Add pagebreaks after each section

_drivers-of-inequality-benin.qmd

@@ -115,7 +115,7 @@ df = df.rename(columns={'\ufeff"DONOR"': 'DONOR'})
 
 ```{python}
 #| label: fig-ben-aid-financetype
-#| fig-cap: "Total ODA for Benin per year, by finance type"
+#| fig-cap: "Total ODA for Benin per year, by financing type"
 #| column: page
 totals = df.loc[
     (df['RECIPIENT'] == 236) & # Benin
@@ -139,10 +139,8 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
 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.
@@ -166,7 +164,7 @@ totals = df.loc[
     ]
 donortotals = totals.copy()
 donortotals["Donortype"] = donortotals["DONOR"].map(donortypes)
-donortotals = donortotals[(donortotals["Donortype"] == "dac") | (donortotals["Donortype"] == "multilateral")] = donortotals["DONOR"].map(donortypes)
+donortotals = donortotals[(donortotals["Donortype"] != "nondac")]
 
 donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': ['sum']})
 donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
@@ -176,14 +174,12 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
 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.
 
-
+{{< pagebreak >}}

_drivers-of-inequality-djibouti.qmd

@@ -110,3 +110,5 @@ Nomadic and pastoralist people in the country's rural regions were hit especiall
 with the nomadic population decreasing by nearly three quarters and many fleeing or becoming sedentary.
 Women face less opportunity in the country with worse upward educational mobility, less participation in the labor force, higher unemployment rates, and a continuing, if closing, gender literacy gap.
 Djibouti is set to miss most of its poverty target levels and move along a growth pathway that does not lend itself to inclusion unless active policy measures changing its economic investment and growth strategies are examined.
+
+{{< pagebreak >}}

_drivers-of-inequality-uganda.qmd

@@ -142,3 +142,5 @@ which in turn worsens food securities, retrenches gender role inequalities and p
 In the district of Isingiro in West Uganda access to water is considerably below the national average,
 with policy failures during implementation now leading to partly or non-functional water sources.
 The problem runs danger of deteriorating with an increased amount of climate shocks such as droughts threatening to exacerbate existing inequalities and drive further households into poverty.
+
+{{< pagebreak >}}

_drivers-of-inequality-vietnam.qmd

@@ -166,3 +166,4 @@ both ethnic minorities and the rural female population are thus at risk of being
 * Wordings do not quite capture quintile poverty assessments for coming descriptive statistics
 -->
 
+{{< pagebreak >}}