Add aid description and water supply aid money analysis to Uganda

_drivers-of-inequality-uganda.qmd

@@ -176,13 +176,22 @@ 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 = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', labels={"Value": "Development aid amount, in millions of USD"}, markers=True, template="seaborn")
 fig.show()
 ```
 
 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,
+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.
+
 ```{python}
 #| label: fig-uga-aid-donortype
 #| fig-cap: "Total ODA for Uganda per year, separated by donor type"
@@ -203,35 +212,90 @@ donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': [
 donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
 donortotals_grouped.columns = donortotals_grouped.columns.to_flat_index()
 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 = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', labels={"Value": "Development aid amount, in millions of USD"}, markers=True, template="seaborn")
 fig.show()
 ```
 
 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,
+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.
+
 ```{python}
-#| label: tbl-uga-aid-electricity
+#| label: tbl-uga-aid-watersupply
-#| tbl-cap: "ODA for transmission and distribution of electric power in Uganda per year, separated by financing type"
+#| tbl-cap: "ODA projects of water and sanitation supply in Uganda per year, separated by project type"
 #| column: page
+pd.options.display.float_format = "{:.2f}".format
+
+# 14020-22 - large scale potable water
+# 14030-32 - individual-level water and sanitation supply
+# 14081 - education and training in water supply
 totals = df.loc[
-    ((df['SECTOR'] == 23630) | (df['SECTOR'] == 23631)) & # Total
+    (   (df['SECTOR'] == 14020) |
+        (df['SECTOR'] == 14021) |
+        (df['SECTOR'] == 14022) |
+        (df['SECTOR'] == 14030) |
+        (df['SECTOR'] == 14031) |
+        (df['SECTOR'] == 14032) |
+        (df['SECTOR'] == 14081)
+    ) &
     (df['CHANNEL'] == 100) &
     (df['AMOUNTTYPE'] == 'D') &
     ((df['FLOW'] == 11) | (df['FLOW'] == 13)) & # Total
     (df['FLOWTYPE'] == 112) &
     (df['AIDTYPE'] == "100") # contains mixed int and string representations
     ]
-electricityaid = totals[totals['DONOR'] < 20000] # drop all 'total' aggregations
+wateraid = totals[totals['DONOR'] < 20000] # drop all 'total' aggregations
+# Aggregate different measures of large scale water supply and sanitation
+wateraid.loc[wateraid[(wateraid['SECTOR'] == 14021) | (wateraid['SECTOR'] == 14022)].index, 'SECTOR'] = 14020
+wateraid.loc[wateraid[(wateraid['SECTOR'] == 14020)].index, 'Sector'] = "Water supply and sanitation - large systems"
+# Aggregate different measures of individual water supply and sanitation
+wateraid.loc[wateraid[(wateraid['SECTOR'] == 14031) | (wateraid['SECTOR'] == 14032)].index, 'SECTOR'] = 14030
+wateraid.loc[wateraid[(wateraid['SECTOR'] == 14030)].index, 'Sector'] = "Basic drinking water supply and basic sanitation"
 
-pd.options.display.float_format = "{:.2f}".format
+# Group by sector per year and sum all values
+wateraid_grouped = wateraid.groupby(['Year', 'Sector']).agg({'Value': ['sum']})
+wateraid_grouped = wateraid_grouped.reset_index(['Year', 'Sector'])
+wateraid_grouped.columns = wateraid_grouped.columns.to_flat_index()
+wateraid_grouped.columns = ['Year', 'Sector', 'Value']
+crosstab = pd.crosstab(wateraid_grouped['Year'], wateraid_grouped['Sector'], margins=True, values=wateraid_grouped['Value'], aggfunc='sum')
 
-el_grouped = electricityaid.groupby(['Year', 'Flow']).agg({'Value': ['sum']})
+# Rename and reorder columns
-el_grouped.style.format(escape="latex")
+crosstab.columns = ['Basic water supply', 'Education and training', 'Large water supply', 'All']
-el_grouped
+crosstab = crosstab[['Basic water supply', 'Large water supply', 'Education and training', 'All']]
+
+crosstab
 ```
 
-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.
+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.
+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.
+
+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.
+
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