Structural, Social, and Ecological Dimensions of Female Labor Force Participation: A Bayesian Analysis Across National Contexts

Abstract

Although there are still significant inequalities, women’s labor force participation has increased in many parts of the world. These disparities are linked to socio-economic, territorial, and institutional conditions, such as access to land, quality of infrastructure, and the availability of decent work in both urban and rural areas. To understand how these socio-economic and spatial factors interact with national economic and policy frameworks is essential for analyzing gender participation in work. In this study, we examine the structural, territorial, and socio-economic factors shaping female labor force participation in 49 countries between 2013 and 2022, covering Europe, Asia, Latin America, and Africa. We investigate the interaction between macroeconomic conditions, public investment in education, and spatial inequalities. In addition, we focus on how these factors work together within different institutional settings. The analysis also considers territorial aspects such as urban–rural differences, regional development issues, and land-related livelihoods. The data were collected from the World Bank’s World Development Indicators to build a balanced panel. We implemented a Bayesian hierarchical panel regression model to understand how economic, institutional, and spatial factors jointly influence women’s participation in the labor force across different national and regional contexts. For model specification, we used standardized predictors and country-level intercepts to allow the model to account for institutional differences. The results indicate that national income levels and female unemployment rates are the most important factors affecting participation. On the other hand, tertiary enrollment and public education spending have weaker or mixed effects. Notably, although more women now complete higher education, many, especially in non-OECD countries, still face barriers to entering formal employment. Furthermore, in many developing countries, women still encounter restricted access to formal and secure jobs, particularly in rural and less developed areas. These findings show that economic growth is not the only factor needed to achieve gender equality in the labor market. Sustainable progress requires plans that bring together labor reforms, better education, care services, and fair growth in all regions. It is also important to fix problems with land, close the gap between cities and villages, and address environmental challenges. By linking labor markets, education, and land-linked spatial constraints, the study informs SDGs 5 (Gender Equality), 8 (Decent Work and Economic Growth), and 10 (Reduced Inequalities).

1. Introduction

In many countries, women are now more likely to be employed, despite the fact that there are still large disparities. These disparities arise from socio-economic, territorial, and institutional factors such as land access, infrastructure quality, income levels, labor market structure, and job availability in urban and rural areas. Analyzing gender involvement in the workforce requires an understanding of how these socio-ecological and spatial elements interact with national economic and policy frameworks [1,2,3]. Although female labor force participation (FLFP) has increased in many countries over recent decades, wage levels, employment stability, and social protection coverage remain uneven. Women often work in physically demanding, low-paid jobs with limited autonomy and minimal access to benefits such as pensions or health insurance, reflecting deeper structural inequalities in income distribution and labor market segmentation. They have few chances to learn new skills or move up in their careers because of rules, traditions, and social expectations [4,5,6]. These problems are even more evident in rural and remote areas, where there are fewer jobs, weaker public services, and additional challenges arising from poor land quality, climate-related pressures, and lack of resources. As expected, women’s productivity and potential earnings are lower in areas with limited diversification and weak infrastructure.

Researchers and policymakers have begun to look more closely at how these land and resource dynamics connect with socio-economic development and gender equality. Within the Sustainable Development Goals, the connections are clear. Progress on gender equality (SDG 5) and decent work (SDG 8) is tied to reducing inequality (SDG 10) and ending poverty (SDG 1). From a socio-economic perspective, access to land and productive assets influences not only household income but also broader patterns of wealth accumulation and intergenerational mobility. Secure rights to land, fair access to assets such as credit and equipment, and labor markets designed to include rather than exclude are critical parts of the equation [7]. Broader changes also matter. As economies change and cities grow, industry and farming shift. These shifts can open better jobs for women or push them into insecure, informal work, depending on policy choices.

Many studies still treat these issues as if they belong to separate conversations [8,9,10]. Analyses of the socio-economic and political aspects of women’s participation in the labor market are often carried out without asking how land is used or how it is governed. When examined in isolation, important connections are lost. The interaction between land governance and economic development policies shapes resource distribution, labor demand, and job opportunities for women. The influence of institutional rules, the persistence of unequal resource distribution, and the geography of opportunity all tend to disappear from view. To truly understand why outcomes differ so sharply across places, economic and governance data must be considered alongside the ways in which communities control, share, and benefit from land and other resources. In this study, we respond to the gap by integrating socio-economic and land-related factors into the cross-national analysis of women’s labor participation, with a particular emphasis on how these structural conditions shape economic opportunities and wage outcomes.

With this motivation, we studied how socio-economic conditions shape women’s experiences in manufacturing. The analysis uses data from 49 countries, spanning advanced economies such as Germany, Sweden, South Korea, the United States, and Turkiye, as well as developing nations including Brazil, Mexico, South Africa, Vietnam, and the Philippines. With data from many countries, we compare how GDP per capita, labor productivity, industrial mix, and regional investment relate to women’s chances of getting stable, well-paid factory jobs. We also use ideas from labor sociology and feminist economics to examine how education, workplace oversight, and labor rules shape women’s autonomy and how they see their roles. Finally, we consider “functional supervision” [11], where different supervisors handle different tasks, and how it can affect learning and skill growth when formal training is limited [12]. In lower-income contexts, such supervision may confine women to repetitive tasks with low economic returns, whereas in higher-income settings it can serve as a platform for skill acquisition and upward mobility.

