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scoping_review.qmd
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@ -1014,6 +1014,39 @@ def crosstab_inequality(df, inequality:str, **kwargs):
return tab.drop(tab[tab[inequality] == 0].index) return tab.drop(tab[tab[inequality] == 0].index)
``` ```
As can be seen in @fig-region-counts, taken by region for the overall study sample,
the evidence base receives a relatively even split between the World Bank regional country groupings.
Studies tend to base their analyses more in national comparative studies for the North American and Europe and Central Asian regions, while relying more on case studies restricted to a single country context for developing countries in other regions, though this trend does not hold strongly everywhere or over time.
A slight trend towards studies focusing on evidence-based research in developing countries is visible, though with an overall rising output, as seen in @fig-publications-per-year,
and the ability for reliance on more recent datasets, this is to be expected.
```{python}
#| label: fig-region-counts
#| fig-cap: Studies by regions analysed
by_region = (
bib_df[["region"]]
.assign(
region = lambda _df: (_df["region"]
.str.replace(r" ?; ?", ";", regex=True)
.str.strip()
.str.split(";")
)
)
.explode("region")
.reset_index(drop=True)
)
ax = sns.countplot(by_region, x="region", order=by_region["region"].value_counts().index)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
def regions_for_inequality(df, inequality:str):
df_temp = df.loc[(df["inequality"] == inequality)]
return sns.countplot(df_temp, x="region", order=df_temp["region"].value_counts().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. 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. 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? --> <!-- TODO have calculation for amount of studies w/ implicit/explicit targeting? -->
@ -1058,54 +1091,30 @@ The following sections will dive deeper into each predominant identified inequal
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. 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]. 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. Studies that solely or mainly 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]. While a pursuit both worthwhile 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. 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. 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. 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]. 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. Similarly, rarely do studies delineate generational outcomes from income, gender or education issues enough to mark their own category of analysis within.
```{python} Thus, for the current state of the literature on analyses of policy interventions through the lens of inequality reduction within the world of work, there are strong gaps of academic lenses for generational inequalities, age inequalities, education inequalities and inequalities of non-ethnic migration processes going purely by quantity of output.
#| label: fig-region-counts Care should be taken not to overestimate the decisiveness of merely quantified outputs ---
#| fig-cap: Studies by regions analysed multiple studies with strong risk of bias may produce less reliable outcomes than fewer studies with stronger evidence bases ---
however, it does provide an overview of the size of evidence base in the first place.
by_region = ( The following sections will instead discuss in more depth the implications for individual inequalities,
bib_df[["region"]] as well as providing a comparative view of the respective intersection with income inequality.
.assign(
region = lambda _df: (_df["region"]
.str.replace(r" ?; ?", ";", regex=True)
.str.strip()
.str.split(";")
)
)
.explode("region")
.reset_index(drop=True)
)
ax = sns.countplot(by_region, x="region", order=by_region["region"].value_counts().index)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
def regions_for_inequality(df, inequality:str):
df_temp = df.loc[(df["inequality"] == inequality)]
return sns.countplot(df_temp, x="region", order=df_temp["region"].value_counts().index)
```
## Gender inequalities ## Gender inequalities
Gender inequality is the second most reviewed horizontal dimension of workplace inequality in the study sample, Due to its persistent characteristics, gender inequality is an often analysed 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. with a variety of studies looking at it predominantly through the lens of female economic empowerment or through gender pay gaps.
@fig-gender-regions shows that there is a somewhat higher output of research into this inequality in both East Asia & the Pacific and Europe & Central Asian regions just ahead of North America,
though the overall sample is relatively balanced between regions.
```{python}
#| label: tbl-gender-crosstab
#| tbl-cap: Interventions targeting gender inequalities
crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
```
```{python} ```{python}
#| label: fig-gender-regions #| label: fig-gender-regions
@ -1139,21 +1148,45 @@ plt.tight_layout()
plt.show() plt.show()
``` ```
<!-- maternal intersection, children --> Looking into the prevalence of individual interventions within the gender dimension,
A variety of studies also look at female economic empowerment outcomes through a more generational lens, @tbl-gender-crosstab shows that subsidies, notions of unionisation and collective action, education and paid leave received the most attention.
focusing on the effects of interventions aimed at maternity support --- Thus there is a slight leaning towards institutional and structural interventions visible, though the dimension seems to be viewed from angles of strengthening individual agency just as well,
childcare programmes, paid leave and maternity benefits. with subsidies often seeking to nourish this approach, and training, and interventions towards financial agency being represented in the interventions.
<!-- gender -->
Approaches of paid leave, child care and education agree with the findings of Zeinali et al. [-@Zeinali2021] on the main barriers at the intersection of gender and social identity:
The main barriers limiting women's access to career development resources can be reduced access to mentorship and sponsorship opportunities, as well as a reduced recognition, respect, and impression of value at work for women in leadership positions, with inequalities entrenching these barriers being an increased likelihood for women to take on the 'dual burdens' of professional work and childcare or domestic work, as well as biased views of the effectiveness of men's over women's leadership styles.
