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feat(script): Add first draft Discussion and Conclusion

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@ -961,7 +961,7 @@ The identified literature rises in volume over time between 2000 and 2023,
with first larger outputs identified from 2014 onwards,
as can be seen in @fig-publications-per-year.
While fluctuating overall, with a significantly smaller outputs 2017 and in turn significantly higher in 2021,
the overall output volume strongly increased during this period.
the overall output volume increased throughout this period.
```{python}
#| label: fig-publications-per-year
@ -1161,8 +1161,180 @@ Another reason could be the actual implementation of different policy programmes
# Discussion
```{python}
#| label: discussion-prep-inequality-df
#| echo: false
# dataframe containing each intervention inequality pair
df_inequality = (
df[["region", "intervention", "inequality"]]
.assign(
Intervention = lambda _df: (_df["intervention"]
.str.replace(r"\(.+\)", "", regex=True)
.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("Intervention")
.explode("inequality")
.reset_index(drop=True)
)
```
Turning to the available studies from a perspective of inequalities,
@tbl-inequality-crosstab breaks down the individually targeted inequalities per intervention type.
```{python}
#| label: tbl-inequality-crosstab
#| tbl-cap: Intervention types by the inequalities targeted
df_temp = df_inequality.loc[
(df_inequality["inequality"] == "income")
| (df_inequality["inequality"] == "gender")
| (df_inequality["inequality"] == "spatial")
| (df_inequality["inequality"] == "disability")
| (df_inequality["inequality"] == "ethnicity")
]
df_temp = df_temp.rename(columns={"inequality": "Inequality"})
tab = pd.crosstab(df_temp["Intervention"], df_temp["Inequality"],
margins=True).reindex(["income", "gender", "spatial", "ethnicity", "disability"], axis="columns").sort_values("income", ascending=False)
del df_temp
tab
```
Most studies focus on some indicator of income inequality within national or regional contexts.
The second most analysed inequality is that of gender,
followed by spatial inequalities, disabilities, ethnicities, age, inequalities of migration, education and intergenerational issues.
Only a small amount of studies carry analysis of inequalities in the world of work surrounding migration,
generational connections, age and education into the world of work.
Age-related inequalities prominently factor 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 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.[^pension-studies]
Equally, for migration few studies can strictly delineate it from racial inequalities or considerations of ethnicity.
[^pension-studies]: Studies focusing on the effects of pensions themselves often do not intersect back into outcomes within the world of work. For an overview on pensions and health effects, see @VanDerHeide2013; for pensions and other intersectional inequalities, see for example @Zantinge2014.
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 remaining gaps of academic lenses for generational inequalities, age-related inequalities, educational inequalities and inequalities of migration processes when looking at the quantity of output.
Care should be taken not to overestimate the decisiveness of merely quantified outputs ---
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.
<!-- crosstab prevalences -->
<!-- gender -->
Looking into the prevalence of individual interventions from the gender inequality dimension,
the crosstab shows that interventions on paid leave, subsidies, collective bargaining, and education received the most attention.
Thus a slight preference towards institutional and structural interventions is visible,
though the dimension seems to be viewed from angles of strengthening individual agency just as well,
with subsidies often seeking to nourish this approach,
and training, and interventions towards financial agency also being represented in the interventions.
<!-- spatial -->
Interventions affecting spatial inequalities are often also primarily viewed through indicators of income.
Interventions aiming at reducing spatial inequalities primarily base themselves on infrastructural changes,
which aligns with expectations of the infrastructural schism between urban and rural regions.
Additionally, education interventions target spatial inequalities,
with the effects of minimum wage, work programmes, interventions strengthening financial agency, trade liberalization and training playing a reduced role.
This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects which should be taken into account to avoid worsening issues:
such as was seen in the further exclusion of already disadvantaged women from employment, infrastructure and training opportunities in India under bad targeting and elite capture [@Stock2021],
or further deprivation of already disadvantaged regions in the UK work programme [@Whitworth2021].
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 results agree with the systematic review of income, employment and poverty correlations by [@Perez2022],
in that employment plays a significant in spatial disadvantages,
however with different primary barriers for different spatial contexts.[^perez-interventions]
On the other hand, as the results by @Hunt2004 have also shown,
individual measures on their own such as commuting subsidies in this case, while having positive results,
may not provide enough lasting impact over the long term and may need embedding in a more holistic approach,
combining multiple policy packages.
[^perez-interventions]: The identified interventions largely overlap with the identified pertinent interventions in this review: credit programmes, institutional support for childcare, guaranteed minimum income/universal basic income or the provision of living wages, commuting subsidies, and housing mobility programs. However, due to their focus on urban contexts, the identified barriers differ.
<!-- disability -->
Few studies approach disability inequalities primarily through the prism of income inequality,
preferring return to work, employment rates or amount of hours worked as indicators.
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.
