diff --git a/02-data/processed/extracted.csv b/02-data/processed/extracted.csv index d8b703d..3275ad5 100644 --- a/02-data/processed/extracted.csv +++ b/02-data/processed/extracted.csv @@ -7,9 +7,6 @@ Xu2021,"Xu, C., Han, M., Dossou, T. A. M., & Bekun, F. V.",2021,"Trade openness, Wong2019,"Wong, S. A.",2019,Minimum wage impacts on wages and hours worked of low-income workers in Ecuador,World Development,https://doi.org/10.1016/j.worlddev.2018.12.004,article,development,Ecuador,2011-2014,12.0,implicit,wage workers,national employment survey (ENEMDU),quasi-experimental,difference-in-difference approach,1624422.0,individual,"national, census",1.0,,some small sort-dependency in panel data; can only account for effects in period of economic growth,,minimum wage,1,1,0,income; gender,0.0,1.0,Gini coeff,"decreased income inequality through significant increase on income of low-wage earners; larger effect for agricultural workers, smaller for women; potentially negative impact on income of high-earners",income-compression effect,-1.0,2.0,5.0,3.0 Wong2019,"Wong, S. A.",2019,Minimum wage impacts on wages and hours worked of low-income workers in Ecuador,World Development,https://doi.org/10.1016/j.worlddev.2018.12.004,article,development,Ecuador,2011-2014,12.0,implicit,wage workers,national employment survey (ENEMDU),quasi-experimental,difference-in-difference approach,1624422.0,individual,"national, census",1.0,,some small sort-dependency in panel data; can only account for effects in period of economic growth,,minimum wage,1,1,0,income; gender,0.0,0.0,hours worked,significant effect on hours worked; no significant spillover effect on workers in control group; significant negative impact on female hours worked,possibly decreased intensive margin for female workers; affecting lower income increase of women,1.0,0.0,5.0,3.0 Whitworth2021,"Whitworth, A.",2021,Spatial creaming and parking?: The case of the UK work programme,Applied Spatial Analysis and Policy,https://doi.org/10.1007/s12061-020-09349-0,article,economics,United Kingdom,2011-2017,72.0,implicit,unemployed,Department for Work and Pensions Work Programme statistics,observational,three-stage linear model,1494.0,individual,national,0.0,social creaming & parking (used spatially),no causal inferrence attempted,,work programme,0,1,0,spatial,1.0,0.0,employment,already deprived areas experience further deprivation,providers de-prioritize job-weak areas (spatial parking),-1.0,2.0,4.0,0.0 -Wang2020,"Wang, C., Deng, M., & Deng, J.",2020,Factor reallocation and structural transformation implications of grain subsidies in China,Journal of Asian Economics,https://doi.org/10.1016/j.asieco.2020.101248,article,economics,China,2007-2016,108.0,implicit,rural workers,TERMCN-Land database; Chinese Input-Output Table 2007,simulation,historical and TERMCN-Land simulation model,,sector,,0.0,,aggregate national employment exogenous to model; strong correlation to Chinese economic characteristics makes generalisability difficult,,subsidy (firm-level),0,1,0,income; spatial,1.0,1.0,income ratio,the rural-urban income inequality is exacerbated if grain subsidies are removed; over the long term this increase attenuates but income ratio remains decreased for rural labour,"displacement of rural unskilled labour; unskilled labour supply increase, labour difficult to absorb into manufacturing/service sectors; low income/price elasticity for agr. products lower rural income",1.0,2.0,0.0,0.0 -Thoresen2021,"Thoresen, S. H., Cocks, E., & Parsons, R.",2021,Three year longitudinal study of graduate employment outcomes for Australian apprentices and trainees with and without disabilities,International journal of disability development and education,https://doi.org/10.1080/1034912X.2019.1699648,article,education,Australia,2011-204,36.0,explicit,disabled,experimental survey,quasi-experimental,"quantitative survey (n=489); qualitative semi-structured face-to-face interviews (n=30); annual postal survey, baseline and 2 follow-ups; generalised estimating equation GEE",489.0,individual,local,0.0,,"non-representative sample, over-representation of learning disability; limited generalisability through sample LFP bias and attrition bias; small control sample size",Disaggregated results for female participants overall more unequal,training,0,1,1,disability; income,1.0,0.0,hours worked,"slightly lower for disabled group initially, increase to no significant difference with non-disabled group at last survey",significant but small overall increase (3.1 hours to 1 hour difference); fluctuations for non-disability group,1.0,2.0,2.0,4.0 -Thoresen2021,"Thoresen, S. H., Cocks, E., & Parsons, R.",2021,Three year longitudinal study of graduate employment outcomes for Australian apprentices and trainees with and without disabilities,International journal of disability development and education,https://doi.org/10.1080/1034912X.2019.1699648,article,education,Australia,2011-204,36.0,explicit,disabled,experimental survey,quasi-experimental,"quantitative survey (n=489); qualitative semi-structured face-to-face interviews (n=30); annual postal survey, baseline and 2 follow-ups; generalised estimating equation GEE",489.0,individual,local,0.0,,"non-representative sample, over-representation of learning disability; limited generalisability through sample LFP bias and attrition bias; small control sample size",Disaggregated results for female participants overall more unequal,training,0,1,1,disability; income,1.0,0.0,hourly/weekly income,wages of disability group substantially lower than non-disability; increases to be non-significant over time; lower for female and disability-pension recipient groups,strong initial diff means disability group potentially more often initially employed at junior rates or skewed through attrition bias,1.0,2.0,2.0,4.0 Suh2017,"Suh, M.-G.",2017,Determinants of female labor force participation in south korea: Tracing out the U-shaped curve by economic growth,Social Indicators Research,https://doi.org/10.1007/s11205-016-1245-1,article,sociology,"Korea, Rep.",1980-2014,,implicit,married women,Statistical Database in Statistical Information Service Korea 2015,quasi-experimental,OLS regression; log-linear analysis; contingency analysis with cross-tab statistics; Gini coeff as income inequality indicator,35.0,case,"national, census",0.0,,,,education,0,1,0,income; generational; gender,1.0,1.0,employment,education significant increase in married women's employment; female labour force participation negative correlation with income inequality; female education also positively affects daughters' education level,"education being necessary not sufficient condition, also influenced by family size and structure",1.0,2.0,5.0,2.0 Stock2021,"Stock, R. (2021).",2021,Bright as night: Illuminating the antinomies of `gender positive’ solar development,World Development,https://doi.org/10.1016/j.worlddev.2020.105196,article,development,India,2018,1.0,implicit,women,"baseline survey, interviews",observational,quantitative survey and in-depth interviews; discourse analysis,200.0,household,"subnational, rural",0.0,authoritative knowledge power framework (Laclau&Mouffe),no causal research,,infrastructure,0,1,0,gender; income; spatial,1.0,0.0,employment,insignificant increased employment probability; advantaged women predominantly belong to dominant castes,project capture by village female elites; women of disadvantaged castes further excluded from training and work opportunities,1.0,0.0,3.0,0.0 Standing2015,"Standing, G.",2015,Why Basic Income’s Emancipatory Value Exceeds Its Monetary Value,Basic Income Studies,https://doi.org/10.1515/bis-2015-0021,article,economics,India,2010-2013,18.0,implicit,low-income households,baseline & 3 follow-up surveys and censuses; structured interviews,experimental,"rural RCT, randomization at village level; 18/12 months of ubi provision with follow up surveys and interviews",1665.0,household,"subnational, rural",1.0,"Lauderdale paradox (money, if scarce becomes even more valuable resource)",,"ubi paid in addition to any other state transfers; included in sample for effects on work choice (forced to work for debtors, free to pursue own-work)",ubi,1,0,1,income; ethnicity,0.0,0.0,debt,ubi significantly decreases debts; results go beyond direct monetary value; households did not have to work for lenders/to pay off debt,directly enables debt reduction; reduces debt-dependency risks; avoids taking on new debt; enables choosing less exploitative forms of borrowing,-1.0,2.0,3.0,5.0 @@ -55,7 +52,6 @@ Adams2015,"Adams, S., & Atsu, F.",2015,Assessing the distributional effects of r Adams2015,"Adams, S., & Atsu, F.",2015,Assessing the distributional effects of regulation in developing countries,Journal of Policy Modeling,https://doi.org/10.1016/j.jpolmod.2015.08.003,article,economics,global,1970-2012,,implicit,developing countries,panel data,quasi-experimental,"system general method of moments, fixed effects, OLS; using Gini coefficient",72.0,country,regional,0.0,,macro-level observations subsumed under region-level scale only,"LM regulations defined as hiring/firing, minimum wage, severance pay; business reg. bureaucracy costs, business starting costs, licensing and compliance costs; credit market oversight of banks, private sector credit, interest rate controls",regulation (labour),1,0,0,income,0.0,1.0,Gini coeff,labour regulations and business regulations negatively related to equitable income distribution while credit market regulation has no effect in income distribution; FDI unlikely to generate equity-oriented welfare effects; trade openness not significantly related,regulatory policies often lack institutional capability to optimize for benefits; policies require specific targeting of inequality reduction,1.0,2.0,4.0,4.0 Adams2015,"Adams, S., & Atsu, F.",2015,Assessing the distributional effects of regulation in developing countries,Journal of Policy Modeling,https://doi.org/10.1016/j.jpolmod.2015.08.003,article,economics,global,1970-2012,,implicit,developing countries,panel data,quasi-experimental,"system general method of moments, fixed effects, OLS; using Gini coefficient",72.0,country,regional,0.0,,macro-level observations subsumed under region-level scale only,"LM regulations defined as hiring/firing, minimum wage, severance pay; business reg. bureaucracy costs, business starting costs, licensing and compliance costs; credit market oversight of banks, private sector credit, interest rate controls",education (school enrolment),1,0,0,income,0.0,1.0,Gini coeff,school enrolment positively related to equitable income distribution,capacity-building for public administration practitioners; more context-adapted policies generated,-1.0,2.0,4.0,4.0 Alinaghi2020,"Alinaghi, N., Creedy, J., & Gemmell, N.",2020,The redistributive effects of a minimum wage increase in New Zealand: A microsimulation analysis,Australian Economic Review,https://doi.org/10.1111/1467-8462.12381,article,economics,New Zealand,2012-2013,,implicit,,New Zealand Household Economic Survey (HES),simulation,microsimulation model; uses Atkinson index,3500.0,individual,national,0.0,,"large sample weights may bias specific groups, e.g. sole parents",,minimum wage,1,1,0,income,0.0,1.0,Atkinson index,"small impact on inequality of income signals bad programme targeting; significant reduction in poverty measures for sole parents already in employment only, but insignificant for sole parents overall",many low-wage earners are secondary earners in higher income households; low-wage households often have no wage earners at all,-1.0,0.0,4.0,0.0 -Wang2016,"Wang, J., & Van Vliet, O.",2016,"Social Assistance and Minimum Income Benefits: Benefit Levels, Replacement Rates and Policies Across 26 Oecd Countries, 1990-2009",European Journal of Social Security,https://doi.org/10.1177/138826271601800401,article,economics,global,1990-2009,,implicit,low-income,"World Bank CPI indicators & Penn World Table; Social Assistance and Minimum Income Protection Dataset (Nelson, 2013)",observational,cross-country comparative analysis,26.0,country,regional,0.0,,some effects may stem from exchange rate/PPP changes instead,due to data availability indicator for real minimum benefits and replacement rates could be constructed for 26 OECD countries,direct transfers (social assistance),1,1,0,income,0.0,1.0,real wage; replacement rate,"real benefit levels increased in most countries, benefit levels increasing more than consumer prices; income replacement rates mixed outcomes with decreases in some countries where real benefit levels increased",bulk of increases comes from deliberate policy changes; but benefit levels not linked to wages and policy