feat(script): Add findings table as computation
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1 changed files with 32 additions and 35 deletions
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@ -560,7 +560,7 @@ Should they point towards gaps (or over-optimization) of specific areas of inter
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#| fig-cap: Predominant type of intervention
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by_intervention = (
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bib_df.groupby(["author", "year", "title", "design", "method", "representativeness"])
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bib_df.groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
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.agg(
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{
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"intervention": lambda _col: "; ".join(_col),
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@ -615,49 +615,46 @@ to better identify areas of strong analytical lenses or areas of more limited an
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from src import prep_data
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validities = prep_data.calculate_validities(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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g = sns.PairGrid(validities[["internal_validity", "external_validity", "identifier"]].drop_duplicates(subset="identifier"),
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x_vars=["internal_validity", "external_validity"], y_vars = ["identifier"]
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)
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# Melt the dataframe to long format for plotting
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# melted_validities = validities.melt(value_vars=['valid_int', 'valid_ext'], id_vars
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# ='intervention', var_name='Validity')
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# Create a stacked histplot using Seaborn
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sns.scatterplot(data=validities, x='external_validity', y='internal_validity', hue='intervention')
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#sns.scatterplot(data=validities, x='external_validity', y='internal_validity', hue='intervention')
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```
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## Institutional
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{{< portrait >}}
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| area of policy | findings | channels |
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|:----------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
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| minimum wage | mixed evidence for short-/medium-term income inequality impacts | can lead to income compression at higher-earner ends |
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| | some evidence for long-term inequality decrease | job loss offsets through higher wages |
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| | | some spatial transfer from urban manufacturing sectors to rural agricultural sectors |
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| | bad targeting can exacerbate existing inequalities | negative effect on women's hours worked if strong household labour divisions |
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| | | low-earners sometimes secondary high-income household earners while low-wage households have no earners at all |
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| | potential impact larger for single parents, rural/disadvantaged locations | women more affected if they make up large share of low-wage earners |
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| | | |
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| paid leave | evidence for significant increase in rtw after childbirth | esp. disadvantaged women benefit due to no prior employer-funded leave |
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| | some evidence for positive rtw effects to occur with medium-/long-term time delay | short-term exit but no long-term increase to hiring pattern discrimination |
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| | | can exacerbate existing household labour division |
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| | mixed evidence for fixed-/short-term contracts counter-acting effect on rtw | fixed-term contracts often insufficiently covered by otherwise applicable labour regulation |
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| | | |
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| collective bargaining | evidence for decreased income inequality with strong unionisation | stronger collective political power vector enables more equal redistributive policies |
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| | | increased probability for employment on formal, standard employment contract |
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| | marginal evidence for increased income/representation of women/minorities in workforce/management | internal heterogeneity due to predominantly affecting median part of wage distribution |
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| | | self-selection of people joining more unionised enterprises/organisations/sectors |
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| | | depending on targeting of concurrent policies can bestow more benefits on men, increasing horizontal inequalit |
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| | | ies |
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| protective environmental policies | evidence for decrease in spatial inequality | increased employment probability through large-scale rural energy projects |
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| | | |
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| | mixed evidence for increase of existing inequalities | elite policy capture can exacerbate existing social exclusion & disadvantages |
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| workfare programmes | evidence for decrease of vertical inequality | |
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| | evidence for possibility of increased spatial inequalities | bad targeting increases deprivations for already job-deprived areas |
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| | evidence for effective outcomes dependent on on prior material equalities | prior inequalities such as land ownership can lead to political capture and less effective policies |
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| social protection | evidence for conditional cash transfers producing short- and long-term inequality reduction | production of short-term cash influx |
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| | mixed evidence for childcare subsidies decreasing gender inequalities | |
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| | evidence for stagnating income replacement rates exacerbating existing vertical inequalities | |
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| | healthcare subsidy impacts strongly dependent on correct targeting | |
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```{python}
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findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
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from src import prep_data
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import math
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validities = prep_data.calculate_validities(by_intervention)
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valid_subset = validities[["internal_validity", "external_validity", "citation"]].fillna(1.0).drop_duplicates(subset=["citation"]).sort_values("internal_validity")
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def combined_validities(df_in, column: str = "internal_validity"):
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if not isinstance(df_in, str):
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return
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combined = 0.0
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for study in df_in.split(";"):
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new = valid_subset.loc[valid_subset["citation"] == study, column]
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if len(new) > 0 and not math.isnan(new.iat[0]):
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combined += new.iat[0]
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if combined:
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return combined
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return 0.0
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def combined_external(df_in, column: str = "external_validity"):
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return combined_validities(df_in, column)
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findings_institutional["internal_validity"] = findings_institutional["studies"].apply(combined_validities)
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findings_institutional["external_validity"] = findings_institutional["studies"].apply(combined_external)
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md(tabulate(findings_institutional[["area of policy", "internal_validity", "external_validity", "findings", "channels"]], showindex=False, headers="keys", tablefmt="grid"))
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```
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{{< landscape >}}
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