjyrmschr1661acqePXvyjje2Z6+tgyS4VWm9evXUiaeBgYF5TfBYXh5hbO1IZHfpYqsdHhOcnJyMd955R9db2eo7b8pjinkwl97FNPYs78PdZXg6Lo+L5rgNCwtD48aN1bitVq2aJrPMx4Ll+DHZM2bMGHVcdbdu3TTdy7yyxyK8zN+1VQ4fPuy2Dnm6IQFeTyvu+fvp9SD3fE+031GA13ngzbx+Hcf69kJgWBhqTp6GALOHuXZP+F4Les0TTwBvakoGLp6LV6KWq1gcYeEhbhOY0LZt2zZ12FLdunVx7do1fPTRR9i5cyeWLFmCYsWKKZDYunWrbsDrjA133303FixYoGDG/GcuMrdcd2MpjjPXOALe33//HeXLl8/XtDdOW9u7dy/69++PpUuXOuy31kFiAl5rfdfatrP1TWPP8voPP/xQfdHhZgO33norUlNTVdBy+fLl6m8t6amWfjUfPyZ7qE2rVq3UvHFXUNRjwMtvS7YmDSe8UYsAr1E957zdej3InbfAd64U4HUeeK+vXY1L385F9L33o0KX133HiTpZotc80Rt4D+y9gCXf7kJaaqZSJqZkOF7s1BjlKxXXrNS5c+fw2GOPKUioUaNGXnuMuPJh3rFjR7W2heDbsGFD9OzZE++++y7+9a9/4eeff0Z8fDxGjhyJZs2aqbrr16/H5MmTVSQ0JiYGo0aNQoUKFbBv3z4MGDAAderUwenTpzF//vy8ezlrw08//aTAhlG1LVu2qJ/btGmDr776Ki/Cu2rVKvAQqfT0dAXvjMIdOXIkX4SXkWxGhfm8j46Oxrhx4xTIugN4qQkjn2w7IiJCRYBr1qwJnuTKff2rVq2qtO7bt6/6QsGo5BdffKF2h6I+AwcOBANsGRkZqFWrlrKf7ZiX//73v+Aiey6m379/v4Lfli1b5vMH78diyx5H9Uz3cwS8fDPAL0LsMwvfkHM83XXXXejXrx+aN28OwnJaWhqGDx+OJk2a2LXLcpxUqVIlb+xNmjQp7wtHbGys0vSHH35Q48BasTbmbI1P1p8+fToWLlyo0lrZNr9cEajNI7z0m2ku0B6ewrt69eq8/mudkB4DXpOh/HbL8Hi5cuXUKwyjFwFeo3vQsf16Pcgd39n3rhDgdR54z4z7EMkH9qPCG90R7YFXk94eLXrNE1eB99A/F12SYsGcHchIz79VZqWqJfBAC9tvJS1vEBwchJtrF1yfwof3Z599hhUrVhSwiQ/0o0ePYujQoQoACBBMaWjbti0mTpyIFi1aYNGiRSqaRoC9fPkynnrqKQUKBDsCHgGDJ5gePHhQpUUMq0XvQQAAIABJREFUHjxYnWBqXpyxgRDdoEEDBXCEU9PPvGevXr0UmFy6dElBICOfhJY+ffoo6L3//vvzromLi8Ojjz4KgjHTNQjkhEv2USvwEtxbt26NZcuWKRupw7x589TfBCVqSPsIwQRO2seTXN966y2lKe1g2gghkoW6ERDNo4cER0YYCZH8QmHyx/jx4xX08l70BaHenj326pn7xhHwMvLZrl071T/aRvDlvQ8cOKB+zy8fHCeE/AkTJqg0FHt2WY6Tq1ev5o09c7vo7xEjRuDXX3+1OZcs27I3PvmlqEOHDgpeqSu/1P31118FgNfSHvLiQw89pKDfHYFRjwHv2bNnVe7H9u3b8wR88MEH1asdUx6TS/9L+cjFArw+4ggdzdDrQa6jybo1LcDrHPBmpaTgaI83gcBA1JoyHYGhobr5xFca1mueuAK8ifFp+HjYao9LElW8GPoMaVHgvt98842CP0Y8LQthjYBJ8DUH3hdffFFFJ1kINsyj3bBhg4JfXj9jxgz1WVJSkooK8xX88ePHFdTt3r27wJtUZ2yYM2eOQ+BldI59MUUbTf2xl8NLOCUkMhfUEfBa5vBSky5duuTl8DLaSGAy9Z9RZkYfGSH/5ZdflA4E6+eee05FzgnojNYyOk7dCF2EX0ZCmzZtqnKpLQujv506dcLGjRvVRwRXngbLcwQs/WHPnvPnz9usZw14yUABAQF5HzEgyC8qLPwixGgu4ZdjhV8yqCUBkuOE9Qjz9evXV5F5wqotnWiX+TixBbwca/xCxS8ItooJ6k1jzt74pFb8EsEoLwt/pq8sI7zW7OEXKs4fRuS1Fo8B75tvvonQ0FB07txZfTvkYOTE4esTvvIwahHgNarnnLdbrwe58xb4zpUCvPl9UapUJOLiUpCVlZ3vg/hNG3Hhi1mIuqsBKnbr6TsO1NESveaJK8DLXNzF83LhxNliLSIcHhGCKtVzF5Q5U3j9s/++q8ClJqDlom3LQnghqDIqaw68fEaaA5fp34xIEhgqVqyYrynCJEGBoMZXwJbFGRsYUXYU4Z05c6YCQAapzIs58BLKmCZACOLznnZxkTqBxRHwOsrhJS8wIm5+f0ZnCdVMcSDMEu5fffVVFSEnUL3//vvo3bu3gm4WakGQIxwzYsuFeubgy4Ac6/A6FstFhOb/tmcPo7G2/GiunaMIr+laQjdzZvkliYVakqnMI7DUgmkpHGu2dKJd5uPEFvCy3UGDBikwtVWohXlb9sYngZdjnfm4LBwf9IszwMv0Er4p4Jc7rcVjwMtvVHz9wklgKszrfeKJJ+yKqrWDetcX4NVbYe+3r9eD3Ps9c90CAd78mtkC3nOfTEbizh0o36kLijfNzb/096LXPHEFeAujMfN3/952Jl/Vx5+9Ffc+eCPntjDtsg5zIRlhZLSudu3aec1wBTzzY1966SWVAuAM8LINwh3B07LY293BGRuYBuEIeBnhZWoGwca8mAMvbeT2o3ztz2c96yxevNgtwEtoYoTZ1H8uoiLkMcrJwBlzW/kWmf9mru7TTz+t4PfYsWMqJcC8XL9+XQEXfcNrTMVahNcWuNqzh2kF7gJechP7TFjt0aOHesVP4GUU2/QmwBTh5at/Rndt6WRply3gZe440z34RYUpHtaK5ZizNz75JYNf4kwRXvaJuenOAK8hI7z33Xef+vZpvu0IE/c54DZt2lTY/0+8Xk+A1+su0N0AvR7kuhuuww0EeB0Db3Z6Oo72fAs5mZlqd4YgK69OdXCN15vUa57oDbwUbvOG47h4Nk5pWOf28qh7W/7dArSIy4gkH/Bc7c7X0YQJ/sxX8Yw8Ety4KwLB8cSJEzZBiWtfGO3ia2bupMDX14RPU+TVHLAs7XVkQ0hIiEPg5VtZQiTBhlFmRqb5Kp7AbsrzZYSVUUHmLTOfl4DGwBbhUGuE98KFC+r+hH4u1GMuK1+jc9ssFi5KO3XqFP7v//5PRW8Z8U1ISFBpDYQmas0INKOlLIxgUkemTZiKyReEsxIlStiN8Nqzx15k2Nw3jiK81I6LG6dNm6aAl4sama/LccIvTPQ9AZgpHYxqMzLtil3UxzT2zIORtJHt8csK85f5ZYi28t70MSPNJ0+ezDdW7Y1P5lTTLwRx6sovJn///XcB4LW0x5TDy3xfLkDUWjwW4WVn+U2Bg4+Ti/k3nBR8hcBvhEYtArxG9Zzzduv1IHfeAt+5UoDXMfAm7tiOc9OmIKJOXVTuN8B3nKezJXrNE08Ar57SMJrLSBkXnxEKCBamaKRp4TZX3xNiuO8r98G1ltJAGwnOBBEWPke5QIt5rI7273XGBkcRXt6Tr8u5UIrww10nCNK02wS8jBgy55jgWKlSJQVoXbt2VZFsRrGt7alrD/osIdl8VwRqx3xcLuBjIZwx15WBtbJly6ovA1zIxS8WXPBEECQU00bmvXKf39GjRxfYpeG1115TYE2gdASutuxxVM803uztw/vGG2/g4sWLCtKZZsHCyDWj2bSNWnIcMaqbkpKiIqamfXudtYttmsbeJ598UmBHBsIt//DLDlM/yHD0NbWzNuZsjU/eh77gl6WSJUuqxYfkvt9++y3fLg2W9jCH3ZC7NNBwfpPiYCPh85UCc3m5ZYg7kpH1/A/LXtsCvN5S3nP31etB7rkeuO9OAryOgffCFzMRv+kP3PSvjij5WMGFTO7zhm+1pNc8MTrw+paXxBpHCjDySDBmPqyWvWYd3UfL597Yn1iLvYWpa9qHl1+qbrvttsI0UaCOxyK8vDO/hXADbm5fwW9g/Lbgjq0m3KJEIRsR4C2kcAaqpteD3EASKFOvrV6F5L93IPX0KYTXrovSrZ5FsaraTtwxmgaW9lrm8OZkZ6vdGbJTU3HzuIkILlHC6F102n695okAr9MukAvdpACj2Ewn4B68vliKAvDy4AtGs5ma4q6iK/Ay5M3QN1/h8GdbxTyZ310d81Q7AryeUtp799HrQe69Hrl+Z9NrevOagRERuHnMRwiMiHS9QT+pYQm83HeX++/yi0C1wcP8pJfOdUOveSLA65z+cpUoIArYV0BX4OVxwkxyJtDK0cLGGoq2Vp8bqxfusVavB7l7rPNMK5e/nYtrawvub1q5b39E1K3nGSN88C6W8+TSvG9wfd0alG7TDqWfae2DFutnkl7zRIBXP59Jy6JAUVJAV+DlalSGpLkzA3+2VYx84ppEeP1/uuj1IDeCcsn79iJ+y59I+GszcjLzn0ZF+wV48+/De+yd3si8eg3Vh49CaMVKRnCx22zUa54I8LrNRdKQKFCkFdAVeO2lMZirLikNvjcGJcJ7wyd6Pch9z+u5FiXt2Y2EbX8hcfs2lYuqSg6AGwcB5f4uMAg1PhiDkDI3+WpXdLfLfJ6knTyJkyOGIKRsWdT4IHeD9aJU9JonArxFaRRJX0UB/RTQFXjtpTGYd4mbPRu1SITXqJ5z3m69HuTOW6DvlTlZWUj+Z18u5O7cgezk5P8BbSAi6tVHdON7EN2wMWKXLsS1NWZpDTk5KPV0K5Rp97y+Bvpw6+bAG7t4IWKXLUXJx1vipvb/8mGr9TFNr3kiwKuPv6RVUaCoKaAr8NpLYzAXWlIafG/YSYT3hk/0epB70+uOILd443sQ2aARgiLzL0gzbUt2dvsenB4zCsjKQoXX30D0PU282R2v3dt8npwc/B7Szp1Flf7vIfyWW7xmk7durNc8EeD1lkflvqKAfymgK/BKSoNxB4sAr/8Br4Lcff+L5O6yiOTWrYfi9zSxCrnmo9h8H964TRtx8YtZCAgORpWBgxBWBLcoM82T1EuXcLx/XwRGRqLmxKlqY/uiVgR4i5rHpb+igLEU0BV4JaXBWIPB3FoBXv8AXh5vm/zPP7npCru2Izs5JbdjgYFqdwUVyW14d4FIrq2Ra3nwxMU5XyHut/UIiolBtaEjEBxd3LiDvhCWm+bJ5eXLceXH+Yh58GGU+79XCtGS8asI8Brfh9IDUcCfFdAVeCWlwbhDR4DXuMCbC7nmkVwzyK1TNzeS6wLk2orw8ve8F1MbUk8cR1j1Gqgy4D0V8S0qxTRPjo8cjtSjR1CpVx9E3nZHUel+vn4K8BZ0u+noWJ4qGhQUpI6J5c88QphH7VqWPXv25B3TW9hB9Pzzz+PHH390urq5jdxRKSMjA3feeSe6deum/tajGOXgBGta2joOmGcODB48WLP/9NBb2sxVQFfglYMnjDvMBHiNBbz2IDe8Tl3EaIBce8DLzzIT4nFy6CBkxcWh+H1NUf7V14w78F20nPMk9uwlHO7+FgJCQlBryvQiBfzmchkZeNNSknHlwmnVnTLlq6BYeISLI8H65SY4+v3331G+fHl10aZNm9TpUatXr1bwa1kIxYTjwhaeYLp161anq1vamJaWhqVLl2L06NGYOXMmGjVq5HRbzl5oFOC1pqU1n5r3W6v/nNVQrnNdAV2BVw6ecN0hvlJDgNd3gDc7OUntjpBy8ACCS5dB8fubqnSEXMjdi4StTFfYieyUG5FcQi7TFaIbNVZ5pe4qlikNpnZTT53E6Q9GKJvKduiIEs1buOuWPt0O58mJJctx4asv1cI9LuArqsWowHts3w6s+fFzpKfm7k4SXbIMnn6pO8pUrKrZlbbg6Nlnn0X37t1RoUIFDBgwAHXq1MHp06fx/vvv50UI27ZtizfeeAOPP/64soOA/Nlnn6no7ffff4+vv/5agXF0dDTGjRungLpv375YsmQJGjZsiEmTJmH//v2YPHmy2gs/JiYGo0aNUvc0L7Zs/PTTT/Hrr7/iu+++U5evX7/ealv27LRVxxJ4f/75Z/B+7E9ERASGDRuGmjVrYu/evejXrx+aN28OfmkgjA8fPhxNmjRRfePRv/zsl19+UX386KOPMGHCBHDnp4cffhhDhw61a7upjZYtW4I28K30yJEjVfTdUkvTFxZ7wGseobfXNo2y5UNH9VatWgUefZyeno66detizJgxCA8Pt+kfzYPYjxrQFXjl4AnjjhQBXt8BXi6Gyoi9km8whde7FWknjuWH3Np1VLqCuyHX/Ma2gJfXJPy1BednTAcCAlDlnXcRfktt404AJy3nPNk7ZAQS9+wu0rtVUC5fAd4TB3Y56b3cy1bOm47MjPR8dcpVuRmNm7dyup2g4BBUqXVrgettwdGTTz6Jd999F+XKlQNfm/NV+AsvvABzYCLcHj9+XAENC9MgCDjPPfccHn30URB8SpUqpSCWaQiEu6tXrypY27dvHy5fvoynnnoKCxYsQNWqVTF37lwFRbNmzXIKeI8cOYKnn35a2RQXF2ezLVt2tmrVymYdc+A9d+4cWrdujWXLlilop73z5s1Tf/O6du3aKcBr0aKFOrmVQLtmzRrwDTJh+/PPP8e9996rUjBOnDiB+fPnIycnB/fddx/Wrl2rFpDa0sHUxvjx40Ho5T1ZnzBqrqUzXxB4jbn/7LVNPW350F69S5cugV+WGIHn24E+ffqoMUGNnPG10wPaTy/UFXj9VLN83ZJ9eP3fy3o9yJ1RLvnAfpwZ96HNS/MiuY2buDWSa+uG9oCXdS7/OB/XVv6MwIgIVB88DMF+fihFTHggtnb8PyVXramfIjAszBm3+uU1es0TV7YlS0q4jtkf9Naur7WDVuy0GhldAp0GTnAIvIzKEewYiWTElrBHaNu9e7eKbpoD06lTpxQE//nnnwrg7r//fixevLhAhJbtEdRmz56dD9IWLVqkopYzZsxQdiUlJanIL6OmISEhebbagvKEhAR1/bZt27Bu3TqbbZ0/f96qnZs3b7ZZhxFYRrgJpD/88IPSwmQnNbr11luxY8cOFfXu0KEDdu7cqcCVYF+/fn1s2bIFV65cQceOHfPSNwjChNQRI0aovhEoCcoHDx60aQe/ULRv3x67duV+STpw4ABef/11bNiwwSHwcjtVRpVNhV80OnXqlBehJ7jaattyoJj70F69hQsXqi86jIabF2d9rX1iGLsFjwEvvy1+8cUX4ORgjot54asZoxYBXqN6znm79XqQO2OBLeANr1kTlXr08QjkmtvpCHh57dlJH6vT