changes not taking into account changes in wages,1.0,,4.0,0.0 Sotomayor2021,"Sotomayor, Orlando J.",2021,Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil,World Development,https://doi.org/10.1016/j.worlddev.2020.105182,article,economics,Brazil,1995-2015,12.0,implicit,workers,national administrative surveys Monthly Employment survey (PME),quasi-experimental,difference-in-difference estimator,40000.0,household,"national, census",1.0,,"survey data limited to per dwelling, can not account for inhabitants moving",,minimum wage,1,0,0,income,0.0,0.0,poverty,within three months of minimum wage increases poverty declined by 2.8%,,-1.0,2.0,5.0,3.0 Sotomayor2021,"Sotomayor, Orlando J.",2021,Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil,World Development,https://doi.org/10.1016/j.worlddev.2020.105182,article,economics,Brazil,1995-2015,12.0,implicit,workers,national administrative surveys Monthly Employment survey (PME),quasi-experimental,difference-in-difference estimator,40000.0,household,"national, census",1.0,,"survey data limited to per dwelling, can not account for inhabitants moving",,minimum wage,1,0,0,income,0.0,1.0,Gini coeff,inequality declined by 2.4%; decreasing impact over time; diminishing returns when minimum is high relative to median earnings,unemployment costs (job losses) overwhelmed by benefits (higher wages); but inelastic relationship of increase and changes in poverty,-1.0,2.0,5.0,3.0 Al-Mamun2014,"Al-Mamun, A., Wahab, S. A., Mazumder, M. N. H., & Su, Z.",2014,Empirical Investigation on the Impact of Microcredit on Women Empowerment in Urban Peninsular Malaysia,Journal of Developing Areas,https://doi.org/10.1353/jda.2014.0030,article,development,Malaysia,2011,2.0,implicit,women,structured face-to-face interviews,quasi-experimental,"cross-sectional stratified random sampling; OLS, multiple regression analysis",242.0,individual,"subnational, urban",1.0,"household economic portfolio model (Chen & Dunn, 1996)",can not establish full experimental design,,microcredit; training,0,0,1,gender; income,1.0,0.0,empowerment index (personal savings; personal income; asset ownership),increase in household decision-making for women; increase in economic security for women; constrained by inability for individuals to obtain loans,individual access to finance; collective agency increase through meetings and training,1.0,2.0,3.0,2.0 @@ -69,4 +65,8 @@ Shin2006,"Shin, J., & Moon, S.",2006,"Fertility, relative wages, and labor marke Alexiou2023,"Alexiou, C., & Trachanas, E.",2023,The impact of trade unions and government party orientation on income inequality: Evidence from 17 OECD economies,Journal of Economic Studies,https://doi.org/10.1108/JES-12-2021-0612,article,economics,Australia; Austria; Belgium; Canada; Denmark; Finland; France; Germany; Italy; Japan; Netherlands; New Zealand; Norway; Spain; Sweden; United Kingdom; United States,2000-2016,,,,Standardized World Income Inequality Database (SWIID) OECD panel data,quasi-experimental,"panel fixed effects approach, Driscoll and Kraay non-parametric covariance matrix estimator",18.0,country,regional,1.0,power resources theory,"can not account for individual drivers such as collective bargaining, arbitration, etc",,collective action (trade unionization),1,1,0,income; gender,0.0,1.0,"Gini coeff (equivalized household disposable income, market income, manufacturing pay)",unionization strongly related with decreasing income inequality; right-wing institutional contexts related with increased income inequality,redistribution of political power under unions; weak unionization increases post-redistribution inequality,-1.0,2.0,4.0,2.0 Mun2018,"Mun, E., & Jung, J.",2018,"Policy generosity, employer heterogeneity, and women’s employment opportunities: The welfare state paradox reexamined",American Sociological Review,https://doi.org/10.1177/0003122418772857,article,sociology,Japan,1992-2009,84.0,explicit,working mothers,Japan Company Handbook for Job Searchers,quasi-experimental,potential outcomes framework; fixed-effects analysis,600.0,enterprise,national,0.0,welfare state paradox (over-representation of women in low-authority jobs in progressive welfare states),limited generalizability with unique Japanese LM institutional features; limited ability to explain voluntary effects as lasting or as symbolic compliance and impression management,,paid leave (childcare),1,0,0,gender,1.0,0.0,job quality,"no change for promotions for firms not previously providing leave, positive promotion impact for firms already providing leave; incentive-based policies may lead to larger effects",voluntary compliance to maintain positive reputations,1.0,1.0,4.0,2.0 Mun2018,"Mun, E., & Jung, J.",2018,"Policy generosity, employer heterogeneity, and women’s employment opportunities: The welfare state paradox reexamined",American Sociological Review,https://doi.org/10.1177/0003122418772857,article,sociology,Japan,1992-2009,84.0,explicit,working mothers,Japan Company Handbook for Job Searchers,quasi-experimental,potential outcomes framework; fixed-effects analysis,600.0,enterprise,national,0.0,welfare state paradox (over-representation of women in low-authority jobs in progressive welfare states),limited generalizability with unique Japanese LM institutional features; limited ability to explain voluntary effects as lasting or as symbolic compliance and impression management,,paid leave (childcare),1,0,0,gender,1.0,0.0,employment,no increase in hiring discrimination against women reflected as decreased employment probability,decreases may be due to supply-side mechanisms based on individual career planning and reinforced existing gender division of household labour,0.0,0.0,4.0,2.0 +Thoresen2021,"Thoresen, S. H., Cocks, E., & Parsons, R.",2021,Three year longitudinal study of graduate employment outcomes for Australian apprentices and trainees with and without disabilities,International journal of disability development and education,https://doi.org/10.1080/1034912X.2019.1699648,article,education,Australia,2011-204,36.0,explicit,disabled,experimental survey,quasi-experimental,"quantitative survey (n=489); qualitative semi-structured face-to-face interviews (n=30); annual postal survey, baseline and 2 follow-ups; generalised estimating equation GEE",489.0,individual,local,0.0,,"non-representative sample, over-representation of learning disability; limited generalisability through sample LFP bias and attrition bias; small control sample size",Disaggregated results for female participants overall more unequal,training,0,1,1,disability; income,1.0,0.0,hours worked,"slightly lower for disabled group initially, increase to no significant difference with non-disabled group at last survey",significant but small overall increase (3.1 hours to 1 hour difference); fluctuations for non-disability group,1.0,2.0,2.0,4.0 +Thoresen2021,"Thoresen, S. H., Cocks, E., & Parsons, R.",2021,Three year longitudinal study of graduate employment outcomes for Australian apprentices and trainees with and without disabilities,International journal of disability development and education,https://doi.org/10.1080/1034912X.2019.1699648,article,education,Australia,2011-204,36.0,explicit,disabled,experimental survey,quasi-experimental,"quantitative survey (n=489); qualitative semi-structured face-to-face interviews (n=30); annual postal survey, baseline and 2 follow-ups; generalised estimating equation GEE",489.0,individual,local,0.0,,"non-representative sample, over-representation of learning disability; limited generalisability through sample LFP bias and attrition bias; small control sample size",Disaggregated results for female participants overall more unequal,training,0,1,1,disability; income,1.0,0.0,hourly/weekly income,wages of disability group substantially lower than non-disability; increases to be non-significant over time; lower for female and disability-pension recipient groups,strong initial diff means disability group potentially more often initially employed at junior rates or skewed through attrition bias,1.0,2.0,2.0,4.0 +Wang2016,"Wang, J., & Van Vliet, O.",2016,"Social Assistance and Minimum Income Benefits: Benefit Levels, Replacement Rates and Policies Across 26 Oecd Countries, 1990-2009",European Journal of Social Security,https://doi.org/10.1177/138826271601800401,article,economics,global,1990-2009,,implicit,low-income,"World Bank CPI indicators & Penn World Table; Social Assistance and Minimum Income Protection Dataset (Nelson, 2013)",observational,cross-country comparative analysis,26.0,country,regional,0.0,,some effects may stem from exchange rate/PPP changes instead,due to data availability indicator for real minimum benefits and replacement rates could be constructed for 26 OECD countries,direct transfers (social assistance),1,1,0,income,0.0,1.0,real wage; replacement rate,"real benefit levels increased in most countries, benefit levels increasing more than consumer prices; income replacement rates mixed outcomes with decreases in some countries where real benefit levels increased",bulk of increases comes from deliberate policy changes; but benefit levels not linked to wages and policy changes not taking into account changes in wages,1.0,,4.0,0.0 +Wang2020,"Wang, C., Deng, M., & Deng, J.",2020,Factor reallocation and structural transformation implications of grain subsidies in China,Journal of Asian Economics,https://doi.org/10.1016/j.asieco.2020.101248,article,economics,China,2007-2016,108.0,implicit,rural workers,TERMCN-Land database; Chinese Input-Output Table 2007,simulation,historical and TERMCN-Land structural simulation model,,sector,,0.0,,aggregate national employment exogenous to model; strong correlation to Chinese economic characteristics makes generalisability difficult,,subsidy (firm-level),0,1,0,income; spatial,1.0,1.0,income ratio,the rural-urban income inequality is exacerbated if grain subsidies are removed; over the long term this increase attenuates but income ratio remains decreased for rural labour,"displacement of rural unskilled labour; unskilled labour supply increase, labour difficult to absorb into manufacturing/service sectors; low income/price elasticity for agr. products lower rural income",1.0,2.0,0.0,0.0 diff --git a/05-final_paper/notes.docx b/05-final_paper/notes.docx index 98f4d99..38de92d 100644 Binary files a/05-final_paper/notes.docx and b/05-final_paper/notes.docx differ diff --git a/05-final_paper/notes.html b/05-final_paper/notes.html index 2e23178..ae48bb7 100644 --- a/05-final_paper/notes.html +++ b/05-final_paper/notes.html @@ -2,7 +2,7 @@ - + @@ -21,7 +21,7 @@ margin: 0 0.8em 0.2em -1em; vertical-align: middle; } pre > code.sourceCode { white-space: pre; position: relative; } -pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } +pre > code.sourceCode > span { line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode > span { color: inherit; text-decoration: inherit; } @@ -57,6 +57,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin div.csl-bib-body { } div.csl-entry { clear: both; +margin-bottom: 0em; } .hanging-indent div.csl-entry { margin-left:2em; @@ -93,7 +94,7 @@ const layoutMarginEls = () => { // Find any conflicting margin elements and add margins to the // top to prevent overlap const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > * " + ".column-margin.column-container > *, .margin-caption, .aside" ); let lastBottom = 0; @@ -102,25 +103,14 @@ const layoutMarginEls = () => { // clear the top margin so we recompute it marginChild.style.marginTop = null; const top = marginChild.getBoundingClientRect().top + window.scrollY; - console.log({ - childtop: marginChild.getBoundingClientRect().top, - scroll: window.scrollY, - top, - lastBottom, - }); if (top < lastBottom) { - const margin = lastBottom - top; + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; marginChild.style.marginTop = `${margin}px`; } const styles = window.getComputedStyle(marginChild); const marginTop = parseFloat(styles["marginTop"]); - - console.log({ - top, - height: marginChild.getBoundingClientRect().height, - marginTop, - total: top + marginChild.getBoundingClientRect().height + marginTop, - }); lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; } } @@ -130,7 +120,15 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // Recompute the position of margin elements anytime the body size changes if (window.ResizeObserver) { const resizeObserver = new window.ResizeObserver( - throttle(layoutMarginEls, 50) + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) ); resizeObserver.observe(window.document.body); } @@ -986,10 +984,10 @@ function nexttick(func) { } - + - - + + + @@ -3142,8 +3202,10 @@ vertical-align: -.125em; + +

