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---
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lang: en
title: Addressing Inequalities in the World of Work
subtitle: Scoping Review on 'What Works'
crossref:
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- kind: float
key: apptbl
latex-env: apptbl
reference-prefix: Table A
space-before-numbering: false
latex-list-of-description: Appendix Table
---
```{python}
#| label: prep-data
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{{< include 01-codechunks/_prep-data.py >}}
```
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# Introduction
This study presents a systematic scoping review of the current literature concerning inequalities in the world of work.
It attempts to trace the main mechanisms and channels of the interventions employed in the global world of work to reduce its inequalities,
while simultaneously investigating the methodologies and indicators used in evidence-based research on them to systematically elaborate the current state of the art on inequalities in the world of work.
The following section presents a typology of policies that directly or indirectly tackle inequalities in the world of work both within the labour market and outside this domain (e.g. education policy).
The section also makes an attempt to clearly identify the theoretical mechanisms and channels through which policies are expected to impact inequalities in forms of work and ultimate labour market outcomes.
The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas:
employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection [@ILO2022b].
Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.
The rest of the study is structured as follows:
Section 2 will introduce the world of work, as well as the ILO's approach to inequalities within it, and provide a variety of other recent approaches to make sense of inequalities in the world of work.
Section 3 will then introduce the method applied in the scoping review of this study, before introducing the initial identified literature as a coherent sample.
Section 4 will synthesize findings on a variety of intervention found in the literature, organized by general policy area of intervention pursued.
Section 5 will then provide a brief discussion on these findings from the perspective of individual inequalities, the interventions found to reduce them, and resulting policy implications, before Section 6 briefly concludes.
# The world of work
The policy areas and their respective focus perspectives are based in the conceptual understanding of the world of work, following the definition of work being activities performed by persons of any sex and age producing goods or providing services for "economic units [which] can be allocated mutually exclusively to one of the following sectors:" the formal sector, the informal sector, or the community and household own-use sector [@ILO2023c, 6].
This is the broader understanding of work which specifically separates itself from a more narrow conception of those in employment who are engaging in "production for pay or profit", whether for the informal or the formal market economy [see especially @ILO2023c, Point 18ff].
The key differentiations for these concepts are founded on an understanding of the production of goods or provision of services, as well as the distinctions between use by others for ultimate own-use and that of working for pay and/or profit – that is, as part of a market transaction in exchange for remuneration or in the form of profits derived from the goods or services.
Whether these services or goods are produced in what is defined as the informal economy, the formal economy or under informal employment outside the informal sector is, for the general encapsulation of no importance – they occur in the world of work.
Here, conceptually, it should be captured under one of the five mutually exclusive forms of work [@ILO2023, p. 4, Point 7c] to be understood as: own-use production work, performing "any activity to produce goods or provide services for own final use" [@ILO2013, p.5]; employment work comprising those performing work for others in exchange for pay or profit introduced above; unpaid trainee work, performing "any unpaid activity to produce goods or provide services […] to acquire workplace experience or skills" [@ILO2013, p.7]; and volunteer work, that being "any unpaid, non-compulsory activity to produce goods or provide services for others" [@ILO2013, p.8].
Any activity falling under work as defined above on the one hand, but not under any of these forms of work on the other, is instead designated as other work activities in the following considerations. The key concepts between these categories come down to a varying intensity of participation, the distinction of working for pay and/or profit mentioned above, whether it is for ultimate own-use or the use by others, and its compulsory nature.
The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas: employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection.
Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.
An exemplary typology of general policy area, related specified policy focus and related focus if any can be found in @tbl-policy-areas.
::: {#tbl-policy-areas}
| area of policy | focus | related |
|-------------------------:|----------------------------------------------------|----------------------------------|
| employment creation | pro-employment framework | |
| | gender-transformative framework | |
| | promotion of business sustainability | productivity increases |
| | | reduction in productivity gaps |
| | promotion of digital infrastructure | technology for decent work |
| | | reducing digital divide |
| access to education | quality of education/training/skills development | green transition |
| | relevance of education/training/skills development | digital transition |
| | gender-transformative career guidance | |
| | improvements of public services/social protection | |
| | work-life balance | juggle paid work and family care |
| | targeted support for disadvantaged groups | targeted education |
| labour rights protection | promotion of rights for all workers | collective bargaining systems |
| | minimum wage | |
| | inclusive labour market institutions | |
| | equal pay for work of equal value | |
| | wage transparency | |
| formalization | equality-driven approach to formalization | gender-responsive |
| | increase decent work in formal economy | country-tailored |
| | absorb informal workers / economic units | comprehensive |
| | | non-discriminatory |
| gender equality | removal of discriminatory practice | removal of stereotypes |
| diversity | promotion of equality of treatment | removal of discriminatory law |
| | promotion of equality of opportunity | |
| | data collection improvements | gender-focus |
| | occupational gender segregation | age-focus |
| | unequal pay for work of equal value | disability-focus |
| | gender-based violence | race-focus |
| | gender-based harassment | ethnicity-focus |
| | gender unequal division of unpaid care work | migrant status-focus |
| social protection | extend reach of social protection schemes | |
| | reach those not adequately protected | |
| | ensure access to social protection | comprehensive social protection |
| | | adequate social protection |
| | | sustainable social protection |
Source: Authors' elaboration based on ILO [-@ILO2022b].
ILO focus areas for inequality reduction
:::
## Inequalities in the world of work
Inequalities in the world of work have to be fundamentally conceptualized along two axes: On the one hand, vertical inequality captures the "income inequality between all households in a country" [@ILO2021].
Measurements of vertical inequalities is a perspective which focuses primarily on incomes as data, with debate of top income percentiles versus the remaining body of people often posing the primary area of debate [@ILO2021a].
Horizontal inequalities, on the other hand, occur when "some groups within the population find themselves disadvantaged and discriminated against on the basis of their visible identity, for example their gender, colour or beliefs, among others" [@ILO2021a].
Importantly, these inequalities do not act in a vacuum but create an interplay through overlaps and accumulations which take on their own driving dynamics for people belonging to multiple disadvantaged groups, captured in the idea of inequality’s intersectionality [@ILO2022b].
Here, especially horizontal inequalities may be hard to disentangle for impact finding, an important aspect of effective rigorous analysis in quantitative studies.
Thus, for a study on inequalities, or in turn a study on policies aimed at reducing inequalities in the world of work to be one of rigorous analysis, it must clearly define the type of policy taken as its object of analysis (its independent variable) as well as the types of inequalities targeted for reduction through the respective policy and measured as channels of impact.
Ultimately, then, the individual outcome measures need to be clearly specified and disentangled, most clearly reflecting in labour market outcome measures (dependent variables).
Only then can the targeted inequality be delineated as a clear channel.
In targeting an increase in equality, there are then two approaches to take: either levelling the playing field so that characteristics beyond an individual’s control can not influence their future perspectives, nor limit the potential of the powers they possess, through achieving equality of opportunity; or strive for an equality of outcomes, in factual observed resulting (in-)equalities.
As the ILO established, such a focus on equality of outcomes can be of great importance since "high levels of inequality today tend to reduce social mobility tomorrow" [@ILO2021a], making it that much more difficult to ultimately ensure equality of opportunity for following generation.
The key concepts here are thus the distinction of within-group and between-group inequalities, their overlapping characteristics, as well as policies enabling an equality of opportunity or of outcome.
Income inequality is still the primary lens of inequality that many approaches target, as well as the main focus point of many inequality measurements such as the Gini coefficient or ratios such as the Palma ratio [@DFI2023].
Following the ILO, "labour income is the main source of income for most households in the world [thus] unequal access to work and working poverty are major drivers of inequalities" [@ILO2021].
Income inequality, here, can be affected by a wide set of factors: status in employment, forms of work, the sector of activity, the respective occupation, type of enterprise, type of contract for those in waged work, and the status of formality among others [@ILO2019].
Income inequality should also not be seen as separate from, nor standing above, other inequalities, but closely linked to other inequalities.
As the ILO states, "income inequality, inequality of employment outcomes more generally and inequality of opportunities are intimately related" [@ILO2022b].
At the same time the exact linkages of these factors remain under-analysed, which is the reason why the channels of inequalities and the policies to reduce them will pose a fruitful space of analysis for this research.
While income inequality holds a primary position of importance for many analyses from a perspective of quantity, it should not be understood as carrying more importance qualitatively for itself compared to other inequalities but rather be understood "like a prism, which reveals many other forms of inequality, including those generated in the world of work" [@ILO2021a, p. 13].
It is the primary measure of vertical inequality, however, with other inequalities describing primarily the concept of horizontal inequality.
Here, of primary focus for the ILO, and many studies on inequality in the world of work, is gender inequality.
It describes the inequalities that arise because of an individual’s gender.
Generally, while the type and extend of other inequalities does vary substantially by global location and country, "gender inequalities, despite some progress over the past decades, remain persistent and pervasive" [@ILO2021].
Following a report on the gendered make-up of work globally, women are making up a larger part of those in underemployment, they primarily make up the service sector – a rising trend – while suffering a persistently substantial wage gap, tend to work shorter hours in employment but in turn have longer working days when including unpaid work, as well as contributing disproportionally to family work [@ILO2016].
The domestic area of work is also dominated by women, who make up 76.2 per cent of it, in addition to domestic work being overwhelmingly informal labour globally [@ILO2023a].
These inequalities in the world of work in turn also reflect in women being hindered in accessing adequate education, training, as well as the possibility for lifelong learning, and furthermore access to quality jobs, housing, mobility, capital, land, and adequate social protection – disparities which, on the basis of deeply rooted inequalities of gender roles, education and places of residence remain largely static if not on the rise.
These channels and outcomes, viewed intersectionally, must thus represent the primary lens through which to disentangle the gender inequality in the world of work today.
There are additional socio-demographic inequalities beyond gender which are based on the innate, most often visible, identification of a person.
These are made up of, though not limited to, ethnic and racial inequalities, those based on religion and beliefs, based on a person’s status as a migrant, a person’s age, sex, or disabilities [@ILO2021a].
For example, young people generally fare significantly worse in labour markets shown through outcomes such as a higher incidence of temporary employment throughout youth and the young labour force [@ILO2023b; @ILO2019].
As a report on the global conditions of work established, over "7% of workers felt they had been discriminated against in the 12 months prior to the survey on grounds of sex, race, religion, age, nationality, disability or sexual orientation" [@ILO2019] in the EU alone, making socio-demographic inequalities a prevalent and important to tackle angle of horizontal inequality.
Here, it will be especially important to correctly disentangle individual sources or contributing characteristics from each other in finding their linkages to relevant outcomes.
Another form of inequality are spatial inequalities, those that arise because of an individual’s location relative to other.
These inequalities exist primarily between different regions of a country: those between urbanity and rurality or more peripheral areas, but also between richer and poorer regions and, as the ILO established, can even lead to a ‘growing sense of fractured societies’ [@ILO2021].
One of the channels this can manifest itself is through an unequal access to decent work opportunities or economic opportunities more generally, an unequal access to financial resources, quality public services or even overall access to an essential social service infrastructure and digital infrastructure, as well as quality access to education or relevant training.
For spatial inequalities it will be especially important to take note of locally bound differences versus more generalizable results, with the dimensions and contributing factors to its outcomes potentially varying widely between different geographies and national contexts.
In mentioning unequal access to quality education or public infrastructure another important dimension of inequalities becomes highlighted: the dimension of pre-existing inequalities, that is, inequalities which exist prior to an individual’s interaction with the labour market and, though closely intertwined with socio-demographic inequalities, will prove useful to analytically differentiate between.
A differentiation which becomes especially important with a view on the inter-generational effects of inequality, highlighted in recognizing the difference between equality of opportunity and outcome.
The level of education, an individual’s poverty, productivity on the labour market and similar inequalities in opportunities are often the result of long-running pre-existing inequalities such as unequal access to health services, education, lacking property rights or clear ownership of assets, the lack of formal recognition as an individual, no access to formal banking [@ILO2021a].
Understanding such channels becomes difficult if not taking pre-existing inequalities into account as a separate category of inequality and long-term impacting channel.
Addressing these inequalities, in turn, is just as important to reducing inequalities within the labour market (as well as beyond) since they do play such a role for intergenerational social mobility and their impacts can be seen, once again, reflecting in the prism of subsequent income inequality.
For pre-existing inequalities, it will be especially important to understand the often delayed and more opaque nature of the roots of many outcomes, with channel being more difficult to identify and clearly label – especially in an intersectional context.
These five dimensions of inequalities --- income inequality, gender inequality, socio-demographic inequality, spatial inequality and pre-existing inequalities --- will thus provide the categorical anchors along which the reviewed studies will be analysed for their policy effects, each with a slightly different focus in linkages between inequality, policy and outcome.
