diff --git a/_drivers-of-inequality-benin.qmd b/_drivers-of-inequality-benin.qmd
index 99151c2..4cd56df 100644
--- a/_drivers-of-inequality-benin.qmd
+++ b/_drivers-of-inequality-benin.qmd
@@ -100,3 +100,25 @@ The primary reasons for not having access to electricity are the lack of physica
 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.
+
+<!-- development assistance -->
+
+```{python}
+#| label: fig-ben-aid-donortype
+#| 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)
+
+donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': ['sum']})
+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.show()
+```
diff --git a/data-prep.qmd b/data-prep.qmd
index dcc40bb..9659082 100644
--- a/data-prep.qmd
+++ b/data-prep.qmd
@@ -61,3 +61,182 @@ def plot_consumption_gini_percapita_ruralurban(country_df):
     gini_plot(gni_cnsmpt)
 ```
 
+```{python}
+## Set up functions to grab development aids by type of donating body
+## ODA donor type map, see DAC code sheet xlsx
+donortypes = {
+    1: 'dac',
+    2: 'dac',
+    3: 'dac',
+    4: 'dac',
+    5: 'dac',
+    6: 'dac',
+    7: 'dac',
+    8: 'dac',
+    9: 'dac',
+    10: 'dac',
+    11: 'dac',
+    12: 'dac',
+    18: 'dac',
+    20: 'dac',
+    21: 'dac',
+    22: 'dac',
+    40: 'dac',
+    50: 'dac',
+    61: 'dac',
+    68: 'dac',
+    69: 'dac',
+    75: 'dac',
+    76: 'dac',
+    301: 'dac',
+    302: 'dac',
+    701: 'dac',
+    742: 'dac',
+    801: 'dac',
+    820: 'dac',
+    918: 'dac',
+    104: 'multilat',
+    807: 'multilat',
+    811: 'multilat',
+    812: 'multilat',
+    901: 'multilat',
+    902: 'multilat',
+    903: 'multilat',
+    905: 'multilat',
+    906: 'multilat',
+    907: 'multilat',
+    909: 'multilat',
+    913: 'multilat',
+    914: 'multilat',
+    915: 'multilat',
+    921: 'multilat',
+    923: 'multilat',
+    926: 'multilat',
+    928: 'multilat',
+    932: 'multilat',
+    940: 'multilat',
+    944: 'multilat',
+    948: 'multilat',
+    951: 'multilat',
+    952: 'multilat',
+    953: 'multilat',
+    954: 'multilat',
+    956: 'multilat',
+    958: 'multilat',
+    959: 'multilat',
+    960: 'multilat',
+    963: 'multilat',
+    964: 'multilat',
+    966: 'multilat',
+    967: 'multilat',
+    971: 'multilat',
+    974: 'multilat',
+    976: 'multilat',
+    978: 'multilat',
+    979: 'multilat',
+    980: 'multilat',
+    981: 'multilat',
+    982: 'multilat',
+    983: 'multilat',
+    988: 'multilat',
+    990: 'multilat',
+    992: 'multilat',
+    997: 'multilat',
+    1011: 'multilat',
+    1012: 'multilat',
+    1013: 'multilat',
+    1014: 'multilat',
+    1015: 'multilat',
+    1016: 'multilat',
+    1017: 'multilat',
+    1018: 'multilat',
+    1019: 'multilat',
+    1020: 'multilat',
+    1023: 'multilat',
+    1024: 'multilat',
+    1025: 'multilat',
+    1037: 'multilat',
+    1038: 'multilat',
+    1311: 'multilat',
+    1312: 'multilat',
+    1313: 'multilat',
+    30: 'nondac',
+    45: 'nondac',
+    55: 'nondac',
+    62: 'nondac',
+    70: 'nondac',
+    72: 'nondac',
+    77: 'nondac',
+    82: 'nondac',
+    83: 'nondac',
+    84: 'nondac',
+    87: 'nondac',
+    130: 'nondac',
+    133: 'nondac',
+    358: 'nondac',
+    543: 'nondac',
+    546: 'nondac',
+    552: 'nondac',
+    561: 'nondac',
+    566: 'nondac',
+    576: 'nondac',
+    611: 'nondac',
+    613: 'nondac',
+    732: 'nondac',
+    764: 'nondac',
+    765: 'nondac',
+    1601: 'private',
+    1602: 'private',
+    1603: 'private',
+    1604: 'private',
+    1605: 'private',
+    1606: 'private',
+    1607: 'private',
+    1608: 'private',
+    1609: 'private',
+    1610: 'private',
+    1611: 'private',
+    1612: 'private',
+    1613: 'private',
+    1614: 'private',
+    1615: 'private',
+    1616: 'private',
+    1617: 'private',
+    1618: 'private',
+    1619: 'private',
+    1620: 'private',
+    1621: 'private',
+    1622: 'private',
+    1623: 'private',
+    1624: 'private',
+    1625: 'private',
+    1626: 'private',
+    1627: 'private',
+    1628: 'private',
+    1629: 'private',
+    1630: 'private',
+    1631: 'private',
+    1632: 'private',
+    1633: 'private',
+    1634: 'private',
+    1635: 'private',
+    1636: 'private',
+    1637: 'private',
+    1638: 'private',
+    1639: 'private',
+}
+
+def totals_by_donortype(oda_frame):
+    totals = oda_frame.loc[
+        (df['RECIPIENT'] == 236) &
+        (df['SECTOR'] == 1000) &
+        (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)
+    return donortotals
+```