Here, a split between frameworks that favour agency-based approaches,
putting organisational barriers and environmental activation, as well as individual (re-)integration within the world of work into focus,
and frameworks which focus on the institutional-structural component,
with a focus on educational inclusion, and selection and eligibility criteria for benefit or vocational programmes.[^disability-approaches]
In addition to employment or return to work based indicators it might thus be pertinent to include a focus on decent work and meaningful achievement as additional indications of successful programmes.
Taken together, these results especially reinforce the results of @Poppen2017 and @Thoresen2021,
for the importance of correct targeting to avoid unintended negative outcomes,
while the evidence base also highlights research gaps in contexts and regions other than developed high-income countries.
[^disability-approaches]: For exemplary frameworks in the agency perspective, see @Martin2020 and @Lindsay2015; for the latter see @Lindsay2015a and @Gruber2014.
<!-- migration/ethnicity -->
Studies on migration- or ethnicity-based inequalities predominantly focus on structural interventions such as education, fiscal policies or infrastructure,
or the effects of institutional contexts such as collective bargaining, unionisation or universal incomes.
The primary indicators are mixed between indicators of income inequality and others such as employment probability,
though with a focus on absolute measures such as poverty, hours worked or debt.
While some frameworks do put agency-driven necessities to the foreground,
there is a consensus for structural approaches required to enable this agency.[^migration-frameworks]
[^migration-frameworks]: For an agency-focused approach, see @Siebers2015; for an example of structural requirements, see @Goodburn2020 or @Samaluk2014 for a discussion of structural power dynamics; for an institutional focus, see @Clibborn2022.
# Conclusions
The preceding study undertook a systematic scoping review of the literature on inequalities in the world of work.
It focused on the variety of approaches to policy interventions, from institutional to structural to more agency-driven programmes,
and highlighted the inequalities targeted, analysed in subsequent study, their methods and limitations,
to arrive at a picture of which lays out the strengths and weaknesses of current approaches.
Wide gaps exist between the research on existing topics within the areas and intersections of inequalities in the world of work.
First, while regionally research on such inequalities seems relatively evenly distributed,
focus prevalence on individual inequalities varies widely.
Research into interventions preventing income inequality are still the dominant form of measured outcomes,
which makes sense for its prevailing usefulness through a variety of indicators and its use to investigate both vertical and horizontal inequalities.
However, care should be taken not to over-emphasize the reliance on income inequality outcomes:
they can obscure intersections with other inequalities,
or diminish the perceived importance of tackling other inequalities themselves, if not directly measurable through income.
Thus, while interventions attempt to approach the inequality from a variety of institutional, structural and agency-oriented approaches already,
this could be further enhanced by putting a continuous focus on the closely intertwined intersectional nature of the issue.
Gender inequality is an almost equally considered dimension in the interventions,
a reasonable conclusion due to the inequality's global ubiquity and persistence.
Most gender-oriented policy approaches tackle it directly alongside income inequality outcomes,
especially viewed through gender pay gaps and economic (dis-)empowerment,
approaching it from backgrounds of structural or agency-driven interventions.
While both approaches seem fruitful in different contexts, few interventions strive to provide a holistic approach which combines the individual-level with macro-impacts,
tackling both institutional-structural issues while driving concerns of agency simultaneously.
Spatial inequalities are primarily viewed through rural-urban divides,
concerning welfare, opportunities and employment probabilities.
Spatially focused interventions primarily tackle infrastructural issues which should be an effective avenue since most positive interventions are focused on the structural dimension of the inequality.
However, too many interventions, especially focused on reducing income inequalities,
still do not take spatial components fully into view,
potentially leading to worse outcomes for inequalities along the spatial dimension.
Disabilities are rarely viewed through lenses other than employment opportunities.
While most interventions already focus on dimensions of strengthening agency and improved integration or reintegration of individuals with disabilities into the world of work,
a wider net needs to be cast with future research focusing on developing regions and the effects of more institutional-structural approaches before clearer recommendations can be given based on existing evidence.
Ethnicity and migration provide dimensions of inequalities which are, while more evenly distributed regionally,
still equally underdeveloped in research on evidence-based intervention impacts.
Currently, there is a strong focus on institutional-structural approaches,
which seems to follow the literature in what is required for effective interventions.
However, similarly to research on inequalities based on disability, there are clear gaps in research
on ethnicity and especially migration, before clearer pictures of what works can develop.
The intertwined nature of inequalities, once recognized, requires intervention approaches which heed multi-dimensional issues and can flexibly intervene and subsequently correctly measure their relative effectiveness.
To do so, perspectives need to shift and align towards a new, more intersectional approach which can incorporate both a wider array of methodological approaches between purely quantitative and qualitative research,
while relying on indicators for measurement which are flexible yet overlapping enough to encompass such a broadened perspective.
# Bibliography
::: {#refs}