## Existing reviews: alternative approaches
Aside from the general typology by the ILO introduced above, there are a variety of differing approaches to the interplay of inequalities and outcomes,
outlined in the following section.
<!-- gender/pre-existing -->
Chaudhuri et al. [-@Chaudhuri2021] conduct a systematic review to look at coping strategies and the effects of food insecurity, often through poverty, on social and health outcomes for women and children.
They find that one of the primary non-food coping strategies for women is to look for outdoor employment, mostly farm work, which can in turn lead to what the authors argue as *time* poverty when their time for childcare or personal nutrition is now cut short.
This in turn can, in combination with food-based coping strategies such as food rationing (in size or frequency), nutritional switches or food sharing, lead to negative health outcomes for children including disrupted socio-cognitive development as well as coping through dropping out of school, thereby furthering the rift of pre-existing inequalities.
<!-- gender -->
Finlay [-@Finlay2021] looks at the effects of female women's reproductive health on female labour force participation, especially career advancement, job quality and hours worked, to find a variety of responses differing between low-income, middle-income and high-income countries.
The main findings are that in low-income countries because of the prevalence of informal work, women are forced to adopt individual strategies of balancing child rearing and labour force participation through job type selection, reliance on other women in the household for child care, or birth spacing.
In middle-income countries, women have to juggle child rearing and labour force participation with an overall income inequality; here, early childbearing or lone motherhood especially can perpetuate poverty.
In high-income countries, social protection policies can assist in balancing child rearing and work but many underlying issues of gender inequality remain.
Throughout all countries, childbearing significantly interrupts career advancement.
<!-- gender -->
Chang et al. [-@Chang2021] use a qualitative systematic review to look at the linkages of breast-feeding and returning to paid employment for women and identify multiple barriers provided through inequalities discouraging continued breast-feeding after return to employment --- an experience often experienced as physically and emotionally difficult and potentially providing a barrier to full labour force participation.
Aside from individual motivation and support from employers, colleagues, and family members, women highlighted the importance of having workplace legislation in place to facilitate breast-feeding during employment, as well as access to convenient child care.
The review concludes indicating remaining gender and employment inequalities in accessing and receiving the support needed: gender role expectations viewing women as responsible for domestic work or childcare, with shorter maternity leave further discouraging breast-feeding especially of women not in managerial roles.
<!-- policy interv -->
Looking strictly at the impact of basic income interventions on labour market, health, educational, housing and other outcomes, Pinto et al. [-@Pinto2021] find that, while workforce participation is the primary outcome in most studies, the evaluations have shifted over time to include a wider array of outcomes, perhaps reflecting an understanding of lower health and social care spending offsetting some of the basic income investments.
Most of the studies investigating basic income perspectives focus on advanced economies such as the US.
<!-- disability -->
Undertaking a systematic review to find the effects of brain tumours in individuals on their labour market outcomes, Silvaggi [-@Silvaggi2020] find an impact of neuropsychological functioning on work productivity, issues for their process of returning to work, and often an exit from employment (job loss) for long-term survivors of brain tumours
While the channels are primarily viewed as stemming from the high short-term mortality and depressive symptoms or cognitive deficits, environmental barriers are identified as one channel as well, with the review ending in the policy recommendation of increased vocational rehabilitation for affected persons.
<!-- basic income -->
De Paz-Banez et al. [-@dePaz-Banez2020] use a systematic review of empirical studies to look at the effects of universal basic income on labour supply to find that, with no evidence of significant reductions in labour supply, instead the labour supply would increase globally among adults, men, women, young and old.
The insignificant reductions they found they assumed functional, since they were in the categories of: children, elderly, sick, people with disabilities, women with young children, young people continuing their studies and were offset by the otherwise increased supply.
<!-- disabilities, gender -->
Looking at the impact of gender on the employment outcomes for young disabled adults, Lindsay et al. [-@Lindsay2018] find that while youth with disabilities are half as likely to be employed, gender inequalities may play a compounding role with men being more likely to be in employment than women, working longer hours and having higher wages.
The identified channels here are different social supports, gender role expectations, as well as women's lower job expectations and overprotection from parents or guardians discouraging their independence.
<!-- gender -->
Kumari [-@Kumari2018] looks at the relationship of both economic growth and gender disparity on the labour supply in investigating their effects on female work participation.
<!-- TODO further explanation of U-shape necessary? -->
They see a U-shaped participation rate and some evidence of cross-sector gender pay disparity which is affected by demographic factors such as migration, marriage, child care and fertility, as well as economic factors such as per capita income, unemployment, infrastructure and the prevalence of non-farm jobs.
Ultimately, they argue that the labour supply inequalities are based on inequality between the sexes and, while regulatory measures such as adequate family and childcare policies, tax regimes and the presence of subsidized healthcare help, changes to the female labour force participation fundamentally require the replacement of such a traditional value system itself.
<!-- income -->
While undertaking a systematic review concerning the effects of adopting technology on employment in LICs or LMICs, Ugur and Mitra [-@Ugur2017] find when adoption favours product innovation positive effects are somewhat likely.
They also find, however, that existing income inequalities can make the possible positive effects of its adoption more ambiguous and may in turn widen the rift of demand for skilled versus unskilled labour.
Lastly, policies favouring green transition technologies may in turn reduce income inequality, providing another possible linkage.
<!-- disability -->
Lettieri and Diez Villoria [-@Lettieri2017] find that hiding mental illness is one of the primary strategies for improved employment outcomes in a meta-review looking at barriers to labour market inclusion for people mental disabilities.
This act of concealment of identity and self-stigmatization can seem necessary, they argue, due to the channels of workplace prejudices, perceiving them missing skills, as dangerous or unpredictable, or seeing the act of their hiring as charity due to expectations of lower productivity; but also due to discriminatory hiring practices and pre-existing inequalities leading to them being lower-skilled individuals due to prior discrimination, cultural and social barriers to training and work inclusion.
Here, relevant policies include interventions of supported employment (removing an environmental barrier), cognitive behavioural or computer-assisted therapies (cognitive barrier) or vocational rehabilitation programmes (human capital).
<!-- gender -->
Taukobong et al. [-@Taukobong2016] review various dimensions of female empowerment and their effects on a variety of health and development outcomes, including the access and use of financial services for the poor.
They find that, aside from gender inequalities being both highly contextual and intersectional, especially the channels of control over one's income, assets, resources, having decision-making power and individual education affected these outcomes across all dimensions, reflecting their position as channels of gender inequality.
Additionally, personal mobility, safety and equitable interpersonal relationships are associated with some health and family planning outcomes.
Ultimately, the review shows that due to the contextual nature, interventions need to identify the variations of inequality at their start, see where inequalities exist, overlap and work as barriers for an effective implementation.
<!-- disability -->
Ruhindwa et al. [-@Ruhindwa2016] review a variety of barriers to adequate workforce inclusion for people with disabilities, proposing an inclusive approach in which the individual is given space to take ownership of the solutions addressing challenges experienced in the employment sector.
Similarly, they view hiring discrimination and workplace stigmatization as the largest channels through which inequalities of disability manifest themselves.
They see especially employment support practices, with focus on enabling this, as relevant policy strategies, as well as national campaigns to ease disclosing one's disability in the labour market.
<!-- disability, gender, age -->
In looking at the various dimensions affecting the labour market outcomes of supported employment interventions for people with disabilities, Kirsh [-@Kirsh2016] finds that most literature still only regards the overall efficacy of the interventions without taking into account compounding intersectional characteristics.
They find that generally men are more likely to find employment through the intervention, possibly resting on current programmes focus on manual labour, as well as younger people generally finding better employment.
This highlights the intersectional nature of inequalities between disability, gender and age.
One relevant policy they see is that of vocational rehabilitation.
<!-- disability -->
Hastbacka et al. [-@Hastbacka2016] undertake a scoping review to find the linkages between societal participation and people with disabilities, looking at specific interventions for the identity of participants, types of participation analysed, and channels of effect.
They see most literature focusing on labour market participation and viewing disabled people as coherent group instead of intersectional.
The main channels of inequality providing barriers they identify are financial factors, attitudes of discrimination, health issues and unemployment, while the main driving mechanisms identified are legislation and disability policies, as well as support from people in close contact with disabled people and attitudes in society and the hiring process.
<!-- disability -->
In a systematic review looking at the effectiveness of workplace accommodations on employment and return to work, Nevala et al. [-@Nevala2015] find few studies with rigorous design leading to conclusive evidence.
They do find moderate evidence that employment in disability can be increased through workplace accommodations such as vocational counselling or guidance, education, self-advocacy, positive perception and help by others.
There is also low evidence for return to work being increased by education, work aids and techniques and cooperation between employers and other professionals (such as occupational health care, or service providers).
# Methodology and data
The following section will discuss the methodology that is proposed to conduct the review of the literature on policy interventions that are expected to address inequalities in forms of work and labour market outcomes,
as well as give an overview of the collected data.
This study follows the principles of a systematic review framework, to systematically assess the impact of an array of policies on inequalities in the world of work.
It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research,
while limiting "bias by the systematic assembly, critical appraisal and synthesis" through applying scientific strategies to the review itself [@Cook1995].
It thereby attempts to provide an improved basis for comparative analysis between studies through the rigorous application of systematic criteria and thus to avoid the potential bias of narrative reviews.
Unlike purely systematic reviews which typically focus on specific policy questions and interventions, systematic scoping reviews focus on a wider spectrum of policies, where different study designs and research questions can be investigated.
Since scoping reviews allow both broad and in-depth analyses, they are the most appropriate rigorous method to make a synthesis of the current evidence in this area [@Arksey2005].
The scoping review allows broad focus to be given to a subject for which no unified path with clear edges has been laid out yet by prior reviews, as remains the case with policies targeting inequalities in the world of work.
It does so through a breadth-first approach through a search protocol which favours working through a large body of literature to subsequently move toward a depth-favouring approach once the literature has been sufficiently delimited.
Its purpose, clearly mapping a body of literature on a (broad) topic area, is thereby useful on its own or in combination with a systematic approach [@Arksey2005].
With an increasingly adopted approach in recent years, with rigorous dichotomy of inclusion and exclusion criteria it provides a way of charting the relevance of literature related to its overall body that strives to be free of influencing biases which could affect the skew of the resulting literature sample [@Pham2014].
## The search protocol
The search protocol was carried out based on the introduced areas of policies as well as the possible combination of definitions and outcomes in the world of work.
For each dimension of definitions, a cluster containing possible utilized terms will be created, that is for: definitions of work and labour, forms of work, definitions of inequality, forms of vertical and forms of horizontal inequalities, labour market outcomes, and definitions of policy.
Each of the clusters contains synonymous terms as well as term-adjacent phrase combinations which are in turn used to refine or broaden the search scope to best encapsulate each respective cluster, based on the above definitions.
The search protocol then follows a three-staged process of execution: identification, screening and extraction.
First, in identification, the relevant policy, inequality and world of work related dimensions are combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential grey literature.
While the resulting study pools could be screened for in multiple languages, the search queries themselves are passed to the databases in English-language only.
Relevant results are then complemented through the adoption of a 'snowballing' technique,
in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.
To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions.
Identified terms comprising the world of work can be found in @tbl-wow-terms,
with the search query requiring a term from the general column and one other column.
```{python}
#| label: tbl-wow-terms
#| tbl-cap: World of work term cluster
terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
The world of work cluster, like the inequality and policy intervention clusters below, is made up of a general signifier (such as "work", "inequality" or "intervention") which has to be labelled in a study to form part of the sample,
as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as "domestic" work, "gender" inequality, "maternity leave" intervention).
At least one general term and at least one additional term have to be mentioned by a study to be identified for the initial sample pool.
For the policy intervention cluster, a variety of terms have been identified both from the ILO policy areas and guidelines as well as existing reviews, as can be seen in @tbl-intervention-terms.
Where terms have been identified from previous reviews outside the introduced ILO policy guidelines,
their sources have been included in the table.
For the database query, a single term from the general category is required to be included in addition to one term from *any* of the remaining categories.
```{python}
#| label: tbl-intervention-terms
#| tbl-cap: Policy intervention term cluster
terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
```
Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality,
as seen in @tbl-inequality-terms.
```{python}
#| label: tbl-inequality-terms
#| tbl-cap: Inequality term cluster
terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean 'AND'),
as well as in turn a single of those from each of the three clusters using an intersection merge.
The resulting sample pool will thus include a term and specific dimension of inequality and of policy intervention within the world of work.