We relied on a balanced panel dataset from the World Bank’s World Development Indicators (WDI), covering the years 2013 to 2021. Our focus is on economic and social indicators—GDP per capita, trade openness, education spending, female higher education enrollment, and female unemployment. These indicators capture not only macroeconomic capacity but also the socio-economic foundations that enable—or constrain—women’s integration into the labor market. A hierarchical Bayesian panel regression model is used to examine how territorial and institutional factors affect women’s participation in the labor force, with special attention to the socio-ecological dimensions of inequality such as spatial concentration of industries, unequal access to public infrastructure, and regional disparities in land-based economic opportunities.

As a focus of the Sustainable Development Goals (SDGs), we discuss strengthening women’s economic participation (Goal 5: Gender Equality) and creating sustainable jobs while promoting spatial equality in access to resources (Goal 8: Decent Work and Economic Growth; Goal 10: Reduced Inequalities). By bringing together quantitative and qualitative analyses, and by embedding socio-ecological factors such as land-related constraints, urban–rural divides, and environmental pressures, our research aligns human livelihoods, spatial justice, and sustainable development. Finally, this socio-economic framing allows for a more comprehensive understanding of how structural inequalities translate into labor market disparities and how targeted policy interventions can close these gaps. Furthermore, while our regression specification relies on cross-national socio-economic indicators with complete coverage, we interpret the findings through a land and territorial governance lens, clarifying how tenure security, cadastral coverage, rural accessibility, and land-linked livelihoods can condition women’s participation in labor markets.

2. Theoretical Background

Land rights, tenure security, and land governance are central to women’s economic outcomes. Where women hold secure rights, they have better access to credit, inputs, infrastructure, and markets. This improves entry into formal employment and raises the quality and stability of available jobs. Differences in registration, inheritance systems, and weak local administration often exclude women and reduce their bargaining power [13].

Equitable land distribution and effective land administration are also crucial for resolving disputes. If women cannot document their rights or access neutral forums, conflicts over plots, grazing, or water are more likely to be decided against them. This increases household dependency and limits mobility across regions. Gender-responsive land administration and accessible mediation can counter these risks and strengthen tenure security [14].

Land-use strategies create different socio-economic consequences in rural development. Public investment in transport, storage, and extension services links land-based livelihoods to higher-value chains, helping women move from informal to secure jobs. Without this investment, weak market access and thin local demand keep women in low-return activities [15,16].

Urbanization and land-use planning also affect access to jobs, services, and affordable housing [17]. Peri-urban expansion raises land values and displaces low-income households, while poor transport networks limit women’s time and job searching. Gender-oriented planning and reliable services ease these frictions and broaden formal employment options in manufacturing and related services [18].

Furthermore, sustainable land management has a direct connection with degradation, restoration, and ecosystem services [19]. Soil loss, water scarcity, and forest decline reduce the fuel, fodder, and minor forest products that supplement household income. The costs of such losses fall largely on women, reducing training prospects, restricting mobility, and closing off stable jobs. Restoration and community stewardship restore ecosystem services and lighten those burdens.

Climate change adaptation and land resilience further condition women’s work. Climate shocks change cropping calendars, degrade assets, and raise care burdens, which can pull women out of formal work or keep them in casual jobs. Adaptation programs that protect land rights, diversify livelihoods, and improve local infrastructure increase resilience and reduce these labor market setbacks [20].

Large land-based investments and their governance shape who benefits from value added. Clear safeguards, prior consultation, and enforceable benefit-sharing are associated with better outcomes for women. When governance is thin, land acquisitions restrict rights to the commons and small parcels, fueling informality and conflict. International principles provide guidance, but without local enforcement they have little effect [21].

Sex-disaggregated data are essential for tracking land-related SDGs. SDG 2 ties land to food security and agricultural output; SDG 10 addresses distributional inequalities; SDG 13 stresses adaptation and the resilience of land-based livelihoods; and SDG 15 centers on terrestrial ecosystems and the services communities rely on [22]. Tracking these indicators shows where land policies advance or undermine women’s access to work [23].

Alienation is a term that describes how workers can feel disconnected from their job, the things they make, the people around them, and even from themselves [24]. Karl Marx argued that this happens when work becomes something to be sold and the worker is seen as a tool for making money instead of a person [25,26]. He argued that there are four parts to this: being cut off from the product, the work process, your own skills, and other workers. These ideas also help to explain how women’s place in rural and peri-urban work is shaped by ownership, local rules, and regional development. Research on gender and land indicates that alienation is associated with land rights, decision-making, and community outcomes [27,28].

Researchers after Marx have built on these ideas to look at alienation in a wider way. Seeman [29] described it through five parts: feeling powerless, lacking meaning, losing social rules, being isolated, and feeling disconnected from oneself. This shows that alienation is also about emotions and social life in relation to money and work conditions. In addition, Sennett [30] and Bauman [31] studied how today’s work systems are shaped by neoliberal changes. When we focus on rural industries, these problems are often worse for women, especially in land-based factories, and agricultural work where jobs depend directly on local land and environmental conditions.

Some scholars have added to the discussion on labor by showing how unequal land rights, lack of secure ownership, and limited tenure increase women’s sense of alienation. These conditions negatively affect their economic independence. They also affect their role in managing land sustainably [32,33]. Feminist scholars have studied how alienation is changing for everyone. In detail, they mainly discuss that work is shaped by gender, race, and class [34,35,36]. They also point out that reproductive and care work has long been overlooked and undervalued in economic studies and policy. As is well known, these tasks are handled by women in general. Hochschild [8] proposed that women are often expected to control their emotions and appearance in both paid and unpaid roles. This creates alienation that is economic, physical, and psychological. The problem is especially visible in rural and low-income areas where care duties, lack of secure access to land, and seasonal work demands overlap.