```{python} ```{python}
#| label: tbl-spatial-crosstab #| label: tbl-gender-crosstab
#| tbl-cap: Interventions targeting spatial inequalities #| tbl-cap: Interventions targeting gender inequalities
crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False) crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
``` ```
Whereas institutional programmes such as minimum wage and structural interventions such as education or the contextual trade liberalization are strongly viewed through the lens of income effects,
with more studies targeting gender along income dimensions and the income dimension on its own,
studies of agency-based interventions approach gender inequalities less through this dimension.
Instead, they tend to rely on employment numbers or representation in absolute terms or as shares for their analyses.
<!-- maternal intersection, children -->
A variety of studies also look at female economic empowerment outcomes through a more generational lens,
focusing on the effects of interventions aimed at maternity support for the mother and/or children ---
childcare programmes, paid leave and maternity benefits.
## Spatial inequalities ## Spatial inequalities
Spatial inequalities are less focused within European, Central Asian and North American regions,
as @fig-spatial-regions shows.
Instead, both Southern Asia and Sub-Saharan Africa are the primary areas of interest,
with studies especially into Tanzania, India and Pakistan.
The distribution of spatial inequality analyses otherwise is primarily conducted in the contexts of the United States and the United Kingdom.
This may point to the countries' large rural populations or wider inequality gaps between rural and urban populations.
While large rural populations are a sign of a predominantly agrarian economy, widening gaps are argued to be specifically appearing between rural and urban locations in post-industrial societies:
Under modes of financialization, a spatial redistribution of high- and low-income sectors and increasing occupational segregation, rural locations are often left behind economically and require structural-institutional interventions to be rectified [@Crouch2019].
```{python} ```{python}
#| label: fig-spatial-regions #| label: fig-spatial-regions
#| fig-cap: Regional distribution of studies analysing spatial inequalities #| fig-cap: Regional distribution of studies analysing spatial inequalities
@ -1166,15 +1199,50 @@ plt.tight_layout()
plt.show() plt.show()
``` ```
Interventions affecting spatial inequalities are often viewed through indicators of income,
as can be seen in @tbl-spatial-crosstab.
The primary intervention aiming at reduction of spatial inequalities is based on infrastructural changes,
which aligns with expectations of the infrastructural rift between urban and rural regions.
```{python} ```{python}
#| label: tbl-disability-crosstab #| label: tbl-spatial-crosstab
#| tbl-cap: Interventions targeting disability inequalities #| tbl-cap: Interventions targeting spatial inequalities
crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False) crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False)
``` ```
Additionally, education interventions target spatial inequalities, with the effects of minimum wage, interventions strengthening financial agency, trade liberalization and training all playing a more marginal role.
Thus, structural interventions are the dominant approach to reducing spatial inequalities, with institutional and agency-driven interventions often not targeting them specifically.
This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects,
be they positive: as is the case for higher positive income effects on rural households due to unintentional good targeting of minimum wage to lower-income households [@Gilbert2001];
or negative: as seen in the further exclusion of already disadvantaged women from employment, infrastructure and training opportunities in India under bad targeting and elite capture [@Stock2021].
Policies, even those of an ostensibly non-spatial nature, must thus keep in mind possibly adverse targeting effects if not specifically adjusting for potential impacts on spatial inequalities.
Rural communities relying on agricultural economies in particular may be vulnerable to exogenous structural shock events such as climate change, which may thus need to be a focal point for future structural interventions [@Salvati2014].
The measures used to investigate spatial effects of policy interventions follow an even split between relative inequality measured through indicators such as the Gini coefficient or urban-rural employment shares, and absolute measures such as the effects on rural employment.
With the level of analysis mostly taking place at the household level, some individual horizontal inequalities such as intra-household gender roles and economic participation or racial intersections can be considered,
however, analyses of spatial inequalities often remain solely focused on spatial employment and income effects.
<!-- income, spatial, pre-existing -->
Spatial inequalities move both ways, however, as also shown by @Perez2022 in a multi-disciplinary systematic review of the association between a person's income, their employment and poverty in an urban environment.
They find, similarly to the rural-urban divide, that employment plays a significant role in the poverty of urban residents, though here the primary barriers are identified as lack of access to public transport, geographical segregation, labour informality and inadequate human capital.
They also agree with the potential policy interventions identified to counteract these inequalities:
credit programs, institutional support for childcare, guaranteed minimum income/universal basic income or the provision of living wages, commuting subsidies, and housing mobility programs,
which largely map onto structural or institutional efforts identified by the studies.
Like the study pool shows, many of the highlighted barriers can be mapped onto channels of inequality:
gender inequality's impact, through traditional gender roles and lack of empowerment, a lack of childcare possibilities, or unequal proportions of domestic work;
spatial inequality, through residential segregation or discrimination, lack of access to transportation, and a limited access to work;
as well as pre-existing inequalities, here defined as the generational persistence of poverty, larger household sizes, and its possible negative impacts on human capital.
