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feat(script): Begin adding summary of findings table

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Marty Oehme 2024-02-09 16:54:48 +01:00
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scoping_review.qmd
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@ -611,6 +611,62 @@ Since policies employed in the pursuit of increased equality can take a wide for
the following synthesis will first categorize between the main thematic area and its associated interventions,
which are then distinguished between for their primary outcome inequalities.
Findings:
| area of policy | findings | channels |
| --- | ---- | ---- |
| minimum wage | mixed evidence for short-/medium-term income inequality impacts | can lead to income compression at higher-earner ends |
| | some evidence for long-term inequality decrease | job loss offsets through higher wages |
| | | some spatial transfer from urban manufacturing sectors to rural agricultural sectors |
| | bad targeting can exacerbate existing inequalities | negative effect on women's hours worked if strong household labour divisions |
| | | low-earners sometimes secondary high-income household earners while low-wage households have no earners at all |
| | potential impact larger for single parents, rural/disadvantaged locations | women more affected if they make up large share of low-wage earners |
| | | |
| paid leave | evidence for significant increase in rtw after childbirth | esp. disadvantaged women benefit due to no prior employer-funded leave |
| | 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 |
| | | can exacerbate existing household labour division |
| | mixed evidence for fixed-/short-term contracts counter-acting effect on rtw | fixed-term contracts often insufficiently covered by otherwise applicable labour regulation |
| | | |
| collective bargaining | evidence for decreased income inequality with strong unionisation | stronger collective political power vector enables more equal redistributive policies |
| | | increased probability for employment on formal, standard employment contract |
| | marginal evidence for increased income/representation of women/minorities in workforce/management | internal heterogeneity due to predominantly affecting median part of wage distribution |
| | | self-selection of people joining more unionised enterprises/organisations/sectors |
| | | depending on targeting of concurrent policies can bestow more benefits on men, increasing horizontal inequalities |
Strength of Evidence
```{python}
# Create a dictionary with the data for the dataframe
data = {
'strong_internal_validity': [
'weak', 'strong', 'strong', 'strong', 'strong', 'weak', 'strong', #minimum wage
'strong', 'weak', 'strong', 'strong', # paid leave
'strong', 'weak' # protective env policies
],
'strong_external_validity': [
'strong', 'strong', 'weak', 'weak', 'strong', 'strong', 'strong', #minimum wage
'strong', 'strong', 'weak', 'weak', # paid leave
'strong', 'weak' # prot env policies
],
'policy': [
'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage',
'paid leave', 'paid leave', 'paid leave', 'paid leave',
'environmental infrastructure','environmental infrastructure'
]
}
# Create the dataframe
test_df = pd.DataFrame(data)
# Assuming df is your dataframe
# Melt the dataframe to long format for plotting
melted_df = test_df.melt(value_vars=['strong_internal_validity', 'strong_external_validity'], id_vars
='policy', var_name='Validity')
# Create a stacked histplot using Seaborn
sns.histplot(data=melted_df, y='policy', hue='Validity', multiple='stack')
```
## Institutional
### Labour laws and regulatory systems