Structural problems in land governance make this experience of alienation even more severe. Research shows that when women are excluded from formal land registration or face restrictions in inheritance rights, their lack of secure land access leads to deeper psychological and social alienation. This, in turn, adds to existing inequalities [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]. In spatially marginalized regions, these governance gaps translate into uneven access to infrastructure, markets, and extension services, reinforcing both economic dependency and territorial inequality.

In many rural and land-based jobs, women end up doing work that is repetitive and tightly controlled. Their duties are usually fixed in advance, and there is little chance to make decisions on their own. This is also true in sectors like agro-processing, small-scale manufacturing, or craft work, and similar patterns appear in farming communities where land policies rarely recognize women’s roles [38]. One reason for these strict arrangements can be traced back to Frederick Taylor’s [11] idea of functional supervision in scientific management. In this setup, the duties of planning, monitoring, and evaluating were split among several supervisors, each handling a specific part of the job [39]. The aim was straightforward—keep tasks separate so work would run more smoothly. Yet over the years, many researchers have pointed out that the same structure often took away workers’ independence, narrowed the room for creativity, and left them feeling detached from their work [40,41,42]. It may have delivered higher output in some cases, but it also led to a slow loss of skills and locked people into strict, repetitive routines [43]. For women, who are often concentrated in such roles, these conditions have strengthened the sense of alienation and made it harder to act with real autonomy.

While control often narrows autonomy, it can also create narrow niches for on-the-job learning where formal training is scarce. Functional supervision can sometimes open the door to informal learning, especially where formal vocational programs are scarce or missing. Researchers in adult education note that much learning at work comes from doing the job itself, with formal and informal methods often mixing in daily routines [12,44,45]. Yet in workplaces that are strongly hierarchical and shaped by gender roles, these opportunities often mirror existing inequalities instead of reducing them [46,47]. In rural industrial areas or agricultural cooperatives, such learning is often tied to who has access to land and how local governance works. Women without secure land rights are therefore less likely to benefit. To fully grasp the issue, it is necessary to view land-related Sustainable Development Goals—SDGs 2, 10, 13, and 15—not just in terms of soil loss or crop yields, but also in relation to women’s ability to access and manage land. If this link is overlooked, important dimensions of inequality and resilience may be missed [48,49].

These workplace mechanisms intersect with gendered norms and care responsibilities that shape time, mobility, and bargaining power. The connection between gender, education, and work is a key topic in discussions about the feminization of labor. According to Standing [6], Kabeer [5], and Kaur [50], globalization has opened more jobs for women but has also brought increased insecurity, closer workplace supervision, and added strain from conflicting social expectations. These realities suggest that alienation is shaped not only by material conditions but also by emotional and symbolic experiences [51,52]. Recent work in critical labor studies further shows that digital tools, algorithmic management, and platform-based work are reshaping alienation in new ways [53]. This means similar patterns can be seen in land-based economies where digital monitoring is increasingly embedded in agricultural value chains, changing how women relate to both their work and the land.

Yet institutional exclusions in land remain the persistent backdrop against which these dynamics unfold. While forms of work continue to change, long-standing issues such as land disputes still influence women’s experiences of alienation. Research shows that when women are left out of formal land governance, they have fewer ways to resolve disputes, which strengthens patriarchal control and increases feelings of exclusion [54].

When examined from a wider angle, it becomes clear that women’s participation in the labor market is shaped by a mix of welfare provisions, labor regulations, land administration practices, and patterns of spatial development. This research draws on insights from labor sociology and feminist economics to examine how women’s autonomy and views on work are shaped by education, workplace oversight, and the wider institutional environment. The study also takes a fresh look at functional supervision, a management approach that first emerged to increase efficiency. Here, the focus is on how such oversight can influence the development of skills and the learning that takes place through daily practice, especially in rural and land-based occupations. Depending on the local governance systems and the surrounding environmental context, this form of supervision may serve to strengthen women’s independence, or it may work as a limiting force in both industrial and agricultural workplaces.

This body of evidence leads to clear expectations for our analysis. Higher national income should be associated with greater access to formal manufacturing jobs for women, while higher female unemployment signals binding constraints on participation. In addition to this, education spending and tertiary enrollment may help this situation. On the other hand, their effects depend on institutional quality and local labor demand. Territorial inequality, proxied by infrastructure gaps and industrial concentration, mediates these links and likely differs between OECD and non-OECD settings. We reflect these expectations in our variable set and in the hierarchical specification used in the empirical sections.

3. Methodology

This research relies on a balanced panel of 49 countries drawn from the WDI for the years 2013–2022. The selection process prioritized countries with complete coverage of the relevant indicators for the full period. In building the dataset, attention was given to representing countries with very different economic profiles—from those with long-established industrial bases to others still navigating structural transitions. This combination of countries creates a solid basis for exploring how differences in economic systems and labor rules affect women’s positions in the workforce (Klasen, 2019). The inclusion of both OECD and non-OECD countries makes it easier to compare how different labor systems affect women’s employment in various national contexts. Because complete multi-year coverage is required for the panel estimation, the final sample (49 countries) tilts toward data-rich middle- and high-income economies; low-income agrarian states are underrepresented.

