From 6678df6d17870c669ba92fd1cf7b2a0d00d71422 Mon Sep 17 00:00:00 2001 From: Marty Oehme Date: Tue, 6 Aug 2024 13:53:26 +0200 Subject: [PATCH] feat(script): Add first draft Discussion and Conclusion --- manuscript/article.qmd | 174 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 173 insertions(+), 1 deletion(-) diff --git a/manuscript/article.qmd b/manuscript/article.qmd index 7b67be9..292c62b 100644 --- a/manuscript/article.qmd +++ b/manuscript/article.qmd @@ -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. + + + +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. + + +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. + + +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. + + +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}