1 Definitions

@@ -3706,13 +3768,17 @@ generally, [from UN, 2023, A call to action to save SDG10, Policy Brief], separa

3.1 Inclusion criteria

-
-
- - +
+
+
+Table 1: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria} +
+
+
+
Table 1: Study inclusion and exclusion scoping criteria
-+ @@ -3739,21 +3805,26 @@ generally, [from UN, 2023, A call to action to save SDG10, Policy Brief], separa - - - + + + + + + + + - + - + @@ -3762,6 +3833,8 @@ generally, [from UN, 2023, A call to action to save SDG10, Policy Brief], separa
gray literature superseded by white literature publication
Study focusinequality or labour market outcomes as primary outcome (dependent variable)neither inequality nor labour market outcomes as dependent variableStudy dataevidence-based study or based on empirical approachno empirical approach or not clearly based on evidential data
Study focuseffects on inequality/equality as primary outcome (dependent variable)neither inequality nor equality outcomes as dependent variable
policy measure or strategy as primary intervention (independent variable) no policy measure/strategy as intervention or relationship unclear
specifically relates to some dimension of world of work exists outside world of work for both independent and dependent variables
focus on dimension of inequality in analysis no focus on mention of inequality in analysis
+ +

not currently used as criteria: - we are probably including qualitative studies (to be tagged) - perhaps studies <2000 (to be tagged) to count quantity?

@@ -4524,7 +4597,7 @@ generally, [from UN, 2023, A call to action to save SDG10, Policy Brief], separa

7 Relevant references

-
+
Chang, Y.-S., Harger, L., Beake, S., & Bick, D. (2021). Women’s and EmployersExperiences and Views of Combining Breastfeeding with a Return to Paid Employment: A Systematic Review of Qualitative Studies. Journal of Midwifery Womens Health, 66(5), 641–655. https://doi.org/10.1111/jmwh.13243
@@ -4728,10 +4801,24 @@ window.document.addEventListener("DOMContentLoaded", function (event) { if (showAllCode) { showAllCode.addEventListener("click", toggleCodeHandler(true)); } - function tippyHover(el, contentFn) { + var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//); + var mailtoRegex = new RegExp(/^mailto:/); + var filterRegex = new RegExp('/' + window.location.host + '/'); + var isInternal = (href) => { + return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href); + } + // Inspect non-navigation links and adorn them if external + var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)'); + for (var i=0; i { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -4798,6 +5013,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) { } div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -4823,6 +5039,32 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -4880,20 +5122,6 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }); } } - var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//); - var filterRegex = new RegExp('/' + window.location.host + '/'); - var isInternal = (href) => { - return filterRegex.test(href) || localhostRegex.test(href); - } - // Inspect non-navigation links and adorn them if external - var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item)'); - for (var i=0; i

Data

-
-
-

The query execution results in an initial sample of 1643 potential studies identified from the database search as well as 753 potential studies from other sources, leading to a total initial number of 2396. 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, 151 have been identified as potentially relevant studies for the purposes of this scoping review, from which 38 have been extracted.

-
-
+

The query execution results in an initial sample of 1749 potential studies identified from the database search as well as 2240 potential studies from other sources, leading to a total initial number of 3989. 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, 244 have been identified as potentially relevant studies for the purposes of this scoping review, from which 52 have been extracted.

-

The currently identified literature rises somewhat in volume over time, with first larger outputs identified from 2014, as can be seen in Figure 2.

+

The currently identified literature rises somewhat in volume over time, with first larger outputs identified from 2014, as can be seen in Figure 2.

-
-
+
+
Code -
df_study_years = (
-    bib_df.groupby(["author", "year", "title"])
-    .first()
-    .reset_index()
-    .drop_duplicates()
-)
-# plot by year TODO decide if we want to distinguish by literature type/region/etc as hue
-# FIXME should be timeseries plot so no years are missing
-ax = sns.countplot(df_study_years, x="year")
-ax.tick_params(axis='x', rotation=45)
-ax.set_xlabel("")
-plt.tight_layout()
-plt.show()
-df_study_years = None
+
df_study_years = (
+    bib_df.groupby(["author", "year", "title"])
+    .first()
+    .reset_index()
+    .drop_duplicates()
+)
+# plot by year TODO decide if we want to distinguish by literature type/region/etc as hue
+# FIXME should be timeseries plot so no years are missing
+ax = sns.countplot(df_study_years, x="year")
+ax.tick_params(axis='x', rotation=45)
+ax.set_xlabel("")
+plt.tight_layout()
+plt.show()
+df_study_years = None
-
-
-

-
Figure 2: Publications per year
+
+
+
+ + + + + + 2024-02-28T08:11:15.704667 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 2: Publications per year +

Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output. While this can mean a decreased interest or different focus points within academia during those time spans, it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions, 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 gray 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.

-

Figure 3 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 with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.

-
-
+

Figure 3 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 with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.

+
+
Code -
bib_df["zot_cited"] = bib_df["zot_cited"].dropna().astype("int")
-grpd = bib_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)
-ax.tick_params(axis='x', rotation=45)
-plt.tight_layout()
-plt.show()
+
bib_df["zot_cited"] = bib_df["zot_cited"].dropna().astype("int")
+grpd = bib_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)
+ax.tick_params(axis='x', rotation=45)
+plt.tight_layout()
+plt.show()
-
-
-

-
Figure 3: Average citations per year
+
+
+
+ + + + + + 2024-02-28T08:11:16.274778 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 3: Average citations per year +

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 approach from a single coherent perspective yet. It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.

-

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 Figure 2 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.

+

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 Figure 2 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.

-
-
-Code -
by_intervention = (
-    bib_df.groupby(["author", "year", "title"])
-    .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()
-by_intervention = None
-
-
-
-
-

-
Figure 4: Predominant type of intervention
-
-
-
-
-

Figure 4 shows the most often analysed interventions for the literature reviewed. Overall, there is a focus on measures of minimum wage and education interventions, as well as collective action, subsidies, trade liberalization changes and training. This points to a spread capturing both institutional, as well as structural and agency-driven programmes.