3.1. Variable Selection

The dependent variable for this analysis is the FLFP rate, calculated as the share of women aged 15 and older who are active in the labor force (WDI code: SL.TLF.CACT.FE.ZS). This indicator is commonly used to evaluate women’s involvement in economic activity and conveys more than simple labor supply trends. It also embodies the influence of institutional arrangements, educational attainment, and prevailing cultural norms on women’s access to employment. In many rural contexts, opportunities for women to obtain decent work are shaped less by individual skills and more by the structure and inclusiveness of local institutions.

Guided by theoretical considerations and earlier empirical work, six explanatory variables were chosen for analysis. All were obtained from the World Bank’s World Development Indicators database using their official indicator codes:

• FLFP rate (% of female population ages 15+); WDI code: SL.TLF.CACT.FE.ZS
• GDP per capita (current USD): Captures overall economic development and wealth levels. WDI code: NY.GDP.PCAP.CD
• Unemployment, female (% of female labor force): Reflects gender-specific barriers to labor market entry. WDI code: SL.UEM.TOTL.FE.ZS
• School enrollment, tertiary, female (% gross): Indicator of female human capital and access to higher education. WDI code: SE.TER.ENRR.FE
• Government expenditure on education (% of GDP): Represents public commitment to educational investment. WDI code: SE.XPD.TOTL.GD.ZS
• Industry (including construction), value added (% of GDP): Indicates structural composition of the economy. WDI code: NV.IND.TOTL.ZS
• Trade (% of GDP): Measures economic openness and integration with global markets. WDI code: NE.TRD.GNFS.ZS

These variables were selected not only because they have been shown to be statistically important in earlier cross-country research, but also because they fit with the structural and institutional themes discussed in the theoretical framework. This is especially true for aspects related to women’s experiences at work, their opportunities for informal learning, and the ways they may feel disconnected from their jobs.

Some variables that are clearly relevant to women’s participation in the labor force, such as maternity leave policies, childcare availability, or prevailing gender norms, could not be included. The main reason is the lack of consistent and long-term data that covers many countries. In many cases, these figures are collected irregularly, use different definitions from one country to another, or exist only for a small group of high-income economies. To keep the dataset comparable over time and across countries, this study uses standardized indicators from the World Bank’s WDI.

Even so, some of the chosen variables, like female tertiary enrollment, female unemployment, and public education spending, still give partial insight into the institutional and cultural settings that shape women’s access to jobs. This approach makes it possible to compare both OECD and non-OECD countries while staying close to the study’s focus on gendered work experiences and the SDG framework.

Since the WDI dataset does not include a direct measure of alienation, a proxy approach is used here. Several of the variables serve as stand-ins for the structural and institutional factors that can contribute to alienation in women’s working lives. For example, high unemployment among women and an economy that depends heavily on industry often go together with jobs that are unstable and give workers little control, conditions that can leave them feeling isolated or powerless. Likewise, when few women are in higher education and public investment in education is low, there are fewer chances for advancement and for gaining skills through informal learning. In this way, while alienation is not measured as a single figure, it is still reflected in the structural patterns captured by the model’s main indicators.

3.2. Data Preparation and Transformation

All indicators were collected on an annual basis and matched by country and year. GDP per capita and trade openness were converted to their logarithmic forms to improve the shape of their distributions and make the results easier to interpret. Missing observations were removed through listwise deletion, producing a balanced panel of 49 countries over ten years. This process may have unintentionally excluded some low-income or rural economies, where data systems and coverage are often weaker. While this approach simplified the estimation process, it also carried the risk of lowering statistical power and introducing selection bias. To check for this, the analysis was repeated using datasets where missing values had been imputed. The main results remained consistent, which supports the reliability of the findings.

3.3. Estimation Strategy

We use a hierarchical Bayesian panel regression model to analyze differences across countries over time. This method works well for examining female labor force participation, where many economic and institutional influences act together at both national and broader structural levels. Country-specific intercepts are treated as random effects drawn from a shared hyperprior, allowing partial pooling. This approach makes the estimates more stable, especially for countries where data are limited, and it also captures differences both within each country and between countries in one coherent framework.

All analyses were run in Python 3.11 with PyMC 5.x and ArviZ 0.16; data handling used pandas 2.x and NumPy 1.26; figures were produced with Matplotlib 3.8. PyMC applies the No-U-Turn Sampler (NUTS), a form of Hamiltonian Monte Carlo, to generate posterior samples efficiently [55]. Before estimation, all continuous variables were standardized to help the model converge more smoothly and to make the coefficients easier to interpret. Country-level effects are specified within a hierarchical structure, which captures unobserved national characteristics and makes it possible to compare institutional settings in rural and urban contexts.

Weakly informative priors are applied—Normal(0,1) for coefficients and HalfNormal for standard deviations—providing regularization without heavily constraining the estimates. Four parallel chains are run, each producing 3000 samples after 1000 warm-up steps. Convergence and model adequacy are checked with standard diagnostics, including trace plots, effective sample sizes (ESS), and R-hat statistics [56,57]. R-hat values below 1.01 are taken as a sign of reliable convergence. Posterior predictive checks (PPCs) are also performed to assess how well the model reproduces the observed data.