2kIrVEK19wcjoFgxZ7pquGsyrlxGxo6/cGb+94i8405U6tnHcH1wp8F6zRNXgDctJQmrv88fwXTUR2sR4WIRUahQtZajqnmfh0VE4rEXutgEXtOiteDgYDDV7+2331aROcINIemPP/5QdS0XrTHyyVf4hECmLTDyyOcno76E5NDQUAVmpUuXVikO5lFJRnKnT5+OihUr5rOLYGyeO2wLeI8ePaoirwRkRlHttWXNTnv3Z6TSBLyEN96LXwJM5fbbb1dfDAjpb775pkqtMP/sp59+Upq89tpr+O2339RHTN3g9YycszAVhJFbwrEt26lX586dsXHjRlWH/jD92x0RXltt2/OhuQ2WNjGnmp+ba8VrnPW10wPaTy/0GPDy1UjJkiXBJHBOevPCBH6jFgFeo3rOebv1epA7YwGh6viAfgUuvenFDijZIje3z5PFGeBlLvHJEUORcekiIu9sgErde3rSRI/cK3bpYvCPqYSWL4+qAwchMMJ9+dIe6Ygbb6LXPHEFeAvTnTU/zMKBHbnAaSrNnv437mqmfX45WuBkCTeWwDtt2jSkpKSo3FWC6yuvvKJydL/88kv16p3Ay6gfI7+WwMvrCI2EJHvFlo2MjtIeRl4dtWXLTlv3N09pYISXUUuTnampqSDwMqrLKDejuPyZxRTh5aI8QrMzwHvs2DGbOtiDSz2B154P7dlEX69YsaJAWooj/xRmXvhjHY8BLwfw9u3b1QT1pyLA60/etN4XvR7kzih34cvPEb/x93yXhteug0rdengFrpwBXhqbfuG8gt6ctDSUfqY1Srdp50x3DXFN2qmTODl8SAFbvfUlxFdE02ue6A281G/XxlW4cv6UkvLm+g1x860N3SKrVuDlK3fmaTJySaBljus333yjFnAxd5a5oD169EBycrJKDWAawt13361AlWtomCvMRW7VqlVTkU5GAi0B2NLG7OxsBYhcOMZ71atXT6UP2GvLmp326pgD74ULF1SuMO/JBXWMYvMV/bfffqtyeNu0aaNsfuihh9TitIkTJ6oUBYKhM8DLNm3Zbg8uzbU05xZnF63Za9ueD+3VI+RTKwIuvwAx95t53C+99JJTvnbLoDZwIx4D3n/9618YO3asSp73pyLA60/e9C3gvbZyBS7/+D0CQkJzo6SBgQiKiEAxL55o5izwUsmkvXtwduJ4JWrFN7shquHdfjFYbKWZcPeM8p2LzpZs5s7MOLQROadz8yADqtyFkNrN3OZrTwCv24y1aEgr8LI5Lv6KiopSAMjCyCPzTBkJrVSpEnr27ImuXbsq6OHfjAJz8dYnn3yioqAERBbm7RJimR9rXiz3Cua/uWsEdylgoMpUuODNXluWdrKerTr2dmlgbizTOMgKpusee+wxlavLaDd3UeBWac4CL/tgyw57cEn7zbU06eYO4LXnw0ceecRmmgVtWrlypcpNph01atRQ6Q3cqcORf/Qa40Zq12PAyxwdrqLkViElSpTIpxEnqVGLAK9RPee83XpFruxZkLDlT5yf+Zm6hLDL1ABfKK4Ar3o4/7wCVxYQ2kNQ9b0hKFa5si90Q5MNiTu249y0KQXaKN26DfinqJX07YuRtv1Gegf7X6xRG4Q2co8WRgbeojYW3N1fo+zX6+5+S3v6KOAx4GXyNldC1qpVK98KUXaL26UYtQjwGtVzztvtaeBN3v8PzkwYB2Rno9zLnRHzwIPOG6vzla4CL805O3UyknbtQHDJUqg2dDiCIqN0tlK/5lNPnMDZKROQdT0OCMh/n2qDh3k1+q5fr+23nDi7K3Iy0vJdFBhdBpH/HucWkwR43SKjIRsR4DWk23zWaI8BL18HcGsT7jvoT0WA15+8ab0vngTetLNncGrUcOSkp6PUU8+gTLvnfUrgwgBvdno6To0chvRzZxFW6xa1R2+A2XY+PtVBO8bE//kHLnz5BZCVhag77kRo5SrIPHkMwRUrI+LOBuowEH8vOSnxyLpwCJkXDiPr4hFkXT6JgJxMq92Ofv1Lt8ghwOsWGaURUaDIK+Ax4OUWIUxEj3TjqU++4D0BXl/wgr42eAp4M2JjcWrUMGTFxyOqUWNUfMP3di8pDPDSOxlXY3Fy6GDw1LiYhx5Buf+8rK/T3Nh6TlYWLs2bg7jfflWHapRp+zxKPfW0uoO/H9CSfeUUsi4dQeb5gwpwcxJjCyobGARk599qMrBUFUQ+n7sfqtYiwKtVQakvCogCVMBjwMtVhTwd5d///neBHF5uJO2Jwr37pk6dqpL9uXKTJ9Tw+EIW7uXHDauZFM+0C+7fZ9qr0N5nArye8Jx37+EJ4M1KTsapEUOQcfkywuvWQ+XefREQFOTdjlu5e2GBl02lHD6E02NHAzk5KPufV1DioYd9rn+WBmVev67ydVOPHUVgeAQqdH0DkbfdWMjjT8Cbk56C7EtHkXnhELIYwb18DLBIVUBwKILK3oygsjURXL4WgsrdguzEWCT/9CFy0nOP5g0IjUBEq/4ILF3NLf4V4HWLjNKIKFDkFfAY8HKzbVuFp67oXU6ePKmO3+OJNNWrV1cnlXCz7zlz5qjtW7gKlJtrc0UnoZirN7mRtb3PaLMAr96e8377egNvdkYGznz4AVJPHEdoxUqo+t5gBProYQ1agJeevL5+LS7NnWOI44dTjh7FuSkTkZWYgNCKFVGxWy+Eli2bb0AaGXiz4y4i6+JhlZ6QffEIsq+dA8Ajxm6UgOibEFSuFoLL34KgsrUQWLoyEHDjdCnza0OuHlH/zCjl/IENzsxuAV5nVJJrRAFRwJECHgNe7hPIoxOtlWIeeLgnJiaqU95M4M1keO4Owf0MuZEzN3Q2nTHOa3kON/cNZlTa1mfcm0+A19EQM/7negJvTnY2zk6agOR9exBcqpTaySA4JsZnRdMKvOzYhc9ngvmwPKSBi9hCSpX2uf5eX78Ol7+bC6YzRDVohPJdXrf6JcRXgDcnLQkBxewcepGdgaxLx1Vagin/FqkJ+XUPCsmN3parieBytyCo/C1AMecXGOo1TwR4fW56iEGigCEV8Bjw+po6hFtuzj1p0iTwlBhuks1NnE2FwMt9DwnDtj5jpFiA19c863579HqQK/j738ESfF1eddDQAhFE9/dGW4vuAN6czEycHjPqRkT7/SEI9JEDaRhtv/jVbCRs3lQgX9eact4G3pRfZyLzUO4pYUwlKHZ/B7UHbk5yXG709vyh3MVlV04VWFyWG739H9yWY/S2is3orTOjRq95IsDrjPpyjSggCjhSoEgC7+bNm9G/f38FtDythPm6PNv6nXfeydOL+wUThHmii63PmHvMU2n8sQQGBiI7m68387/i9Me+OupTQEDu/lM5Oe7V4swPC3B63ndqj9pbRw1DtJ20H0c2eu7zAAQGBmge9xlxcfi7d19kXLuOkvc0Rt13b8w9z/Ul/53SrsTiwKjRSD5xUh3wUbtvb5RocJddc7w5T+K3LsfVNQV3QgiKLoWshKv57A4IDkVohZoIq1QbYZXrILRSHQRFFHer1HrNE2osRRQQBUQBrQoUOeDlmd1jxoxROby1a9dW+k2fPh2XL1/OF+Hl8Yw8kpHAa+szRnhjY5O0+sAn65coEY6EhFRkZbkX8nyysw6MCg8PAR/mycnpbjM/7s8/cg+WCAhA5V5vI+r2O9zWtp4NBQUFIDo6DNevp2i+TerJEzjJLdgyM1GmTTuU8eKhDcmHDuLMlEnITkpUedSVezBf1/EWit6cJ8nrZyDj4EYrfshBYL7c25oqVUHvosc8oc2lS9tJ1dC7U9K+KCAK+I0CRQp4N2zYoHZimD17NiqbnfjEo/p4fvcXX3yhHBsbGwse78cc3rVr19r8jEc1SkqD38wFmx1x96tadbDExx+p3Qp87WAJR950R0qD+T0S/tqC8zOmq19V6tkHkV4A/+urf8GlH+argz7s5eta08abKQ1pm+Yife/qAmaFt+iO4BqNHLnS7Z+7e56YDJSUBre7ShoUBYqkAkUGeLkQrWXLlvjyyy/VtmPmhZ89+uijatEad2kYN24cLl68qM6otvcZ2xDg9f95484Heeqpkyp/1VcPlnDkTXcDr5pD33+Ha6tWIqBYMVQdOAjFKnnm+GEeiHFh9iwkbv3LqXxdXwPerHP7kbzsw3xm8YSziHbD7C9gc+TkQn7uznliboIAbyEdItVEAVEgnwIeA14eK/zxxx/jzJkzKifWvHCfW70Ld1p47733ULp0/hXhy5cvR0xMDDZu3IihQ4ciKSkJPBWO0FuiRAlllr3PBHj19pz323fXg5wHS5wcPhjZSUk+e7CEI7X1AF7uVMGId8qB/R47fjgj9grOTvoY6efOWd1f15EOps+9GeHNunwCyYuGKljn1mEBYVEo1qiN2/a/dVYD03XumieW9xXgddUTcr0oIApYU8BjwPvkk0+qLcG4GMxye7Jnn33WsN4R4DWs65w23B0P8qzERHWKmq8fLOFIFD2Al/dUB28MH4KMK5d1P36YKSXnpk1Fdkqyzf11HengC8CbtGSk2j+3WOPnENqglbMm63adO+aJNeMEeHVzmTQsChQpBTwGvHfccQd27NiB4OBgvxJYgNev3Gm1M1of5Nzq6vQHI5B2+pTPHyzhyJt6AS/vm37hPE6OGIqctDSUaP4YynZ4yZE5Ln9+dcVyXFn0o8qfdjVf19rNvBXhzTqzB8krxiMgvDgi/z0eAcEhLmvh7gpa54ktewR43e0paU8UKJoKeAx4n3nmGZU/azqu11/kFuD1F0/a7oeWB7nRDpZw5E09gZf3TtqzW6UasJTr3AUx9zdzZJJTn2enpeHCrBlI3Lm90Pm6vgS8ST+8j+xrZxDW7D8Iqf+oUxrofZGWeWLPNgFevT1K27MpAAAgAElEQVQn7YsCRUMBjwEvd0L47rvv8MorrxTIo+VCMaMWAV6jes55u7U8yM/P+gwJm/9UeaJGOFjCkSp6Ay/vH7tsKWIXLwSCglBlwHsIr6FtS630S5cURGdcvKApX9dXgDfj6Bakrp2OgOgyiHxxLAJ8ZJ9aLfNEgNfRzJPPRQFRQKsCHgNe05G+1gw+fPiw1n54rb4Ar9ek99iNC/sgj12yCLE/LVGvmyv3f1czuHmsw3Zu5Ang5e3PTp2MpF07EBQVjWpDRyD4fwtIXdUgae8enP/0E2SnpmrO1/UF4OUbg6T57yAn4QrCmndFSK37XJVEt+sLO08cGSQRXkcKyeeigCjgjAIeA97k5OQCi9VMBhYrVswZW33yGgFen3SLW40qzIM8/s9NuPD5DPX6XO0ve5tx32KYi+kp4OWWYadGDkP6ubMoVqUqqgwchMAQF/JUc3JwddlSXFmySJnvjnxdXwDe9H/WIW3j1wgsWRmRL4x06zjX2lhh5okz9xTgdUYluUYUEAUcKaAr8B46dAg8jSw0NBT82VYxnXjmyFhf/FyA1xe94l6bXH2QM6qo8lANeLCEI+U8Bby0I+NqLE4OHYzs5CRENb4HFbu+6cg89XlWSoqK6ibv2+vWfF1vA29OZgaSvn0bOSnxiHiyD4Kq+NbpfK7OE6ecCUCA11ml5DpRQBSwp4CuwMs0Bu5zS6CVlAZjDURvrT73RZVceZCrgyU+GAnCSamnnkGZds/7YpcKbZMngZdGphw+hNNjR6svD9SSmtor3OlB5etevuz2fF1vA2/6zmVI2/ojgsrdgohn3yu0D/Wq6Mo8ccUGAV5X1JJrRQFRwJYCugJvfHw8oqKiEBgYCP5sqxQvXtywHpIIr2Fd57Thzj7IuTiKe+0a+WAJR6J4Gnhpz/W1q3Hp27nKNHvHDyf+vQvnP5umTrELrVgRFbv1QmjZso66pOlzT30xzElPQeLc3kBGKiLaDkHQTTU02a1HZWfniav3FuB1VTG5XhQQBawpoCvwFgXJBXj938vOPMh5sARPUcu8ehXhdeuhcu++CAgK8jtxvAG8FPHC5zMR/+cf6vjhaoOGIrR8hRvaZmfjyuIF4B67LHrl61pzpqeAN3XzfGTs/hnB1Rsi/PEePjmunJknhTFcgLcwqkkdUUAUsFRAgFfjmBDg1SigAao7epBzj9fTY0b5xcESjtzhLeDNycxUGqeeOI7gmBIofn9TBISEqC8XsUsXq2OJuUCwTFumPTztqBtu+9wTwJudHIekb/sCWZmIbP8BAkuYwb7beqK9IUfzpLB3EOAtrHJSTxQQBcwVEODVOB4EeDUKaIDq9h7kOVlZODNhnAKu4FKlUPW9IQiOiTFArwpnoreAl9ZmJSTg+MB3kJ2Skt/4HCAwMhwVX38LEbfdVriOFbKWJ4A3dcMXyDiwASG1myHs4S6FtFT/agK8+mssdxAFRIHCKyDAW3jtVE0BXo0CGqC6vQf5+VkzkLB5EwIjIlHtvcEIKVfOAD0qvIneBF5afeztXsiMu56/A0GBqDFiDEJ0zte1pprewJt9/QKSvn8XCAhE5L8/QmBUqcI7T+eaArw6CyzNiwKigCYFBHg1ySfAq1E+Q1S39SC/svCHvLzRqgMHIezmmobojxYjvQ28h7q8YtX82rO+1NKtQtfVG3hTVk9F5vFtCL3jCRS799+FttMTFQV4PaGy3EMUEAUKq4DXgfedd94Bd2lo0qQJWrRoUdh+eK2eRHi9Jr3HbmztQR7326+4OCcXsip174nIOxt4zB5v3sjbwHu8f19kxF7JJ0GxylXUaWzeKHoCb9blE0heNBQIDkVUh48REBbljS46fU8BXqelkgtFAVHACwp4HXhfe+019OvXDytXrkSPHr65+tieXwR4vTBqPXxLywd54q6dOPfJZL88WMKRtN4G3sQd23Fu2pR8ZlZ8szuiGjZyZLoun+sJvElLRiL74hH8P3vnAR9lkf//z/Zsek8gVEkooUPo6KEgFhQrp3KnZ+Fs59mP0/P+nnee53l2j9OzoP6wIIJwKiJSVJRO6KGFhJpAet/dbP+/ZjabZFO3PM+z+2y+8yKvlJ3ync/M8Lx39jvf0Y6/Drrx14hiv5CVEvAKqSbVRQqQAkIrEHTgFbpDUtdHwCu14tK31/pBbjp5gkcLgN0elhdLdKdusIGX2WetKEfD3r3c1OixY6FJTunObNFeFwt47UUHYVzzMqCLcu3uakL/+nUCXtGmGVVMCpACAihAwBugiAS8AQoog+LuB3l14Smcee5ZOExGRI+fgN73/U4G1gtrYigAr7A9Cqw2sYDXsPzPcFQXIWLar6AZLg9XLwLewOYSlSYFSAFxFRAVeGfPnt2t9Xa7HRs3buw2X6hmIOAN1ZEJ3C6H0YDKr76EtfgsWPgx46nTcFoaoR+aHbYXS3SnGgGvp0JiAK+1cAcaN74FRUwyom76JxRKdXfDEhKvE/CGxDCQEaQAKdCJAqIC7+eff97c7JkzZ7if7ty5c5GUlITz58/j66+/xvz583HPPffIdoAIeGU7dN0aXvL+u6jbusUjnzJSjwtefA1KXeh/xNxtB/3IQMArLvA6HQ4Yli2Es74CERffDU3WVD9GKThFCHiDozu1SgqQAt4pICrwtjbhpptuwmuvvYZevVpuCTp27BiefvppLFu2zDtrQzAXAW8IDopAJhX8/n7uvtA6KfV6ZP77LYFakF81BLziAq/l8Pcwb14CZUIfRM37u6wmCAGvrIaLjCUFepwCkgHvhAkTsGvXLg+BmTvD2LFjceDAAdkKT8Ar26Hr0nCHwYCChzr20Q1WzNdQUJqAVzzgddqsMCx9DE5THfSXPwp1v1GhMORe20DA67VUlJEUIAWCoIBkwMti7N51112YN28eVCoVGOx+/PHH+Oijj7Bhw4YgdF2YJgl4hdExZGpxOFD9/UZUfrUSDoMRUCg8TIseMxa9H3goZMyV2hACXvGA17L3a5h3fQFVWhYir3lK6qENuD0C3oAlpApIAVJARAUkA94ffvgBjzzyCGw2G+Li4lBbW8u79fLLL+Oyyy4TsYviVk3AK66+UtZuPHIYpZ98BGvJed6sJj0dCocDlrIy/ju74CD9zgXQ9esvpVkh1RYBrzjA67SY0PDJI4C1EZHX/QWqlIEhNe7eGEPA641KlIcUIAWCpYBkwMs6WFdXh9zcXFRVVSEhIYG7MyQmhu7d8N4MCgGvNyqFdh5reTnKln4Cw4F93FB1fAKSb5iH2MlT+A6vWA/y0FalY+sIeMUB3sbtn8F6YC3U/cdCf5k8P0EQa52kpMTIcamQzaQAKRBiCogKvPn5+RgwYAC0Wi3Yz52lwYMHh5gs3ptDwOu9VqGW02k2o+LrL1G9fh1gt0Gh0SJh9mVImnM1FFpts7liPchDTQ9v7CHgFR54HYZqGD5byOdg1C//AWV8y8Feb8YkVPKItU4IeENlhMkOUkDeCogKvFlZWfjmm2/AgJb93Fk6fvy4bFUk4JXh0DmdqNuyGeUrV8Be53KtiZ4wEanzboE6MaFdh8R6kMtQORDwCg+8jZveh/XYT1BnTYX+4rvlOC24zWKtEwJe2U4JMpwUCCkFRAVe5sIQHR0NpVLJ3Rk6S7GxsSElii/GEPD6olbw85pOFKLskyUwnz7NjdH174/UX90G/QWDOjVOrAd58NXw3QICXmGB11FTAsPnTwIKJaJueRHKaPm6eIm1Tgh4fV+nVIIUIAXaKyAq8Hoj+BNPPIF//vOf3mQNyTwEvCE5LO2MstXUoHz5Z6jfsZ2/po6PR/L18xA7ZWq7SAxtC4v1IJeHcp5WEvAKC7ymdf+G7dRuaEdcCt3UX8lxSjTbLNY6IeCV9bQg40mBkFFAMuCtr6/HO++8g8LCQjgcDi4A2/Vll0/s3r07ZATx1RACXl8Vkzi/zYqq79ai8puv4bRYALUGiZdd3s5PtyurxHqQS6yEIM0R8AoHvPbyUzCuegZQaxE9/xUoIqIFGaNgVSLWOiHgDdaIUrukQHgpIBnwPvTQQ/zg2tSpU7Fq1SpceeWV2LZtG1544QXk5OTIVlUC3tAduobduSj7fClslZXcyJgJE5HSiZ8uAa9340jAKxzwGr78OxylBdCOvxa68dd6NwAhnIuAN4QHh0wjBUgBSAa87KY1dsEEi8E7a9Ys/vPXX3+No0eP4g9/+INsh4KAN/SGzlxcjNIlH6CxsIAb542fLgGvd+NIwCsM8NqLDsK45mVAF+Xa3dXovBuAEM5FwBvCg0OmkQKkgHTAO378eB6DV6FQgN26tn79elgsFlxyySXYvHmzbIeCgDd0ho5dB1y+agVqf9oEOBxQxcUhhcXTnTKtWz9dAl7vxpGAVxjgNSz/MxzVRdxvl/nvhkMi4A2HUaQ+kALhq4BkO7w33HADv1Ht7rvvxty5c/H8888jLS0Ns2fPxp49e2SrMAFvCAxd83XAq+AwGrmfLounm3zVXI94uv5aKtaD3F97glmOgDdw4LUWbEfj9/+FIiqBR2ZQKNXBHFLB2hZrnZAPr2BDRBWRAj1aAcmAd+fOnfjTn/7EXRmWLFmCf/zjH9Dr9bjooovw+uuvy3YQwg14G/bsRsmHi13gCCBh1myk3Dw/ZMen7XXAMTkTkPLL+R3G0/W3E2I9yP21J5jlCHgDA16nwwHDsoVw1ldAP+O3UA+eFszhFLRtsdYJAa+gw0SVkQI9VgHJgJcpzKIzsJi8LG3fvh3V1dWYOXMmv4lNrimcgNdaUY7Tf/tLM+y6xyT9jgWInTY9pIaIXwf82acw7N/L7dL26Yu0X98GfWbnF5z42wGxHuT+2hPMcgS8gQGv9fBGNG7+CIq4dH6rmkLh+v8wHJJY64SANxxmB/WBFAi+ApICb/C7K7wF4QS8hsOHUPzKi+1E0iSnIHL4cGgSEqGKj4c6PgGaBNd3ZZS0oZRc1wH/D9Xr1/OrWLmfLounOzUwP92uZoZYD3LhZ6P4NRLw+g+8TpsVhqWPwWmqg/6yh6DuP1b8AZOwBbHWCQGvhINITZECYawAAW+AgxsOwGvKP4babVtQv3MHGFC2T04Aik6VUiclNcFwHNRxDIYToGZf8QlQxcVDk5QIhca3XXyH0QDjsWMwnz2DyCFDoR8yFHXbtqJ8+bLm64ATrrwKyXOuhkIn7gl3sR7kAU69oBQn4PUfeC17v4Z51xdQJg9A1PXPBGX8xGxUrHVCwCvmqFHdpEDPUYCAN8Cxlivwmk+dRO32bWjI3QVbTTVXQRkRAafVCqfd7qFK0pVzoE5KgbWqEraaWthrq2Gtrga7vcxhaPBKQaVeD1U8g+B4FxA37RQzKFaz3eK4xGa/245cK1h5h8nE24rOyUEqi6eblORV24FmEutBHqhdwShPwOsf8DotJjR88ghgbYR+7p+gTh8cjOETtU2x1gkBr6jDRpWTAj1GAQLeAIdaTsBrPX8ONdu2omHXDjAfWHdiB71iJk5G9LjxMJ85jcqv/ge266vr2w9RY8Yh4dLZXapkqyjn8Msg2F5bA2tNNWxV1bDV1nCYtlVXu2458yKpYmIBpQL22tp2uTVp6Ui//U7os6SFBbEe5F7IEXJZCHj9A97G7Z/BemAt1P1GQX/5oyE3rkIYJNY6IeAVYnSoDlKAFJAMeP/85z/j73//u4fiVqsVDz74IN566y1JRoJdcsEiQrBIETt27EBiYiJv12w2Y8SIETxMmjux6BEskgRLmzZtwrPPPguTyYTMzEy8/PLLSE5O5q+FOvDaKitQt3076nZuh6W4qLl/kSNGIHbSVESPHQ9lRHuXgMTEKNTWmmC3u66BDjSx3Vk7g9+6Olirqjgg26rZd9dOMYNi9rsrdexC0efxPyJy6LBATfG5vFgPcp8NCYECBLy+A6/DUA3DZwu5zzk7qKaM7xUCIym8CWKtEwJe4ceKaiQFeqICogPv999/z3X94x//yK8Rbp3Ky8s5SObl5Umi/cMPPwwWD3jBggX8WmM38JaVleHqq6/mENw21dXV8ZvhFi9ejJEjR2LRokX8iuQ33ngjZIGX7Y7W5+5A3Y4daDxR2NyliMwsxE2eyq/YVUZFdam50MDr7QDb6+tQ8b9VqN30Q7si/Z/+K3T9+ntblWD5xHqQC2aghBUR8PoOvI2b3of12E/QZE1BxMX3SDha0jYl1joh4JV2HKk1UiBcFRAdeFns3UOHDuHw4cOIjvY80a/RaHDttdfy+LxSpiFDhngAb0FBAe655x5s3LixnRlr1qzBypUr8d577/HXGhoaMGXKFOzevZuHUwuVHV67wQDDnlzul8vcEeBku6SArm9fxE6aguiJk6Fp2tH2RutgAS+zjfvw/vUvcJhcsYBZ0g8egr4Ln/TGdMHziPUgF9xQCSok4PUNeB01JTB8/iSgUPJLJpTRrk+VwjGJtU4IeMNxtlCfSAHpFRAdeN1dYkD59ttvS9/DDlpsC7x79+7Fvffei/79+4Pt9qampuK5555DVlYW3nzzTVRUVODpp59urokB79KlSzFgwICgAi+LqNCwb68Lcg/nNR8206SmIWbiJMROngpterpfmgcTeJnBLEpD7ZYtHHo1SclBjQMs1oPcr4EJciECXt+A17Tu37Cd2g3N8FmImPbrII+euM2LtU4IeMUdN6qdFOgpCkgGvKEkaFvgLS4uxvr16/luc3x8PD788EN89tlnWLt2LffXtdvtWLhwYXMXZsyYwUE4OzsbRqN3h7GE6r/TZkPd3r2o2rIVdXv3wNF0GIxFPoifMgWJ06Yi8oJBATen12vQ2GiDs2mnOOAKZVyBRqPi1lutntErZNwlv01XKBSIiFDDZLL6XUc4FexqnVhLT6Dy06egUOuQ8ttFUEZIG7Naap3FWicMpCmRAqQAKRCoApIB7759+/DKK6+gqKiIA2TrxA6FSZnaAm/bttmNcMxfl7k4rFq1CszXuPUOb05ODlasWMF3eA2GjuLW+tcbS3k5TKdP88L6/v2hTUlxVeRwoD4vD1Vbt6J2V27zR/2qqCjETZzEITd66FCg6RY7/1r3LKXXa2E2W+FwuFwjenJiD3IGehaLrSfLwPuuVCqg02lgMkn7Ri9Uhe9qnVQtewbW8/mInnw9oibfGKpdEMwusdZJVJS4cbYFE4AqIgVIgZBWQDLgveKKK7iLANsdValcO2budM0110gqUkfAyyI16JouMGDRI1jUBnb9MTvI9vnnn+P999/nNlZWVuLiiy/mPrzMB1koH96GPbtx7s1/e+iQfP0NPHpBfe4u2Ovr+WsKrRbRY8YhdvJkRGaPgEKtFkW7YLs0iNIpPysV66NaP80JajFyafCUv7N1Yi86COOalwFdFKLnvwKFJvyhTax1Qi4NQV3y1DgpEDYKSAa8o0aNwp49e6AWCdB8GZG2wPvtt9/yqAvLli1DbGws3nnnHe7isHz5cn5IbebMmfzQGtv1femll1BaWooXX3RdwSsU8J5+5v/BXHTWsxvMnUDhuuEsatQYxE6ZiugxY3y+tcwXbdx5CXhbVBPrQe7PuAS7DAGvd8BrWP5nOKqLoJt8C7SjLgv2sEnSvljrhIBXkuGjRkiBsFdAMuC96qqruG+sO36t1MrW1tZizpw5vFkGrCkpKVAqlfjqq6+QkJCA1157jf/MPrpmrgp/+9vf0KdPH55/8+bNeOaZZ2AwGDB8+HAOvczXV0jgzV9we4eSpP/mDkSNywFzX5AyEfAS8HY03wh4uwdea8F2NH7/XyiiEnhkBoVSnE9hpPz/wJu2CHi9UYnykAKkQLAUkAx42QEwdhDs9ttvR1KbK2HZzqlck1A7vCf/+DislRUeMrDoBANfeCko0hDwEvAS8Ha/9NquE6fDAcOyhXDWVyBixgJoBk/vvpIwyUHAGyYDSd0gBcJUAcmAl/nvdpaOHz8uW3mFAl52nS/7ap2S5l4L9hWMRMBLwEvA2/3Ka7tOrIc3onHzR1DEpfNb1RQKZfeVhEkOAt4wGUjqBikQpgpIBrxGo7HdYTW3pu7DYnLUWCjgZX03Hj3iujSi6aKFYFyj6x4DAl4CXgLe7v9Har1OnDYrDEsfg9NUB/3sB6EeMK77CsIoBwFvGA0mdYUUCEMFJANet3YsAgK73CEjIyMs5BQSeENJEAJeAl4C3u5XZOt1YtnzFcy5K6FMHoCo65/pvnCY5SDgDbMBpe6QAmGmgGTAW1dXh7/+9a9YvXo1IiMjwW43e/DBB/H444+jX79+spWVgFe2Q+e14WI9yL02IIQy0qE1z8FwA6/NZEDDJ48A1kbo5/4J6vTBITRq0pgi1jqhKA3SjB+1QgqEuwKSAe8TTzyBmpoafoXvHXfcwYH31VdfRV5eHhYvXixbnQl4ZTt0Xhsu1oPcawNCKCMBb8fAa9zyKSwH1kLVdyQir3gshEZMOlPEWicEvNKNIbVECoSzApIB74UXXoh169ZBr9dj7NixHHiZe8P06dP55Q5yTQS8ch057+y2HFwHx9m9PFydos8YaEfO9q5gGOaynzsCS94GqByNcMZlQDPiUihjmm4DDMP+etMltsNbXVyMuo8fA5x2RN74LFSJfb0pGnZ5CHjDbkipQ6RAWCkgGfBOnTqVx7NlsW/dwGuz2TjwshvN5JoIeOU6ct3bbd76CSx56z0yakdcCt3UX3VfOMxyMNg1rn7Bo1fKmGRE3RKcsHmhIi8D3vP/+zcsR3+CJnMKIi65J1RMk9wOAl7JJacGSQFSwAcFJAPeO++8E9nZ2dxvd9KkSfxq3kWLFmH//v3k0uDDgEmVlQ6tAQ0f3g+nxeghuUKlgf7yh/nOpiI2VarhCFo7TlMtnHXlsBz4FtaTu9vZEXnVH6HqPSxo9gWrYaaFedNij/kRzS6Z6ME73gS8wZqN1C4pQAp4o4BkwFtYWIi77rqLR2hgrgwsFBm77Yxd2Tto0CBvbA3JPLTDG5LDErBRtlO7YVr3727rUUQnQRmbykFHFZsGRSz7ngJlbAqgi+62fChkcNYUw15XAUddORz15XDUlsLJvteVAXYrN9EJJxRwXXPdOkVM/RV3behJiWlk/OIv7d4M9bSLJtqOOQFvT1oF1FdSQH4KSAa8TJrGxkbk5uZy6E1PT8f48eM5+Mo5EfDKefTa2G4zw3p0Eyx5G+GoK+WYhzaQ5wZcBoPOhsquO6/RQRmb1rwbzEGY7wynQBmX7rNwjsrTUEQnQ6Hz7Zppp6GagyzbqXU0VMDeCmidxtqu7YiIcfnpOuxg7bdPTqh6DYNuwvVQ9ZDIBB25dzBdeqq7i3tOEPD6vKSpAClACkiogKTAK2G/JGuKgFcyqUVryNlQAfP+b2E9thmwmXk7isg4aAaMg+3MfjgaqvjfmM+qfvbvoUzq32yLs64Mjnq2O1rW/MXq47ukFlOXNiuiE1xAHJ0MZZwLjNnOMNstVuhjm8ta8zfDvPXT5h1FdqEBu9igOdktrvZ4u2WwMxivL4e9tpxfcQuHa5e2s8Tg27VLnQxVXCq3idnA/ga1lhdzmg0wrXsD9vOui1FYUqVnwV7SckuistdQRORcB1WvIaKNVbArtp/ZB/POFbBXFbUzhYBXyw93GgyuNSRUoigNQilJ9ZACPVsByYC3oKAA77//Ps6fPw+73e6h+pIlS2Q7CgS8sh06Dm8sCgNzX3AnZVomdCMuhXrQpOa/aQ3n+IPcHNnLp84y4HXUlrh2VuvZzmoJB1DX7nBF13WptVDGpHLwtZ873C4vh26VGgy4nY31XdaliIiBggE1A2vubsGgNtVVf3SCT31SGCsR6aiDIXqgC4RNtTDv/QbWvHUtINxraNjt+NqObYJ5/1o4as4DTieg6MC949LfQzNwvE96hlNm2uENp9GkvpAC4aeAZMA7Z84cJCQkYMKECVCr1R5K/u53v5OtsgS88hs667GfYTn4HRytdunUg6dBN2I2lMktu7funon1IOd+sk2+su4dYg7EHrvD7d0qOGi28alt3qV1+xDHpXH3B7ZzDJVrl1aI1FkcXnadrnnvak/w7T0MOrbjK1dXB3MDLIc28lBszW8qImKgHTYD6oxsWPLWwXH+GJRJ/aDqP65Hh6xjc0usdUI7vEKsXKqDFCAFJAPekSNH8sgMWq1wD99QGD4C3lAYhe5tYL6qlkMbYDnyI9C0I6qIjIdu+Eyoh80A2wXtLIn1IO/KavfuMP8IffeX7bIytwbtyMv82qXtXq3Oc3R38UQ4gC97I8JdXI7+3OwOwt44aEdfAc3QGR7iUDSTFjnEWicEvIGsWCpLCpACbgUkA96bb74Z//rXv2R9jXBH04aAN7QXk6PyDMz718B2Yhc/eMWSKi2LRxbgHz8rVd12QKwHebcNN/nOGlf+hbtEtE7BCgfWHfC6bWQ7ouZ938B6aGNzpAcWviyUd3zt5Sdh2bcatpN7mg4sgvsj60ZfDlW/Me0OMLK+EvAS8HqzjikPKUAKBF8ByYCXhSV74IEHMGPGDMTHx3v0/J575BusnYA3+JO4nQVOB2wncmFmHzmXFvCXnUo1tJmToR11GZQ+3oQVTOBltjO3B/Pu/7kOoAHQjJgdNF9Rb4G3eUzMBjTu/RrWw98DNovrDUfvYYiYeCOUqaEQjtAJ26k9sOxfC3tp0wE8hRLqgTnQjbkKyuR+XU5wAl4C3hD8H5BMIgVIgQ4UkAx42cUT+/btQ2ZmJjQajYcpn3zyiWwHh4A3dIbOaTbCeuRHWA6vh7OhmhumiEqANnsmtNkXAz6G83L3LNjAGzoKAz4Dr9t4Dr6rYT28sRX4ZiNi4g3BAV+HlUflYKDrCkHH3knooBnyC+hGXeH1YT4CXgLeUFqfZAspQAp0roBkwDt8+HB8//33SEtLC6vxCEfgZfFWo9AAo00NRVroh5hiJ+fNB9bCdnwbYG/aRUzPgnbEbDBfV2/cFg7ueJkAACAASURBVLqalAS8Ler4DbytwJe5OjB/6pYdXwnB19wA88F1sBz6HjA3uN4URSfwA4vqoTOg0Op9+v+JgJeA16cJQ5lJAVIgaApIBryzZ8/GqlWrEBXlW9D8oCnjZcPhBrymH9+FLX9Lc+9Z7NnI6//q82UHXsoXQDYnbGcO8GgL9uKmsF1KDTSZk/xyWyDg9W4oAgZeD/BdzaMgNINvRjYiJoiz48t2cc371sCWv7XlIFpSP+hGXwH1BRP9flNEwEvA693KoVykACkQbAUkA94vv/wSGzZswC233NLOhzc7OzvYOvjdfjgBL9vZNXzxl3ZaaAZNgib7EigioqDQRkLBrsxtupDAb+G8LMguPPC4WazpNjTzgXXNsWzZJRG64bO4jf66LRDwejcgggFv2x3fvPUth9v6jOAXWAjh49tRrGVVv7HQjZrNfYkDTQS8BLyBziEqTwqQAtIoIBnwZmVlddqj48dbbmuSptvCtRJOwGvJ/QLmPV93IE7HsWDZpQgcRrWRULLvOgbDUfzL9Tv7Hglo2d8iodBGQxERCai7v07atO512E7t5bYwyNaOuwaOhkp+9a/7NjR205eWhRUbNFm4Ae2gJnJpaBFFcOBtqpr5X1sOfAPLgXWCgK+tcDsPLeaoaLkOWT2U+edeDmW8bxeIdDW5CHgJeEX9z4cqJwVIAcEUkAx4jUYjVKqOQ0DpdN0DkGA9FrgiuQMvC9vFYtPaCrY1XV3b/gYpZXQSFNGJcDQaAYsBDE7cvrL+yslv/2JQHBHN/SYVbijWRcFRXdQMu571u8BbM+RC7p+rTOrrb/M+lSPgFR94m1tgPrbMxzdvg+/ga7PAeuwnDrrOhkpXlbpofmCRXfvb+rpmnyZAF5kJeAl4hZpLVA8pQAqIq4BkwMu64XQ6UVFRgerqaiQlJfEvuSc5Ai8DVga4lmOb4Kg40zwEipgUKOBsF/M16pYXoYxJaTdU7DIHBsAOcxMEm9nPDIqNcDQ2wMngmEGy1Qgn/931HXZrl8Pe9hYxd2Yt26GbNI9DjJSJgFdC4HU31Qn4sggK1oJtsFe65q0qqR+0Y+bAenwzrId/cL0ZA6CMZRdFXA7N4GmC3jTXdt4R8BLwSvl/EbVFCpAC/isgGfAePnwYjz32GAoKXHFRWWK+uy+99BK6cnfwv2vSlJQT8LLDXZajP8JWuLNFHKUG6kETwGCSBdlnPrPW/C3Qq6xobLRBlTW1Q9gNVF2nqdYFJ82QzKDZAHbDmPXELrCd57YpWJctEPAGAXibwdcA8/5vYDno8vHt6M1Q67+xa4xZrGX1gPGBTlGvygcbePeV5+HHItch00h1BK7PvBrJ+kSvbBc6k1jrhG5aE3qkqD5SoGcqIBnwXnfddRg0aBBuv/12JCYm8l3ejz/+mAPw8uXLZat+qAOvs6ECliOb+A6YOzYtE1uZPBDaYRdBkzkF0ES00z+YD3L7uSMwrn7Bw6ZgRosQ60Eux0kvlg9vt1qYm8B335r2WZ1OqAdNhG70lVCmDOy2KiEzBHOd5FcX4vW9b3t0J0mfiCcmPIhIdaSQ3fSqLrHWCQGvV/JTJlKAFOhGAcmAd9SoUdi1axda++va7XZMmjQJubm5sh2okARehxW2E7thOboJDB6bky4Kmsyp0A2fAUV8RpeaB/NBzgyz5m+G9djP3EZ24E03Zb4oO83eTDyxHuTetB1qeYIGvE1C1L9ze4eSxNz9YVCkCuY6WXH8S/xwtiWEoFuAyb1yMCQxEyn6ZKToExGtkcYFSKx1QsAblKlNjZICYaeAZMA7a9YsrFy5ErGxsc0iGgwG3Hjjjfj2229lK2woAa+9/CSsR3/iPo6wNjZrqsrIhnboDL4L5m0K5oPcWxulyifWg1wq+4VsJ9jA2zp6h7tf6gFjoZ/9kJDd9LquYK6TJYeXYUfJ7na2to2polVqkB6ViqSIBCTrk5AalcJhOCkiCYkRcV73tbuMYq0TAt7ulKfXSQFSwBsFJANe5rawZs0a/OY3v0Fqaio/vLZkyRJMmzYN06dP5wfaWBo8eLA3dodMnqADL/O5Pb4FlqM/wVFV1KyLIjoJ2qEXQT34QiijfffpC+aDPGQGt8kQsR7kodZPb+wJNvCyWNGNWz8Fi6/LEvM7j5ixIGi7/8FcJ9+cXIc1Jze0G7as+EGotzSgsrESVoet22FNi0zlO8Gpkanc/zdFn4SUSLY77Nuh4jOmM1AoFOgbIWz0FALeboeQMpACpIAXCkgGvN4eTJNbTN6gAK/TAXvRIR5lwXpyHxRO10PNqVRDM2ActMNmQJXBguq3DzHmxZzgWYL5IPfWRqnyEfC2KB1s4JVqzL1tJ5jr5IVdr+N0fREUTetcr47AjVlzwVwa3KnOUo8KUyUqGqtQaapCubESFaYq/rdaS12X3VQqlEiMiOe7wgx+UzkEJ7ugODIZaoWaly9qKMZre96ByWbiv+vVejw87m70ie7abcpbjQl4vVWK8pECpEBXCkgGvOyQWmdxeFsb2NrlQQ5DJyXwstii5sM/wHZ8C5yG6mZ5WDxaFmVBnTmV+7sKkYL5IBfCfiHrIOAl4O1sPgVrnXxxfDW+P/sTojVReGrSo6iz1PkFmCXGUlQYq1DRWMlhuNzEwNgFxbamN9Kd9T1OG8shuNhwHkarC3bdqU90bzw58WFBliEBryAyUiWkQI9XQDLgdSvNwJe5M6SlpXn488p1JIQEXkd9efOFC8wvkce+tVtgO5ELy7GfYD93tFkmdlmDOnMKB11lcn/B5QvWg1zwjghQIQEvAW8oAe/BisP474EP+c7ug2PvxuCEQQLM8vZVsB1gBr5sZ5jtEJcbKvh3tjvMdo67S/+55F/dZfHqdQJer2SiTKQAKdCNApIBb3FxMY/Du3t3yyGLiy66CC+++CIPUybXJBTwWk/uRuP6f3vIoMoYDntZoecBtF5DoR32C6gvyAGUGtFkI+BtkZaAl4A3VICXAejzO19Fo92MuYMux2X9LxHt/4CuKrY6rCg3VaKqsRrvHfyona9wYkQCnp36pCC2EfAKIiNVQgr0eAUkA977778fWq0Wd955J5KTk1FWVob//ve/iI6O5pdPyDUJBbyGFf8PjqqzbWRwnbdWRMZDPXg6dNkXgx1GkyIR8BLwdjTPyIfXUxUp1wk7gPavXW/gnKEEQxOz8MDoBfyQWLDTNyfXY83J9R5mXDnwUswZeKkgphHwCiIjVUIK9HgFJANeFo3hhx9+4NDrTkajEZdddhl+/tkVb1WOSSjg7Sy+qP6KR6HuMwJQKCWVR8oHuaQd86Mx2uFtEY2AN3jA+8nR5dh6bheY7+yfJz2GSI3ej9ksTpHt53ORV32YVz4iIdvj4FygLRLwBqoglScFSAGmgGTAO2XKFGzZsgVKZQu4ORwOHpJs69atsh0NoYDXsPRxOOorPHRgt4tF3RKc3W8C3pahIOAl4O3sPyip1snOkj34v8OfgUVO+MP4B9Avtk/I/Z8p1joh4A25oSaDSAFZKiAZ8N52222YMGEC7r33Xmg0GlgsFrz99tvcp/fDD4NzS5IQIyYU8LKbxRp/fM/DJBZfVDN4uhBm+lyHVA9ynw0LQgGxHuRB6ErATdIOr/Q7vCWGUjy/8zXYnHbMGzwXM/oE5/+E7iaPWOuEgLc75el1UoAU8EYByYD36NGjWLBgAaqqqhAfH4+amhruy/v+++8jMzPTG1tDMo9QwMs6x4Lq207t5f3kURqShI++4K2IBLy0w9vRXCHglRZ4zTYz/rHrNR4ZYUTSMNw3+g5vl7Dk+Qh4JZecGiQFSAEfFJAMeJlNJpMJubm5KC8v57etsR1fnU7ng7mhl1VI4A2l3hHwEvAS8Ha/IsVeJ2/t/wB5lUf45Q9/mvAwdOrQ/f+SgLf7+UI5SAFSIHgKiAq8+fn5XvVMbtcJt+4UAa9XQyzrTGI9yOUoCu3wSrfD+8PZLVhx/EuoFSp+iUN6VFpITxmx1gm5NIT0sJNxpIBsFBAVeMP1OmECXtnMb0EMFetBLohxEldCwCsN8J6pK8KLuxfB4XTgN9k3Y2L6OIlH2vfmxFonBLy+jwWVIAVIgfYKiAq8dXVd39XuNkeq64SZH/Hrr7+ODRs2YMeOHR4XXmzatAnPPvssd7tgPsUvv/wy9zFmqavXaIc3/JeVWA9yOSpHwCs+8LJrev++42Wwm86m9Z6I+UNvlMVUEWudEPDKYvjJSFIg5BUQFXg76r3VauWXTmRkZEguzsMPP4wbbriBH57btm1bM/AyMJ81axYWL16MkSNHYtGiRWDuGG+88Qa6eo11gIBX8mGUvEGxHuSSd0SABgl4xQVep9OJRfvfw9Gq4+gdlY6FEx6ERqkWYOTEr0KsdULAK/7YUQukQE9QQDLgZeD417/+FatXr0ZkZCT27t2LBx98EI8//jj69esnqdZDhgzxAN41a9Zg5cqVeO89V1iwhoYGsLjBLGQa2w3u7DV2iQYBr6RDF5TGxHqQB6UzATZKwCsu8K45uQHfnFwHrUrLL5dIikgIcMSkKy7WOiHglW4MqSVSIJwVkAx4n3jiCR6KjMXhveOOOzjwvvrqq8jLy+M7q1KmtsD75ptvoqKiAk8//XSzGQx4ly5dCgbDnb02YMAAVFQ0SGm6ZG0lJESirq4RdrtDsjZDtaHISA2/wtVgsISqiZLZxYA3NjYC1dVGydoM5YaEXCf51YV4fc/bcMKJe0fdgVEp2aHc9Xa2ibVOkpOjZaUDGUsKkAKhqYBkwHvhhRdi3bp10Ov1GDt2LAde5t7Ablpj/rRSprbAy/x17XY7Fi5c2GzGjBkzwED422+/7fS17OxssI8gwzExwAvXvvk+XoqmIuE51r7qQXOjRTGhtKhprMNja59Fg8WAq4fMwq9HX+/rsIRAfnHWCdOYEilACpACgSogGfBOnToVmzdv5lcLu4HXZrNx4N2+fXug/fCpfFvgfeutt3hs4NY7vDk5OVixYgUH3s5eYzu85NLgk/SyzCzWR7VyFINcGjxHTYg4vHaHHa/ueQsn685gYGw/PDLuPqiUKtlND7HWCbk0yG4qkMGkQEgqIBnw3nnnnWA7osxvd9KkSdw/lh0O279/f9BdGtauXYvPP/+c3/rGUmVlJS6++GJu48aNGzt9jV2RTMAbkvNaUKPEepALaqRElRHwCg+8KwtWY+OZnxCljsSfJz+GWG2MRKMpbDNirRMCXmHHiWojBXqqApIBb2FhIe666y4eoYG5MrAb1lJSUvhBsUGDBkmqf9sdXnZIbebMmdwWFqXhpZdeQmlpKV588UV+gK2z15jRBLySDl1QGhPrQR6UzgTYKAGvsMB7sOIw/nvgQyigwINj78bgBGn/LwxwOngUF2udEPAKOUpUFynQcxWQDHiZxI2NjfxqYQa96enpGD9+vGRXC9fW1mLOnDl8pBnMMthm7hVfffUVD0/G3C2eeeYZGAwGDB8+nENvfHw8z9/VawS84b94xHqQy1E5Al7hgLeysZrH27XYLbjqgtm4YsAsOU6JZpvFWicEvLKeFmQ8KRAyCkgGvPPnz8dNN92Eyy+/XDLIlUJlAl4pVA5uG2I9yIPbK/9aJ+AVBnitDhv+tesNnDOUYGhiFh4YvYBHApFzEmudEPDKeVaQ7aRA6CggGfA+//zzfDfVYrHg2muv5fA7ePDg0FHCT0sIeP0UTkbFxHqQy0iCZlMJeIUB3k+PrsCWczsRp43l8XYjNXo5TgcPm8VaJwS8sp8a1AFSICQUkAx4WW9Z6K+ffvoJq1at4ofB2CG2X/7yl5g3b15IiOGPEQS8/qgmrzJiPcjlpYLLWgLewIF3Z8ke/N/hz6BUKPGH8Q+gX2wfOU6FdjaLtU4IeMNielAnSIGgKyAp8LbuLQv1xW5e++6773D8+PGgC+GvAQS8/ionn3JiPcjlo0CLpQS8gQFviaEUz+98DTanHfMGz8WMPtPlOA06tFmsdULAGzZThDpCCgRVAUmB1+Fw8EsmvvzySw66qampuOGGG3D33XcHVYRAGifgDUQ9eZQV60Euj957WknA6z/wssNpz+18FRWmSoxJGYnfjrxVjlOgU5vFWicEvGE1TagzpEDQFJAMeF944QV8/fXXqK+v59ESbrzxRowbNy5oHReqYQJeoZQM3XrEepCHbo87tuz7sz/jYOVhWBxmxGnicOXAWegTnSG3bghqry8XT7x78CPsKz+IZH0Snpr4CLQqraC2BLsysdYJAW+wR5baJwXCQwHJgPfmm2/mkHvllVciMjIyPNSjOLxhM45ddUSsB7mcxNtXnod3Dy7xMFmv1uNvU/