Synthesis: A multitude of lenses

-

This section will present a synthesis of evidence from the scoping review. The section will also present a discussion on the implications of the current evidence base for policy and underscore key knowledge gaps.

-

One of the primary lenses through which policy interventions to reduce inequalities in the world of work are viewed is that of income inequality, often measured for all people throughout a country or subsets thereof. At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities may lead to adverse targeting or intervention outcomes, as can be seen in the following studies on policies to increase overall income equality.

-

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 categorize between the main thematic area and its associated interventions, which are then distinguished between for their primary outcome inequalities.

+

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.

+
+
+Code +
by_intervention = (
+    bib_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()
+
+
+
+
+
+ + + + + + 2024-02-28T08:11:16.553579 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 4: Available studies by primary type of intervention +
+
+
+
+
+

Figure 4 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

- -

Whitworth (2021) 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.

- -

Carstens & Massatti (2018) 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 barriers of 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.

-
-

Minimum wage

+

+
+Code +
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"))
+
+
+
+
+Table 6: Summary of main findings for institutional policies +
+
+
+
+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
area of policyinternal strengthexternal strengthmain findingschannels
minimum wage+++mixed evidence for short-/medium-term income inequality impactscan lead to income compression at higher-earner ends
+++some evidence for long-term inequality decreasejob loss offsets through higher wages
some spatial transfer from urban manufacturing sectors to rural agricultural sectors
-++bad targeting can exacerbate existing inequalitiesnegative effect on women’s hours worked if strong household labour divisions
low-earners sometimes secondary high-income household earners while low-wage households have no earners at all
-++potential impact larger for single parents, rural/disadvantaged locationswomen more affected if they make up large share of low-wage earners
labour regulation++++mixed evidence for effects of labour regulation on income inequalitywith lacking institutional capabilities no effective targeting possible
paid leave+++evidence for significant increase in rtw after childbirthesp. disadvantaged women benefit due to no prior employer-funded leave
+++some evidence for positive rtw effects to occur with medium-/long-term time delayshort-term exit but no long-term increase to hiring pattern discrimination
can exacerbate existing household labour division
-+mixed evidence for fixed-/short-term contracts counter-acting effect on rtwfixed-term contracts often insufficiently covered by otherwise applicable labour regulation
collective bargaining-+evidence for decreased income inequality with strong unionisationstronger collective political power vector enables more equal redistributive policies
increased probability for employment on formal, standard employment contract
++marginal evidence for increased income/representation of women/minorities in workforce/managementinternal heterogeneity due to predominantly affecting median part of wage distribution
self-selection of people joining more unionised enterprises/organisations/sectors
depending on targeting of concurrent policies can bestow more benefits on men, increasing horizontal inequalities
workfare programmes-+evidence for decrease of vertical inequality
--evidence for possibility of increased spatial inequalitiesbad targeting increases deprivations for already job-deprived areas
-+evidence for effective outcomes dependent on on prior material equalitiesprior inequalities such as land ownership can lead to political capture and less effective policies
social protection++evidence for conditional cash transfers producing short- and long-term inequality reductionproduction of short-term cash influx
conditioning on school attendance can decrease educational inequalities over long-term
++++mixed evidence for childcare subsidies decreasing gender inequalitieslifting credit constraints greater effect on low-income households
--evidence for stagnating income replacement rates exacerbating existing vertical inequalitiesbenefit levels unlinked from wages can widen division between income groups
--healthcare subsidy impacts strongly dependent on correct targetingdependence on non-participation in labour market may generate benefit trap
+
+
+

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 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.

+
+
+
+

+
+

Labour laws and regulatory systems

+

Adams & Atsu (2015) 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.

+ +

Broadway et al. (2020) 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.

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(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.1 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.

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In a study on the effects of introductions of a variety of maternity leave laws in Japan, Mun & Jung (2018) 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.

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Davies et al. (2022) 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.

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Minimum wage laws

Chao et al. (2022), 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.

Alinaghi et al. (2020) 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 Wong (2019) 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.

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Gilbert et al. (2001) 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, Silveira Neto & Azzoni (2011) 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.

Militaru et al. (2019) 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.

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Sotomayor (2021) 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.

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Unionization & collective action

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Alexiou & Trachanas (2023) study on the effects of both political orientation of governments’ parties and a country’s trade unionization on its income inequality. It finds that, generally, strong unionization 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 unionization, 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.

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Ferguson (2015) conducts a study on the effects of a more unionized workforce in the United States, on the representation of women and minorities in the management of enterprises. It finds that while stronger unionization 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 unionized enterprises. It uses union elections as its base of analysis, and thus can not exclude self-selection effects of people joining more heavily unionized enterprises rather than unionization increasing representation in its conclusions.

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Collective bargaining

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Alexiou & Trachanas (2023) 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.

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Dieckhoff et al. (2015) 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.

Cardinaleschi et al. (2019) 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.

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Dieckhoff et al. (2015) undertake a study on the effect of trade unionization in European labour markets, with a specific emphasis on its effects on gender inequalities. It finds, first of all, that increased unionization 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 unionization, although in combination with temporary contract and family policy re-regulations, men do seem to 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, pose the question of the allocation of relative benefits between the genders through unionization 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.

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Ahumada (2023 MAR 26 2023) 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 unionization in the country, the primary channel for the 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 unionization, 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.

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Ferguson (2015) 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.

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Ahumada (2023) 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.

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Workfare programmes

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Whitworth (2021) 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.

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Li & Sunder (2022) 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.2 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.

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Social protection

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J. Wang & Van Vliet (2016) 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.

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Debowicz & Golan (2014) 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.

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In a study on the labour force impacts for women Hardoy & Schøne (2015) 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.

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Carstens & Massatti (2018) 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.

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Cieplinski et al. (2021) 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

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Shin & Moon (2006) 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.

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Trade liberalization

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Adams & Atsu (2015) study the effects of labour, business and credit regulations, FDI and school enrolment looks at their long-term correlations to income inequality in developing countries from 1970 to 2012. 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. Similarly, FDI is negatively related and the authors suggest it is unlikely to generate general welfare effects in developing countries as it often has the wrong targeting incentive structure and can only generate more equity when correctly targeting connections from the local to surrounding economies. 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. On the other, they find school enrolment and thus education-oriented policies to be positively related with an equitable income distribution, suggesting it increases the capacity of public administration practitioners and in turn lead to more adapted policies specific to developing countries’ institutional contexts. 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 makeup. The study is limited in its design focus that lying purely on the macro-level regional analyses and can thus, when finding correlations towards income inequality, also only identify far-reaching structural and institutional possible root causes. While the literature on policy efforts towards income redistribution is large, studies which focus on the direct effects of individual policy interventions on income inequality and its possible linkages with other inequalities tends to focus on policies such as minimum wage impositions, direct transfers from the state or subsidies for individual life aspects.

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Xu et al. (2021) 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 a variety of previous studies. The authors suggest 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 provide clear channels through which education 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.

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A simulation study on the effects of trade liberalization through FTA by Khan et al. (2021) 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.

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+Code +
from src.model import validity
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+findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
+fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
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+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"))
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+Table 7: Summary of main findings for structural policies +
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+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
area of policyinternal strengthexternal strengthmain findingschannels
trade liberalisation+++evidence for slightly negative effects on income equalityhighly dependent on targeting/micro-economic factors
increase in sectorial wage differences
growing income gap if transfers to low-income households do not rise with liberalisation
-+evidence for reduction of absolute poverty
++mixed evidence for effect of FDI on long-term income equalityrequires incentive structure to directly connect local business with outside economies
correctly targeted FDI can generate low-skill agricultural employment
fiscal policies-++evidence for wage/firm subsidies increasing income equalityeffective targeting crucial to reach disadvantaged sectors
wage subsidy increases formal employment but can lead to wage compression
--evidence for wage/firm subsidies to reduce absolute povertylifting of credit constraints through income gains
techn. change--evidence for legal contraceptive access increasing gender income equalityeducational attainment, occupational upgrading and later labour market exit
infrastructure--evidence for increase in spatial equalityincreased employment probability through large-scale rural energy projects
-+mixed evidence for increase of existing inequalitieselite policy capture can exacerbate existing social exclusion & disadvantages
++mixed evidence for transport infrastructure effects on income inequalitydeficit-/tariff-financing can exacerbate spatia inequality
transit-rich area creation alone not enough for employment gains
access to education++++evidence for increasing income equalityhuman capital building
occupational upgrading and increased probability for formal employment
+++evidence for increasing gender and spatial income equalitygendered occupational upgrading can decrease gender pay gap
education alone necessary but not sufficient condition for increased FLFP
higher overall access but more inequal access can generate new inequalities
++++evidence for increased employment equality for people with disabilitiesincreased employment probability and hours worked
strong remaining intersectional gender inequalities require effective targeting
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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 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.

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Fiscal growth and trade liberalisation

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Complementing their research on institutional labour regulation, Adams & Atsu (2015) 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.

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Xu et al. (2021) 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.

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A simulation study on the effects of trade liberalization through free trade agreements (FTA) by Khan et al. (2021) 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.

Liyanaarachchi et al. (2016) 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.

Rendall (2013) 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.

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C. Wang et al. (2020) 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.

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Go et al. (2010) 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.

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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.

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Education

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Looking at the returns of the Tanzanian ‘Universal Primary Education’ programme on consumption and on rural labour market outcomes, Delesalle (2021), 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, however, 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.

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Pi & Zhang (2016) 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 hukou1 systems.

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Suh (2017) studies the effects of structural changes on married women’s employment in South Korea, looking specifically at the impact of education and family structure. It 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.

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Coutinho et al. (2006) 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.

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Mukhopadhaya (2003) 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.

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Automation and technological change

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Bailey et al. (2012) 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.

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Infrastructural change

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Infrastructure

Kuriyama & Abe (2021) 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, Stock (2021) 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.

