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Make table and figure notes caption styled

This commit is contained in:
Marty Oehme 2022-09-08 12:34:59 +02:00
parent daa8c03ee4
commit 170ac4747e
Signed by: Marty
GPG Key ID: B7538B8F50A1C800
4 changed files with 44 additions and 4 deletions
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12
_drivers-of-inequality-benin.qmd
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@ -22,12 +22,16 @@ with the reduction threatened to be slowed further through increased prices on f
```{python}
#| label: fig-ben
#| fig-cap: "Gini index of consumption per capita for Benin. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
#| fig-cap: "Gini index of consumption per capita for Benin"
gni_cnsmpt = ben[ben['resource'].str.contains("Consumption")]
gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
gini_plot(gni_cnsmpt_percapita)
```
::: {custom-style="caption"}
Source: Author's elaboration based on UNU-WIDER WIID (2022).
:::
<!-- poverty -->
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 [@WorldBank2022b].
@ -139,8 +143,10 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
fig.show()
```
::: {custom-style="caption"}
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).
:::
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 @fig-ben-aid-financetype 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.
@ -174,8 +180,10 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
fig.show()
```
::: {custom-style="caption"}
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).
:::
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 @fig-ben-aid-donortype.
@ -202,8 +210,10 @@ el_grouped.style.format(escape="latex")
el_grouped
```
::: {custom-style="caption"}
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).
:::
@tbl-ben-aid-electricity 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.

12
_drivers-of-inequality-djibouti.qmd
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@ -19,12 +19,16 @@ Additionally in many cases there is a lack of data or the data itself are lackin
```{python}
#| label: fig-dji
#| fig-cap: "Gini index of consumption per capita for Djibouti. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
#| fig-cap: "Gini index of consumption per capita for Djibouti"
gni_cnsmpt = dji[dji['resource'].str.contains("Consumption")]
gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
gini_plot(gni_cnsmpt_percapita)
```
::: {custom-style="caption"}
Source: Author's elaboration based on UNU-WIDER WIID (2022).
:::
<!-- poverty -->
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,
@ -148,8 +152,10 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
fig.show()
```
::: {custom-style="caption"}
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).
:::
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 @fig-dji-aid-financetype.
@ -190,8 +196,10 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
fig.show()
```
::: {custom-style="caption"}
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).
:::
The primary donor type of development assistance to Djibouti has been through bilateral donors for the majority of time between 2011 and 2020,
see @fig-dji-aid-donortype.
@ -262,8 +270,10 @@ crosstab = crosstab[['Trade development', 'Business growth', "Women's rights sup
crosstab.fillna('0.00')
```
::: {custom-style="caption"}
Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions.
Source: Author's elaboration based on OECD ODA CRS (2022).
:::
The sector-based breakdown of aid contributions for inclusive business growth in Djibouti can be seen in @tbl-dji-aid-projects.
It shows that overall development assistance to the necessary inclusive growth sectors in Djibouti is still small in absolute terms, especially for those in vulnerable positions.

12
_drivers-of-inequality-uganda.qmd
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@ -26,12 +26,16 @@ These inequality levels remained mostly unchanged between 2012/13 and 2019/20 bu
```{python}
#| label: fig-uga
#| fig-cap: "Gini index of consumption per capita for Uganda. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
#| fig-cap: "Gini index of consumption per capita for Uganda"
gni_cnsmpt = uga[uga['resource'].str.contains("Consumption")]
gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
gini_plot(gni_cnsmpt_percapita)
```
::: {custom-style="caption"}
Source: Author's elaboration based on UNU-WIDER WIID (2022).
:::
<!-- poverty -->
The World Bank [-@Atamanov2022] 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,
@ -180,8 +184,10 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
fig.show()
```
::: {custom-style="caption"}
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).
:::
Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in @fig-uga-aid-financetype.
The Official Development Assistance overall further increased to over 2.2bn USD in 2019,
@ -216,8 +222,10 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
fig.show()
```
::: {custom-style="caption"}
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).
:::
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,
@ -270,8 +278,10 @@ crosstab = crosstab[['Basic water supply', 'Large water supply', 'Education and
crosstab
```
::: {custom-style="caption"}
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).
:::
The breakdown of development aid to water supply infrastructure and education projects can be seen in @tbl-uga-aid-watersupply.
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.

12
_drivers-of-inequality-vietnam.qmd
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@ -32,7 +32,7 @@ one which exogenous shocks can rapidly exacerbate as the example of the COVID-19
```{python}
#| label: fig-vnm
#| fig-cap: "Gini index of consumption per capita for Vietnam. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
#| fig-cap: "Gini index of consumption per capita for Vietnam"
gni_cnsmpt = vnm[vnm['resource'].str.contains("Consumption")]
gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
@ -40,6 +40,10 @@ gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
gini_plot(gni_cnsmpt)
```
::: {custom-style="caption"}
Source: Author's elaboration based on UNU-WIDER WIID (2022).
:::
<!--
* estimated Gini coeff, overall income distribution: [@Le2021]
* fluctuating 0.42-0.44 (2010-2018)
@ -203,8 +207,10 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
fig.show()
```
::: {custom-style="caption"}
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).
:::
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 @fig-vnm-aid-financetype.
Decreasing continuously after 2014,
@ -244,8 +250,10 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
fig.show()
```
::: {custom-style="caption"}
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).
:::
Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in @fig-vnm-aid-donortype.
Both bilateral and multilateral contributions increase from 2011 to 2014 and subsequently begin decreasing.
@ -306,8 +314,10 @@ crosstab = crosstab[['Basic water supply', 'Large water supply', 'Disaster risk
crosstab
```
::: {custom-style="caption"}
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).
:::
The breakdown of project-based development aid for water supply infrastructure and disaster risk reduction in Vietnam can be seen in @tbl-vnm-aid-water.
It shows the funds broken down into their use for three categories: