Add first draft of aid analysis to Benin

_drivers-of-inequality-benin.qmd

@@ -102,18 +102,71 @@ 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.
 
 <!-- development assistance -->
+### Development assistance to Benin
 
 ```{python}
-#| label: fig-ben-aid-donortype
+# Load CRS data
-#| fig-cap: "Total ODA for Benin per year, separated by donor type. Source: "
-#| column: page
 dfsub1 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_11-13_05092022185506030.csv', parse_dates=True, low_memory=False)
 dfsub2 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_14-16_05092022192438936.csv', parse_dates=True, low_memory=False)
 dfsub3 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_17-20_05092022192856890.csv', parse_dates=True, low_memory=False)
 df = pd.concat([dfsub1, dfsub2, dfsub3], ignore_index=True)
 df = df.rename(columns={'\ufeff"DONOR"': 'DONOR'})
+```
 
-donortotals = totals_by_donortype(df)
+```{python}
+#| label: fig-ben-aid-financetype
+#| fig-cap: "Total ODA for Benin per year, by finance type"
+#| column: page
+totals = df.loc[
+    (df['RECIPIENT'] == 236) & # Benin
+    (df['SECTOR'] == 1000) & # Total
+    (df['CHANNEL'] == 100) &
+    (df['AMOUNTTYPE'] == 'D') &
+    (df['FLOWTYPE'] == 112) &
+    (df['AIDTYPE'] == "100") # contains mixed int and string representations
+    ]
+financetotals = totals.copy()
+financetotals = financetotals[financetotals['DONOR'] < 20000] # drop all 'total' aggregations
+
+## count amount of development aid financing instruments (grants/loans) by year and display
+## count USD amount of development aid financing instumrnets by year and display
+financetotals_grouped = financetotals.groupby(['Flow', 'Year']).agg({'Value': ['sum']})
+financetotals_grouped = financetotals_grouped.reset_index(['Flow', 'Year'])
+financetotals_grouped.columns = financetotals_grouped.columns.to_flat_index()
+financetotals_grouped.columns = ['Financetype', 'Year', 'Value']
+
+fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', labels={"Value": "Development aid, in millions"}, markers=True, template="seaborn")
+fig.show()
+```
+
+::: {.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.
+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.
+
+```{python}
+#| label: fig-ben-aid-donortype
+#| fig-cap: "Total ODA for Benin per year, separated by donor type"
+#| column: page
+totals = df.loc[
+    (df['RECIPIENT'] == 236) & # Benin
+    (df['SECTOR'] == 1000) & # Total
+    (df['FLOW'] == 100) &
+    (df['CHANNEL'] == 100) &
+    (df['AMOUNTTYPE'] == 'D') &
+    (df['FLOWTYPE'] == 112) &
+    (df['AIDTYPE'] == "100") # contains mixed int and string representations
+    ]
+donortotals = totals.copy()
+donortotals["Donortype"] = donortotals["DONOR"].map(donortypes)
+donortotals = donortotals[(donortotals["Donortype"] == "dac") | (donortotals["Donortype"] == "multilateral")] = donortotals["DONOR"].map(donortypes)
 
 donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': ['sum']})
 donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
@@ -122,3 +175,15 @@ donortotals_grouped.columns = ['Donortype', 'Year', 'Value']
 fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', labels={"Value": "Development aid, in millions"}, markers=True, template="seaborn")
 fig.show()
 ```
+
+::: {.caption}
+Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac), bilateral non-DAC countries (nondac) and multilateral donors (multilateral), 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.
+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.
+
+