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Blumenberg & Pierce (2014) 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.

Adam et al. (2018) 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.

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Agency-oriented

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Training & accommodation

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Similarly, Shepherd-Banigan et al. (2021) 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.

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An experimental study on the impacts of benefits and vocational training counselling for disabled veterans in the United States by Rosen et al. (2014) 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.

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The studies thus not only reinforce recommendations for strength-based approaches, emphasizing 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.

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Education access

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In addition to the institutional effects of regulation above, Adams & Atsu (2015) 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.

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Mukhopadhaya (2003) 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.

+ +

Looking at the returns of the Tanzanian ‘Universal Primary Education’ programme on consumption and on rural labour market outcomes, Delesalle (2021), 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.

+ +

Pi & Zhang (2016) 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 hukou3 systems.

+

Suh (2017) 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.

+

Coutinho et al. (2006) 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-Banigan et al. (2021) 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, Gates (2000) 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, Poppen et al. (2017) 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.

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Thoresen et al. (2021) 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.

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An experimental study on the impacts of benefits and vocational training counselling for disabled veterans in the United States by Rosen et al. (2014) 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.

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Direct transfers

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Emigh et al. (2018) study the effects of direct state transfers to people in poverty in the post-socialist market transition countries of Hungary, Romania and Bulgaria. It 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 should not be understood too generalizable.

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Wang & Van Vliet (2016) undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 developed 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.

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An experimental study of providing UBI for villages in India by Standing (2015) 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, 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 neighbors 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 houshold 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.

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Cieplinski et al. (2021) 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 modeling 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.

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Microfinance

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Agency

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+Code +
from src.model import validity
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+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"))
+
+
+
+
+Table 8: Summary of main findings for agency-based policies +
+
+
+
+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
area of policyinternal strengthexternal strengthmain findingschannels
direct transfers++++evidence for increasing gender equalitylifted credit constraints and debt dependency increases employment probability
requires effective targeting to disadvantaged women
can counter negative rtw effects of childbirth
-+evidence for reduction of absolute povertypositive short-term effects but mixed evidence long-term
individual microfinance++evidence for increased gender equalityincreased personal economic security and household decision-making long-term
can decrease local discriminatory gender norms
constrained by loan obtainment abilities through individual focus
+
+
+

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 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.

+
+
+
+

+
+

Occupational segregation and social exclusion

+

Emigh et al. (2018) 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.

+

Bartha & Zentai (2020) 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 Standing (2015) 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.

+ +

Clark et al. (2019) 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.

+

Hojman & López Bóo (2019), 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

+ +

Al-Mamun et al. (2014) 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, Field et al. (2019) 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 MGNREGS2 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.

+

In turn, Field et al. (2019) 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 MGNREGS4 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.

-
-

Discussion & policy implications

-
-
+
+

Discussion and policy implications

+
+

Robustness of evidence

+
+
Code -
# dataframe containing each intervention inequality pair
-df_inequality = (
-    bib_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)
+
# dataframe containing each intervention inequality pair
+df_inequality = (
+    bib_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)
-

As can be seen in Figure 5, taken by region for the overall study sample, the evidence base receives a relatively even split between the World Bank regional country groupings. Studies tend to base their analyses more in national comparative studies for the North American and Europe and Central Asian regions, while relying more on case studies restricted to a single country context for developing countries in other regions, though this trend does not hold strongly everywhere or over time. A slight trend towards studies focusing on evidence-based research in developing countries is visible, though with an overall rising output, as seen in Figure 2, and the ability for reliance on more recent datasets, this is to be expected.

-
-
+
+

Regional spread

+

As can be seen in Figure 5, 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.

+
+
Code -
by_region = (
-    bib_df[["region"]]
-    .assign(
-        region = lambda _df: (_df["region"]
-            .str.replace(r" ?; ?", ";", regex=True)
-            .str.strip()
-            .str.split(";")
-        )
-    )
-    .explode("region")
-    .reset_index(drop=True)
-)
-ax = sns.countplot(by_region, x="region", order=by_region["region"].value_counts().index)
-plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
-         rotation_mode="anchor")
-plt.show()
-
-def regions_for_inequality(df, inequality:str):
-    df_temp = df.loc[(df["inequality"] == inequality)]
-    return sns.countplot(df_temp, x="region", order=df_temp["region"].value_counts().index)
-
-
-
-
-

-
Figure 5: Studies by regions 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 Figure 6 which breaks down available studies by targeted inequalities, income inequality is the type of inequality traced in most of the relevant studies. This follows the identified multi-purpose lens income inequality can provide, through which to understand other inequalities — many studies use income measurements and changes in income or income inequality over time as indicators to understand a variety of other inequalities’ linkages through. Often, however, income inequality is not the primary inequality being targeted, but used to measure the effects on other inequalities by seeing how the effects of respective inequality and income intersect, as will be discussed in the following section.

-
-
-Code -
by_inequality = (
-    bib_df[["inequality"]]
-    .assign(
-        inequality = lambda _df: (_df["inequality"]
-            .str.replace(r"\(.+\)", "", regex=True)
-            .str.replace(r" ?; ?", ";", regex=True)
-            .str.strip()
-            .str.split(";")
-        )
-    )
-    .explode("inequality")
-    .reset_index(drop=True)
-)
-
-fig = plt.figure()
-fig.set_size_inches(6, 3)
-ax = sns.countplot(by_inequality, x="inequality", order=by_inequality["inequality"].value_counts().index)
-plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
-         rotation_mode="anchor")
-plt.show()
-by_inequality = None
-
-
-
-
-

-
Figure 6: Types of inequality analysed
-
-
-
-
-

With income inequality on its own often describing vertical inequality within a national context, the remaining inequalities gathered from the data rather form horizontal lenses to view their contexts through. The second most analysed inequality is that of gender, followed by spatial inequalities, disabilities, generational inequalities, inequalities of migration, education and age. The following sections will dive deeper into each predominant identified inequality, discuss what the main interventions analysed in the literature are and where gaps and limitations lie.

-

Only a small amount of studies carried analysis of inequalities surrounding migration, generational connections, education and age into the world of work, being the focal point of almost no studies at all. Age-related inequalities predominantly factored into studies as an intersection with disability, in focusing on the effects of older people with disabilities on the labour market (Kirsh, 2016). 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 (see Van Der Heide et al., 2013; Zantinge et al., 2014). Equally, for migration few studies strictly can delineate it from racial inequalities or considerations of ethnicity. For the purposes of discussion, studies analysing both inequalities concerning ethnicity and migration will be discussed as part of one socio-demographic point of view, though results that do only speak to migration will be highlighted accordingly.

-

Surprisingly few studies focus on the eventual outcomes in the world of work of earlier education inequalities. The majority of studies analysing education-oriented policies focus on direct outcomes of child health and development, education accessibility itself or social outcomes (see Curran et al., 2022; Gutierrez & Tanaka, 2009; Newman et al., 2016; Stepanenko et al., 2021; Zamfir, 2017). Similarly, rarely do studies delineate generational outcomes from income, gender or education issues enough to mark their own category of analysis within.

-

Thus, for the current state of the literature on analyses of policy interventions through the lens of inequality reduction within the world of work, there are strong gaps of academic lenses for generational inequalities, age inequalities, education inequalities and inequalities of non-ethnic migration processes going purely by quantity of output. 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

-

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. Figure 7 shows that there is a somewhat higher output of research into this inequality in both East Asia & the Pacific and Europe & Central Asian regions just ahead of North America, though the overall sample is relatively balanced between regions.

-
-
-Code -
by_region_and_inequality = (
-    bib_df[["inequality", "region"]]
+
by_region = (
+    bib_df[["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()
+ ) + ) + .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)
-
-
-

-
Figure 7: Regional distribution of studies analysing gender inequalities
+
+
+
+ + + + + + 2024-02-28T08:11:16.940160 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 5: Studies by regions analysed +
-

Looking into the prevalence of individual interventions within the gender dimension, Table 6 shows that subsidies, notions of unionisation and collective action, education and paid leave 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.

- -

Approaches of paid leave, child care and education agree with the findings of Zeinali et al. (2021) 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.

-
-
+

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 gray 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.

+

As can be seen in Figure 6, the relationship between the internal dimension and the external dimension of validity for the study pool follows a normal distribution. 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.

+
Code -
crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
+
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')
+
+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
+)
+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
+)
-
-
- +
+
+
+
+
+
+
+ + + + + + 2024-02-28T08:11:17.600329 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 6: Relation between internal and external validity +
+
+
+
+
+
+
+
+
+
+ + + + + + 2024-02-28T08:11:18.346691 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 7: Distribution of internal validities +
+
+
+
+
+
+
+

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 Thoresen et al. (2021), which had the limitation of its underlying data being non-representative.

+

Looking at the overall density of studies along their external validity dimension, Figure 7 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 Thoresen et al. (2021) already mentioned.

+

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 seen in Figure 2, 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 Figure 8 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.

+
+
+Code +
targeting_majority = bib_df["targeting"].value_counts().index.tolist()[0]
+targeting_minority = bib_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 implicit focus on all the inequalities analysed in the respective study, with only a minority of studies looking at policies with an explicit targeting on the inequalities itself.

+
+
+Code +
by_inequality = (
+    bib_df[["inequality"]]
+    .assign(
+        inequality = lambda _df: (_df["inequality"]
+            .str.replace(r"\(.+\)", "", regex=True)
+            .str.replace(r" ?; ?", ";", regex=True)
+            .str.strip()
+            .str.split(";")
+        )
+    )
+    .explode("inequality")
+    .reset_index(drop=True)
+)
+
+fig = plt.figure()
+fig.set_size_inches(6, 3)
+ax = sns.countplot(by_inequality, x="inequality", order=by_inequality["inequality"].value_counts().index)
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+         rotation_mode="anchor")
+plt.show()
+by_inequality = None
+
+
+
+
+
+ + + + + + 2024-02-28T08:11:18.692397 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 8: Types of inequality analysed +
+
+
+
+
+

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 (Kirsh, 2016). 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.5

+

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.6 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.

