feat(script): Begin adding summary of findings table
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@ -611,6 +611,62 @@ Since policies employed in the pursuit of increased equality can take a wide for
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the following synthesis will first categorize between the main thematic area and its associated interventions,
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which are then distinguished between for their primary outcome inequalities.
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Findings:
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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 inequalities |
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Strength of Evidence
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```{python}
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# Create a dictionary with the data for the dataframe
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data = {
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'strong_internal_validity': [
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'weak', 'strong', 'strong', 'strong', 'strong', 'weak', 'strong', #minimum wage
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'strong', 'weak', 'strong', 'strong', # paid leave
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'strong', 'weak' # protective env policies
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],
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'strong_external_validity': [
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'strong', 'strong', 'weak', 'weak', 'strong', 'strong', 'strong', #minimum wage
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'strong', 'strong', 'weak', 'weak', # paid leave
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'strong', 'weak' # prot env policies
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],
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'policy': [
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'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage',
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'paid leave', 'paid leave', 'paid leave', 'paid leave',
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'environmental infrastructure','environmental infrastructure'
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]
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}
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# Create the dataframe
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test_df = pd.DataFrame(data)
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# Assuming df is your dataframe
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# Melt the dataframe to long format for plotting
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melted_df = test_df.melt(value_vars=['strong_internal_validity', 'strong_external_validity'], id_vars
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='policy', var_name='Validity')
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# Create a stacked histplot using Seaborn
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sns.histplot(data=melted_df, y='policy', hue='Validity', multiple='stack')
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```
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## Institutional
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### Labour laws and regulatory systems
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