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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, 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. 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 ## Institutional
### Labour laws and regulatory systems ### Labour laws and regulatory systems