+ + +

The effects of automation on income inequality are more clearly put into focus by Eckardt (2022) 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

+ +

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 Figure 9 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.

+
+
+Code +
by_region_and_inequality = (
+    bib_df[["inequality", "region"]]
+    .assign(
+        region = lambda _df: (_df["region"]
+            .str.replace(r" ?; ?", ";", regex=True)
+            .str.strip()
+            .str.split(";")
+        ),
+        inequality = lambda _df: (_df["inequality"]
+            .str.replace(r"\(.+\)", "", regex=True)
+            .str.replace(r" ?; ?", ";", regex=True)
+            .str.strip()
+            .str.split(";")
+        )
+    )
+    .explode("inequality")
+    .explode("region")
+    .reset_index(drop=True)
+)
+
+ax = regions_for_inequality(by_region_and_inequality, "gender")
+ax.set_xlabel("")
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+         rotation_mode="anchor")
+plt.tight_layout()
+plt.show()
+
+
+
+
+
+ + + + + + 2024-02-28T08:11:19.035158 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 9: Regional distribution of studies analysing gender inequalities +
+
+
+
+
+

Looking into the prevalence of individual interventions within the gender dimension, Table 9 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.

+
+
+Code +
crosstab_inequality(df_inequality, "gender").sort_values("gender", ascending=False)
+
+
+
+
+Table 9: Interventions targeting gender inequalities +
+
+
+
+
+ + + @@ -4567,25 +17819,25 @@ text-align: right; - - + + + + + + + - + - - - - - @@ -4594,7 +17846,7 @@ text-align: right; - + @@ -4609,7 +17861,7 @@ text-align: right; - + @@ -4621,63 +17873,1182 @@ text-align: right; + + + + + + + + + +
inequality
subsidy5paid leave7 1
subsidy54
collective action 4 3
education 4 6
paid leave41
minimum wage 3
training 313
infrastructure
direct transfers 134
microcredit1 2
social security11
technological change11
-
+
+ +
+
+ +

Approaches of paid leave, child care and education agree with the findings of Zeinali et al. (2021) 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 Grotti & Scherer (2016) 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.

-

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 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 Mun & Jung (2018) 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, Suh (2017) also agreed with this and sees family structure, alongside education, having a direct impact on labour market participation (see also Ochsenfeld, 2012).

+

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.

+ +

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, Williams et al. (2012) 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 (Edgell et al., 2012). 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 Figure 8 shows. Instead, both Southern Asia and Sub-Saharan Africa are the primary areas of interest, with studies especially into Tanzania, India and Pakistan. The distribution of spatial inequality analyses otherwise is primarily conducted in the contexts of the United States and the United Kingdom.

-

This may point to the countries’ large rural populations or wider inequality gaps between rural and urban populations. While large rural populations are a sign of a predominantly agrarian economy, widening gaps are argued to be specifically appearing between rural and urban locations in post-industrial societies: Under modes of financialization, a spatial redistribution of high- and low-income sectors and increasing occupational segregation, rural locations are often left behind economically and require structural-institutional interventions to be rectified (Crouch, 2019).

-
-
+

Spatial inequalities are less focused within European, Central Asian and North American regions, as Figure 10 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.

+
+
Code -
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()
+
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()
-
-
-

-
Figure 8: Regional distribution of studies analysing spatial inequalities
+
+
+
+ + + + + + 2024-02-28T08:11:19.411856 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 10: Regional distribution of studies analysing spatial inequalities +
-

Interventions affecting spatial inequalities are often viewed through indicators of income, as can be seen in Table 7. The primary intervention aiming at reduction of spatial inequalities is based on infrastructural changes, which aligns with expectations of the infrastructural rift between urban and rural regions.

-
-
+

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 (Crouch, 2019).

+

Interventions affecting spatial inequalities are often also viewed through indicators of income, as can be seen in Table 10. 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.

+
+
Code -
crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False)
+
crosstab_inequality(df_inequality, "spatial").sort_values("spatial", ascending=False)
-
+
+
+
+Table 10: Interventions targeting spatial inequalities +
+
+
+
- -
- - + +
Table 7: Interventions targeting spatial inequalities
@@ -4707,90 +19078,906 @@ text-align: right; + + + + + + + + + + - + - - - - - - - - - - - - + +
inequality2
subsidy42
work programme12
direct transfers34 1
subsidy11
trade liberalization 7 1
training11
work programme0training3 1
-
-

Additionally, education interventions target spatial inequalities, with the effects of minimum wage, interventions strengthening financial agency, trade liberalization and training all playing a more marginal role. Thus, structural interventions are the dominant approach to reducing spatial inequalities, with institutional and agency-driven interventions often not targeting them specifically.

-

This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects, be they positive: as is the case for higher positive income effects on rural households due to unintentional good targeting of minimum wage to lower-income households (Gilbert et al., 2001); 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 (Stock, 2021).

-

Policies, even those of an ostensibly non-spatial nature, must thus keep in mind possibly adverse targeting effects if not specifically adjusting for potential impacts on spatial inequalities. Rural communities relying on agricultural economies in particular may be vulnerable to exogenous structural shock events such as climate change, which may thus need to be a focal point for future structural interventions (Salvati, 2014).

+
+
+
+
+

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 (Gilbert et al., 2001) — 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 (Stock, 2021).

+

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 (Salvati, 2014).

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.

-

Spatial inequalities move both ways, however, as also shown by Perez et al. (2022) 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.

+

Spatial inequalities move both ways, however, as also shown by Perez et al. (2022) 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, Hunt & Czerwinski (2004) 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 strictly focused on developed nations, through analysis of effects on inequality in the world of work in a context of the United States labour market, as can be seen in Figure 9.

-
-
+

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 Figure 11.

+
+
Code -
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()
+
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()
-
-
-

-
Figure 9: Regional distribution of studies analysing disability inequalities
+
+
+
+ + + + + + 2024-02-28T08:11:19.696055 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 11: Regional distribution of studies analysing disability inequalities +
-

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 Table 8. 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.

-
-
+

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 Table 11. 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.

+
+
Code -
crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False)
+
crosstab_inequality(df_inequality, "disability").sort_values("disability", ascending=False)
-
+
+
+
+Table 11: Interventions targeting disability inequalities +
+
+
+
- -
- - + +
Table 8: Interventions targeting disability inequalities
@@ -4805,15 +19992,15 @@ text-align: right; + + + + + - - - - - @@ -4822,65 +20009,1052 @@ text-align: right; - +
inequality
training43
counseling 2 0
training21
education 1
subsidy 114
-
+
+
+
+

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 (Carstens & Massatti, 2018).

-

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 (Martin & Honig, 2020). Here, in addition to the predominantly used measures of employment and return to work rates, meaningful achievement and decent work should be measured from individual economic and social-psychological indicators, especially in view of the already predominantly agency-based variety of interventions. There is a clear bias in studies on disability interventions towards studies undertaken in developed countries and, more specifically, based on the Veteran Disability system in the United States which has been the object of analysis for a variety of studies but equally highlights gaps in research on the topic in other contexts and other regions.

+ +

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 (Martin & Honig, 2020). Kim et al. (2020) 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, C. Lindsay et al. (2015) 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 Gruber et al. (2014), 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 Poppen et al. (2017) and Thoresen et al. (2021) 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 & ethnic inequalities

-

The effects of policy interventions targeting migratory and ethnic inequalities in the world of work are viewed primarily through the regions of North America, Europe, and Central, South and East Asia, and the Pacific, as can be seen in Figure 10. Especially the specifics regarding migration are reviewed in an Asian context, while studies in North America focus predominantly on ethnicity in their analyses, though both dimensions are deeply intertwined and hard to disentangle for most studies.

-
-
+
+

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 Figure 12. 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.

+
+
Code -
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()
+
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()
-
-
-

-
Figure 10: Regional distribution of studies analysing migration and ethnicity inequalities
+
+
+
+ + + + + + 2024-02-28T08:11:20.018351 + image/svg+xml + + + Matplotlib v3.8.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+Figure 12: Regional distribution of studies analysing migration and ethnicity inequalities +
-

As seen in Table 9, in terms of primary interventions analysed for these dimensions, most focus on structural interventions such as education or infrastructure, as well as institutional contexts such as the possibility for collective bargaining and unionisation, or the effects of universal income on the world of work.

-
-
+

As seen in Table 12, 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.

+
+
Code -
crosstab_inequality(df_inequality, "ethnicity").sort_values("ethnicity", ascending=False)
+
crosstab_inequality(df_inequality, "ethnicity").sort_values("ethnicity", ascending=False)
-
+
+
+
+Table 12: Interventions targeting migration and ethnicity inequalities +
+
+
+
- -
- - + +
Table 9: Interventions targeting migration and ethnicity inequalities
@@ -4912,7 +21086,7 @@ text-align: right; - + @@ -4927,26 +21101,29 @@ text-align: right; - +
inequality
direct transfers 134
infrastructure
subsidy 114
-
-

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 (Samaluk, 2014). 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 (Landsbergis et al., 2014).

-

While some frameworks do put agency-driven necessities to the foreground (see Siebers & van Gastel, 2015), the consensus seems a requirement for structural approaches enabling this agency and their institutional embedding before more agency-driven interventions alone increase their effectiveness (see for structural necessities Do et al., 2020; Goodburn, 2020; for institutional contexts see Clibborn & Wright, 2022).

+
+
+
+
+

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 (Samaluk, 2014).