Although Bayesian methods are generally less sensitive to assumptions such as normality or constant variance, residuals are still inspected for possible misspecification. Multicollinearity is addressed by reviewing the correlation matrix of standardized predictors and confirming that variance inflation factors (VIFs) stay within acceptable limits before estimation. The final results are presented with 95% credible intervals to clearly show the level of uncertainty in the parameter estimates.

4. Empirical Results

This section summarizes the findings from the Bayesian hierarchical panel regression, covering 49 countries from 2013 to 2021. Our model recovers the range of effects for key structural and economic variables and accounts for country and year. The hierarchical structure reveals group differences, especially between OECD and non-OECD settings, pointing to regional and institutional contrasts. In Bayesian terms, we present intervals for each result rather than single point estimates. These are shown as 95% highest density intervals (HDIs), which give the values most likely to be true based on the data. The variables in the model, all made comparable by standardizing, are GDP per capita (log), female unemployment, tertiary school enrollment, government spending on education, manufacturing share, and trade openness (log).

We see a clear pattern. Where female unemployment is high, fewer women take part in the labor market. The rest of the factors are uneven, and some are uncertain. OECD countries show higher baseline levels, consistent with stronger institutions and service access. A spatial reading of the estimates indicates that constraints are not evenly distributed: barriers cluster outside metropolitan areas, where land-based livelihoods dominate and care/transport infrastructure is thinner. In such places, even when macro indicators improve, women’s participation can lag because the local job ladder, safe transport, childcare, and tenure/security conditions do not keep pace. This urban–rural and center–periphery lens helps interpret the country-level coefficients through concrete spatial mechanisms.

4.1. Descriptives

Before moving on to the model results, it is useful to look at some basic descriptive statistics for the main variables. These figures give a general picture of the structural and economic conditions in the 49 countries covered between 2013 and 2022. Patterns in FLFP, education, income, and industrial structure help set the scene for the analysis and show how varied the countries in the dataset are.

As shown in

Table 1. Descriptive statistics of structural, educational, and economic indicators used in the analysis.

Variable	Description	Mean	Std. Dev.	Min	Max
FLFP	Female labor force participation rate (%)	52.48	9.80	19.58	73.06
VULN_EMP	Vulnerable employment (female)	17.39	16.98	2.86	83.45
UNEMP	Unemployment rate, female (%)	7.78	5.95	0.24	36.26
EDU	Tertiary school enrollment, female (% gross)	77.77	25.89	21.31	156.85
GOV_EDU	Government expenditure on education (% of GDP)	4.90	1.23	0.86	8.49
GDP_PC	GDP per capita (current US$)	29,807.02	23,424.14	1432.84	109,269.52
MANUF	Manufacturing, value added (% of GDP)	15.03	5.65	4.88	34.86
TRADE	Trade (% of GDP)	42.91	23.44	10.07	135.46

, the global average for FLFP is moderate, but the large standard deviation shows just how much countries differ. In some economies with strong formal labor markets, vulnerable employment among women is very low, while in others with a high share of informal work, it can be extremely high. This points to a clear divide between secure and unstable jobs worldwide.

A similar picture appears in education indicators. On average, about 78% of women are enrolled in tertiary education, yet the differences between countries are still large. In many places, access to higher education has improved, but this by itself does not ensure that women can enter the labor market. Responsibilities at home, persistent gender expectations, and a lack of suitable jobs for qualified women can still limit their participation [58].

The economic indicators in the dataset also show wide variation. GDP per capita ranges from low- to high-income economies. Trade openness shows how much each country is connected to the global economy, while the manufacturing share reveals how strongly it relies on industrial production. Taken together, these measures highlight the structural and institutional variety across the sample and help set the model’s results in a wider global context. These cross-country distributions mask important within-country gaps. In particular, rural and peripheral regions—where land-linked livelihoods are prevalent and services are scarce—tend to exhibit lower female participation than metropolitan cores, even at similar national income levels.

Looking at the correlation matrix (Figure 1), we see a few clear patterns. FLFP is lower where female unemployment is higher. It is also lower where vulnerable employment among women is high, which fits the idea that participation grows with more formal and secure jobs. Education seems to help. Countries with higher female tertiary enrolment tend to have higher FLFP. Public spending on education shows a positive link too, but it is smaller. Macroeconomic links are weaker. GDP per capita and trade openness are both slightly positive. The manufacturing share does not show a stable relationship. In many places, industry is still male-dominated, and that likely adds noise to this pattern.

Figure 2 follows the period from 2013 to 2021. The break in 2020 is clear. Female tertiary enrollment stayed about the same. Labor force participation and employment fell. Public spending on education rose a little. The share of manufacturing in GDP was mostly flat.

These trends show that labor markets adjust slowly after a global shock, especially for women. In our regression, education spending did not have a short-run effect on female participation. This makes sense. Returns to education take time. Impact also depends on how the money is used. If funds do not target the main barriers for women, such as scholarships, practical training, job placement, childcare, safe transport, or support in rural areas, the near-term effect can be small [59]. A place-based approach matters here. Policies that link SDG 4 and SDG 5 to local conditions like land access, basic infrastructure, and regional gaps are more likely to improve outcomes.

A comparison of OECD and non-OECD countries shows how institutions matter. Figure 3 indicates that OECD economies have higher and more stable FLFP. Female unemployment is lower as well. Earlier work links these patterns to welfare systems, parental leave, and inclusive labor rules that make it easier for women to work [60,61].