Second, in screening, duplicate results are removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on: language, title, abstract, full text and literature supersession through newer publications.
Properties in these characteristics are used to assess an individual study on its suitability for further review.
Narrowing criteria are applied to restrict the sample to studies looking at i) the effects of individual evidence-based policy measures or intervention initiatives ii) attempting to address a single or multiple of the defined inequalities in the world of work.
iii) using appropriate quantitative methods to examine the links of intervention and impact on the given inequalities.
The narrowing process makes use of the typology of inequalities, of forms of work, and of policy areas introduced above as its criteria.
An overview of the respective criteria used for inclusion or exclusion can be found in @tbl-inclusion-criteria.
It restricts studies to those that comprise primary research published after 2000,
with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
::: {#tbl-inclusion-criteria}
```{python}
#| label: inclusion-criteria
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
Source: Author's elaboration
Study inclusion and exclusion scoping criteria
:::
To facilitate the screening process, with the help of 'Zotero' reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,such as ‘excluded::language’, ‘excluded::title’, ‘excluded::abstract’, and ‘excluded::superseded’.
This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.
To that end, a ‘country::’, ‘region::’ and ‘income::’ are used to disambiguate between the respective characteristics, such as ‘region::LAC’ for Latin America and the Caribbean, ‘region::SSA’ for Sub-Saharan Africa; as well as for example ‘income::low-middle’, ‘income::upper-middle’ or ‘income::high’.
These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings [@ILO2022].
Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system, through for example ‘inequality::income’ or ‘inequality::gender’.
The complete process of identification and screening is undertaken with the help of the Zotero reference manager, ultimately leaving only publications which are relevant for final full-text review and analysis.
Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with ‘excluded::full-text’ as explained above and relevant studies then ingested into the final sample pool.
Should any literature reviews be identified as relevant during this screening process,
they will in turn be crawled for cited sources in a 'snowballing' process,
and the sources will be added to the sample to undergo the same screening process explained above.
```{python}
#| label: calculate-scoping-flowchart
#| echo: false
#| output: asis
{{< include 01-codechunks/_prisma-flowchart.py >}}
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
The results to be identified in the matrix include a study’s: i) key outcome measures (dependent variables), ii) main findings, iii) main policy interventions (independent variables), iv) study design and sample size, v) dataset and methods of evaluation, vi) direction of relation and level of representativeness, vii) level of statistical significance, viii) main limitations.
Finally, following @Maitrot2017, the relevant studies are ranked for their validity.
Here, a 2-dimensional approach is taken to separate the external validity from the internal validity of the studies.
The ranking process then uses the representativeness of a study's underlying dataset,
from a non-representative survey sample, through a sub-nationally representative sample, a nationally representative and the use of census data,
to arrive at a ranking between 2.0 and 5.0 respectively.
Similarly, the studies are ranked for internal validity using the study design,
with only quasi-experimental and experimental studies receiving similar rankings between 2.0 and 5.0 depending on the individually applied methods due to their quantifiability,
while observational and qualitative studies go without an internal validity rank (0.0) due to the more contextual nature of their analyses.
For a full list of validity ranks, see @apptbl-validity-external and @apptbl-validity-internal.
## Data
The query execution results in an initial sample of `{python} nr_database_query_raw` potential studies identified from the database search as well as `{python} nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} FULL_RAW_SAMPLE_NOTHING_REMOVED`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
Of these, `{python} FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract-nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
from which in turn `{python} nr_extraction_done` have ultimately been extracted.
The currently identified literature rises somewhat in volume over time,
with first larger outputs identified from 2014,
as can be seen in @fig-publications-per-year.
```{python}
#| label: fig-publications-per-year
#| fig-cap: Publications per year
df_study_years = (
df.groupby(["author", "year", "title"])
.first()
.reset_index()
.drop_duplicates()
["year"].value_counts()
.sort_index()
)
# use order to ensure all years are displayed, even ones without values
years_range = list(range(df_study_years.index.min(), df_study_years.index.max()+1))
ax = sns.barplot(df_study_years, order=years_range)
ax.set_ylabel("Count")
ax.set_xlabel("Year")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # ensure rotated right-anchor
plt.tight_layout()
plt.show()
df_study_years = None
```
Anomalies such as the relatively significant dips in output in 2016 and 2017 become especially interesting against the strong overall increase of output.
While this can mean a decreased interest or different focus points within academia during the time span,
it may also point towards newly arising alternative term clusters, or a re-focus towards different interventions,
either of which may not be completely captured in the data and should thus be kept in mind for future scoping efforts.
The predominant amount of literature is based on white literature, with only a marginal amount solely published as grey literature.
This represents a gap which seems reasonable and not surprising since the database query efforts were primarily aimed at finding the most current versions of white literature.
Such a stark gap speaks to a well targeted identifaction procedure, with more up-to-date white literature correctly superseding potential previous publications.
@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
From the literature sample, several patterns emerge:
First, in general, citation counts are slightly decreasing --- as should generally be expected for newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.
```{python}
#| label: fig-citations-per-year-avg
#| fig-cap: Average citations per year
df["zot_cited"] = df["zot_cited"].dropna().astype("int")
grpd = df.groupby(["year"], as_index=False)["zot_cited"].mean()
fig, ax = plt.subplots()
ax.bar(grpd["year"], grpd["zot_cited"])
sns.regplot(x=grpd["year"], y=grpd["zot_cited"], ax=ax)
#ax = sns.lmplot(data=grpd, x="year", y="zot_cited", fit_reg=True)
years_list = np.arange(2000, 2024).tolist()
ax.set_xticks(years_list)
ax.tick_params(axis='x', rotation=45)
ax.set_ylabel("Citations")
ax.set_xlabel("Year")
plt.tight_layout()
plt.show()
```
Second, while such a decrease is visible the changes between individual years are more erratic due to strong changes from year to year.
This suggests, first, no overall decrease in academic interest in the topic over this period of time,
and second, no linearly developing concentration or centralization of knowledge output and dissemination,
though it also throws into question a clear-cut increase of *relevant* output over time.
Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest,
a possible sign of still somewhat unfocused research production which does not yet approach from a single coherent perspective.
It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the literature review process,
a possible sign of more focused knowledge production and thus valuable to more closely review during screening.
It may also suggest that clearly influential studies have been produced during those years, a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013).
This is because, as @fig-publications-per-year showed, the overall output was nowhere near as rich as in the following years, allowing single influential works to skew the visible means for those years.
In all of these cases, such outliers should provide clear points of interest during the screening process for eventual re-evaluation of utilized scoping term clusters and for future research focus.
Should they point towards gaps (or over-optimization) of specific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking future identification queries to better align with the prevailing literature output.
# Synthesis: A multitude of lenses
This section will present a synthesis of evidence from the scoping review,
analysing the main findings per policy area, as well as underscore individual studies' approaches and limitations.
```{python}
#| label: fig-intervention-types
#| fig-cap: Available studies by primary type of intervention
by_intervention = (
df
.fillna("")
.groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
.agg(
{
"intervention": lambda _col: "; ".join(_col),
}
)
.reset_index()
.drop_duplicates()
.assign(
intervention=lambda _df: _df["intervention"].apply(
lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
),
)
.explode("intervention")
)
sort_order = by_intervention["intervention"].value_counts().index
fig = plt.figure()
fig.set_size_inches(6, 3)
ax = sns.countplot(by_intervention, x="intervention", order=by_intervention["intervention"].value_counts().index)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
```
@fig-intervention-types shows the predominant interventions contained in the reviewed literature.
Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.
The entire spread of policies captures interventions aimed primarily at institutional and structural mechanisms, but also mechanisms focused on individual agency.
Since policies employed in the pursuit of increased equality can take a wide form of actors, strategy approaches and implementation details,
the following synthesis will first categorise between the main thematic area and its associated interventions.
Individual observations are then descriptively distinguished between for the primary outcome variables (inequalities) of interest.
Thus, in the following synthesis each reviewed study will be analysed through the primary policies or mechanisms they use as independent variables to analyse the effects on a variety of inequalities.
One of the primary lenses of inequality in viewing policy interventions to reduce inequalities in the world of work is that of income,
often measured for all people throughout a country (vertical inequality) or subsets thereof (horizontal inequality).
At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities could lead to diminished intervention outcomes through adverse targeting.
Each main thematic area will be preceded by a table presenting a summary of findings for the respective policies,
their identified channels and an estimation of their strength of evidence base.
Afterwards, the analytical lens will be inverted for the discussion (Section 5) and the reviewed studies discussed from a perspective of their analysed inequalities and limitations,
to better identify areas of strong analytical lenses or areas of more limited analyses.
## Institutional
{{< portrait >}}
::: {#tbl-findings-institutional}
```{python}
#| label: tbl-findings-institutional
from src.model import validity
study_strength_bins = {
0.0: r"\-",
5.0: r"\+",
10.0: r"\++",
}
def strength_for(val):
return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
Summary of main findings for institutional policies
:::
{{< landscape >}}
### Labour laws and regulatory systems
@Adams2015 study the effects of labour, business and credit regulations and looks at their long-term correlations to income inequality in developing countries from 1970 to 2012.
Additionally, the study looks at the effects of FDI and school enrolment, which will be reviewed in their respective policy sections.
They find that in MENA, SSA, LAC and to some extend AP increased labour and business regulations are actually negatively related to equitable income distribution, with market regulation not having significant effects.
The authors identify developing countries lacking in institutional capability to accomplish regulatory policies optimized for benefits and see the need for policies requiring more specific targeting of inequality reduction as their agenda.
Overall, the authors suggest that regulatory policy in developing countries needs to be built for their specific contexts and not exported from developed countries due to their different institutional capabilities and structural make-up.
The study is limited in its design focus relying purely on the macro-level regional analyses and can thus,
when finding correlations towards income inequality, not necessarily drill down into their qualitative root causes.
<!-- maternity leave and benefits -->
@Broadway2020 study the introduction of universal paid maternal leave in Australia, looking at its impacts on mothers returning to work and the conditions they return under.
It finds that, while there is a short-term decrease of mothers returning to work since they make use of the introduced leave period, over the long-term (after six to nine months) there is a significant positive impact on return to work.
Furthermore, there is a positive impact on returning to work in the same job and under the same conditions,
the effects of which are stronger for more disadvantaged mothers (measured through income, education and access to employer-funded leave).
This suggests that the intervention reduced the opportunity costs for delaying the return to work, and especially for those women that did not have employer-funded leave options, directly benefiting more disadvantaged mothers.
Some potential biases of the study are its inability to account for child-care costs, as well as not being able to fully exclude selection bias into motherhood.
There also remains the potential of results being biased through pre-birth labour supply effects or the results of the financial crisis, which may create a down-ward bias for either the short- or long-term effects.
@Dustmann2012 analyse the long-run effects on children's outcomes of increasing the period of paid leave for mothers in Germany.
While the study focuses on the children's outcomes, it also analyses the effects on the return to work rates and cumulative incomes of the policies within the first 40 months after childbirth.
It finds that, while short-term increases of paid leave periods (up to 6 months) significantly increased incomes, over longer periods (10-36 months) the cumulative incomes in fact decreased significantly,
marginally for low-wage mothers for 10 month periods, and across all wage segments for 36 month periods.
For the share of mothers returning to work, it finds that there is a significant increase in the months away from work among all wage segments for all paid leave period increases, positively correlated with their length.
Still similar numbers of mothers return once the leave period ends, though with significant decreases for leave periods from 18 to 36 months.
For its analysis of long-term educational outcomes on children, however, it does not find any evidence for the expansions improving children's outcomes, even suggesting a possible decrease of educational attainment for the paid leave extension to 36 months.[^dustmann-childoutcomes]
Some limitations of the study include its sample being restricted to mothers who go on maternity leave and some control group identification restrictions possibly introducing some sampling bias.
[^dustmann-childoutcomes]: The authors suggest that the negative effect for children under the long-term paid leave program of 36 months may stem from the fact that children require more external stimuli (aside from the mother) before this period ends, as well as the negative long-term effects of the mother's significantly reduced income for the long-term leave periods.
In a study on the effects of introductions of a variety of maternity leave laws in Japan, @Mun2018 look at the effects on employment numbers and job quality in managerial positions of women.
Contrary to notions of demand-side mechanisms of the welfare state paradox, with women being less represented in high-authority employment positions due to hiring or workplace discrimination against them with increased maternity benefits,
it finds that this is not the case for the Japanese labour market between 1992 and 2009.
There were no increases in hiring discrimination against women, and either no significant change in promotions for firms not providing paid leave before the laws or instead a positive impact on promotions for firms that already provided paid leave.