+

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 (Landsbergis et al., 2014). With a focus on remittances of temporary migratory work, Rosewarne (2012) 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 Siebers & van Gastel, 2015), the consensus seems a requirement for structural approaches enabling this agency and their institutional embedding before more agency-driven interventions alone increase their effectiveness.7

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 tackle 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, tackling 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.

+

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.

@@ -4955,7 +21132,7 @@ text-align: right;

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+Rosewarne, S. (2012). Temporary international labor migration and development in south and southeast asia [Article]. FEMINIST ECONOMICS, 18(2, SI), 63–90. https://doi.org/10.1080/13545701.2012.696314 +
Ruhindwa, A., Randall, C., & Cartmel, J. (2016). Exploring the challenges experienced by people with disabilities in the employment sector in Australia: Advocating for inclusive practice-a review of literature. Journal of Social Inclusion, 7(1), 4–19. https://doi.org/10.36251/josi.99
@@ -5178,9 +21400,6 @@ Samaluk, B. (2014). Whiteness, ethnic privilege and migration: A Bourdieui
Shepherd-Banigan, M., Pogoda, T. K., McKenna, K., Sperber, N., & Van Houtven, C. H. (2021). Experiences of VA vocational and education training and assistance services: Facilitators and barriers reported by veterans with disabilities. Psychiatric Rehabilitation Journal, 44(2), 148–156. https://doi.org/10.1037/prj0000437
-
-Shin, J., & Moon, S. (2006). Fertility, relative wages, and labor market decisions: A case of female teachers. Economics of Education Review, 25(6), 591–604. https://doi.org/10.1016/j.econedurev.2005.06.004 -
Siebers, H., & van Gastel, J. (2015). Why migrants earn less: In search of the factors producing the ethno-migrant pay gap in a Dutch public organization [Article]. WORK EMPLOYMENT AND SOCIETY, 29(3), 371–391. https://doi.org/10.1177/0950017014568138
@@ -5190,6 +21409,9 @@ Silvaggi, F., Leonardi, M., Raggi, A., Eigenmann, M., Mariniello, A., Silvani, A
Silveira Neto, R. D. M., & Azzoni, C. R. (2011). Non-spatial government policies and regional income inequality in brazil. Regional Studies, 45(4), 453–461. https://doi.org/10.1080/00343400903241485
+
+Sotomayor, O. J. (2021). Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil. World Development, 138(105182). https://doi.org/10.1016/j.worlddev.2020.105182 +
Standing, G. (2015). Why Basic Income’s Emancipatory Value Exceeds Its Monetary Value. Basic Income Studies, 10(2). https://doi.org/10.1515/bis-2015-0021
@@ -5205,18 +21427,27 @@ Suh, M.-G. (2017). Determinants of female labor force participation in south kor
Taukobong, H. F. G., Kincaid, M. M., Levy, J. K., Bloom, S. S., Platt, J. L., Henry, S. K., & Darmstadt, G. L. (2016). Does addressing gender inequalities and empowering women and girls improve health and development programme outcomes? Health Policy and Planning, 31(10), 1492–1514. https://doi.org/10.1093/heapol/czw074
+
+Thoresen, S. H., Cocks, E., & Parsons, R. (2021). Three year longitudinal study of graduate employment outcomes for australian apprentices and trainees with and without disabilities [Article]. INTERNATIONAL JOURNAL OF DISABILITY DEVELOPMENT AND EDUCATION, 68(5), 702–716. https://doi.org/10.1080/1034912X.2019.1699648 +
Ugur, M., & Mitra, A. (2017). Technology Adoption and Employment in Less Developed Countries: A Mixed-Method Systematic Review. World Development, 96, 1–18. https://doi.org/10.1016/j.worlddev.2017.03.015
Van Der Heide, I., Van Rijn, R. M., Robroek, S. J., Burdorf, A., & Proper, K. I. (2013). Is retirement good for your health? A systematic review of longitudinal studies. BMC Public Health, 13(1), 1180. https://doi.org/10.1186/1471-2458-13-1180
+
+Wang, C., Deng, M., & Deng, J. (2020). Factor reallocation and structural transformation implications of grain subsidies in China [Article]. JOURNAL OF ASIAN ECONOMICS, 71(101248). https://doi.org/10.1016/j.asieco.2020.101248 +
Wang, J., & Van Vliet, O. (2016). Social Assistance and Minimum Income Benefits: Benefit Levels, Replacement Rates and Policies Across 26 Oecd Countries, 1990-2009. European Journal of Social Security, 18(4), 333–355. https://doi.org/10.1177/138826271601800401
Whitworth, A. (2021). Spatial creaming and parking?: The case of the UK work programme. Applied Spatial Analysis and Policy, 14(1), 135–152. https://doi.org/10.1007/s12061-020-09349-0
+
+Williams, C. L., Muller, C., & Kilanski, K. (2012). Gendered organizations in the new economy [Article]. GENDER & SOCIETY, 26(4), 549–573. https://doi.org/10.1177/0891243212445466 +
Wong, S. A. (2019). Minimum wage impacts on wages and hours worked of low-income workers in Ecuador. World Development, 116, 77–99. https://doi.org/10.1016/j.worlddev.2018.12.004
@@ -5235,180 +21466,590 @@ Zeinali, Z., Muraya, K., Molyneux, S., & Morgan, R. (2021). The Use
-
-

Appendix

-
-

Full search query

-
TS=
-(
-    (
-        work OR
-        labour OR
-        production of goods OR
-        provision of services OR
-        own-use OR
-        use by others OR
-        of working age OR
-        for pay OR
-        for profit OR
-        remuneration OR
-        market transactions
-    ) AND
-    (
-        (
-            own-use OR
-            employment OR
-            unpaid trainee OR
-            volunteer OR
-            other work activities OR
-            wage-employed OR
-            self-employed OR
-            formal work OR
-            informal work OR
-            domestic work OR
-            care work OR
-            unpaid work
-        ) OR
-        (
-            employment outcomes OR
-            labour rights OR
-            equality of oppoertunity OR
-            equality of outcome OR
-            labour force participationOR
-            labour force exit OR
-            job quality OR
-            career advancement OR
-            hours worked OR
-            wage OR
-            salary OR
-            return to work
-        )
-    )
-) AND
-
-TS=
-(
-    (
-        intervention OR
-        policy OR
-        participation OR
-        targeting/targeted OR
-        distributive OR
-        redistributive
-    )
-    AND
-    (
-        (
-            support for childcare OR
-            labour rights OR
-            minimum wage OR
-            collective bargaining OR
-            business sustainability promotion OR
-            work-life balance promotion OR
-            equal pay for work of equal value OR
-            removal of (discriminatory) law OR
-            law reformation OR
-            guaranteed income OR
-            universal basic income OR
-            provision of living wage OR
-            maternity leave
-        )
-        OR
-        (
-            cash benefits OR
-            services in kind OR
-            green transition OR
-            infrastructure OR
-            digital infrastructure OR
-            quality of education OR
-            public service improvement OR
-            lowering of gender segregation OR
-            price stability intervention OR
-            extended social protection scheme OR
-            comprehensive social protection OR
-            sustainable social protection OR
-            supported employment OR
-            vocational rehabilitation
-        )
-        OR
-        (
-            credit programs OR
-            career guidance OR
-            vocational guidance OR
-            vocational counselling OR
-            counteracting of stereotypes OR
-            commuting subsidies OR
-            housing mobility programs OR
-            encouraging re-situation/migration OR
-            encouraging self-advocacy OR
-            cognitive behavioural therapy OR
-            computer-assisted therapy OR
-            work organization OR
-            special transportation
-        )
-    )
-) AND
-
-TS=
-(
-    (
-        inequality OR
-        inequalities OR
-        barriers OR
-        advantaged OR
-        disadvantaged OR
-        discriminated OR
-        disparity OR
-        disparities
-    )
-    NEAR/5
-    (
-        (
-            income OR
-            "Palma ratio" OR
-            "Gini coefficient" OR
-            class OR
-            fertility OR
-            "bottom percentile" OR
-            "top percentile"
-        )
-        OR
-        (
-            identity OR
-            demographic OR
-            gender OR
-            colour OR
-            beliefs OR
-            racial OR
-            ethnic OR
-            migrant OR
-            spatial OR
-            rural OR
-            urban OR
-            mega-cities OR
-            "small cities" OR
-            "peripheral cities" OR
-            age OR
-            nationality OR
-            ethnicity OR
-            "health status" OR
-            disability OR
-            characteristics
-        )
-    )
-)
+ + + +