+ISHX4rGdfx8Rb4P2xaDOW538FtUKFJyc+jPSoNF+bCvn8Yq0TAt6QH3oykBSQhQKSAa9bDXbpBIvDm5ERHjtDtMMri3kekJFiPcgDMkqiwnanDUX1JfjyxDc4VlXYrtWHxt4j68sSApXRG+A9U1eEF3cvgsPpwG+yb8bEdPl/stWRbmKtEwLeQGcplScFSAGmgGTAW1dXxw+prV69mu/w7t27l18z/Pjjj6Nfv36yHQ0CXtkOndeGi/Ug99oAiTJaHVYUNZzDmbpinK1nX0U4byiF3emAk/1n0YEdD4xZgGGJ8g8v6K/E3QGv0Wril0vUWuowrfdEzB96o79NhXw5sdYJAW/IDz0ZSArIQgHJgPeJJ55ATU0N7r33Xtxxxx0ceF999VXk5eVh8eLFshCrIyMJeGU7dF4bLtaD3GsDRMhotltwpr4IRQ3FOFVbhOKG8zhvKOmwpT7RvaBTRaCw9mSb151I1afi3tG3Iy0yRQQrQ7/K7oB30b73cKQqH32ie3NXhnBOYq0TAt5wnjXUN1JAOgUkA94LL7wQ69atg16vx9ixYznwMveG6dOn88gNck0EvHIdOe/tFutB7r0FgeU0WE0cbBngnq0rwpn6YpSbKttVqlIo0SsqHX1j+qB/bB/0i+mDPjHpUCnUPO+Sw8uwo2Q3/1mn0vA9XwbOGqUGN2RdjQszJgdmqAxLdwW8357agNUn1vHDaexyiaSIBBn20HuTxVonBLzejwHlJAVIgc4VkAx4p06dis2bN0OpVDYDr81m48C7fft22Y4RAa9sh85rw8V6kHttAIAKUxWqzTXQq3VdRkZosDbgdF0xB9zTdUXcNaGqsboTuE1rgtu+6BebAbaT64bbzmxrHZaszlKP9/M+xfEal2/vqOThuC37l2CH2XpK6gx486sL8cbed+CEE/eOuh0jk7PDXhKx1gkBb9hPHeogKSCJApIB75133slvVmN+u5MmTcLu3buxaNEi7N+/n1waJBlq3xrp7qNa32qTd26xHuTeqvLNyfVYc3J9c/ashEG4e+StMNutKKpvAVvmf1tjrm1XLbvRq1dUKvrF9EX/WO/htiP72sbhZUC38cxP+KrwW+7rG6+Lw4IRv8bAuP7edk/W+TpaJ+yNwN+3vwyDzYiZfS/C9VlXybqP3hov1joh4PV2BCgfKUAKdKWAZMBbWFiIu+66i0doYK4MOp0OKSkpeO+99zBo0CDZjhLt8Mp26Lw2XKwHuTcGsJ3a53e+3i6rVqmBxWHtsAq2U9snJoO7JLBrawfGCncotLOLJ9hu8geHPml2lbjqgtm4YsAsb7oo6zwdAe8ru9/i/s6D4gbi0fH3ybp/vhgv1joh4PVlFCgvKUAKdKaAZMDLDGhsbERubi6H3vT0dIwfP56Dr5wTAa+cR88728V6kHvTOvto/PW9b7fL6o6aICbcerPD2zoP8+f97NhK7CzZw//MgO/OEfP5rm+4prbAu6rgG2w4swnRmig8NelRxGpjwrXr7fol1joh4O0xU4g6SgqIqoAkwMtAd9euXSgqKoJWq+U7uqNHj4ZC0VGgI1H7K3jlBLyCSxpyFYr1IPemo50B75TeE/HrIIS48uZq4dyyffj0yAp+oC1Srcdt2TeFrQ9ra+DNqzyCt/Z/wIf1oXH3YHC8fD+58mZuts0j1joh4PVnNKgMKUAKtFVAdODduHEjnnrqKVRWViImxrXbwa4XZrF3//nPf2LChAmyHhUCXlkPn1fGi/Ug96ZxduDs/239J8Aj4bakJyc+FJRrfb0BXmZlpakK7+Z9xA/NscQiONw4eC7UTREfvOm7HPK4gbfMUMnj7VrsFlx9wWW4fMBMSczfsqEAB3JdGmt1alw8ZwguGJwsSdsEvEGRmRolBUgBPxUQFXgPHTqEefPm4aabbsL999/PfXZZOnv2LN555x3873//wxdffIHBg+UbuJ6A18+ZJ6NiwQJeBk8v5i5CseE80iPTEKuNRp+YXhiVPCJot5t5C7xseNkhtq8K12LDmR/5aPeKSsNvR9yKtKhUGY1+16Yy4K2qbsBz21/FOUMJhiZm4YHRCyT59OrowRL88M0xDwMZ9M67Yzxi4yMk11isdUI7vJIPJTVICoSlAqIC70MPPQSNRoOXXnqpQ/GefPJJGAwGvPHGG7IVl4BXtkPnteFiPci7M+Cdg0uwvzyPX+rwxISHeDzXYCdfgNdta351AQ9fVm9t4DF72U7v9N6Tgt2VgNpnhwnXnNyA4zUnoIACBqsBcdo4Hm83UiNNWLbvVx/FsbzSdv2Ye8toZPSPD6h//hQWa50Q8PozGlSGFCAF2iogKvCy2Lv/+c9/eNzdjtLRo0dx++23UxzeEJyXFJasZVDEepB3News1NfKgtWIUEVw2E2JTAqJWeIP8DLDGywGfHD4UxytOs77IeeYvUabEU9vfQEmm8ljTK7LnINZ/X4h2Ti1dmdo3SgBr2RDQA2RAqSAjBQQFXiHDBmCbdu2ITExsUNJGhoaOAwfP+56CMox0Q6vHEfNN5ulBt78mkK8vscVmeH+0XdheNIQ3wwWMbe/wOs26Yein7Ei/2v+a4IuDneO+BUuiBsgosXCV32o8hje3N/+OvSL+07DjVnXCN9gJzWeyK/AdysPebyqUivxmwcmQxfBbsKTNom1TmiHV9pxpNZIgXBVQFTgzcrKwr59+xAVFdWhfsydYcyYMQS8ITi7aIe3ZVDEepB3NOzs4ojndrwCo82EOQMvxZUDLw2p2REo8LLOFNWfw7t5S/jtcUqFgvfxsv4z+c+hmuxOGxjo5pbsxb7yQ7A77e1MlRp4zY02fPD6VjgdDqg1KtisdqjUKvz6/kmIjJLe/UWsdULAG6qrguwiBeSlgOjA++ijj3I/3o4Su4DilVdeIeANwTlDwCs98FodNrycuwhnG85heNJQ3DfqDkkOP/ky/YQAXtYeuzRj2bFV2H4+lzfPdnnZDW1xulhfzBE1r8Pp5Ncm7+KQexAmWyNvzx0DuW3jtw77JSb3yhHVptaV79h0Enu2ncHgEamYedUwfLfqME4cK8egoSmYfa30VxkT8Eo29NQQKUAK+KGAqMA7e/Zsr0xat26dV/lCMRO5NITiqAhrk1gP8rZWfnDoU+SW7kOyPhF/mvAIdOrQu5RFKOB193136X58fHQ5D+elV+txe/bNGJE8TNgB9LG2U3VnOOTuKTsAdk2wO+nVOoxMHo6ctLEwOyxYdXw1WNg4loKxu7vkP9tgszow/56JiEvQw9hgwSdv7+B/mzt/NDL6SXtwTax1Qju8Pk5gyk4KkAIdKiAq8PYEzQl4w3+UxXqQt1bu5+Jt+OzYKh6J4Y85DyI9REN3CQ28TINQiNlbYizDzvN7+BuOysaq5qFhVziPSM7GhPQx3Jda1SaOcLA+Cdm1+RRyN59ut5t7YFcxtmwsQExcBG757QQwn16pkljrhIBXqhGkdkiB8FaAgDfA8SXgDVBAGRQX60Hu7vrJ2tN4Zc9bcDgd3I0h2DucXQ2JGMDL2mMxe1efWIv1pzfBCackMXurzDXILdmHXSV7eAxdd2KXYwxPHoqctDEYmTyMh1LrLAUDeK0WO9jursVsx00LcpCY3HJGwuFwYvn7uaiqMGLclH6Y9IuBkq0wsdYJAa9kQ0gNkQJhrQABb4DDS8AboIAyKC7Wg5x1vd7SwG/oarAaMLv/xbhm0BUhrYhYwOvuNIvZ+8GhpdyVgIHmvMFzMU3AmL1MZ+ZGwVwWTtadbtZapVBhWOJg5KSP4SHTdF7GPA4G8DK/Xea/OzArCZffMKLdfCkvqceKD/eAnQG8ecEExCdFSjKnxFonBLySDB81QgqEvQIEvAEOMQFvgALKoLhYD3K7w853dpnP6JCETDwwZgGUCuk+gvZHerGBl9lktBrx4eHPcKjyKDcx0Ji9jXYz9pUdxK7SvWBAzQ6jscS0ZrqPTxuNMSmjwHx0fU1SAy+LxPDRm9vRaLLhxjvGISXNdV172/TzugLk7SlGWu9YXH9bx3HQfe1rd/nFWicEvN0pT6+TAqSANwoQ8HqjUhd5CHgDFFAGxcV6kDOfXea7m6CLx1OTHuGHtkI9SQG8bg1+LNqClce/AQsJFq+L41EcBsb190oiq8OKvIqjHHLZd1YHS+xWtMz4gchJH4uxKaMQFeCtaFID774dRdj2QyH6XZCAOb8c1akWzO2BHWAzGayYceUQDBuV7pVugWQSa50Q8AYyKlSWFCAF3AoQ8AY4Fwh4AxRQBsXFeJCzj9Q/PLyUf2z/h5wHkBHdSwZKAFICLxPkXEMJ3s37CGXG8qaYvbNxYcZknDe4rtTNiE5HpNr1kT3zgWY3uTHIZVcym+2WZk1Z2DO2k8u+YjTRgmktJfDa7Q4sWbSt291dd+eOHy7Dhq+OQKtT4Vf3TkKEXtzLKMRYJ6wvBLyCTVeqiBTo0QoQ8AY4/AS8AQoog+JCP8jP1BfzeLs2px2/HXErxqSOlIEKLhOlBl7WJtut/fzY/7D1/C4eA5e5IjidDm4P2xW/6oJLUWIow96yg9wX2p36xfThPrnjU0fzHWIxkpTAezC3CJs3FKLPgARcfXPnu7ut+/nlp/tw7kwt85TCKAAAIABJREFUhoxMwyVzhoohQXOdQq8Td8UEvKIOG1VOCvQYBQh4AxxqAt4ABZRBcSEf5AabEc/veBXV5lpc0vdC3JB1tQwUaDExGMDrbn1veR4W5y1BkwtuK91aroLoE90LY1NH8zBiSREdX2kupOBSAS/b3f34zR0wGiy4Zv5o9PYyxm5djQlL390Fh92J624dg/QMccCfaSrkOmk9RgS8Qs5YqosU6LkKEPAGOPYEvAEKKIPiQj7IX9/zNvJrCpEVfwEeHnevDHrvaWIwgZdZ8rvvF3ao2VUXzMa41NFIi0yRVFOpgPfQ3vP46bt8vw6h5W45jV0/n0Jcop5HbVAqxbnCWch1QsAr6TSmxkiBHqEAAW+Aw0zAG6CAMigu1IN85fHV2Hj2JyTo4vDExIcRrWmJnyoDGbiJwQbe/7f1+ebbzdyaJUYk4NmpTwZFQimAl8XW/eS/O9BQZ8ZVN41E34G+7Vyz8p++vRP1tY2YcOEA5Ezz7uCfr4IKtU7atks7vL6OBOUnBUiBjhQg4A1wXhDwBiigDIoL8SBnYbHY4Svmf/qHnN+jX0yGDHre3sRgA+83J9djzcn1HoZdOfBSzBl4aVD0lAJ4jx4swQ/fHENyajTm3Tner34Wn6nBV5/uh1Kl4DewxcYLHxFEiHXSUecIeP0acipECpACbRQg4A1wShDwBiigDIoH+iAvbjiPF3MX8cNXvx46D1N6T5BBrzs2MdjAy6zafj4XRQ3F3MA+0RmY3CsnaHqKDbxOp2t3tq6mEVfeOBL9M33b3W0tzMbVR5CfV4be/eJwzfwxgmsW6DrpzCACXsGHiiokBXqkAgS8AQ47AW+AAsqgeCAPcpPNhOf4IbUaTO89GbcMvV4GPe7cxFAA3lASUGzgzT9Uio1fHw1od9etV6PJyl0j2JXEs+YOQ1Z2qqBSBrJOujKEgFfQYaLKSIEeqwABb4BDT8AboIAyKO7vg5ztzv1737s4Vl2AAbH98Oi4+6BSqmTQYwJebwdJTOBtvbt72XXDccGQZG/N6jTfob3n8NN3x6GLUOPW+ydDoxVuPvq7TrrrFAFvdwrR66QAKeCNAgS83qjURR4C3gAFlEFxfx/kX5/4DmtPbeSH0/486THEaIW78CBYstEOr6fyYgJv4dFyrPvfYSQkReKmBTlQKISJrrD8g92oKG3A8LG9cdFlWYJNJX/XSXcGEPB2pxC9TgqQAt4oQMDrjUoEvAGqJO/i/jzI8yqO4K0DH/CrbB8bf7/XV+KGulIEvNIB72fv7kJ1pRGXXjMMmcOEcz+oLDdg+fu5PJ7xjbePQ0p6jCDTzp914k3DBLzeqER5SAFSoDsFCHgBmM1mjBgxAmlpac16XXTRRfjHP/7Bf9+0aROeffZZmEwmZGZm4uWXX0ZysuvjRdrh7W6Kyf91Xx/k5cZK/GPXq7DYLbhpyLW4KGOq/EVo6gEBrzTAe/J4JdZ+kYfY+AjMv2eiYLu7buu3fV+IfTuLkJgciXl35ggSm9fXdeLtoiDg9VYpykcKkAJdKUDAC6CsrAxXX301duzY0U6ruro6zJo1C4sXL8bIkSOxaNEi5Ofn44033iDg7SFry5cHudlmxgu5b6DUWI6ctDG4Y/j8sFKJgFca4F3+/m5UlDXw64DZtcBCJ5vVjk/f2QlDvQVTLhmEMRP7BNyEL+vEl8YIeH1Ri/KSAqRAZwoQ8AIoKCjAPffcg40bN7bTac2aNVi5ciXee+89/lpDQwOmTJmC3bt3Q6vV0g5vD1hbvjzI39z/Pg5VHkXf6N54LOcBaJTqsFKIgFd84D1zogrffH4Q0bE6/Pq+SYLv7rp7cCK/At+tPASVWon5d0/k7QWSfFknvrRDwOuLWpSXFCAFCHi7mAN79+7Fvffei/79+/Pd3tTUVDz33HPIysrCm2++iYqKCjz99NPNNTDgXbp0KQYMGEDA2wPWlrcPcnZAjR1Ui1Tr8dSkRxGviws7dQh4xQfelUv2ovRcHWZcMRjDRvcSdQ6tXnYAZ09WY2BWEi6/YURAbXm7TnxthIDXV8UoPylACnSkAO3wAiguLsb69etx7bXXIj4+Hh9++CE+++wzrF27lvvr2u12LFy4sFm/GTNmcBDOzs5GY6M1LGeWTqeGxWIHC43U05NarQSggM1m71SKvPJj+NfW//BDak9O+z2GJmeGpWwsUoBWq4LZbAvL/vnaKaHXyenCSix7PxdR0Vrct/AXUKrY3BMvseuG333lZ9hsDsy7fTwGZvkf+sybdeJPTyIiNP4UozKkAClACngoQMDbwYRwOBzcX5e5OKxatQrl5eUeO7w5OTlYsWIF3+Gtr28MyykVFaWDyWSBw0HAq9WqwSJCdQZ5laZq/OXnf8Fka8Qvh12DywbOCMs5wTqlVCqg12thMJjDto++dEzodbL8w90oOlXNfXdHTwjcr9abvuRuOY2f1x/nLg13/H4q1Br/YvN2t068saWjPDExEf4WpXKkAClACjQrQMDbJAWL1KDTuXzYrFYrj9qwfft2fpDt888/x/vvv89fq6ysxMUXX8x9eDUaDbk09IDF1NVHtSwSA7s2+JyhBKNTRuDukbeFtSLk0uA5vELG4S0prsWqj/YhMkqLX98/CUxrKRJ7U/vZe7tQW2Xih9fYITZ/Erk0+KMalSEFSAGpFCDgBfDtt9/yqAvLli1DbGws3nnnHe7isHz5cn5IbebMmfzQGtv1femll1BaWooXX3yRjxGFJZNqqgavna4e5O8cXIL95XnoHZWOP+Q8AK1KGzxDJWiZgFc84F297CDOnqzCtJmZGDUhQ4LRbGnCDdvskwwWpiwpJcrn9gl4fZaMCpACpICEChDwAtxP9bXXXsNXX33FT0QzV4W//e1v6NPH9ZHi5s2b8cwzz8BgMGD48OEcepmvLwGvhDM1iE119iDfeOYnrCxYjQhVBJ6a9AgSIxKCaKU0TRPwigO85aX1WPHBHkToXVf++utWEMgs+PHbfBzZfx7JadH8Qgpfb3Yj4A1EfSpLCpACYitAwBugwrTDG6CAMije0YP8eM0JvL7nbTjhxO/H/BZDE4W7ojWUJSHgFQd4v12Rh1MFlZg84wKMndw3KFPA3GjDJ//dAfadXTnMrh72JRHw+qIW5SUFSAGpFSDgDVBxAt4ABZRB8bYP8hpzLZ7b8QqMNhOuvuAyXD5gpgx6IYyJBLzCA29VhQHL3suFVqfCbx6YEpTdXXevjuWV4vvVR6HWKPlOc4Te+wgJBLzCrDGqhRQgBcRRgIA3QF0JeAMUUAbFWz/IrQ4bXs5dhLMN5zA8aSjuH32nDHognIkEvMID73erDuHEsQpMuHAAcqb1F26w/KzJHQc4KzsVs+YO87oWAl6vpaKMpAApEAQFCHgDFJ2AN0ABZVC89YP8g0OfIrd0H9IiU/DHnAehUwd2O5UMuu9hIgGvsMBbW23Cp2/v5Duqt/9+KjRa/0KCCTmPaiqNPGoDC8E9d/5oZPRznVfoLhHwdqcQvU4KkALBVICAN0D1CXgDFFAGxd0P8rX5P+KzY6t4JIY/TXgEKZFJMrBeWBMJeIUF3g1fHcHxw2V8Z5ft8IZK2vnTSezeegYxcRG45bcT+PXD3SUC3u4UotdJAVIgmAoQ8AaoPgFvgAKGcHGjzYg1JzfgnLEEjTYzTtee5YfU7h91J0Yke/9Rbwh30WfTCHiFA97Wu7u3/W4KdBFqn8dDrAJ2mwNL390FdhPb+Kn9MfGi7mGcgFes0aB6SQFSQAgFCHgDVJGAN0ABQ7j4ksPLsKNkt4eFvaN74amJj4Sw1eKaRsArHPCyw2HskBiLysCiM4RaKj5Tg68+3c9vGbx5wQTEJ0V2aSIBb6iNINlDCpACrRUg4A1wPggJvNvP54KFu2IpK2EQJqePD9A6/4sLeYOU/1YEp2S5qRLs650DH4IdUmud9Go9Xrror8ExLARaJeAVBnjZzikLAaZUuaIh6CO9j4Yg5TRY97/DKDxajrTesbj+trEEvFKKT22RAqSAoAoQ8AYop1DA+83J9Vhzcr2HNVcOvBRzBl4aoIW+Fy9qKIZar4DBYMag2IG+VyCDEqXGcpQbK1DRWIkyYwUHXPY7+95d+s8l/+ouS9i+TsArDPBuWpuPw/vOY/SEDEydmRmy88XYYMEnb++AzerAJXOGYsjItE5tpR3ekB1GMowUIAUAEPAGOA2EAt7/t/V5VDVWe1jDDkeNTx0NrUoDnUoLrUoHnUoDjZL9rOG/a5XsuwZapRY6Nfu7tulvru++prbg3Se6Nx4adzci1V1/nOlrO1LkLzGWotzIdmsrPKC2so3OrW2J18UiVZ+ClMhkFNaeRImhzMPUUSnZuGfk7VKYH5JtEPAGDryGBjM++s92fpPZbQ9MCdndXXdPD+YWYfOGQu5j/Kt7J3Xqa0zAG5JLlowiBUiBJgUIeAOcCkIB7+++XxigJR0XdwGxJwi74JlBchM0NwFzo92ELed2tavooj5TMDF9fDNss/IMuhl8KxXdn972p2MMvA+UH0JRwznu3nHlgFkYnDDIoyqrw+ramTVVcLB17dS6fmaXQ7ADZh2lGE00UiOTkRqZ0vSV1Py7WtFycIjtdC85/DmKG87zajKie+G27F+iT3SGP10KizIEvIED78/rCpC3pxgjxmXgwtmhu7vr7im7ev3zxbmoqjBi6Kh0XHzlkA7nMgFvWCxx6gQpELYKEPAGOLRCAe8/dr7aDFZukxIi4nHFgFmwOMww26yw2C0w2y2w2M2wOGz8d4vDAqvdBrPdDAaA7LvFbuW3gPmaGB4qOijEwFHR4SuuzHo122nWNe06u3abXTvN7p1o1/e2u9QcnFVa6JQaaPjrrjoOVRzFyoLVHpaw+mb2uwg1jbWoaKxCmaEctZa6TrvI6u4VlY5kfRLSo1w7tins58g0bocvSawHuS82hEpeAt7AgNdktGLJom1gEHnr7yYjKloecZzLS+qx4sM9vPPX3ToG6Rlx7aakWOskJSUmVKY/2UEKkAIyVoCAN8DBEwp486sL8c7B/4PJ1tgEkRG4e+Rv2u1q+mpuIwdgSxMMu74zIDbbzDA7rLA2QbTVYcF5QxnYwbm2KTEiAWxX1MLLMuB2gTaDb3FSx+jd9q9sN7ZXdCqSIxKRGpnKd2nTGNzqkxCtiRbMNLEe5IIZKGFFBLyBAe/W7wuxf2cRssf0wi8uHyzhyAXelHtnOi5Rz6M2KJWeb4/FWicEvIGPHdVACpAC5MMb8BwQCniZISzua1G96+PzPjG9guI329FO85MTH+r0Y3y704ZGmxUMmBlIMxB2QXGrn5tA2dwE2h295vqba3eauSN0BNPjUkZhaNJgDrYMauN17XeZAh7QDioQ60Euhq1i10nA6z/wst3dj97cDofdwX1h2aUOckpWi50fYDMZrJj0i4EYN6Wfh/lirRMCXjnNErKVFAhdBWiHN8CxERJ4AzRFkOIMuref341ySxliVHEYlTxMcp9Vtsv80ZHPPfqjV0fgiQkPI1mfKEg/falErAe5LzaESl4CXv+Bd/uPJ7F3+xkMGZGGS64aGipD6pMdBUfKsP7LI1CqFPwGtth4fXN5sdYJAa9PQ0SZSQFSoBMFCHgDnBrhBrxuOYIdh5cdWvvh7M/cxYO5VNyQdTXGpIwIcLT8Ky7Wg9w/a4JbioDXP+A1N9qw5D/beHiv+fdMRFxCCygGd0R9b/3LT/fh3Jla9O4Xh2vmjyHg9V1CKkEKkAJBUICAN0DRCXgDFFAGxQl4WwaJgNc/4N318ynkbjmNrOxUzJor72up62pM/Nphh92JS68ZhsxhqVwUsdYJ7fDK4D9JMpEUkIECBLwBDhIBb4ACyqC4WA9yGXS9nYkEvL4DL/N9Zbu7FrMdNy3IQWJylByH3sNmBu8M4vVRGvzqnknQaFUEvLIfVeoAKRDeChDwBji+BLwBCiiD4gS8tMPb2TT1xvVn99bT2PnTKVwwJBmXXTdcBjO+exMdDic+e28XaqtMzfGExVontMPb/XhQDlKAFOheAQLe7jXqMgcBb4ACyqC4WA9yGXSddni7GaTugNdmteP/FoXX7q5bkpLiWqz6aB//9cbbx6H/BUn89jh2JbmQiYBXSDWpLlKg5ypAwBvg2BPwBiigDIoT8NIOr787vPt2nMW2H05gQGYSrrgxOIcuxVxiG1cfRX5eKRKTI/GbB6aCubwQ8IqpONVNCpAC/ipAwOuvck3lCHgDFFAGxQl4CXj9AV62u8vi7jaabLjxjnFISQu/G8MaTVZ88t8dMJvtGJiVhN594xETH4ELBicLtrJph1cwKakiUqBHK0DAG+DwE/AGKKAMihPwEvD6A7wHdhVjy8YC9B2YiKtuGimDme6fid+uyMOpgkqPwhOmD0DO9P7+VdimFAGvIDJSJaRAj1eAgDfAKUDAG6CAMihOwEvA6w/w/t+/t8FosOC6W8ciPSNWBjPdPxM/fmsH6mtdV6K7E7tF7tf3TfKvQgJeQXSjSkgBUsBTAQLeAGcEAW+AAsqgOAFv6ACvudGKA7nF/OIDltjlB2w3MVips0Nrh/edx6a1+e0uZwiWnWK2+9Y/N3VY/X1P/EKQZmmHVxAZqRJSoMcrQMAb4BQg4A1QQBkUJ+ANHeDdsqGAA2/rNConA9NmZQZlJnUEvCxkF/Nrbagz4+qbR6HPgISg2CZVo5+/vxuVZQ0ezSWlRuOXd44XxAQCXkFkpEpIgR6vAAFvgFOAgDdAAWVQnIA3NICXXcv74RtbYbXaPWaNVqfGXY9MC8pM6gh4jx4owQ9rjiEtIxbX3zo2KHZJ2eiJ/Ar88M0xWMw23iwbj4vnDBHs4BoBr5SjSW2RAuGrAAFvgGNLwBuggDIoTsArPfBaLQ6Ul9ShorQBpefqUVHWgJpKY6ezhe3yZmWnIbW3tJEQOgJetrtbV9OIq24ahb4Dw3t31z0grL9Ws+uNiEanQmx8hGArm4BXMCmpIlKgRytAwBvg8BPwBiigDIoT8IoLvOzq3fKSBpSX1KPsPINcA2qqOoZbrU7Fr+j1SE4noFDwP7HDUlnZKRx+E1PEv8K3LfAeP1SGDV8fQUp6NG68XZiP9GWwRLiJYq0TAl65zACykxQIbQUIeAMcHwLeAAWUQXGxHuQy6Ho7E9nFAnFxelRVGfwyn8Eq27UtK6lDGdu5LW1AbbWpXV1KpRJJqZFI7RXDv5LTYpGYEomqcgPWrjzUHBWAAe7ECweivLQehUfLYKi3NNeVkBSJwSPSkJWdykFYjNQaeJ1OJz59eyff3b38hhE8Lm1PSmKtEwLenjSLqK+kgHgKEPAGqC0Bb4ACyqC4WA9yGXQ9IOBlcMt2bd1fZefrOQy2TS64jUJqr2gPuFUqXbu2HSV3PR4fnTuB88W1KDhchsJjFTAZWuCX7bgOHp6GzOxUREZpBZO+NfAWHCnD+i+PgIH2zb+dIFgbcqlIrHVCwCuXGUB2kgKhrQABb4DjQ8AboIAyKC7Wg1wGXfcwcf+uYuTnlfDd1d794pEzrT+S06J5Hga3DGhdcFuHsvMN7WKzsnxKlRJJKb7BrT86MS+H82drcfxwCQqPVcJssrqqUYDfBsZ2fQcNTYEuQu1P9c1l3MBrs9mx7L1cVFcaMfvabF53T0tirRMC3p42k6i/pIA4ChDwBqgrAW+AAsqguFgPcm+7zj72z91yGsWnazhgDsxKxqgJGd4WFyQfO4n/3cpDHnUx94Y+A+O5m0F9rbldO1LBbXcddDqcKDpdw3d+T+SXN/sAK5QKfqgsa1gqBg5OgUar7K6qdq+7gff4kTKuD9txnn/PRCiafIp9rlDGBcRaJwS8Mp4UZDopEEIKEPAGOBgEvAEKKIPiYj3Ivek6u2jh47d2Nod8cpdhYZ/69E+AzeaAw+6A3e4AC9vFdjatVhvsNifsNvZ3O2zsZ5aH/439zn52vWa3O3k59t3B8zoAZ3vLmJ+toaHFRcCdo9V5sVb+ttFITovhB7dCMZ0uqMLxI6U4cayC6+BOg4Ym88Nu/QYlgsG8N8kNvMsW7+IH7y69JhuZw3re7i7TSqx1QsDrzUykPKQAKdCdAgS83SnUzesEvAEKKIPiYj3IO+u6scHCoxTUVjfi7MkqFB4tb5+VQWnnLq7Cq9pJe2Mm9cHg4elITI4E2zGVU2JvFE4VVKHgSCn/7oZfttPLdnzZzm+fgQnoypeYAe/+3UVYvexAj97dJeCV08wnW0mBnqkAAW+A4y4U8HZ0ZSqLLaqL0ARooX/FO7sy1b/a5F1KDOBlcWarKwyoqTahtsrYBLgmVJUb+W6sO7XeQW2tItuBjNBroFIr+G6kSt30pVJArVZCqVJBxX7WKPl3FftdreSvqdTsZ1c5Nf+u4r61ak1TvqZy7DCZuwxzBdiyodBjINkFA/PuGC9ozNVgzRS2y32qoAL5h8pwuqCy2Qzm48t3foeno3ffuHbmWUx2fLPiAEqK63DJnKEYMjItWF0IertirBPWKdrhDfrQkgGkQFgoQMAb4DAKBbzfrz6KY3mlHtYMGZGGS64aGqCF/hUn4G3Rzd8HucPhQG2VCTXNXwxs2e9GNBqbDlF1MDws1mxcQiTik/SIjNLh0N5i7nbQOjGXhqEj0/0bXD9Ltb7Wl4X5mjpzkGC3aflpkijF2JuRk/nlYFEXzp6sBrsqmKWoaB0GDUvB4OGpqCw38DcA7tvF2BuDBY9O73I3WBRjQ6hSf9dJd10g4O1OIXqdFCAFvFGAgNcblbrIIxTwLn51Szs/TaVKgemzshAZreEPWwY/0bHChVTqqusEvN4DLwuRxXxc2Y4t++6G2oa69ge5WmvOwlfFJuiRlMK+RyIhUY+4xEjoIz139dte3cp2/qfNygxw5vpXPNA4vP61GrxSLPpE4bFyFBwuRdGpGm5IZ7vuU2dmYrTEhwmDp0z7lgl4Q2k0yBZSgBRoqwABb4BzQijgfeufm7y2JCKSAbCWfzEIjorRIiomAlEMjGN00EcGDsYEvJ7AazRYca6ohrsf1FYbUV3RArZdDRzbCY1L0HMfVwa18Yl6xCdGIiZO5/V4h1LGnga8rbU3N9pw/HAZDu05h6qK9hdvBPMTmVCYIwS8oTAKZAMpQAp0pgABb4BzQyjgXftFHk4eb/EdZGYxUOrVNw7GBjM/Ic9ukWp0xxP1wm62UxjJwDjGtTvsAuIIFyBzWHa91lHqicDL3AYYyLBd2pYd20YeW9VmbXOdbSvR9FFavjvrBlp2I1hcvB7xSZFejJK8svRk4HWPFAsP99XS/e0GbsL0AciZ3l9eAyqgtQS8AopJVZECpIDgChDweiHppk2b8Oyzz8JkMiEzMxMvv/wykpOTeUmhgJfFWm17Zerl1w9vDuzvNpP5ExobrDAazE0gbIWhvpHDMPsbB+MGS5c+oh5dVgD6yCYAbto1Zu4TqemxYC4V7t1kBs9yO4Xf0dA67E7U1rj8aJl/bXUlg1uXb23rm7nalmUHtOISIvjuLANZ9p29IWFuCexgWE9JBLwAO2C6/IM97S7WmHvLaGT0j+8pU8Gjnw21lagrP8XjD8ck90d0nHDXKpMPb4+cUtRpUkBwBQh4u5G0rq4Os2bNwuLFizFy5EgsWrQI+fn5eOONNwQFXrcZ7GHKUqDRGToC44a6RrCQV/6AMYujz8E4Rgu2o8l2iKNjI5pcKjRNO8iu14Iec98J1NeZOdQykK2tMjT71bK/dxRnlmnOIhUwiHW5HbhcD+IS9eiVEc/7azB07ZMr+OoMwQpDAXgtjUbUVJzj6sQn94Y2QvqddOa3vXVjAdgbKBa7eERORlge4PNmChYdP4AtX7/nkXXa1QvQJ2uUN8W7zUPA261ElIEUIAW8UICAtxuR1qxZg5UrV+K991z/oTc0NGDKlCnYvXs3tFqtYDu87CF+aPta1JQXux7kKRkYPvly0R/mrcHY0GBGWbkBuXklcFgcgM2OWJ0GDqvd6x1jDsZNQOzyL25yo+CH7rRNLhZaDs9dgfEXi9+ErfYo18IBLTInXIcJF07zGC2T0eoJtZWNrp3bmkZ+GUNHibXJQJ0DbdNOrRtuo5l7R5tQsicP7UDpqUOwmE3oN3QCBmRP9GJZCZulpqyIz43SswVISM1AxqBRGDzuF8I24kVtZWeP4/jen+CwmxGdkI6ssb8QdCfPCxPAbNj81WJYzUaeXaOLxPS5dyG1b5Y3xQXL4x6T2spziIxJQO8LgjMmx3b/iHMnDqK6rBhpfTP5/xnxqX0E6yeryG6zwGZ1fzXCbrU2/d7Iv+/f9CWMDa4Dfe4Un9IHl926UBA7CHgFkZEqIQV6vAIEvN1MgTfffBMVFRV4+umnm3My4F26dCkGDBggGPDuWPsxTh3e6WENg6tJl/9asklqbLTimQ9yUVFr8mhz4S1jMbR/AljUAbZDzMDY5VPc+nf2s9UnH2OXD7Hr4B2LRMFAlLlT5OdtQ2PpDx42OJwapGf/CjaL3uVfW2loF6qrdQHmgsGjIDT50jK/WvYz+5u3ae8PXyB/r+dhwhFTrsDwKVd0WYXD7tqld7Lj/E0E7fq56QozJ/up5eeWvK2vOHP9bDEZse6Tl2C1eI7JxNnz0Wtgtque5mKuH3hTzanlF26D54tNNrYu1/5nBZyoLDmNHWs/8ei3PjoOv7j+Ps/mm+pvbZfrZ1e97jc5Lj1cqbU2LX9uKcNzNr2w87tP0VBb4WFHdHwKXyfs43TXlb7su9L1s1LBR8D9mhOuvzd/KZk7CvudW+fxGhRNr7E6eBlWFrBZrVi9+G/N0O02RshdTW/maEc7q2qtHhff+AB3P7JbzdxWG//Ovhi0mmGzmGG1mmFnv1vMsHGgZb9bYbM0Nr3WUs4bWzrKc9Ojrk/BAk0EvIEqSOVJAVKA/w/vbP3kIU3aKcD8ddkVrAsXtuxWzJgxAwyEs7OzUVM/AsMJAAAThUlEQVTj2mkKNC179fF2UMMesMm9B7ixAE6ne/vR2Ypb3IDSAhAMUFoDVVu4YoU7et1ssaG8tpG3p2gGFAXYLasqdosWa74NXLE/uJCCU0mLDG64cjZBDuc99ouT/WuTv3XFrD4bFIr2O7QOp7oJaJqqcgOUkqMKhxEXuHheC9Z6irth1Kvx6uB2MakvOOvUzpAxxCsle0YmqcdEwvaY24hao4Nao4VGq4NKo23+/dyJwxyYW6eo2ERcd9+zgox7fLz3b1IFaZAqIQVIgbBUgIC3m2F96623UF5e7rHDm5OTgxUrVvAdXmsXp/d9mTFvP73Al+zhnVfCB7nYQqrULTF1XbuPLcm9G+l6h+GCdtfP7h1HcNcMq8X1JqR1Uqk00OgimrdMeVl38VY/u3ct27XbZAt7c9Tat6TFRldl7p1PY0MtrOb2dsTEJULN7HDtj7axodmgrl9v2oFt6rxHu61tYD+XF5+C3W5ro4UaSb36wel08HdC7A2O68vR/HPbv7t+d+0ut83H999b1dG6rN3W+YUhbd5niT21Oq0/MjoOEVHR0Or0UKldgKrWNn3X6PjvWl1E09+bIFYXwQFWw750LM//b+/eY6so0ziOP7a0IHLpcmsE1qgITShFmjUYkcQScCkCki1REhQjASQG10tQQLxsDUHk5gYkgJGbkEgst4BiKcLKpVwEyRZhEdwlXLIrNlC3siXlInTzvHhOe4qnpz1z5nTmPd9J/EOYmfM+n/ednl/feWfQQNtMUpvdHrGOE3/fIzs2LA/ZL+dPoyUjO3QJUsQThdkhJSU52kM5DgEEEAgKEHgjDIYtW7ZIQUGBLFu2zOxZVlYm/fr1M2t4U1JSYrakoXjjR/Kfk0dCWtOu473Ss++QX3NA9TzqzRRSM5D8GjdqBKpAcAneItYQVZ2IQsJV4I/L/3dF/lpwuNYzXbfJg93TJeuetsFgZOZ0g9O0Ned3A7eUa5RRo63V4SXw94EAWB2M9GyHdu2Tphd3h1jo7G527gS5t0vgXxerGR5rfG7E0FcVnCVOSq77n20uWjlTyi/cXFMd2Dp1yZK+w8bF7UeIPv2+ddWsW5c0DHxK7sl8MG7t0LWiJTvX3/J5g8f8Ja7reP+xr1CO7isMaUd9lpnEEsorfaLryw8UhS4zSUm9Xf44alJc+0RtdW11eekpw5yWfk9M11SzpCGWo5dzIZC4AgTeCH2vD6n179/fPLSmb2mYM2eOlJaWyuzZs82RsXotmT4Eo2skAwErrV0neTD3qZg/gBJpqBcfOSert/1TKq/cnEXL7tpO/jw8Nk9bR/rsmn+/9IMV0vTKWUmSa/JLVXP5XcYjMvTxPg05heN9tU/+VvBBMGzqbdq+j4+Ne5/oWk0NNoF1vN2yH5HsfsMd19eQE+hDlcWblsj5f/8reFivR/Ik4w85DTlNTPbVoKcBSzd9WC2ewT9QgBf6RNtSc525ht3eA5+K2dsRGtpZvIe3oWLsjwAC8RQg8NZDu7i4WPLz8+XSpUuSmZlpQm9a2s33bcYq8NajGXHdpbzyF2nbIlWuh3nbQbwac768UtqnRb6t6mZ7Ll8sNW9paNU+sJ7azU/z9rkrK/4rt12vkGatf+/thsapdYn4D7SEoyXwxmnQ8TEIIBCVAIE3Krbqg2wNvHyRV/exW1/kDodeoxzuhffwNkrhYT6U68T964QlDV4a8bQFAf8KEHgd9h2B1yGgDw4n8FZ3EoE3dMASeAm8PvgRRhMRQIDXkjkfAwRe54ZePwOBl8AbbowSeAm8Xv/5RfsQQOCmADO8DkcCgdchoA8OJ/ASeAm8kS9Ut64TljREtmcPBBCILEDgjWxU5x4EXoeAPjjcrS9yH5R+SxNZ0hBKwgwvM7x+vI5pMwKJKEDgddjrBF6HgD44nMDLDC8zvJEvVLeuE2Z4I9uzBwIIRBYg8EY2YobXoZHfD3fri9yPLszwMsMbbty6dZ0QeP34k4I2I+A9AQKvwz5hhtchoA8Od+uL3Ael39JEAi+Bl8DrxyuXNiOAAIHX4Rgg8DoE9MHhBN7qTiLwEngJvD74oUUTEUDgFgECr8NBQeB1COiDwwm8BN5ww5SH1qpl3LpOWNLggx+SNBEBHwgQeB12EoHXIaAPDnfri9wHpbOkIUInEXgJvH68jmkzAokoQOB12OsEXoeAPjicwMsMLzO8kS9Ut64TZngj27MHAghEFiDwRjaqcw8Cr0NAHxzu1he5D0pnhpcZ3noPU7euEwJvvbuAHRFAoA4BAq/D4UHgdQjog8Pd+iL3QekEXgJvvYepW9cJgbfeXcCOCCBA4HVvDBB43bP1ypnd+iL3Sn0NaQdvaQjVYg1vtYdb1wmBtyFXKPsigEA4AWZ4HY4NAq9DQB8c7tYXuQ9KZ4aXGd56D1O3rhMCb727gB0RQIAZXvfGAIHXPVuvnNmtL3Kv1NeQdjDDywxvuPHi1nVC4G3IFcq+CCDADK9LY4DA6xKsh07r1he5h0qsd1MIvAReAm+9Lxd2RAABDwmwpMFDnUFTEEAAAQQQQAABBGIvQOCNvSlnRAABBBBAAAEEEPCQAIHXQ51BUxBAAAEEEEAAAQRiL0Dgjb0pZ0QAAQQQQAABBBDwkACB10Od4WZTPvvsM1mwYIFcvnxZ7rzzTpk+fbp06dLFfOTOnTtl2rRpUllZKffdd5/MnTtX2rVrZ/7u+PHjMm/ePNm2bZt8/fXX0qZNm2Az6zqnm7U4PbcbFoE2FRcXy+jRo2XLli1BX6ftdfN4Nyx++OEHee211+T06dOSlpZmxlqvXr3cLCNm53bDQ8+5ZMkSSUlJkWbNmkl+fr65zry+RWsR7XFe96B9CCDgbwECr7/7r16tP3PmjOTl5cm6devk7rvvlsWLF8uePXtk1apVcvHiRRkwYIAsXbpUsrKyTCj+/vvvZf78+ebcL7/8sgwfPlzGjh0r+/btCwbeus5Zr0Y10k5uWARK0V8mRowYIT/99JOsWLHC84HXLYuRI0cah2HDhsnmzZtl//795hcqr29uePz4448yePBgKSoqMr9E6jVYUFAgn376qac5orWI9jhPY9A4BBCwQoDAa0U31l1ERUWFnDt3Trp27Wp2PHbsmIwfP152794tX3zxhaxfv97MQOmm+z700ENy6NAhSU1NDZ44IyMjJPDWdU4vk7phEah31qxZ0r59exNo9BeHwAy6Vz3csDh79qyZ4d6+fbtXyw7bLjc8Dh48KG+//bYUFhaazz1x4oQ888wz5m6Jl7doLa5ever4Z42XXWgbAgj4V4DA69++i7rlGm6PHDliliosXLhQLly4YL6UA5sG3tWrV5vZ4MBWO/DW/vCa54y6YY1wYKwsvvvuO5k6daqsXbtWhgwZ4ovAW1cfRjsuvvzyS1m5cqV06NDB/GLVtGlTeffdd6V79+6N0LvOPjIWY0OXCQ0aNMgYZGdnm3GhdwBmzJjhrHFxPjoaC21itMfFuTw+DgEEEkCAwJsAnVyzRL29PHnyZBNoO3bsaNbrXr9+XSZNmhTcLScnxwThmiGlrsBb+5x+IY2VxY0bN8wt/DfeeMOsVdWA44cZXjfGhd6y11+e9K6BjplNmzaZsaRrmv20xWpsaM1qoYFXlzToshcNgX5Ywxvor2gtoj3OT+OEtiKAgH8ECLz+6SvHLd26dau89957Zg1vt27dzPkWLVok58+fD5nhfeCBB8xMZX1meH/rnI4bGocTxNLi448/llOnTpmHkXTzW+CNpYXO8GrY37hxo7GoqqqSHj16mOUzNR94jEMXR/0RsfQoKSmRiRMnyoYNG6RVq1Zy4MAB8/9fffWVNGnSJOo2xuvAaC2iPS5edfE5CCCQeAIE3gTp8127dpkHh5YvXy6dO3cOVq0zb7rmdNmyZebPysrKpF+/fmYNrz5VHth+a4Y33Dm9Thpri7feeku++eabYNm6jlXfhKEBuG/fvp7miLWFvtVD14frmz9009nvzMxM0bWsLVq08LSFNi7WHnq96fKh999/P1i73jnRNc46Rry8RWsR7XFetqBtCCDgfwECr//7MGIF+gBKbm6ueXNA7Vup+nf9+/c3t1n1LQ1z5syR0tJSmT17dsh5f+uhtXDnjNigRtzBDYva5fhlhtctC13DPGHCBDPTvWbNGvNGAr1j4PXNDQ99s4mu7dalHS1btjTB/4UXXpC9e/dKcnKyZ0mitYj2OM9C0DAEELBGgMBrTVeGL0TXEOr60rZt24bspK+Mat26tei7Y3U28tKlS2Y2TkOvvj/1559/Nq9U0k1DsL6BICkpyXx579ixo85zepXVDYvat+r9EnjdstBZ3ilTpphX3qWnp5sHtGouj0m0saG/TKq1vvVE75roGnpdNuTlLdqxEe1xXragbQggYIcAgdeOfqQKBBBAAAEEEEAAgTACBF6GBgIIIIAAAggggIDVAgReq7uX4hBAAAEEEEAAAQQIvIwBBBBAAAEEEEAAAasFCLxWdy/FIYAAAggggAACCBB4GQMIIIAAAggggAACVgsQeK3uXopDAAEEEEAAAQQQIPAyBhBAAAEEEEAAAQSsFiDwWt29FIcAAggggAACCCBA4GUMIIAAAggggAACCFgtQOC1unspDgEEEEAAAQQQQIDAyxhAAAEEEEAAAQQQsFqAwGt191IcAggggAACCCCAAIGXMYAAAggggAACCCBgtQCB1+rupTgEEEAAAQQQQAABAi9jAAEEEEAAAQQQQMBqAQKv1d1LcQgggAACCCCAAAIEXsYAAggggAACCCCAgNUCBF6ru5fibBVYt26dTJs2TYqKiiQ9PT2kzLFjx0pVVZUsXbrU1vKpCwEEEEAAgQYJEHgbxMXOCHhHYNSoUdKmTRuZN29esFFbt26VV199VQoLC6VTp07eaSwtQQABBBBAoBEFCLyNiM9HI+BE4MyZMzJ48GD58MMP5eGHH5bKykoZOHCgjB492vzHhgACCCCAAAI3BQi8jAQEfCywaNEiWb9+vWzevNnM9O7Zs0d0uUNycrKcPHlSpk+fLsePH5cmTZqYUPz6669Lq1atTMUHDx6UmTNnyunTp83+ffr0McskWrRoIRUVFZKdnS2zZs2SGTNmyIsvvihPP/20j6VoOgIIIIBAIgsQeBO596nd9wLXrl2TYcOGSc+ePeXzzz+XgoIC6d69u1y+fFlyc3Plsccek4kTJ8rVq1fllVdekTvuuEPmzp0rN27ckEcffVSefPJJee6556S8vFxGjhwpAwYMMPvr8VlZWZKTkyMvvfSSdO7cWdLS0nzvRQEIIIAAAokpQOBNzH6naosESkpK5IknnpAxY8bIlClTTGXFxcUybtw4OXz4sKSmppo/0/1GjBghR48elZSUlFsEdHb33LlzsnDhQrly5Yr06NFD5s+fL4MGDbJIi1IQQAABBBJRgMCbiL1OzdYJZGRkyIIFC8ysrW66zGHy5MlmeULtTWeC9YG2VatWyZo1a8zaX93Kysqkd+/esnjx4mDgXbt2rdx///3WeVEQAggggEBiCRB4E6u/qdZSgdqBd+/evWapwrfffitJSUm3VL1//3559tlnzXrfzMxM8/dvvvmmXLhwISTwanDWpQ1sCCCAAAII+FmAwOvn3qPtCPwqUDvw6pIEXcObl5cnzz//vHkvry5V0AfZdJmCvrZs6tSpsn37dmnZsqVs27bNrO3t0KGDfPLJJ8EZXgIvQwwBBBBAwAYBAq8NvUgNCS9QO/AqSOAtDceOHTMPqema3Pz8fLnrrrvMQ2z6cJqu9W3evLkMHTrUPLA2fvx4syzinXfeMfsTeBN+aAGAAAIIWCFA4LWiGykCAQQQQAABBBBAIJwAgZexgQACCCCAAAIIIGC1AIHX6u6lOAQQQAABBBBAAAECL2MAAQQQQAABBBBAwGoBAq/V3UtxCCCAAAIIIIAAAgRexgACCCCAAAIIIICA1QIEXqu7l+IQQAABBBBAAAEECLyMAQQQQAABBBBAAAGrBQi8VncvxSGAAAIIIIAAAggQeBkDCCCAAAIIIIAAAlYLEHit7l6KQwABBBBAAAEEECDwMgYQQAABBBBAAAEErBYg8FrdvRSHAAIIIIAAAgggQOBlDCCAAAIIIIAAAghYLUDgtbp7KQ4BBBBAAAEEEECAwMsYQAABBBBAAAEEELBagMBrdfdSHAIIIIAAAggggACBlzGAAAIIIIAAAgggYLUAgdfq7qU4BBBAAAEEEEAAAQIvYwABBBBAAAEEEEDAagECr9XdS3EIIIAAAggggAACBF7GAAIIIIAAAggggIDVAgReq7uX4hBAAAEEEEAAAQQIvIwBBBBAAAEEEEAAAasFCLxWdy/FIYAAAggggAACCBB4GQMIIIAAAggggAACVgsQeK3uXopDAAEEEEAAAQQQIPAyBhBAAAEEEEAAAQSsFiDwWt29FIcAAggggAACCCBA4GUMIIAAAggggAACCFgtQOC1unspDgEEEEAAAQQQQIDAyxhAAAEEEEAAAQQQsFqAwGt191IcAggggAACCCCAAIGXMYAAAggggAACCCBgtQCB1+rupTgEEEAAAQQQQAABAi9jAAEEEEAAAQQQQMBqAQKv1d1LcQgggAACCCCAAAIEXsYAAggggAACCCCAgNUCBF6ru5fiEEAAAQQQQAABBP4P8wPUPFKn/UEAAAAASUVORK5CYII=" 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>