The authors suggest the additional promotions were primarily based on voluntary compliance of firms in order to maintain positive reputations,
signalled through a larger positive response to incentive-based laws than for mandate-based ones.
Additionally, the authors suggest that the welfare paradox may rather be due to supply-side mechanisms, based on individual career planning, as well as reinforced along existing gender divisions of household labour which may increase alongside the laws.
Limitations of the study include foremost its limited generalizability due to the unique Japanese institutional labour market structure (with many employments, for example, being within a single firm until retirement), as well as no ability yet to measure the true causes and effects of adhering to the voluntary incentive-based labour policies, with lasting effects or done as symbolic compliance efforts and mere impression management.
@Davies2022 conduct a study on the return to work ratios for high-skill women workers in public academic universities in the United Kingdom, comparing the results for those in fixed-term contract work versus those in open-ended contracts.
It finds that there is a significantly decreased return to work probability for those working under fixed-term contracts, and most universities providing policies with more limited access to maternity payment for fixed-contract staff.
This is possibly due to provisions in the policies implicitly working against utilization under fixed-terms:
there are strict policies on payments if a contract ends before the maternity leave period is over, and obligations on repayments if not staying in the position long enough after rtw.
Additionally, most policies require long-term continuous service before qualifying for enhanced payments in the maternity policies.
There is high internal heterogeneity between the universities, primarily due to the diverging maternity policy documents, only a small number of the overall dataset providing favourable conditions for fixed-term work within.
### Minimum wage laws
@Chao2022, in a study looking at the effects of minimum wage increases on a country's income inequality, analyse the impacts in a sample of 43 countries, both LMIC and HIC.
Using a general-equilibrium model, it finds that there are differences between the short-term and long-term effects of the increase:
In the short term it leads to a reduction of the skilled-unskilled wage gap, however an increase in unemployment and welfare,
while in the long term the results are an overall decrease in wage inequality as well as improved social welfare.
It finds those results primarily stem from LMIC which experience significant effects driven by a long-term firm exit from the urban manufacturing sector thereby increasing available capital for the rural agricultural sector, while in HIC the results remain insignificant.
The study uses the Gini coefficient for identifying a country's inequality.
Some limitations of the study include the necessity to omit short-term urban firm exit for the impact to be significant, as well as requiring the, reasonable but necessary, prior assumption of decreased inequality through increased rural agricultural capital.
@Alinaghi2020 conduct a study using a microsimulation to estimate the effects of a minimum wage increase in New Zealand on overall income inequality and further disaggregation along gender and poverty lines.
It finds limited redistributional effects for the policy, with negligible impact on overall income inequality and the possibility of actually increasing inequalities among lower percentile income households.
Additionally, while it finds a significant reduction in some poverty measures for sole parents that are in employment, when looking at sole parents overall the effects become insignificant again.
The authors suggest this points to bad programme targeting, which at best has negligible positive impact on income equality and at worst worsens income inequality in lower income households, due to may low-wage earners being the secondary earners of higher-income households but low-wage households often having no wage earners at all.
A pertinent limitation of the study includes its large sample weights possibly biasing the impacts on specific groups such as sole parents and thus being careful not to overestimate their significance.
In a study on the impacts of minimum wage increases in Ecuador @Wong2019 specifically looks at the income and hours worked of low-wage earners to analyse the policies effectiveness.
The study finds that, generally, there was a significant increase on the income of low-wage earners and also a significant increase on wage workers hours worked which would reflect positively on a decrease in the country's income inequality.
At the same time, it finds some potential negative effects on the income of high earners, suggesting an income-compression effect as employers freeze or reduce high-earners wages to offset low-earners new floors.
The findings hide internal heterogeneity, however:
For income the effect is largest for agricultural workers while for women the effect is significantly smaller than overall affected workers.
For hours worked there is a significant negative impact on women's hours worked, a fact which may point to a decreased intensive margin for female workers and thus also affect their lower income increases.
Limitations of the study include some sort-dependency in their panel data and only being able to account for effects during a period of economic growth.
Thus, while overall income inequality seems well targeted in the intervention, it may exacerbate the gender gap that already existed at the same time.
<!-- non-spatial policy but spatial effects -->
@Gilbert2001 undertake a study looking at the distributional effects of introducing a minimum wage in Britain, with a specific spatial component.
Overall it finds little effect on income inequality in the country.
It finds that the effects on rural areas differ depending on their proximity to urban areas.
While overall income inequality only decreases a small amount, the intervention results in effective targeting with remote rural households having around twice the reduction in inequality compared to others.
Rural areas that are accessible to urban markets are less affected, with insignificant impacts to overall income inequality reduction.
One limit of the study is that it has to assume no effects on employment after the enaction of the minimum wage for its results to hold.
In a study on the impacts of minimum wage and direct cash transfers in Brazil on the country's income inequality,
@SilveiraNeto2011 especially analyse the way the policies interact with spatial inequalities.
It finds that incomes between regions have converged during the time frame and overall the cash transfers under the 'Bolsa Familia' programme and minimum wage were accounting for 26.2% of the effect.
Minimum wage contributed 16.6% of the effect to overall Gini reduction between the regions while cash transfers accounted for 9.6% of the effect.
The authors argue that this highlights the way even non-spatial policies can have a positive (or, with worse targeting or selection, negative) influence on spatial inequalities,
as transfers occurring predominantly to poorer regions and minimum wages having larger impacts in those regions created quasi-regional effects without being explicitly addressed in the policies.
Some limitations include limited underlying data only making it possible to estimate the cash transfer impacts for the analysis end-line,
and minimum wage effects having to be constructed from the effects wages equal to minimum wage.
@Militaru2019 conduct an analysis of the effects of minimum wage increases on income inequality in Romania.
They find that, generally, minimum wage increases correlate with small wage inequality decreases, but carry a larger impact for women.
The channels for the policies effects are two-fold in that there is an inequality decrease as the number of wage earners in total number of employees increases,
as well as the concentration of workers at the minimum level mattering --- the probable channel for a larger impact on women since they make up larger parts of low-income and minimum wage households in Romania.
Limitations to the study are some remaining unobservables for the final inequality outcomes (such as other wages or incomes), the sample over-representing employees and not being able to account for any tax evasion or behavioural changes in the model.
@Sotomayor2021 conducts a study on the impact of subsequent minimum wage floor introductions on poverty and income inequality in Brazil.
He finds that in the short-term (3 months) wage floor increases reduced poverty by 2.8% and reduced income inequality by 2.4%.
Over the longer-term though these impacts decrease,
the minimum wage increases only show diminishing returns when the legal minimum is already high in relation to median earnings.
It suggests that additional unemployment costs, created through new job losses through the introduction, are offset by the increased benefits --- the higher wages for workers.
The authors also suggest an inelastic relationship between increases and poverty incidence.
One limitation of the study is the limit of tracking individuals in the underlying data which can not account for people moving household to new locations.
The data can only track individual dwellings --- instead of the households and inhabitants within --- and thus resembles repeated cross-sectional data more than actual panel data.
### Collective bargaining
@Alexiou2023 study the effects of both political orientation of governments' parties and a country's trade unionisation on its income inequality.
They find that, generally, strong unionisation is strongly related to decreasing income inequality, most likely through a redistribution of political power through collective mobilization in national contexts of stronger unions.
It also suggests that in contexts of weaker unionisation, post-redistribution income inequality is higher, thus also fostering unequal redistributive policies.
Lastly, it finds positive relations between right-wing orientation of a country's government and its income inequality, with more mixed results for centrist governments pointing to potential fragmentations in their redistributive policy approaches.
The study is mostly limited in not being able to account for individual drivers (or barriers) and can thus not disaggregate for the effects for example arbitration or collective bargaining.
@Dieckhoff2015 undertake a study on the effect of trade unionisation in European labour markets, with a specific emphasis on its effects on gender inequalities.
It finds, first of all, that increased unionisation is related to the probability of being employed on a standard employment contract for both men and women.
It also finds no evidence that men seem to carry increased benefits from increased unionisation alone,
although in combination with temporary contract and family policy re-regulations, men can experience greater benefits than women.
At the same time women's employment under standard contracts does not decrease, such that there is no absolute detrimental effect for either gender.
It does, however, leave open the question of the allocation of relative benefits between the genders through unionisation efforts.
The study is limited in that, by averaging outcomes across European nations, it can not account for nation-specific labour market contexts or gender disaggregations.
@Cardinaleschi2019 study the wage gap in the Italian labour market, looking especially at the effects of collective negotiation practices.
It finds that the Italian labour market's wage gap exists primarily due to occupational segregation between the genders, with women often working in more 'feminized' industries, and not due to educational lag by women in Italy.
It also finds that collective negotiation practices targeting especially managerial representation and wages do address the gender pay gap, but only marginally significantly.
The primary channel for only marginal significance stems from internal heterogeneity in that only the median part of wage distributions is significantly affected by the measures.
Instead, the authors recommend a stronger mix of policy approaches, also considering the human-capital aspects with for example active labour-market policies targeting it.
@Ferguson2015 conducts a study on the effects of a more unionised workforce in the United States, on the representation of women and minorities in the management of enterprises.
It finds that while stronger unionisation is associated both with more women and more minorities represented in the overall workforce and in management, this effect is only marginally significant.
Additionally, there are drivers which may be based on unobservables and not a direct effect ---
it may be a selection effect of more unionised enterprises.
It uses union elections as its base of analysis, and thus can not exclude self-selection effects of people joining more heavily unionised enterprises rather than unionisation increasing representation in its conclusions.
@Ahumada2023 on the other hand create a study on the effects of unequal distributions of political power on the extent and provision of collective labour rights.
It is a combination of quantitative global comparison with qualitative case studies for Argentina and Chile.
It finds that, for societies in which power is more unequally distributed, collective bargaining possibilities are more limited and weaker.
It suggests that, aside from a less entrenched trade unionisation in the country, the primary channel for its weakening are that existing collective labour rights are often either restricted or disregarded outright.
Employers were restricted in their ability to effectively conduct lobbying, and made more vulnerable to what the authors suggest are 'divide-and-conquer' strategies by government with a strongly entrenched trade unionisation, due to being more separate and uncoordinated.
A limit is the strong institutional context of the two countries which makes generalizable application of its underlying channels more difficult to the overarching quantitative analysis of inequality outcomes.
### Workfare programmes
@Whitworth2021 analyse the spatial consequences of a UK work programme on spatial factors of job deprivation or opportunity increases.
The programme follows a quasi-marketized approach of rewarding employment-favourable results of transitions into employment and further sustained months in employment.
The author argues, however, that the non-spatial implementation of the policy leads to spatial outcomes.
Founded on the approach of social 'creaming' and 'parking' and applied to the spatial dimension,
the study shows that already job-deprived areas indeed experience further deprivations under the programme,
while non-deprived areas are correlated with positive impacts, thereby further deteriorating spatial inequality outcomes.
This occurs because of providers in the programme de-prioritizing the already deprived areas ('parking') in favour prioritizing wealthier areas for improved within-programme results.
@Li2022 conduct a study on the effects of previous inequalities on the outcomes of a work programme in India intended to provide job opportunity equality for already disadvantages population.
It specifically looks at the NREGA programme in India, and takes the land-ownership inequality measured through the Gini coefficient as its preceding inequality.[^nrega]
The study finds that there is significantly negative relationship between the Gini coefficient and the provision of jobs through the work programme.
In other words, the workfare policy implemented at least in part to reduce a district's inequality seems to be less effective if there is a larger prior capital inequality.
The authors see the primary channel to be the landlords' opposition to broad workfare programme introduction since they are often followed by overall wage increases in the districts.
They suggest that in more inequally distributed channels the landlords can use a more unequal power structure to lobby and effect political power decreasing the effectiveness of the programmes,
in addition to often reduced collective bargaining power on the side of labour in these districts.
The results show the same trends for measurement of land inequality using the share of land owned by the top 10 per cent largest holdings instead.
[^nrega]: The National Rural Employment Guarantee Scheme (NREGA) is a workfare programme implemented in India, the largest of its kind, which seeks to provide 100 days of employment for each household per year. It was rolled out from 2005 over several phases until it reached all districts in India in 2008.
### Social protection
<!-- TODO Should we include Pi2016 on social security? -->
<!-- social assistance benefits and wages -->
@Wang2016 undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries.
It finds that real minimum income benefit levels generally increased in most countries from 1990 to 2009, with only a few countries, mostly in Eastern European welfare states, showing decreases during the time frame.
The majority of changes in real benefit levels are from deliberate policy changes and the study calculates them by a comparison of the changes in benefit levels to the changes in consumer prices.
Secondly, it finds that changes for income replacement rates are more mixed, with rates decreasing even in some countries which have increasing real benefits levels.
The study suggests this is because benefit levels are in most cases not linked to wages and policy changes also do not take changes in wages into account resulting in diverging benefit levels and wages, which may lead to exacerbating inequality gaps between income groups.
<!-- conditional cash transfer -->
@Debowicz2014 conduct a study looking at the impact of the cash transfer programme Oportunidades in Mexico, conditioned on a household's children school attendance, on income inequality among others.
It finds that a combination of effects raises the average income of the poorest households by 23 percent.
The authors argue in the short run this benefits households through the direct cash influx itself, as well as generating a positive wage effect benefitting those who keep their children at work.
For the estimation of income inequality it uses the Gini coefficient.
Additionally, over the long-term for the children in the model there is a direct benefit for those whose human capital is increased due to the programme, but also an indirect benefit for those who did not increase their human capital, because of the increased scarcity of unskilled labor as a secondary effect.
Due to the relatively low cost of the programme if correctly targeted, it seems to have a significantly positive effect on the Mexican economy and its income equality.
In a study on the labour force impacts for women @Hardoy2015 look at the effects of reducing overall child care costs in Norway through subsidies.
It finds that overall the reductions in child care cost increased the female labour supply in the country (by about 5 per cent),
while there were no significant impacts on mothers which already participated in the labour market.
It also finds some internal heterogeneity, with the impact being strongest for low-education mothers and low-income households,
a finding the authors expected due to day care expenditure representing a larger part of those households' budgets thus creating a larger impact.
Though it may alternatively also be generated by the lower average pre-intervention employment rate for those households.
Interestingly when disaggregating by native and immigrant mothers there is only a significant impact on native mothers,
though the authors do not form an inference on why this difference would be.
A limitation of the study is that there was a simultaneous child care capacity increase in the country,
which may bias the labour market results due to being affected by both the cost reduction and the capacity increase.
<!-- health care -->
@Carstens2018 conduct an analysis of the potential factors influencing mentally ill individuals in the United States to participate in the labour force, using correlation between different programmes of Medicaid and labour force status.
In trying to find labour force participation predictors it finds employment motivating factors in reduced depression and anxiety, increased responsibility and problem-solving and stress management being positive predictors.
In turn increased stress, discrimination based on their mental, loss of free time, loss of government benefits and tests for illegal drugs were listed as barriers negatively associated with labour force participation.
For the government benefits, it finds significant variations for the different varieties of Medicaid programmes, with the strongest negative labour force participation correlated to Medicaid ABD, a programme for which it has to be demonstrated that an individual cannot work due to their disability.
The authors suggest this shows the primary channel of the programme becoming a benefit trap, with disability being determined by not working and benefits disappearing when participants enter the labour force, creating dependency to the programme as a primary barrier.
Two limitations of the study are its small sample size due to a low response rate, and an over-representation of racial minorities, women and older persons in the sample mentioned as introducing possible downward bias for measured labour force participation rates.
<!-- UBI -->
<!-- TODO Potentially mention single sentence of Standing also looking into UBI -->
@Cieplinski2021 undertake a simulation study on the income inequality effects of both a policy targeting a reduction in working time and the introduction of a UBI in Italy.
It finds that while both decrease overall income inequality, measured through Gini coefficient, they do so through different channels.
While provision of a UBI sustains aggregate demand, thereby spreading income in a more equitable manner,
working time reductions significantly decrease aggregate demand through lower individual income but significantly increases labour force participation and thus employment.
It also finds that through these channels of changing aggregate demand, the environmental outcomes are oppositional, with work time reduction decreasing and UBI increasing the overall ecological footprint.
One limitation of the study is the modelling assumption that workers will have to accept both lower income and lower consumption levels under a policy of work time reduction through stable labour market entry for the results to hold.
## Structural
{{< portrait >}}
::: {#tbl-findings-structural}
```{python}
#| label: tbl-findings-structural
from src.model import validity
findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
Summary of main findings for structural policies
:::
{{< landscape >}}
### Fiscal growth and trade liberalisation
Complementing their research on institutional labour regulation,
@Adams2015 study the effects of business and credit regulations and FDI on long-term income inequality in developing countries.
While for them business regulations seemed to have mixed relationships with income inequality,
they find that, FDI is positively related with income inequality and the authors suggest it is unlikely to generate general welfare effects in developing countries.
This, they argue, is due to FDI often operating on the wrong targeting incentive structure and only able to generate more equity when correctly targeting the creation of connections from the local to surrounding economies.
While a long-term study, its scale is purely on the macro-level without delving deeper into individual-level changes per country.
@Xu2021 study the effects of trade liberalization and FDI on income inequality in 38 countries in the Sub-Saharan region.
It finds that increased FDI is negatively correlated with income inequality measured through the Gini coefficient, while trade liberalization is positively correlated with income inequality ---
as are corruption, political stability, rule of law and education, which contradicts some findings of the previous study.
The authors argue this may be due to the difference in sample and variables used, and the periods under study.
They suggest that FDI may primarily go to the agricultural sector which can employ low-skilled labour and thereby reduce inequalities,
while trade openness in fact creates jobs in other countries through higher import than export rates.
They do not clearly identify channels through which a higher overall education level positively correlates with inequality,
though some possibilities are an unequal access to education (through excluding factors such as those based on spatial, gender or financial inequalities),
as well as a differentiated quality of education.
Limitations of the study are the region-wide level of analysis which may obscure context-dependent mechanisms within the different institutional-structural contexts of the countries and potential hidden unobservables which may bias the results.
A simulation study on the effects of trade liberalization through free trade agreements (FTA) by @Khan2021 looks at income inequality in Pakistan between different households, measured through the Gini coefficient.
It finds that there is no clear general direction for changes through FTA visible, with its impact primarily depending on micro-economic factors.
Some large trade agreements are negatively correlated with the Gini while others are positively related, similar to regional and bilateral agreements.
Generally, this is due to increases in the income of poor rural agricultural farm households being dependent on grain (which is the largest export good often rising under FTA), while livestock predominantly owned by poor rural households decreases in returns under FTA.
The deciding channel can then be increases on the wages of farm workers (after among others grain export increases) increasing income equity, which, when they do not happen, can in turn lead to an overall decrease.
Lastly, there are wage compression effects between urban and rural households, with richer urban households often decreasing processed food or service production.
A greater mobility would dissipate all short-term gains and losses, as changes would get more evenly distributed across regions and households, while over the long term some positive aspects on income equality are visible if increased agricultural growth can be sustained.
The study may have some limits to its generalizability due to the production factor reallocations for agricultural households being specific to the rural poor context in Pakistan.
@Liyanaarachchi2016 run a simulation model on the effects of trade liberalization in Sri Lanka on income inequality and absolute poverty.
It finds that the complete elimination of tariffs results in an overall reduction in absolute poverty, while tariff elimination with resulting fiscal policy responses to balance the budget would result in more mixed results but still pointing to an absolute reduction in poverty.
On the other hand, income inequality is seen to increase for most sectors over the short term and for all sectors over the long term.
The primary channels for this change are increased wage differences --- especially the increased wages for managers, professionals and technicians, as well as increased differences between urban workers ---
and low-income households being more dependent on private or government transfers, which do not increase with trade liberalization.
@Rendall2013 undertake a cross-country analysis on the impacts of structural changes in Brazil, Mexico, Thailand and India from 1987 to 2008, and its effects on female labour market participation and the gender wage gap.
Basing its analysis on the theory of capital displacing brawn in production for transition economies, it finds that all countries had reduced brawn requirements over time, though with large heterogeneity: Thailand lead the change with 15 percentage points while India had the smallest change with 0.2 percentage points.
Following this, there was the largest steady labour market participation inequality in India, while there were mixed results for Mexico and Thailand, with Brazil having female employment shares changes similar to that of the United States.
The channels here are seen as a reduced requirement for physical labour replaced by for example more service-oriented economies ('brawn' to 'brain').
For female wage shares, in Brazil the wage gap closed most rapidly, though it began widening in 2005, while Thailand and India had converging but mixed changes.
In Mexico, while the gap widened during the 1990s, it began closing again afterwards.
The differences in wage gap effects compared to both other countries and the respective country's physical labour market requirements show that contextual structural changes played a large role in each case:
with erstwhile reduced returns on Brazilian returns for brain intensive occupations, the introduction of a female-lead manufacturing sector in Mexico in the 90s, and widely diverging basic labour market skill structures in Thailand and India necessitating subsistence-oriented participation; the results show impacts of structural changes, though limited through a variety of mediating factors influencing each case.
@Wang2020 conduct a simulation to examine the impact of terminating subsidies for the agricultural grain sectors in China, with a particular focus on analysing the effects on rural-urban income inequality.
The findings indicate that the removal of grain subsidies would lead to gradual improvements in the industrial economic structure.
However, in the short term, it is observed that rural-urban income inequality is exacerbated.
Over an extended period, the decrease in real wages for rural workers would alleviate, suggesting an increase in the rural income ratio, yet the gap remains incompletely closed.
The study attributes this outcome to the displacement of rural unskilled labour, resulting in an increased supply of unskilled labour that is challenging to absorb into the manufacturing or service sectors.
Additionally, the low income and price elasticity of agricultural products contribute to an overall decline in rural incomes.
Consequently, the authors identify a trade-off between long-term national economic output, adversely affected by the removal of subsidies, and the reduction in rural-urban income ratios facilitated by the subsidies, albeit with diminishing contributions over time.
Limitations of the study include the need to assume static national employment and, notably, limited generalizability due to the simulation of specific Chinese structural economic characteristics in the model.
@Go2010 model the effects of a targeted wage subsidy aimed at low- and medium-skilled workers and provided to their employers as an incentive for new job creations, looking at its effects on poverty and income inequality in South Africa.
The study finds that, using the Gini coefficient, the overall income inequality reduced by 0.5 percentage points, which provides an insignificant outcome.
This primarily occurs because of an overall income redistribution and especially an increase in formal employment for low- and medium-skill workers.
Using an absolute poverty headcount ratio, it finds that a significant 1.6 per cent of households move out of poverty, with similar changes observed across urban and rural spaces.
They attribute this primarily to income gains for poorer households and the targeting benefiting the poorest households most by providing them greater income gains.
Limitations of the study include the general equilibrium model approach being potentially restricted by its prior assumptions in validity and generalizability, as well as potentially not accounting for unobservables or exogenous shocks.
Due to the high number of studies on these policy areas being based on equilibrium modelling simulations,
there are some potentially exacerbated blind-spots:
they can possess a higher reliance on prior assumptions for their results to hold,
which includes the effort to subsume all potentially relevant channels and mediators into the equilibrium models.
They are generally more prone to disregarding exogenous factors which may provide shock effects into the system under analysis, and often can not cleanly account for longer-term dynamics.
Lastly, they can not address practical implementation challenges which may be faced by those implementing such policies, the institutional context and political ability to pursue the results modelled therein.
These limitations should be taken into consideration when evaluating their results.
### Automation and technological change
@Bailey2012 undertake a study on the effects of the introduction of legal access to contraceptive measures for women in the United States, measuring the impacts on closing the gender gap through the gendered hourly working wage distribution.
The study finds that of the closing gender pay gap from 1980 to 2000, legal access to 'the pill' as contraceptive from an early age contributed by nearly percent in the 1980s and over 30 percent in the 1990s.
Thus, overall the authors estimate that nearly one third of total female wage gains during this time were attributable to legal access to contraception.
The primary channels identified are greater educational attainment, occupational upgrading, and increased labour market experience made possible due to no early exit.
The authors also argue that the pill spurred individual agency to invest in personal human capital and career.
However, there are some limitations to the findings: The dataset cannot capture specific access to contraception beyond age 20, which makes the window of analysis more restricted and especially focused on the segment of women under 21.
Additionally, the study can not control for social multiplier effects such as employers reacting with changed hiring or promotion patterns or expectations about marriage and childbearing, as well as the overall coinciding paradigmatic change in norms and ideas about women's work and end of the national baby boom.
<!-- ### Informal Economy -->
### Infrastructure
@Kuriyama2021 look at the effects of Japan's move to decarbonise its energy sector on employment, especially rural employment.
It finds that, while employment in general is positively affected, especially rural sectors benefit from additional employment probability.
This is due to the renewable energy sector, while able to utilise urban areas for smaller scale power generation, being largely attached to rural areas for larger scale projects such as geothermal, wind power or large-scale solar generation.
The study also suggests some possible inequality being created in between the different regions of Japan due to the Hokkaido region having limited transmission line capacity and locational imbalance between demand and potential supplies.
Limitations include its design as a projection model with multiple having to make strong assumptions about initial employment numbers and their extrapolation into the future,
as well as having to assume the amount of generated power to increase as a stable square function.
In an observational study looking at the inclusive or exclusionary effects of infrastructure development, @Stock2021 analyses the 'gender inclusive' development of a solar park in India which specifically aims to work towards micro-scale equality through regional uplifting.
The project included a training and temporary employment to local unskilled/semi-skilled labour.
It finds that the development instead impacted equality negatively, creating socio-economic exclusion and disproportionately negatively affected women of lower castes.
While acquiring basic additional skills, none of the women participating in training remained connected to the operators of the solar park and none were hired.
An insignificant amount of women from local villages were working at the solar park, of which most belonged to the dominant caste, and the redistributive potential was stymied through capture by village female elites.
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.
<!-- explicitly spatial policies -->
@Blumenberg2014 look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households,
and comparing its impacts to the ownership of a car for the same sample.
It follows the 'Moving to Opportunity' programme which provided vouchers to randomized households for movement to a geographically unrestricted area or to specifically to a low-poverty area (treatment group),
some of which are in areas with well-connected public transport opportunities.
The sample for the study is made up predominantly of women (98%).
No relationship between programme participation and increased employment probability could be established.
However, a positive relationship exists between owning an auto-mobile and improved employment outcomes for low-income households,
as well as including those households that are located in 'transit-rich' areas.
Access to better transit itself is related to employment probability but not gains in employment -
the authors suggest this reflects individuals' strategic relocation to use public transit for their job.
However, moving to a better transit area itself does not increase employment probability,
perhaps pointing to a certain threshold required in transit extensiveness before it facilitates employment.
Ultimately, the findings suggest the need to further individual access to auto-mobiles in disadvantaged households or for extensive transit network upgrade which have to cross an efficiency threshold.
Some limitations of the study are its models low explanatory power for individual outcomes, more so among disadvantaged population groups,
as well as some remaining possibility of endogeneity bias through unobserved factors such as individual motivation or ability.
@Adam2018 model the effects of transport infrastructure investments in Tanzania on rural income inequalities and household welfare inequalities, modelled through consumption indicators.
Generally it finds that the results of public investment measures into transport infrastructure largely depend on the financing scheme used.
Comparing four financing schemes when looking at the effects on rural households, it finds that they are generally worse off when the development is deficit-financed or paid through tariff revenues.
On the other hand, rural households benefit through increased income from measures financed through consumption taxes, or by external aid.
The general finding is that there is no Pareto optimum for any of the investment measures for all locations,
and that much of the increases in welfare are based on movement of rural workers out of quasi-subsistence agriculture to other locations and other sectors.
The study creates causal inferences but is limited in its modelling approach representing a limited subset of empirical possibility spaces,
as well as having to make the assumption of no population growth for measures to hold.
### Education access
In addition to the institutional effects of regulation above, @Adams2015 analyse the effects of school enrolment and on income inequality in developing countries between 1970 and 2012.
Contrary to the regulatory policies, they find school enrolment and thus well-effected education-oriented policies to be positively related with an equitable income distribution.
They suggest additional enrolment increases the capacity of public administration practitioners and in turn lead to more adapted policies specific to developing countries' institutional contexts.
Due to the often limited contexts of institutional capabilities such policies thus have a two-fold function:
they increase human capital in the medium term, but may also function as capability-building measures long-term.
It is important to keep in mind that the recommendations of the study should be understood as made from a macro-perspective, detached from the more micro-oriented contexts of individual countries or regions.
@Mukhopadhaya2003 looks at the income inequality in Singapore and how national education policies impact this inequality, focusing especially on the 'Yearly Awards' scheme and the 'Edusave Entrance Scholarship for Independent Schools'.
It finds that, generally, income inequality for migrants in Singapore is relatively high, primarily due to generated between-occupational income inequalities and migration policies which further stimulate occupational segregation.
Then, for the higher-education interventions, it identifies issues which may exacerbate the existing inequalities along these lines:
Already-advantaged (high-income) households generally stem from non-migration households and are also reflected in higher representation of high-achievement education brackets.
The education policies thus may exacerbate income inequality through their bad targeting when considering inter-generational academic achievements with high-education households remaining the primary beneficiaries of the policies, a finding which is more significant for the 'Edusave Entrance Scholarship for Independent Schools' than the 'Yearly Awards' scheme which has fewer benefit accruals to wealthier households.
More generally, the study suggests that the system of financing for higher education in Singapore aiming for providing equal education opportunity for all, may in fact further disadvantage poorer, low-income households that have a low-education parental background.
<!-- education -> gender economic empowerment -->
Looking at the returns of the Tanzanian 'Universal Primary Education' programme on consumption and on rural labour market outcomes, @Delesalle2021, finds outcomes that additionally differ along spatial and gender lines.
The programme both attempted to increase access to schools but also changed curricula to contain more technical classes, judged relevant to increase equity in rural areas.
Even though the programme aims to increase universal equality of access to education, the study finds that gender, geographical and income inequalities persist throughout, with individuals that complete primary education more likely to be male urban wage workers.
The study measures returns purely on consumption of households to show the estimated effect on their productivity ---
here, it finds generally positive returns but greatest for non-agricultural work, self-employed or as wage work.
Importantly, the introduction of more technical classes also changes employment sector choices, with men working less in agricultural work and more in non-farm wage sectors and an increased probability for rural women to both work in agriculture and to work formally.
Limitations of the study include the inability to directly identify intervention compliers and having to construct returns for each household head only and a possibly unobserved 'villagization' effect by bringing people together in community villages for their education leading to other unobserved variable impacting the returns.
<!-- increased education access for migrants -> wage inequality -->
@Pi2016 conduct a study on the impacts of allowing increased access to social welfare provisions and education to urban migrants in China, looking at the effects on wage inequality between skilled and unskilled sectors and workers.
It uses skilled-unskilled inequality instead of rural-urban inequalities since the real wages of the rural sector are already much lower in China, making comparisons along the 90th to 10th decile ratios more difficult.
The study finds that reforms to increase access to social security and education for urban migrants decreases wage inequality between the sectors if the skilled sector is more capital intensive than the unskilled sector, though it makes no specific identification of individual channels.
There are several limitations to the study such as no disaggregation between the private and the (very important for the Chinese economy) public sector, job searching not being part of the model, and, most importantly, a severely restricted generalizability due to the reform characteristics being strongly bound to the institutional contexts of Chinese *hukou*[^hukou] systems.
[^hukou]: The hukou system generally denotes a permission towards either rural land-ownership and agricultural subsidies for the rural hukou or social welfare benefits and employment possibilities for the urban hukou, and children of migrants often have to go back to their place of registered residence for their college entrance examination. This study looks at reforms undoing some of the restrictions under the sytem.
@Suh2017 studies the effects of structural changes on married women's employment in South Korea, looking specifically at the impact of education and family structure.
The study finds that educational interventions significantly increase the employment probability of married women, and it finds overall female labour force participation showing a negative correlation with income inequality.
However, education alone is only a necessary not a sufficient condition for increased employment, with a married woman's family size and family structure having an impact as well.
Finally, education also has an intergenerational impact, with the female education also positively relating to daughters' education levels.
@Coutinho2006 study the impacts of special education between young men and women on their relative employment probabilities and incomes.
It finds that, overall, young women with disabilities were significantly less likely to be employed, earned less than males with disabilities, had lower likelihood of obtaining a high school diploma and were more likely to be a biological parent.
For the employment outcomes, the primary channels identified were men with disabilities being in employment both more months in the preceding period and more hours per week on average than women with disabilities.
Overall, more women were employed in clerical positions and substantially more men employed in technical or skilled positions for both special education and the control samples.
Similarly, for income there was a gender-based difference for the whole sample, though with substantial internal heterogeneity showing only marginal differences between men and women in the high-achieving subsample and the largest differences in the low-achieving and special needs subsample.
The suggestions include a strengthening of personal agency to remain in education longer and delay having children through self-advocacy and -determination transition services for young women to supplement structural education efforts.
Some limitations include initial subsample selection based on parent-reporting possibly introducing selection bias and the special education sample not including students with more severe impairments due to the requirement of self-reporting.
@Shepherd-Banigan2021 undertake a qualitative study on the significance of vocational and educational training provided for disabled veterans in the United States.
It finds that both the vocational and educational services help strengthen individual agency, autonomy and motivation but impacts can be dampened if the potential for disability payment loss due to the potential for job acquisition impedes skill development efforts.
The primary barriers of return to work efforts identified are an individual's health problems as well as various programmes not accommodating the needs of disabled veteran students,
while the primary Facilitators identified are financial assistance provided for education as well as strengthened individual agency through motivation.
Some limitations include a possible bias of accommodations required through the sample being restricted to veterans with a caregiver, which often signals more substantial impairments than for a larger training-participatory sample, as well as the data not being able to identify the impact of supported employment.
The studies thus not only reinforce recommendations for strength-based approaches, emphasising the benefits of work, but also highlight the targeting importance of subsidy programmes in general on the one hand,
in the worst case reducing equity through bad targeting mechanisms,
and their negative reinforcement effects widening existing inequalities of gender, age and racial discrimination through such targeting on the other.
With a similar focus on agency, @Gates2000 conducts a qualitative study on the mechanisms of workplace accommodation for people with mental health conditions to allow their successful return-to-work.
The intervention is based on an accommodation which disaggregates the effects of social and technical components of the process and included a disclosure and psycho-educational plan.
It finds that successful return-to-work through accommodation requires consideration of the social component ('who is involved'), with unsuccessful accommodation often only relying on the functional aspect ('what is involved').
The primary barrier identified for successful return-to-work are actually relationship issues not functional ones, with supervisors playing a key role for the success of the accommodation process.
Additionally, it highlighted the necessity of strengthening the individual agency of the returnee, accomplished in the intervention through a concrete training plan with the worker but also with other key workplace players such as the supervisors.
Additionally, providers must be willing to develop a disclosure plan with the employee and enter the workplace itself to adequately assist in the accommodation process.
Limitations to the study include the limited generalizability of its findings with a small non-randomized sample size and restriction to mental health disability.
A study looking at the effects of vocational rehabilitation on employment probabilities, @Poppen2017 look at the factors influencing successful employment for disabled people in the United States.
It finds that the primary factors negatively correlated with successful employment were for women in the sample, for having mental illness or traumatic brain injury as the primary disability, having multiple disabilities, an interpersonal or self-care impediment and receiving social security benefits.
On the other hand, having participated in a youth-transition training programme, as well as making use of more vocational rehabilitation services, are correlated with an increased employment probability.
It thereby highlights the gendered dimension of employment probabilities and points to a necessity to focus training and rehabilitation efforts along multiple dimensions.
Some limitations of the study include its limited generalizability, having a sample located in a single state, as well as a dataset intended for service provision not academic pursuits possibly introducing unreliability in its data and not measuring service quality.
@Thoresen2021 conduct a survey combined with qualitative interviews for the participants of a vocational training programme in Australia, looking at the effects on participants' hours worked and incomes.
It finds, foremost, that initially both the hours worked and the income of people with disabilities are lower on the Australian labour market in general and this reflects in the results for the disability group of participants, which have significantly lower weekly incomes and hours worked than the control group.
Over time, hours worked increase for the disability group to no longer be significantly different but still lower than for the control group (from 3.1 hours to 1 hour difference per week),
however there are large fluctuations in the control group.
Similarly, the wages of the disability group are initially substantially lower than of the control group,
which increases to be non-significant though still lower over time, more so for the earnings of female participants and participants which received a disability pension.
Relevant limitations of the study include the use of a non-representative sample for the national representativeness,
and the overall generalisability being low due to an increased labour force participation bias and attrition bias of the surveys,
as well as only having access to a small control sample size.
Thus, findings should be understood as guiding policy directions, while generalisations should be done with care as some of the larger changes may be due to those limitations,
such as the increased survey response of those with positive wage outcomes.
An experimental study on the impacts of benefits and vocational training counselling for disabled veterans in the United States by @Rosen2014 measures the effects on return to work through average hours worked.
It identifies time worked through a timeline follow-back calendar, measuring the change in days worked in the 28 days preceding the final study measurement.
Here, it finds the sessions having a significant increase on more waged days worked, with an additional three days for the 28 preceding days on average.
One limitation is the inability of the study to locate an active ingredient:
Though the intervention clearly aims at strengthening some aspect of individual agency, the exact mediators are not clear, with neither beliefs about work, beliefs about benefits, nor provided service use for mental health or substance abuse impacted significantly.
