From 94cccab78c48e8636f005ea497fb7e324e80f4da Mon Sep 17 00:00:00 2001 From: Marty Oehme Date: Tue, 6 Sep 2022 16:28:30 +0200 Subject: [PATCH] Add first draft of aid analysis to Benin --- _drivers-of-inequality-benin.qmd | 73 ++++++++++++++++++++++++++++++-- 1 file changed, 69 insertions(+), 4 deletions(-) diff --git a/_drivers-of-inequality-benin.qmd b/_drivers-of-inequality-benin.qmd index 4cd56df..8b8a20d 100644 --- a/_drivers-of-inequality-benin.qmd +++ b/_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 to Benin ```{python} -#| label: fig-ben-aid-donortype -#| fig-cap: "Total ODA for Benin per year, separated by donor type. Source: " -#| column: page +# Load CRS data 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. + +