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feat(script): Add inequality and region breakdowns per inequality

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Marty Oehme 2023-12-22 19:27:52 +01:00
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scoping_review.qmd
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@ -879,7 +879,7 @@ 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.
#### Transport networks
#### Transport mobility
<!-- explicitly spatial policies -->
@Blumenberg2014 look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households,
@ -1022,6 +1022,59 @@ def crosstab_inequality(df, inequality:str, **kwargs):
return tab.drop(tab[tab[inequality] == 0].index)
```
Policy interventions undertaken either with the explicit aim of reducing one or multiple inequalities, or analysed under the lens of such an aim implicitly, appear in a wide array of variations to their approach and primary targeted inequality, as was highlighted in the previous section.
To make further sense of the studies shining a light on such approaches, it makes sense to divide their attention not just by primary approach, but by individual or overlapping inequalities being targeted, as well as the region of their operation.
<!-- TODO have calculation for amount of studies w/ implicit/explicit targeting? -->
As can be seen in @fig-inequality-types which breaks down available studies by targeted inequalities,
income inequality is the type of inequality traced in most of the relevant studies.
This follows the identified multi-purpose lens income inequality can provide, through which to understand other inequalities ---
many studies use income measurements and changes in income or income inequality over time as indicators to understand a variety of other inequalities' linkages through.
Often, however, income inequality is not the primary inequality being targeted, but used to measure the effects on other inequalities by seeing how the effects of respective inequality and income intersect, as will be discussed in the following section.
```{python}
#| label: fig-inequality-types
#| fig-cap: Types of inequality analysed
by_inequality = (
bib_df[["inequality"]]
.assign(
inequality = lambda _df: (_df["inequality"]
.str.replace(r"\(.+\)", "", regex=True)
.str.replace(r" ?; ?", ";", regex=True)
.str.strip()
.str.split(";")
)
)
.explode("inequality")
.reset_index(drop=True)
)
fig = plt.figure()
fig.set_size_inches(6, 3)
ax = sns.countplot(by_inequality, x="inequality", order=by_inequality["inequality"].value_counts().index)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
by_inequality = None
```
With income inequality on its own often describing vertical inequality within a national context,
the remaining inequalities gathered from the data rather form horizontal lenses to view their contexts through.
The second most analysed inequality is that of gender, followed by spatial inequalities, disabilities, generational inequalities, inequalities of migration, education and age.
The following sections will dive deeper into each predominant identified inequality, discuss what the main interventions analysed in the literature are and where gaps and limitations lie.
Only a small amount of studies carried analysis of inequalities surrounding migration, generational connections, education and age into the world of work, being the focal point of almost no studies at all.
Age-related inequalities predominantly factored into studies as an intersection with disability, in focusing on the effects of older people with disabilities on the labour market [@Kirsh2016].
Studies that solely or majorly target age-related inequalities themselves often do so with a stronger focus on the effects on seniors' health outcomes and long-term activation measures, with some extending into the effects of differentiated pension systems.
While a pursuit both worthwile in its own right and, by the nature of pensions, closely tied to labour markets, the studies ultimately focus on impacts which rarely intersect back into the world of work itself and are thus beyond the scope of this review [see @VanDerHeide2013; @Zantinge2014].
Equally, for migration few studies strictly can delineate it from racial inequalities or considerations of ethnicity.
For the purposes of discussion, studies analysing both inequalities concerning ethnicity and migration will be discussed as part of one socio-demographic point of view, though results that do only speak to migration will be highlighted accordingly.
Surprisingly few studies focus on the eventual outcomes in the world of work of earlier education inequalities.
The majority of studies analysing education-oriented policies focus on direct outcomes of child health and development, education accessibility itself or social outcomes [see @Curran2022; @Stepanenko2021; @Newman2016; @Gutierrez2009; @Zamfir2017].
Similarly, few studies delineate generational outcomes from income, gender or education issues enough to mark their own category.
```{python}
#| label: fig-region-counts
@ -1051,17 +1104,48 @@ def regions_for_inequality(df, inequality:str):
## Gender inequality
Gender inequality is the second most reviewed horizontal dimension of workplace inequality in the study sample,
with a variety of studies looking at it predominantly through the lens of female economic empowerment or through gender pay gaps.
```{python}
#| label: tbl-gender-crosstab
#| tbl-cap: Interventions targeting gender inequality
#| tbl-cap: Interventions targeting gender inequalities
crosstab_inequality(df_inequality, "gender")
crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
```
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.
```{python}
#| label: fig-gender-regions
#| fig-cap: Regional distribution of studies analysing gender inequalities
{{++ insert regional breakdown++}}
by_region_and_inequality = (
bib_df[["inequality", "region"]]
.assign(
region = lambda _df: (_df["region"]
.str.replace(r" ?; ?", ";", regex=True)
.str.strip()
.str.split(";")
),
inequality = lambda _df: (_df["inequality"]
.str.replace(r"\(.+\)", "", regex=True)
.str.replace(r" ?; ?", ";", regex=True)
.str.strip()
.str.split(";")
)
)
.explode("inequality")
.explode("region")
.reset_index(drop=True)
)
ax = regions_for_inequality(by_region_and_inequality, "gender")
ax.set_xlabel("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.tight_layout()
plt.show()
```
<!-- maternal intersection, children -->
A variety of studies also look at female economic empowerment outcomes through a more generational lens,
@ -1074,31 +1158,69 @@ childcare programmes, paid leave and maternity benefits.
```{python}
#| label: tbl-spatial-crosstab
#| tbl-cap: Interventions targeting spatial inequality
#| tbl-cap: Interventions targeting spatial inequalities
crosstab_inequality(df_inequality, "spatial")
crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False)
```
{{++ insert regional breakdown++}}
```{python}
#| label: fig-spatial-regions
#| fig-cap: Regional distribution of studies analysing spatial inequalities
{{++ insert intervention/outcome breakdown ++}}
ax = regions_for_inequality(by_region_and_inequality, "spatial")
ax.set_xlabel("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.tight_layout()
plt.show()
```
## Disability
```{python}
#| label: tbl-disability-crosstab
#| tbl-cap: Interventions targeting disability inequality
#| tbl-cap: Interventions targeting disability inequalities
crosstab_inequality(df_inequality, "disability")
crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False)
```
{{++ insert regional breakdown++}}
```{python}
#| label: fig-disability-regions
#| fig-cap: Regional distribution of studies analysing disability inequalities
ax = regions_for_inequality(by_region_and_inequality, "disability")
ax.set_xlabel("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.tight_layout()
plt.show()
```
<!-- LFP and RTW -->
There is a clear bias in studies on disability interventions towards studies undertaken in developed countries and, more specifically,
based on the Veteran Disability system in the United States which has been the object of analysis for a wide variety of studies.
{{++ insert intervention/outcome breakdown ++}}
## Racial and migratory inequalities
```{python}
#| label: tbl-migration-crosstab
#| tbl-cap: Interventions targeting migration inequalities
crosstab_inequality(df_inequality, "migration").sort_values("migration", ascending=False)
```
```{python}
#| label: fig-migration-regions
#| fig-cap: Regional distribution of studies analysing racial and migration inequalities
ax = regions_for_inequality(by_region_and_inequality, "migration")
ax.set_xlabel("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.tight_layout()
plt.show()
```
# Conclusion