## Agency
{{< portrait >}}
::: {#tbl-findings-agency}
```{python}
#| label: tbl-findings-agency
from src.model import validity
findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
Summary of main findings for agency-based policies
:::
{{< landscape >}}
### Occupational segregation and social exclusion
@Emigh2018 study the effects of direct state transfers to people in poverty in the post-socialist market transition countries of Hungary, Romania and Bulgaria.
To do so, the study first looks at the correlations of socio-demographic characteristics with poverty to find that in each country there was an increased probability for poverty of low-education, larger and predominantly Roma households.
It also found that poverty itself was most feminized Hungary, the country with the most advanced market transition in the study period, and least feminized in Bulgaria, the country with the least advanced market transition, and suggests that poverty may have feminized as the market transitions progressed.
For the state transfers it found that while the level of payments may have been too small to eliminate longer-term adverse effects of the market transitions,
in each country's case the transfers to individuals reduced their poverty and were beneficial at least in the short term.
The authors thus suggest that their findings may be compatible both with an institutionalist perspective seeing poverty-eliminating benefits in the short term and with an underclass perspective which contends that nonetheless the transfers do not eliminate the deprivations members of disadvantaged groups face, while providing little evidence for generating welfare dependency proposed in a more neoclassical perspective.
However, due to no long-term panel data available to fully analyse the underclass and neoclassical arguments, these findings' generalizability should be understood as more restricted.
@Bartha2020 conduct an observational study on the effects of the policy trajectories of European countries concerning long-term care work, with a special focus on the impacts on gender equality.
The trajectories for the study are mostly described through measures of social protection and social security such as pensions or the provision of residential or at-home care facilities, regulation and fiscal policies.
Regarding the effects on the labour market it uses the full-time equivalent employment rate gap between men and women.
It finds that few countries in Europe fit one of the ideal-type household their ranking predicted, between male bread-winner, unsupported double-earner and supported double-earner households.
Only half of the countries clearly fall into one of the three ideal-types and no countries fall into the category of male bread-winner.
While supported double-earner type is mostly prevalent in Western Europe and the Scandinavian countries, Southern and Eastern Europe are predominantly shaped by the unsupported double-earner type.
Generally, more women will take on more unpaid care work in this model especially, though the prevalence exists in all models, which also explains the employment rate gap not decreasing significantly.
Where it decreases, the 'familialization' of care work is often undergoing a process of being taken on as cash-for-care work by migrants in a rising work sector in the former countries, which in turn may slightly increase the overall female labour force participation.
However, relying on this type of work may not be sustainable or provide decent work, as it often remains poorly regulated and low paid, and may in turn have negative consequences on gender inequality in migrant communities or home countries.
Some limitations of the study include its scarce underlying data for comparable care work and care migration data, as well as the weak categorization possibilities perhaps obscuring incongruent patterns of policy effects.
@Shin2006 look at the effects of providing relatively higher wages for teachers, as well as fertility differences, on labour market participation of young female teachers.
They find that providing relatively higher wages for teaching professions as compared to non-teaching professions significantly increases female labour force participation for teachers, though the strongest determinant for it is possessing a college major in education, with overall education level being another determinant.
The study also looks at the effects of the presence of a new-born baby and finds that it significantly decreases female labour force participation and is almost twice as large for women in the teaching profession as compared to non-teaching jobs, though it does not have an effect on the choice of job between teaching or non-teaching.
The authors suggest this relatively higher exit from the labour market for women with new-born babies in teaching professions may once again be due to low wages: teachers leaving the labour market experience relatively lower temporary wage losses than in other professions, decreasing the exit-cost.
A limitation of the study is its restricted focus on strictly female underlying panel data which does not allow for comparisons between genders within or across professions.
An experimental study of providing UBI for villages in India by @Standing2015 looks at the effects on absolute low-income household debts, utilizing a combination of qualitative and quantitative experimental research.
It finds that the provision of UBI significantly reduced household debts,
a finding generally agreeing with assumptions in the literature,
but goes beyond this by investigating the qualitative causes going beyond purely monetary value into what the authors call 'emancipatory value'.
They find UBI reduces dependency risk - primarily to lenders with high associated fees by allowing the repayment of existing debt, not having to work for the lender directly or by providing them parts of their wages, avoiding taking on new debts and, if new debts have to be taken on, allows choosing less exploitative forms of borrowing (such as from relatives or friends).
The last channel especially is a point of interest of the study: the intervention did not just reduce absolute debts through an individual possessing more money, it generally infused more money into the local contexts, reducing its scarcity and allowing others such as neighbours and friends to provide more collective risk spreading in the villages..
The intervention also significantly increased possibility of saving in treatment households, allowing for an increased economic security and empowerment, which was also influenced by household head education, landholding, the household's caste and size.
The main channel this is accomplished through is a shift to institutionalized saving, with provides increased resilience against shock events.
<!-- childcare subsidy -->
@Clark2019 undertake an experimental study on the impacts of providing vouchers for childcare to poor women in urban Kenya, estimating the impacts on their economic empowerment.
The empowerment is measured through disaggregated analyses of maternal income, employment probability and hours worked.
It finds that, for married mothers there was a significantly positive effect on employment probability and hours worked, suggesting their increased ability to work through lower childcare costs increasing personal agency.
For single mothers, it finds a negative effect on hours worked, though with a stable income.
The authors suggest this is due to single Kenyan mothers already working increased hours compared to married mothers, though the effect shows the ability of single mothers to shift to jobs with more regular hours, even if they are not compatible with childcare.
Minor limitations of the study are its restriction to effects within a period of 1 year, and a somewhat significant attrition rate to the endline survey.
@Hojman2019, in an experimental study looking at the effects of providing childcare vouchers for poor urban mothers in Nicaragua under the 'Programo Urbano', examine the effects on inequality for mothers and children.
It finds that providing free childcare for young children of poor mothers significantly increases the employment probability of the mothers (14ppts) independently of the childcare quality.
It also finds significantly positive impacts on the human capital of the children, though the results are also dependent on the quality of childcare facilities.
This suggests childcare costs being removed through a quasi-transfer reducing the required childcare time burden on mothers, increasing parental agency and employment choices.
Some limitations to the study include a relatively small overall sample size, as well as employment effects becoming insignificant when the effect is measured on randomization alone (without an additional instrumental variable).
### Unconscious bias and discriminatory norms
<!-- TODO should we include hiring / firing patterns of Mun2018? mat laws -->
@Al-Mamun2014 conduct a study on the impacts of an urban micro-finance programme in Malaysia on the economic empowerment of women.
The programme introduced the ability for low-income urban individuals to receive collateral-free credit.
The study finds that the programme, though not specifically aimed at women, indeed increased women's economic empowerment with an increase in household decision-making, as well as increased personal economic security.
Primarily this is due to the increased access to finance, though it also functions thorugh an increase of collective agency established for the women in organised meetings and trainings.
It also finds, however, that the empowerment outcomes are constrained by the inability for individuals to obtain loans, with the programme only disbursing group loans which are harder to achieve through obstacles to collective organisation by different racial and socio-demographic backgrounds in each dwelling.
The study is somewhat limited in its explanatory power since even through its random sampling design it can not establish control for all factors required in experimental design.
In turn, @Field2019 undertake an experimental study looking at the effects of granting women increased access to their own financial accounts and training,
on their employment and hours worked, as well as more long-term economic empowerment.
The background of the experiment was the rural Indian MGNREGS[^1] programme which, despite ostensibly mandated gender wage parity, runs the risk of discouraging female workers and restricting their agency by depositing earned wages into a single household account --- predominantly owned by the male head of household.
To grant increased financial access, the treatment changed the deposits into newly opened individual accounts for the women workers, as well as providing additional training to some women.
It found that, short-term, the deposits into women's individual accounts in combination with provided training increased their labour supply, while longer-term there was an increased acceptance of female work in affected households and a significant increase in women's hours worked.
The impacts on increased hours worked were concentrated on those households where previously women worked relatively lower amounts and there were stronger norms against female work while less constrained households' impacts dissipated over time.
The authors suggest the primary channel is the newly increased bargaining power through having a greater control of one's income, and that it in turn also reflects onto gender norms themselves.
[^1]: The Mahatma Gandhi National Rural Employment Guarantee Scheme, one of the largest redistribution programmes on the household level in the world, entitling each household to up to 100 days of work per year.
# Discussion and policy implications
## Robustness of evidence
```{python}
#| label: prep-inequalities-crosstabs
# 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)
)
def crosstab_inequality(df, inequality:str, **kwargs):
df_temp = df.loc[(df["inequality"] == inequality) | (df["inequality"] == "income")]
tab = pd.crosstab(df_temp["Intervention"], df_temp["inequality"], **kwargs)
return tab.drop(tab[tab[inequality] == 0].index)
```
### Regional spread
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 with the exception of the Middle East and North Africa (MENA) region,
in which fewer studies have been identified.
```{python}
#| label: fig-region-counts
#| fig-cap: Studies by regions analysed
by_region = (
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)
```
Most studies come from a context of East Asia and the Pacific, though with an almost equal amount analysing Europe and Central Asia.
With slightly fewer studies, the contexts of North America, Sub-Saharan Africa follow for amount of anlalyses,
and in turn Latin America and the Caribbean and South Asia with an equal amount of studies for each region.
The lower amount of studies stemming from a MENA context can point to a variety of underlying causes:
First, it is possible that there is simply not as much evidence-based analysis undertaken for countries in the region as for other national or subnational contexts,
with research either following a more theoretical trajectory, or missing the underlying data collection that is available for other regional contexts.
However, it cannot be ruled out that the search protocol itself did not capture the same depth of analytical material as for other contexts,
with each region often having both a specific focus in policy-orientations and academically,
and in some cases also differing underlying term bases.
Such a contextual term differences may then not be captured adequately by the existing query terms and would point to a necessity to re-align it to the required specifics.
One reason for such a differentiation could be a larger amount of grey literature captured compared to other regions,
which may be utilising less established terms than the majority of captured literature for policy implementations.
Another reason could be the actual implementation of different policy programmes which are then equally not captured by existing term clusters.
### Internal and external validity
Using the validity ranking separated into internal and external validity for each study,
it is possible to identify the general make-up of the overall sample,
the relationship between both dimensions and the distribution of studies within.
@fig-validity-relation shows the relation between each study's validity on the internal dimension and the external dimension,
with experimental studies additionally distinguished.
Generally, studies that have a lower internal validity, between 2.0 and 3.5, rank higher on their external validity,
while studies with higher internal validity in turn do not reach as high on the external validity ranking.
```{python}
#| label: fig-validity-relation
#| fig-cap: "Relation between internal and external validity"
from src.model import validity
validities = validity.calculate(by_intervention)
validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
#validities["external_validity"] = validities["external_validity"].astype('category')
validities["internal_validity"] = validities["internal_validity"].astype('category')
validities["External Validity"] = validities["external_validity"]
validities["Internal Validity"] = validities["internal_validity"]
plt.figure().set_figheight(5)
sns.violinplot(
data=validities,
x="Internal Validity", y="External Validity", hue="design",
cut=0, bw_method="scott",
orient="x"
)
sns.swarmplot(
data=validities,
x="Internal Validity", y="External Validity", legend=False,
color="darkmagenta",
s=4
)
```
Studies with an internal validity ranking of of 3.0 (primarily made up of difference-in-difference approaches) and an internal ranking of 5.0 (randomized control trials) have the same tight clustering around an external validity between 4.0 (national) and 5.0 (census-based), and 2.0 (local) and 3.0 (subnational), respectively.
This clearly shows the expected overall relationship of studies with high internal validity generally ranking lower on their external validity.
The situation is less clear-cut with the internal rankings of 2.0 (primarily ordinary least squares) and 4.0 (primarily instrumental variable),
which show a larger external validity spread.
For 2.0-ranked studies, there is an overall larger spread with most using nationally representative data,
while a significant amount makes use of census-based data and others in turn only being subnationally representative.
Studies ranked 4.0 internally have a higher heterogeneity with the significant outlier of @Thoresen2021,
which had the limitation of its underlying data being non-representative.
Looking at the overall density of studies along their external validity dimension,
@fig-validity-distribution reiterates this overall relationship with internal validity.
It additionally shows that studies with low internal validity make up the dominant number of nationally representative analyses and the slight majority of census-based analyses,
while locally or non-representative samples are almost solely made up of internally highly valid (ranking 4.0 or above) analyses,
again with the exception of @Thoresen2021 already mentioned.
```{python}
#| label: fig-validity-distribution
#| fig-cap: "Distribution of internal validities"
sns.displot(
data=validities,
x="External Validity", hue="Internal Validity",
kind="kde",
multiple="fill", clip=(0, None),
palette="ch:rot=-0.5,hue=1.5,light=0.9",
bw_adjust=.65, cut=0,
warn_singular = False
)
```
Looking at the data per region, census-based studies are primarily spread between Latin America and the Caribbean, as well as Europe and Central Asia.
