feat(script): Begin adding horizontal inequalities outcomes

scoping_review.qmd

@@ -848,7 +848,7 @@ Limitations of the study are the region-wide level of analysis which may obscure
 A simulation study on the effects of trade liberalization through FTA by @Khan2021 looks at income inequality in Pakistan between different households, measured through the Gini coefficient.
 It finds that there is no clear general direction for changes through FTA visible, with its impact primarily depending on micro-economic factors.
 Some large trade agreements are negatively correlated with the Gini while others are positively related, similar to regional and bilateral agreements.
-Gnerally, this is due to increases in the income of poor rural agricultural farm households being dependent on grain (which is the largest export good often rising under FTA), while livestock predominantly owned by poor rural households decreases in returns under FTA.
+Generally, this is due to increases in the income of poor rural agricultural farm households being dependent on grain (which is the largest export good often rising under FTA), while livestock predominantly owned by poor rural households decreases in returns under FTA.
 The deciding channel can then be increases on the wages of farm workers (after among others grain export increases) increasing income equity, which, when they do not happen, can in turn lead to an overall decrease.
 Lastly, there are wage compression effects between urban and rural households, with richer urban households often decreasing processed food or service production.
 A greater mobility would dissipate all short-term gains and losses, as changes would get more evenly distributed across regions and households, while over the long term some positive aspects on income equality are visible if increased agricultural growth can be sustained.
@@ -869,11 +869,9 @@ In Mexico, while the gap widened during the 1990s, it began closing again afterw
 The differences in wage gap effects compared to both other countries and the respective country's physical labour market requirements show that contextual structural changes played a large role in each case:
 with erstwhile reduced returns on Brazilian returns for brain intensive occupations, the introduction of a female-lead manufacturing sector in Mexico in the 90s, and widely diverging basic labour market skill structures in Thailand and India necessitating subsistence-oriented participation; the results show impacts of structural changes, though limited through a variety of mediating factors influencing each case.
 
-## Diversity
+## Infrastructure: Transport networks and climate adaption
 
-{{++ TODO: Subsume under individual other intervention types? ++}}
-
-## Infrastructure/Climate Change
+### Climate change
 
 @Kuriyama2021 look at the effects of Japan's move to decarbonise its energy sector on employment, especially rural employment.
 It finds that, while employment in general is positively affected, especially rural sectors benefit from additional employment probability.
@@ -890,7 +888,7 @@ An insignificant amount of women from local villages were working at the solar p
 The author suggests this is an example of institutional design neglecting individual agency and structural power relations, especially intersectional inequalities between gender and caste.
 The study is limited in explanatory power through its observational design, not being able to make causal inferences.
 
-## Infrastructure/Transport
+### Transport networks
 
 <!-- explicitly spatial policies -->
 @Blumenberg2014 look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households,
@@ -941,16 +939,30 @@ It finds that educational interventions significantly increase the employment pr
 However, education alone is only a necessary not a sufficient condition for increased employment, with a married woman's family size and family structure having an impact as well.
 Finally, education also has an intergenerational impact, with the female education also positively relating to daughters' education levels.
 
-# Results for inequalities
+# Results for horizontal inequalities
 
-## Income inequality through a vertical lens
+```{python}
+# dataframe containing each intervention inequality pair
+df_inequality = bib_df[["intervention", "inequality"]].copy().reset_index(drop=True)
+df_inequality['Inequality'] = df_inequality['inequality'].str.split(";").explode(ignore_index=True).str.strip()
+df_inequality['Intervention'] = df_inequality['intervention'].str.split(";").explode(ignore_index=True).str.replace(r"\(.+\)", "", regex=True).str.strip()
 
-{{++ insert regional breakdown++}}
+def crosstab_inequality(df, inequality:str):
+    df_temp = df.loc[(df["Inequality"] == inequality) | (df["Inequality"] == "income")]
+    tab = pd.crosstab(df_temp["Intervention"], df_temp["Inequality"])
+    return tab.drop(tab[tab[inequality] == 0].index)
 
-{{++ insert intervention/outcome breakdown ++}}
+```
 
 ## Gender inequality
 
+```{python}
+#| label: tbl-gender-crosstab
+#| tbl-cap: Interventions targeting gender inequality
+
+crosstab_inequality(df_inequality, "gender")
+```
+
 Gender inequality is the second most reviewed dimension of workplace inequality in the study sample,
 with a variety of studies looking at predominantly it through the lens of female economic empowerment or through closing gender pay gaps.
 
@@ -965,12 +977,26 @@ childcare programmes, paid leave and maternity benefits.
 
 ## Spatial inequality
 
+```{python}
+#| label: tbl-spatial-crosstab
+#| tbl-cap: Interventions targeting spatial inequality
+
+crosstab_inequality(df_inequality, "spatial")
+```
+
 {{++ insert regional breakdown++}}
 
 {{++ insert intervention/outcome breakdown ++}}
 
 ## Disability
 
+```{python}
+#| label: tbl-disability-crosstab
+#| tbl-cap: Interventions targeting disability inequality
+
+crosstab_inequality(df_inequality, "disability")
+```
+
 {{++ insert regional breakdown++}}
 
 <!-- LFP and RTW -->