<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>
<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. 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="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">&#39;SECTOR&#39;</span>] <span class="op">==</span> <span class="dv">1000</span>) <span class="op">&amp;</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">&#39;FLOW&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;CHANNEL&#39;</span>] <span class="op">==</span> <span class="dv">100</span>) <span class="op">&amp;</span></span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AMOUNTTYPE&#39;</span>] <span class="op">==</span> <span class="st">&#39;D&#39;</span>) <span class="op">&amp;</span></span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;FLOWTYPE&#39;</span>] <span class="op">==</span> <span class="dv">112</span>) <span class="op">&amp;</span></span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a> (df[<span class="st">&#39;AIDTYPE&#39;</span>] <span class="op">==</span> <span class="st">&quot;100&quot;</span>) <span class="co"># contains mixed int and string representations</span></span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> totals.copy()</span>
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a>donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">=</span> donortotals[<span class="st">&quot;DONOR&quot;</span>].<span class="bu">map</span>(donortypes)</span>
<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>donortotals <span class="op">=</span> donortotals[(donortotals[<span class="st">&quot;Donortype&quot;</span>] <span class="op">!=</span> <span class="st">&quot;nondac&quot;</span>)]</span>
<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals.groupby([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>]).agg({<span class="st">&#39;Value&#39;</span>: [<span class="st">&#39;sum&#39;</span>]})</span>
<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a>donortotals_grouped <span class="op">=</span> donortotals_grouped.reset_index([<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>])</span>
<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> donortotals_grouped.columns.to_flat_index()</span>
<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a>donortotals_grouped.columns <span class="op">=</span> [<span class="st">&#39;Donortype&#39;</span>, <span class="st">&#39;Year&#39;</span>, <span class="st">&#39;Value&#39;</span>]</span>
<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.line(donortotals_grouped, x<span class="op">=</span><span class="st">&#39;Year&#39;</span>, y<span class="op">=</span><span class="st">&#39;Value&#39;</span>, color<span class="op">=</span><span class="st">&#39;Donortype&#39;</span>, labels<span class="op">=</span>{<span class="st">&quot;Value&quot;</span>: <span class="st">&quot;Development aid, in millions&quot;</span>}, markers<span class="op">=</span><span class="va">True</span>, template<span class="op">=</span><span class="st">&quot;seaborn&quot;</span>)</span>
<span id="cb22-18"><a href="#cb22-18" 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>
<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>
<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 style="page-break-after: always;"></div>
</section>
</section>
<section id="references" class="level2">
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</section>
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"> http://documents.worldbank.org/curated/en/449741576097502078/Challenges-to-Inclusive-Growth-A-Poverty-and-Equity-Assessment-of-Djibouti </a>
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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 </a>
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"> http://documents.worldbank.org/curated/en/203361579888116251/Location-Matters-Welfare-Among-Urban-and-Rural-Poor-in-Djibouti </a>
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"> http://documents.worldbank.org/curated/en/099929206302212659/IDU068dce0c7003280435b099f8040232925d37f </a>
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"> http://documents.worldbank.org/curated/en/099930404182210208/IDU0ef8057e509b5f0432c0b50d00f85b54deb33 </a>
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</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>The Balbala area comprises the 4th and 5th district out of the five districts of Djibouti city.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
</ol>
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<div class="sourceCode" id="cb23" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a><span class="an">nocite:</span><span class="co"> |</span></span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a><span class="co"> | @UNU-WIDER2022a</span></span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="co"> | @UNU-WIDER2022</span></span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a><span class="co"> | @OECD2022</span></span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a>{{&lt; include data-prep.qmd &gt;}}</span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a>svg_render <span class="op">=</span> <span class="va">True</span></span>
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>pd.options.display.float_format <span class="op">=</span> <span class="st">&quot;</span><span class="sc">{:.2f}</span><span class="st">&quot;</span>.<span class="bu">format</span></span>
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-17"><a href="#cb23-17" aria-hidden="true" tabindex="-1"></a>{{&lt; include _drivers-of-inequality-vietnam.qmd &gt;}}</span>
<span id="cb23-18"><a href="#cb23-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-19"><a href="#cb23-19" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb23-20"><a href="#cb23-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-21"><a href="#cb23-21" aria-hidden="true" tabindex="-1"></a>{{&lt; include _drivers-of-inequality-uganda.qmd &gt;}}</span>
<span id="cb23-22"><a href="#cb23-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-23"><a href="#cb23-23" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb23-24"><a href="#cb23-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-25"><a href="#cb23-25" aria-hidden="true" tabindex="-1"></a>{{&lt; include _drivers-of-inequality-benin.qmd &gt;}}</span>
<span id="cb23-26"><a href="#cb23-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-27"><a href="#cb23-27" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb23-28"><a href="#cb23-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-29"><a href="#cb23-29" aria-hidden="true" tabindex="-1"></a>{{&lt; include _drivers-of-inequality-djibouti.qmd &gt;}}</span>
<span id="cb23-30"><a href="#cb23-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-31"><a href="#cb23-31" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb23-32"><a href="#cb23-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-33"><a href="#cb23-33" aria-hidden="true" tabindex="-1"></a><span class="fu">## References</span></span>
<span id="cb23-34"><a href="#cb23-34" 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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