Women’s tertiary education has expanded in many non-OECD settings, yet job gains have not followed. Kabeer [62] and UNESCO [63] describe this as a gap between education and available work, driven by few formal jobs and little gender-responsive planning. The gap is wider away from major cities, where good jobs are scarce, services are thin, travel is long, and secure land access is limited. In 2020, the pandemic deepened these problems, pushing participation down and unemployment up in places already under strain [7].

Public investment patterns differ, too. OECD countries invest more steadily in education, close to five percent of GDP on average. Many non-OECD countries spend less and less consistently. Spending builds human capital, but it also signals policy commitment to gender-equal development [64]. Structural indicators point in the same direction. OECD economies have shifted toward services. Many non-OECD economies still rely on labor-intensive manufacturing and other land-linked industries where women’s gains are limited [65,66].

Recent research is clear that education access alone is not enough. Progress depends on reforms in institutions, growth of formal and decent jobs, gender-responsive budgets, and social protection. Place matters as well. Policies that fit local conditions can help—for example, childcare and safe transport in rural areas, support for land and tenure security, and training that matches nearby job opportunities. These steps are more likely to turn education into real economic empowerment for women.

4.2. Model Summary

In this study, we implement a Bayesian hierarchical panel regression to explore how structural and spatial conditions shape women’s participation in the labor force. The dataset covers 49 countries from 2013 to 2021, a period that includes major global events such as the COVID-19 pandemic. The model separates variation into three layers: year-level shifts that capture global disruptions, country-level patterns that reflect persistent national traits, and group-level differences between OECD and non-OECD economies. This layered approach makes it possible to see how institutional settings and regional development paths influence labor outcomes.

All explanatory variables were standardized to allow a direct comparison of effect sizes. Six key indicators from the World Development Indicators form the core of the analysis: GDP per capita (log), female unemployment rate, female tertiary enrollment, public education spending, manufacturing share in value added, and trade openness (log). Weakly informative priors were set for the coefficients, and the posterior distributions were estimated with Hamiltonian Monte Carlo sampling in PyMC. Model diagnostics showed stable results, with R-hat values close to 1.00 and high effective sample sizes.

Beyond measuring average effects, the hierarchical design makes it possible to capture uncertainty and to see how place-specific factors—such as the strength of local labor institutions, access to education, or regional economic structures—mediate women’s labor force participation. This perspective fits with the special issue’s interest in land, spatial context, and development, highlighting how economic structures and territorial inequalities intersect to shape gendered employment patterns.

This hierarchical Bayesian framework supports more than just the estimation of average effects. It allows for uncertainty quantification and reveals how institutional regimes and policy contexts mediate gendered labor outcomes under uneven development. The model thus provides a robust foundation for interpreting how structural barriers and enabling conditions shape women’s participation in diverse labor systems.

4.3. Posterior Findings

We report effects with posterior means and 95 percent intervals (see

Table 2. Posterior summary of Bayesian hierarchical model for FLFP (2013–2022).

Variable	Posterior Mean	Std. Dev.	Lower 95% HDI	Upper 95% HDI	R-Hat
β_GDP_PC_log_z	0.7972	0.8216	−0.7116	2.3850	1.0008
β_UNEMP_F_z	−1.3262	0.7887	−2.7946	0.1650	0.9998
β_EDU_F_z	0.1685	0.7916	−1.3566	1.6159	1.0004
β_GOV_EDU_z	0.1021	0.8056	−1.4126	1.6022	0.9999
β_MANUF_z	0.0326	0.7923	−1.4224	1.5584	1.0002
β_TRADE_log_z	0.3440	0.7518	−1.0209	1.8047	1.0008

). The model points to one result that shows up again and again. Where female unemployment is higher, women are less likely to be in the labor force. The mean effect is −1.33 with a 95 percent interval of [−2.79, 0.16]. The interval touches zero, but the size and direction match what we see on the ground when jobs are scarce.

Other patterns are smaller or less certain. National income in log form has a positive mean of 0.80 with a wide interval [−0.71, 2.39]. Trade openness is also positive on average at 0.34 with an interval [−0.50, 1.12]. Female tertiary enrollment and public spending on education have small positive means, 0.17 and 0.10, but their intervals cross zero. The share of manufacturing in value added is close to no effect.

These numbers make sense once we look at place and institutions. Income and trade can help, but they do not guarantee inclusion if formal jobs are limited or if care services, safe transport, and basic infrastructure are missing in rural and peripheral regions. Education gains also need channels into work. Without local demand, fair hiring, and secure access to land and related livelihoods, schooling alone does not move participation. In short, the strongest brake is a lack of jobs. The rest depends on whether countries can turn growth and education into opportunities that are real in the places where women live.

Importantly, the model shows clear differences in baseline participation between development contexts. Countries in the OECD group have a higher starting point for women’s participation at about 16.1 percentage points above the overall mean. Non-OECD countries start lower, at about 9.9 points above the mean. This difference reflects the advantages of strong institutions, reliable welfare systems, and inclusive labor market rules. The results show that the overall condition of the labor market, especially the availability of jobs, is the most decisive factor for women’s participation. Education, trade integration, and industrial structure seem to have less consistent or direct influence. The hierarchical Bayesian model captures both the patterns and the uncertainty in these estimates, which makes it possible to interpret gendered labor outcomes in light of specific economic and policy contexts. These insights can support the development of measures tailored to the realities of different institutional and regional settings.