Appendix

+ +
+

Full search query

+
+
TS=
+(
+    (
+        work OR
+        labour OR
+        production of goods OR
+        provision of services OR
+        own-use OR
+        use by others OR
+        of working age OR
+        for pay OR
+        for profit OR
+        remuneration OR
+        market transactions
+    ) AND
+    (
+        (
+            own-use OR
+            employment OR
+            unpaid trainee OR
+            volunteer OR
+            other work activities OR
+            wage-employed OR
+            self-employed OR
+            formal work OR
+            informal work OR
+            domestic work OR
+            care work OR
+            unpaid work
+        ) OR
+        (
+            employment outcomes OR
+            labour rights OR
+            equality of oppoertunity OR
+            equality of outcome OR
+            labour force participationOR
+            labour force exit OR
+            job quality OR
+            career advancement OR
+            hours worked OR
+            wage OR
+            salary OR
+            return to work
+        )
+    )
+) AND
+
+TS=
+(
+    (
+        intervention OR
+        policy OR
+        participation OR
+        targeting/targeted OR
+        distributive OR
+        redistributive
+    )
+    AND
+    (
+        (
+            support for childcare OR
+            labour rights OR
+            minimum wage OR
+            collective bargaining OR
+            business sustainability promotion OR
+            work-life balance promotion OR
+            equal pay for work of equal value OR
+            removal of (discriminatory) law OR
+            law reformation OR
+            guaranteed income OR
+            universal basic income OR
+            provision of living wage OR
+            maternity leave
+        )
+        OR
+        (
+            cash benefits OR
+            services in kind OR
+            green transition OR
+            infrastructure OR
+            digital infrastructure OR
+            quality of education OR
+            public service improvement OR
+            lowering of gender segregation OR
+            price stability intervention OR
+            extended social protection scheme OR
+            comprehensive social protection OR
+            sustainable social protection OR
+            supported employment OR
+            vocational rehabilitation
+        )
+        OR
+        (
+            credit programs OR
+            career guidance OR
+            vocational guidance OR
+            vocational counselling OR
+            counteracting of stereotypes OR
+            commuting subsidies OR
+            housing mobility programs OR
+            encouraging re-situation/migration OR
+            encouraging self-advocacy OR
+            cognitive behavioural therapy OR
+            computer-assisted therapy OR
+            work organization OR
+            special transportation
+        )
+    )
+) AND
+
+TS=
+(
+    (
+        inequality OR
+        inequalities OR
+        barriers OR
+        advantaged OR
+        disadvantaged OR
+        discriminated OR
+        disparity OR
+        disparities
+    )
+    NEAR/5
+    (
+        (
+            income OR
+            "Palma ratio" OR
+            "Gini coefficient" OR
+            class OR
+            fertility OR
+            "bottom percentile" OR
+            "top percentile"
+        )
+        OR
+        (
+            identity OR
+            demographic OR
+            gender OR
+            colour OR
+            beliefs OR
+            racial OR
+            ethnic OR
+            migrant OR
+            spatial OR
+            rural OR
+            urban OR
+            mega-cities OR
+            "small cities" OR
+            "peripheral cities" OR
+            age OR
+            nationality OR
+            ethnicity OR
+            "health status" OR
+            disability OR
+            characteristics
+        )
+    )
+)
+
+
+
+

Validity rankings

+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
RepresentativenessRanking
non-representative survey/dataset2.0
subnationally representative survey/dataset3.0
nationally representative survey/dataset4.0
census-based dataset5.0
+
+
+Table A1: External validity ranking. Adapted from Maîtrot & Niño-Zarazúa (2017). +
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodRanking
ordinary least squares & fixed-effects2.0
discontinuity matching3.0
difference in difference (& triple difference)3.0
propensity score matching3.5
instrumental variable4.0
general method of moments4.0
regression discontinuity4.5
randomised control trial5.0
+
+
+Table A2: Internal validity ranking. Adapted from Maîtrot & Niño-Zarazúa (2017). +
+
+
+
+
+

Extraction matrix

+
+
+Code +
bib_df
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Extraction matrix {#cell-apptbl-extraction-matrix}
citationauthoryeartitlepublisheruripubtypedisciplinecountryperiod...channelsdirectionsignificancedoidatezot_citedzot_usagezot_keywordsregionincome_group
0Adam2018Adam, C., Bevan, D., & Gollin, D.2018Rural-urban linkages, public investment and tr...World Developmenthttps://doi.org/10.1016/j.worlddev.2016.08.013articledevelopmentTanzania2001...movement of rural workers out of quasi-subsist...-1.02.010.1016/j.worlddev.2016.08.0132018-01-0113.0Nonecountry::Tanzania,done::extracted,inequality::...Sub-Saharan AfricaLower middle income
1Rosen2014Rosen, M. I., Ablondi, K., Black, A. C., Muell...2014Work outcomes after benefits counseling among ...Psychiatric Serviceshttps://doi.org/10.1176/appi.ps.201300478articlehealthUnited States2008-2011...not clear, neither belief about work, benefits...1.02.010.1176/appi.ps.2013004782014-01-0110.0Nonecountry::US,done::extracted,inequality::age,in...North AmericaHigh income
2Xu2021Xu, C., Han, M., Dossou, T. A. M., & Bekun, F. V.2021Trade openness, FDI, and income inequality: Ev...African Development Reviewhttps://doi.org/10.1111/1467-8268.12511articledevelopmentAngola; Benin; Botswana; Burkina Faso; Burundi...2000-2015...primarily goes to agriculture which can employ...-1.01.010.1111/1467-8268.125112021-01-0142.0Nonedirection::vertical,done::extracted,indicator:...Sub-Saharan AfricaUpper middle income;Lower middle income;High i...
3Xu2021Xu, C., Han, M., Dossou, T. A. M., & Bekun, F. V.2021Trade openness, FDI, and income inequality: Ev...African Development Reviewhttps://doi.org/10.1111/1467-8268.12511articledevelopmentAngola; Benin; Botswana; Burkina Faso; Burundi...2000-2015...higher import than export, creating jobs in ot...1.02.010.1111/1467-8268.125112021-01-0142.0Nonedirection::vertical,done::extracted,indicator:...Sub-Saharan AfricaUpper middle income;Lower middle income;High i...
4Xu2021Xu, C., Han, M., Dossou, T. A. M., & Bekun, F. V.2021Trade openness, FDI, and income inequality: Ev...African Development Reviewhttps://doi.org/10.1111/1467-8268.12511articledevelopmentAngola; Benin; Botswana; Burkina Faso; Burundi...2000-2015...potentially inequal access to education throug...1.02.010.1111/1467-8268.125112021-01-0142.0Nonedirection::vertical,done::extracted,indicator:...Sub-Saharan AfricaUpper middle income;Lower middle income;High i...
..................................................................
65Mun2018Mun, E., & Jung, J.2018Policy generosity, employer heterogeneity, and...American Sociological Reviewhttps://doi.org/10.1177/0003122418772857articlesociologyJapan1992-2009...decreases may be due to supply-side mechanisms...0.00.010.1177/00031224187728572018-01-0114.0Nonecountry::Japan,done::extracted,inequality::gen...East Asia & PacificHigh income
66Thoresen2021Thoresen, S. H., Cocks, E., & Parsons, R.2021Three year longitudinal study of graduate empl...International journal of disability developmen...https://doi.org/10.1080/1034912X.2019.1699648articleeducationAustralia2011-204...significant but small overall increase (3.1 ho...1.02.010.1080/1034912X.2019.16996482021-01-012.0Nonecountry::Australia,done::extracted,inequality:...East Asia & PacificHigh income
67Thoresen2021Thoresen, S. H., Cocks, E., & Parsons, R.2021Three year longitudinal study of graduate empl...International journal of disability developmen...https://doi.org/10.1080/1034912X.2019.1699648articleeducationAustralia2011-204...strong initial diff means disability group pot...1.02.010.1080/1034912X.2019.16996482021-01-012.0Nonecountry::Australia,done::extracted,inequality:...East Asia & PacificHigh income
68Wang2016Wang, J., & Van Vliet, O.2016Social Assistance and Minimum Income Benefits:...European Journal of Social Securityhttps://doi.org/10.1177/138826271601800401articleeconomicsglobal1990-2009...bulk of increases comes from deliberate policy...1.0NaN10.1177/1388262716018004012016-01-0110.0Nonedone::extracted,inequality::income,region::EU,...Europe & Central Asia;Sub-Saharan Africa;Latin...
69Wang2020Wang, C., Deng, M., & Deng, J.2020Factor reallocation and structural transformat...Journal of Asian Economicshttps://doi.org/10.1016/j.asieco.2020.101248articleeconomicsChina2007-2016...displacement of rural unskilled labour; unskil...1.02.010.1016/j.asieco.2020.1012482020-01-0114.0Nonecountry::China,done::extracted,inequality::inc...East Asia & PacificUpper middle income
+ +

70 rows × 43 columns

+
+
+
+
-
- - -

Footnotes

+

Footnotes

    -
  1. 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.↩︎

  2. -
  3. 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.↩︎

  4. +
  5. 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.↩︎

  6. +
  7. 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.↩︎

  8. +
  9. 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.↩︎

  10. +
  11. 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.↩︎

  12. +
  13. For an overview of how retirement and pensions reflect on health aspects in ageing, see Van Der Heide et al. (2013), for a review of pensions intersecting with other possible inequalities and also health outcomes, see Zantinge et al. (2014).↩︎

  14. +
  15. For gender inequalities within education paths themselves, see Stepanenko et al. (2021). For possible ways to integrate gender-transformative interventions into professional education, see Newman et al. (2016). For the effects of prior inequalities on taxation preferences, school enrolment and educational choices, see Gutierrez & Tanaka (2009) and Zamfir (2017). For interactions between policies for the knowledge translation of sexual education and their barriers, see Curran et al. (2022).↩︎

  16. +
  17. See for structural necessities Do et al. (2020) and Goodburn (2020). For institutional contexts see Clibborn & Wright (2022).↩︎

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+ } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -5612,6 +22395,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) { } div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -5637,6 +22421,32 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -5694,1422 +22504,1868 @@ window.document.addEventListener("DOMContentLoaded", function (event) { }); } } - var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//); - var filterRegex = new RegExp('/' + window.location.host + '/'); - var isInternal = (href) => { - return filterRegex.test(href) || localhostRegex.test(href); - } - // Inspect non-navigation links and adorn them if external - var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item)'); - for (var i=0; i + \ No newline at end of file diff --git a/05-final_paper/scoping_review.pdf b/05-final_paper/scoping_review.pdf index 684342a..46fc92e 100644 Binary files a/05-final_paper/scoping_review.pdf and b/05-final_paper/scoping_review.pdf differ diff --git a/pyproject.toml b/pyproject.toml index 8ad89c1..bc3a204 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "scoping-review" -version = "0.3.0" +version = "0.5.0" description = "" authors = ["Marty Oehme "] readme = "README.md" @@ -50,6 +50,6 @@ quarto render --output-dir 05-final_paper poe extract VERSION="$(poetry version -s minor)" git add pyproject.toml 02-data 05-final_paper -git commit -m "Publish version $VERSION" +git commit -m "Publish version $VERSION" --no-gpg-sign git tag -a -m "new bundle for $(date -Isecond)" "$VERSION" """