Meanwhile, studies using nationally, subnationally or non-representative data then to have a larger focus on North America, as well as East Asia and the Pacific.
A slight trend towards studies focusing on evidence-based research in developing countries is visible,
though with an overall rising output as could be seen in @fig-publications-per-year, and the possibly a reliance on more recent datasets, this would be expected.
### Inequality types analysed
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.
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.
```{python}
#| label: inequality-targeting-implicit-explicit
targeting_majority = df["targeting"].value_counts().index.tolist()[0]
targeting_minority = df["targeting"].value_counts().index.tolist()[-1]
```
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.
The majority of policies under analysis had an `{python} targeting_majority` focus on all the inequalities analysed in the respective study,
with only a minority of studies looking at policies with an `{python} targeting_minority` targeting on the inequalities itself.
```{python}
#| label: fig-inequality-types
#| fig-cap: Types of inequality analysed
by_inequality = (
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, ethnicities, age, inequalities of migration, education and intergenerational issues.
The following sections will dive deeper into the identified predominant inequality areas,
discuss what the main interventions analysed in the literature are,
and where overlaps between theoretical approaches and qualitative considerations are,
as well as where gaps and limitations lie.
Only a small amount of studies carried 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.
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.[^pension-studies]
[^pension-studies]: For an overview of how retirement and pensions reflect on health aspects in ageing, see @VanDerHeide2013, for a review of pensions intersecting with other possible inequalities and also health outcomes, see @Zantinge2014.
Equally, for migration few studies strictly 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.[^education-studies]
Educational inequalities themselves were the outcome-focus of almost no studies,
often analysed as a different dimension from the world of work and more focused on educations systems for children and youth,
especially early childhood development.
Similarly, rarely do studies delineate generational outcomes from income, gender or education issues enough to mark their own category of analysis.
[^education-studies]: For gender inequalities within education paths themselves, see @Stepanenko2021. For possible ways to integrate gender-transformative interventions into professional education, see @Newman2016. For the effects of prior inequalities on taxation preferences, school enrolment and educational choices, see @Gutierrez2009 and @Zamfir2017. For interactions between policies for the knowledge translation of sexual education and their barriers, see @Curran2022.
<!-- frameworks/qualitative discussion -->
The effects of automation on income inequality are more clearly put into focus by @Eckardt2022 by studying income inequality and under the effects of various kinds of automation and a minimum wage within the economy.
He considers several types of automation, with automation on the extensive margin (automation of more tasks) leading to decreased wage inequality between low-skill and high-skill earners if it results in decreased overall outputs due to wage compression, and vice versa for increased total outputs.
Automation on the intensive margin (increased productivity of automating existing tasks) has ambiguous effects on the employment share of low-skill workers (who are possibly displaced) and a higher minimum wage here decreases the inequality between low-skill wages and higher-skill wages.
However, it may also result in a ripple effect which results in the overall share of income of low-skill workers not increasing, if more machines or high-skill workers displace them.
Then, while the wage differences may decrease, the low-skill workers share of national income is identified as non-increasing and the share of low-skill employment could decrease.
The effects on low-skill income share under a system of minimum wage are thus primarily dependent on the amount of low-skill job displacement, as well as the effects of the minimum wage on overall economic output in the first place.
Ultimately, the author also suggests the institution of low-skill worker training programmes either targeting enhanced productivity for their existing tasks ('deepening skills') or enabling their capability for undertaking tasks previously only assigned to high-skill workers ('expanding skills') which would respectively counteract the negative automation effects on both margins.
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, educational inequalities and inequalities of non-ethnic 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.
The following sections will instead discuss in more depth the implications for individual inequalities,
as well as providing a comparative view of the respective intersection with income inequality.
## Gender inequalities
<!--
findings - channels - policy recc
- persistent discrimination and cultural views (strenghtening female agency, vicious circle of low FLFP and education)
- supply-side effects, esp maternal (family planning; care work)
- organisational disadvantagement in new economy (networking needs, self-promotion, managerial discretions)
TODO perhaps add specific section for unionisation effects on gender?
-->
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.
As @fig-gender-regions shows there is a somewhat higher output of research into this inequality in the Europe and Central Asian region, ahead of East Asia and the Pacific and North America,
with the other regions trailing further behind in output.
```{python}
#| label: fig-gender-regions
#| fig-cap: Regional distribution of studies analysing gender inequalities
by_region_and_inequality = (
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()
```
Looking into the prevalence of individual interventions within the gender dimension,
@tbl-gender-crosstab shows that paid leave, subsidies, collective bargaining, and education received the most attention.
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,
with subsidies often seeking to nourish this approach, and training, and interventions towards financial agency being represented in the interventions.
```{python}
#| label: tbl-gender-crosstab
#| tbl-cap: Interventions targeting gender inequalities
crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
```
<!-- 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.
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.
As @Grotti2016 demonstrate, an increased gender equality does not engender an increase in overall economic inequality.
Using the Theil index, they decompose a method to account for the different mediating effects of employment similarity and earnings similarity between the genders and find that neither correlated with an increased income inequality.
In fact the opposite seems the case, at least in their analysis of developed nations, with increased female employment reducing the economic inequality,
which they see rather generated by a polarisation between high-income and low-income households.
<!-- 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.
A reoccurring question is that of the reasons for inequality in female leadership positions, between institutional discrimination, self selection and family life trajectories.
Like @Mun2018 identified for Japan, while a complex interplay of a variety of factors,
the primary channel seems to lie in a combination of the self-selection of women into different individual career plans,
and reproductions of the existing gender divisions when confronted with the household responsibility for care labour.
While focused more on the effects of education itself, @Suh2017 also agreed with this and sees family structure,
alongside education, having a direct impact on labour market participation [see also @Ochsenfeld2012].
These findings of supply-side channels does not imply non-applicability of policy interventions,
but points to a necessity to focus on supporting those causes directly,
through parental leave policies, childcare subsidies and strengthening their return to work effect.
Generally, a reduced cost of child care or expansion of the costs on both parents has been identified to increase mothers' potential to participate in the labour force and pursue further career choices.
On the other hand, currently the presence alone of a new-born child in a household has been identified to strongly negatively correlate with labour force participation,
which can simultaneously foreclose further career choices or advancements.
<!-- organisational structure -->
At the same time, within organisations in the new economy's logic of not being bound to a single employer,
different focal points gain importance: team structures, career maps and networking receive more emphasis,
and often reflect gendered organisational logics.
In a quantitative study, @Williams2012 identify the necessity of maintaining large networks, engage in self-promotion, and supervisory discretion as potentially prominent intra-organisational barriers to workplace gender equality,
suggesting suitable policy efforts to focus on an increased managerial accountability,
inclusive efforts regarding corporate-sponsored events as well as counter-acting more informally driven male-only events,
and the general publication of co-workers salaries and individualised career development plans.
Finally, it is important to reiterate the cross-dimensional nature of such inequalities.
While the changing face of the economy directly affects organisational processes and structural discrimination,
it also has an impact on the work-family relations and thus, ultimately,
the gender inequalities affected on the supply side [@Edgell2012].
These inequalities surface particularly across the intersection of structural disadvantages and should thus provide the foundation for a holistic picture on inequality instead of one closed off between structural economic concerns and family and maternal decision-making.
## 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.
In the European and North American context, the distribution of spatial inequality analyses is primarily conducted in the countries of the United States and the United Kingdom.
```{python}
#| label: fig-spatial-regions
#| fig-cap: Regional distribution of studies analysing spatial inequalities
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()
```
This spread may point to those 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 industrial and 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 improved [@Crouch2019].
Interventions affecting spatial inequalities are often also 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 schism between urban and rural regions.
```{python}
#| label: tbl-spatial-crosstab
#| tbl-cap: Interventions targeting spatial inequalities
crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False)
```
Additionally, education interventions target spatial inequalities, with the effects of minimum wage, work programmes, interventions strengthening financial agency, trade liberalization and training also playing a role.
Thus, structural interventions are the dominant approach to reducing spatial inequalities,
with institutional and agency-driven interventions often less specifically targeted.
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 correctly 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.
On the other hand, @Hunt2004 show that individual measures on their own such as commuting subsidies in this case, while having positive results,
may not provide significantly lasting impact over the long term and thus may need to be undertaken in a more holistic approach, combining multiple policy packages.
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
The dimension of disabilities in inequalities remains strongly focused on developed nations,
primarily 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}
#| 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()
```
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.
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.
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}
#| label: tbl-disability-crosstab
#| tbl-cap: Interventions targeting disability inequalities
crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False)
```
Studies into interventions within the dimension of disabilities are predominantly focused on agency-based perspectives, with counselling and training being the primary approaches.
Structurally approached interventions are also pursued, looking at the overall effects of education, or subsidies in health care, though even here,
the individual effects of activation play a role [@Carstens2018].
<!-- frameworks/qualitative discussion -->
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].
@Kim2020 find the environmental factors in workplaces can significantly affect the individual job retention wishes of disabled employees, through the provided disability facilities influencing both work satisfaction and perceived workplace safety.
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.
Similarly, @Lindsay2015 highlight a variety of barriers to activation such as limited network ties to working population, skills problems and lower levels of qualification for those receiving disability benefits,
though also emphasising environmental factors of workplaces not facilitating integration measures or issues of spatial exclusion from labour markets through being located in areas of large-scale industrial restructuring and low geographic mobility.
One framework which approaches the discussion from an almost entirely institutional-structural view is provided by the systems level theoretical grounding of @Gruber2014,
separating into the exclusionary effects of disability into institutional factors at the macro level, at the meso level and factors influencing the micro level,
and directly focusing on the separation or inclusion of education, recognition of eligibility for vocational rehabilitation and self-recognition as pre-condition for effective programme undertaking respectively.
These discussions reinforce the necessity of correct targeting, as @Poppen2017 and @Thoresen2021 highlighted in the fears of losing existing benefits, or negative relation between benefits and employment probabilities.
The case seems not one of benefits on their own diminishing the readiness for work activation, but the monetary assistance often being provided instead of effective methods of activation, environmental support and agency-driven motivating factors to their respective recipients.
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 simultaneously highlights gaps in research on the topic in other contexts and other regions.
A recurring focus in all these discussions is their insistence on the intersectional nature of the issue, with gender, ethnicity, location, type and level of disability among others often creating more adverse conditions for disabled individuals.
This constitutes a second gap which should provide stronger focus in empirical works, in attempts to disaggregate analyses beyond disability and control group to further understand factors of inequality at work.
## Migration and 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, 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 aspects of 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("")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.tight_layout()
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, fiscal policies, 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].
With a focus on remittances of temporary migratory work,
@Rosewarne2012 similarly argues for the necessity to allow for greater continuity of employment to counteract while cementing the workers' bounds to their respective home countries,
through circular labour migration being supported by formal embedding in employment contract through contract succession negotiations and shifting the focus to labour rights specifically for the temporary nature of such work.
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.[^structural-frameworks]
[^structural-frameworks]: See for structural necessities @Do2020 and @Goodburn2020. For institutional contexts see @Clibborn2022.
# Conclusion
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.
{{< pagebreak >}}
# References
::: {#refs}
:::
{{< pagebreak >}}
# Appendix {.appendix .unnumbered}
## Full search query {.unnumbered}
```{python}
#| label: full-search-query
#| echo: false
#| output: asis
with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
query = f.read()
t3 = "`" * 3
print(f"""
```sql
{query}
{t3}
""")
```
## Validity rankings {#sec-appendix-validity-rankings .unnumbered}
::: {#apptbl-validity-external}
| Representativeness | Ranking |
| --- | --- |
| non-representative survey/dataset | 2.0 |
| subnationally representative survey/dataset | 3.0 |
| nationally representative survey/dataset | 4.0 |
| census-based dataset | 5.0 |
External validity ranking. Adapted from @Maitrot2017.
:::
::: {#apptbl-validity-internal}
| Method | Ranking |
| --- | --- |
| ordinary least squares & fixed-effects | 2.0 |
| discontinuity matching | 3.0 |
| difference in difference (& triple difference) | 3.0 |
| propensity score matching | 3.5 |
| instrumental variable | 4.0 |
| general method of moments | 4.0 |
| regression discontinuity | 4.5 |
| randomised control trial | 5.0 |
Internal validity ranking. Adapted from @Maitrot2017.
:::
## Extraction matrix {.unnumbered}
```{python}
#| label: apptbl-extraction-matrix
#| tbl-cap: Extraction matrix
df
```
{{< pagebreak >}}
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