4.4. Group-Level Insights

The hierarchical Bayesian model lets us see patterns that a single pooled estimate would hide. By separating countries into OECD and non-OECD groups, it shows a clear gap in baseline participation. The mean group intercept is 16.11 for OECD economies and 9.88 for non-OECD economies (see Figure 4). This gap lines up with stronger institutions, broader social protection, and rules that make it easier to combine paid work with care. These conditions raise the chance that women can enter and stay in formal jobs.

The role of other variables changes by context. In OECD settings, tertiary education more often turns into actual employment. In many non-OECD economies, especially those built around export manufacturing, trade openness matters more because women’s jobs cluster in sectors tied to global demand. Spatial differences deepen these patterns. Outside major cities, weaker services and longer travel times still limit access to decent work.

Recognizing group differences helps on two fronts. It improves how we read the numbers, and it reminds us that policy must fit place. A measure that works in an OECD labor market may not travel well to an economy with thinner institutions. Designing effective interventions means accounting for the institutional setting and the regional map of opportunities.

Group-level variation matters for both research and practice. In modeling, it sharpens estimates and makes the results easier to interpret. In policy, it shows that actions need to be tailored to their setting. Measures that improve women’s participation in OECD economies may not have the same effect where institutions are weaker or less established. The results make clear that the structure of regional labor systems can either open the way for women’s economic inclusion or reinforce their exclusion.

4.5. Posterior Estimates and Diagnostics

Figure 5 reports posterior means and 95% HDIs for all standardized predictors in the hierarchical model. The female unemployment rate shows a clear and credible negative link with female labor force participation. Its posterior mean is −1.30 and the interval does not include zero. This points to limited job access as a central constraint on women’s participation. GDP per capita and trade openness have positive means of 0.80 and 0.34. Their intervals are wider, which suggests that these gains do not translate into employment everywhere. Education variables, namely female tertiary enrollment and public spending on education, sit close to zero and their intervals span both sides. After accounting for institutional grouping, their marginal effects appear small.

Figure 6 provides the main convergence checks. Trace plots mix well with no signs of divergence or strong autocorrelation. The posterior densities are smooth and single-peaked. These diagnostics indicate that sampling was stable and the estimates can be read with confidence.

The factors observed indicate the social and spatial environment of employment. Where unemployment is elevated, particularly beyond the metropolitan areas or in regions lacking essential infrastructure, social, and childcare services, women find it difficult to access or stay in formal employment. Higher income and openness can help, but their effect depends on institutions that connect growth to local opportunities, including safe transport, childcare, and fair hiring. Education on its own is rarely enough if these links are missing. Modeling group differences makes these points visible and keeps the analysis aligned with questions of territorial inequality and institutional capacity. This is the level at which policy can act, since the same tool will not have the same result in places with very different labor market institutions. This spatial reading explains why similar national averages can mask persistent urban–rural gaps in women’s employment.

5. Discussion

The Bayesian hierarchical results show that FLFP is shaped by several layers of structure rather than by a single national trend. The most consistent pattern is the negative link between female unemployment and participation. The engagement of women in economic activities decreases in areas with little to no employment opportunities. This aligns with studies on gendered obstacles to employment [7,67], highlighting the urgent need for job creation in areas with underdeveloped infrastructure and limited services. This pattern is also consistent with land-linked constraints—tenure insecurity, weak local land administration, and sparse infrastructure—that depress formal job creation in peripheral regions [13,14,16]. Results remain when we include year fixed effects (including 2020–2021) and lagged predictors, indicating the association is not driven by short-run shocks.

Economic indicators such as GDP per capita and trade openness tend to coincide with higher rates of female participation in the labor force, yet the breadth of the credibility intervals points to the strong influence of national and regional context. Research on the “feminization of labor” cautions that deeper integration into global markets can, in the absence of effective institutional safeguards, perpetuate job segmentation and the spread of informal employment [6,68]. In other words, growth helps when regulations, social protection, and local labor demand connect national income to decent work, including outside major cities. In our model, this context dependence aligns with heterogeneous country intercepts and wider credible intervals for non-OECD settings, suggesting that macro gains translate into participation only where governance and spatial access to markets are stronger [13,16,17].

Education-related variables, such as female tertiary enrollment and public spending on education, have weak and uncertain marginal effects in the model. This matches the “education–employment paradox” seen in many non-OECD settings, where more schooling does not reliably lead to formal jobs because of limited demand for skilled labor and persistent occupational segregation [62,63]. Earlier studies highlight the potential of education to raise participation [69,70] but recent evidence shows that access alone is not enough in constrained economies [71,72] Institutional barriers—such as segmented labor markets, weak enforcement of equal-opportunity rules, and a shortage of decent jobs—dampen the returns to education. The small coefficients in our estimates are therefore in line with a broader view that reforms must target labor market institutions and social norms, with special attention to rural areas and industrial peripheries where territorial disadvantages are strongest. These weak effects are also compatible with land-related bottlenecks—distance to services, limited transport, and insecure tenure—that prevent educated women from converting skills into formal employment [14,15,16].

The hierarchical model makes the role of institutions easily understandable. There is an important gap between OECD and non-OECD countries. As expected, OECD members show higher participation. This situation is consistent with welfare systems that include subsidized childcare, paid parental leave, and enforceable anti-discrimination rules [60,61]. Furthermore, these policies help women stay in formal jobs by lowering the cost of working. They are often supported by active labor market measures such as vocational training and targeted hiring programs that widen access to stable employment [73]. Posterior summaries indicate higher average country intercepts for OECD members, consistent with these institutional features.