## Disability inequalities ## Disability inequalities
The dimension of disabilities in inequalities remains strictly focused on developed nations,
through analysis of effects on inequality in the world of work in a context of the United States labour market,
as can be seen in @fig-disability-regions.
```{python} ```{python}
#| label: fig-disability-regions #| label: fig-disability-regions
#| fig-cap: Regional distribution of studies analysing disability inequalities #| fig-cap: Regional distribution of studies analysing disability inequalities
@ -1187,25 +1255,41 @@ plt.tight_layout()
plt.show() plt.show()
``` ```
<!-- LFP and RTW --> Few studies approach disability inequalities primarily through the prism of income inequality, with more analyses focusing on other outcome measures as can be seen in @tbl-disability-crosstab.
There is a clear bias in studies on disability interventions towards studies undertaken in developed countries and, more specifically, The interventions targeting such inequalities in the world of work favour an approach to measuring inequalities through employment, by absolute amounts of hours worked, return to work numbers or employment rates instead.
based on the Veteran Disability system in the United States which has been the object of analysis for a wide variety of studies. Only when looking at the intersection of disability and gender is income the more utilized indicator, through measuring female income ratios compared to those of males.
```{python} ```{python}
#| label: tbl-migration-crosstab #| label: tbl-disability-crosstab
#| tbl-cap: Interventions targeting migration inequalities #| tbl-cap: Interventions targeting disability inequalities
crosstab_inequality(df_inequality, "migration").sort_values("migration", ascending=False) crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False)
``` ```
## Migration & ethnic inequalities Studies into interventions within the dimension of disabilities are predominantly focused on agency-based perspectives, with counselling and training being the primary approaches.
```{python} Structurally approached interventions are also pursued, looking at the overall effects of education, or subsidies in health care, though even here,
#| label: fig-migration-regions the individual effects of activation play a role [@Carstens2018].
#| fig-cap: Regional distribution of studies analysing racial and migration inequalities
ax = regions_for_inequality(by_region_and_inequality, "migration") The findings for a need toward agency-based interventions reflect in frameworks which put the organizational barriers into focus and simultaneously demand a more inclusive look into (re)integration of people with disabilities into the labour market and within the world of work [@Martin2020].
Here, in addition to the predominantly used measures of employment and return to work rates,
meaningful achievement and decent work should be measured from individual economic and social-psychological indicators, especially in view of the already predominantly agency-based variety of interventions.
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 variety of studies but equally highlights gaps in research on the topic in other contexts and other regions.
## Migration & ethnic inequalities
The effects of policy interventions targeting migratory and ethnic inequalities in the world of work are viewed primarily through the regions of North America, Europe, and Central, South and East Asia, and the Pacific,
as can be seen in @fig-ethnicity-regions.
Especially the specifics regarding migration are reviewed in an Asian context, while studies in North America focus predominantly on ethnicity in their analyses,
though both dimensions are deeply intertwined and hard to disentangle for most studies.
```{python}
#| label: fig-ethnicity-regions
#| fig-cap: Regional distribution of studies analysing migration and ethnicity inequalities
by_region_and_inequality.loc[by_region_and_inequality["inequality"] == "migration", "inequality"] = "ethnicity"
ax = regions_for_inequality(by_region_and_inequality, "ethnicity")
ax.set_xlabel("") ax.set_xlabel("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor") rotation_mode="anchor")
@ -1213,6 +1297,26 @@ plt.tight_layout()
plt.show() plt.show()
``` ```
As seen in @tbl-ethnicity-crosstab, in terms of primary interventions analysed for these dimensions, most focus on structural interventions such as education or infrastructure,
as well as institutional contexts such as the possibility for collective bargaining and unionisation, or the effects of universal income on the world of work.
```{python}
#| label: tbl-ethnicity-crosstab
#| tbl-cap: Interventions targeting migration and ethnicity inequalities
crosstab_inequality(df_inequality, "ethnicity").sort_values("ethnicity", ascending=False)
```
There is a mixed approach to using income-based indicators of inequality or other markers such as employment.
At the same time, there is a somewhat stronger focus on absolute measures of inequality, such poverty, debt or savings, or hours worked in absolute terms.
Relative indicators have a wider spread with the Gini coefficient, the Theil index, decile ratios or employment rates for sub-samples used.
From an organisational perspective, the focus on structural effects is in agreement with perspectives which highlight the conceptualisation of workplace ethnicity as separate from the majority in many places as a structural power structure [@Samaluk2014].
At the same time in a broader context, job insecurities, both produced by the dis-embeddedness of migrants and the broader contemporary institutional work organisational context speak to the same institutional-structural focus required as is already pursued in the literature [@Landsbergis2014].
While some frameworks do put agency-driven necessities to the foreground [see @Siebers2015],
the consensus seems a requirement for structural approaches enabling this agency and their institutional embedding before more agency-driven interventions alone increase their effectiveness [see for structural necessities @Do2020; @Goodburn2020; for institutional contexts see @Clibborn2022].
# Conclusion # Conclusion
The section with conclude with reflections on the implications of findings for policy. The section with conclude with reflections on the implications of findings for policy.