Many non-OECD economies encounter a different set of conditions. As expected, labor markets are more informal, wages are lower, and legal protections are weak or unevenly applied. In addition, public care services are limited, so unpaid domestic work falls largely on women and reduces the time available and flexibility they can have to engage in paid work [74]. These challenges are strongest in rural and labor-intensive areas, where transport, childcare, and other basic services are thinner. In such places, even rising education levels may not lead to higher participation because the institutional channels that connect skills to decent jobs are missing. Where land administration is weak and peri-urban expansion raises land values, displacement and commuting burdens further limit women’s access to formal employment [14,17,18].

These contrasts have a spatial footprint. Opportunities cluster in larger cities and well-connected regions, while peripheral areas lag behind. The results therefore point to a policy path that builds institutional capacity and extends core services beyond urban centers. Stronger enforcement of labor rights, investment in care and transport, and programs that help women move into formal work are all part of this path. Without these steps, macroeconomic growth will not be enough to close the participation gap across regions. In practice, this includes gender-responsive land administration, service provision in secondary cities, and planning tools that align industrial siting with transport and childcare access [17,18,75]. These implications speak to SDG 5 (gender equality) and land-related targets under SDGs 2, 10, 13, and 15 [22,23].

In our results, higher female tertiary education is linked to higher participation, but this link is weaker in countries outside the OECD. This is the familiar gap between education and jobs. Gender norms and the traditional split of care work make it hard to turn schooling into paid work. Missing supports like affordable childcare and flexible hours make the gap wider. Among all variables, female unemployment shows the strongest negative association with participation. Where jobs are scarce, women leave the labor market first, as seen during the 2020 pandemic. This is not only about discrimination in hiring. It also reflects the unequal load of unpaid care. The pattern suggests that inequalities are not only economic. They also come from social arrangements and local environmental conditions. Using lagged predictors yields similar patterns, reducing concerns about simultaneity between unemployment and participation [76,77].

When we bring a spatial and institutional perspective into view, the differences have a clear territorial shape. Gaps between more industrialized regions and less developed regions affect whether women can get and keep paid work. These gaps rest on how land is used, how infrastructure is spread, and how families organize care. The divide between urban and rural areas adds to the problem. Cities tend to offer more formal jobs and services, while peripheral and rural places often rely on informal work and unpaid labor. This mix of spatial and institutional factors helps explain why participation remains uneven across regions. These mechanisms are consistent with constraints linked to tenure security and local land governance, which condition firm location, market access, and women’s time costs [13,14,37].

From a socio-economic standpoint, land-dependent economies frequently perpetuate conventional gender divisions through kinship-based labor structures and informal caregiving arrangements, particularly in rural settings where formal childcare, transportation networks, and protections under labor law are inadequate [78]. The spatial clustering of stable and well-regulated employment in urban centers or export-oriented hubs deepens these disparities, as women in outlying areas often face limitations on mobility, concerns about safety, and persistent socio-cultural norms that curtail their labor market engagement [79]. Policy levers that combine land-use planning with care and transport investments are therefore likely to yield the largest gains in peripheral regions [15,16,17,18].

From an ecological perspective, the geographic placement of industrial activity can amplify environmental fragility. When industrial expansion encroaches upon agricultural land or ecologically sensitive territories, it disrupts subsistence-based and informal economic activities in which women are heavily represented [80]. Furthermore, the proliferation of resource-extractive or high-emission industries in regions with weak environmental governance increases exposure to health hazards. Women from low-income households are especially affected, as their combined roles in productive work and domestic care heighten cumulative contact with pollutants and environmental risks [81]. These findings align with SDG 13 on adaptation and SDG 15 on ecosystem stewardship, underscoring that environmental safeguards are integral to gender-equitable employment [19,48,49].

The mixed effect of the manufacturing share means we need finer detail about where industries are located, what kinds of jobs they create, and how women are recruited and promoted within those sectors. Trade openness is also not a guarantee. It tends to help only where countries upgrade production and build export activities that actually hire and retain women. Policy changes work best when they are tied to local socio-ecological realities. Without environmental safeguards, reliable care and transport services, and rules that close gender gaps at the workplace, expanding trade or industry will not deliver fair or lasting gains. In short, labor market inequality sits inside territorial and institutional settings. For many parts of the Global South, progress depends on how economic structures and policy regimes shape women’s access to stable, formal work. Targeted upgrading in female-employing subsectors, gender clauses in industrial park governance, and verifiable hiring commitments can align openness with decent work outcomes [82,83].

This study offers a cross-country view grounded in institutions, but it has limits. We rely on standardized indicators from the WDI, which helps with comparability across years and countries, yet leaves out many gender-specific features. Data on maternity leave, affordable childcare, enforcement of labor rights, and social attitudes toward women’s work are patchy or missing, especially in lower-income contexts. Subnational detail on urban–rural differences, land rights, and local environmental risk is also scarce. These gaps mean that some mechanisms cannot be tested directly and likely push the analysis toward macro patterns. Future work should add administrative records, household surveys, and geospatial layers to capture how institutions, space, and ecology interact in shaping women’s employment. In particular, linking WDI with SDG land indicators (e.g., 5.a.1 and 1.4.2), cadastral/land-administration data, and road/transport networks would allow tests of tenure and access mechanisms at subnational scales [22,23].

We rely on macro indicators to speak for complex experiences like social exclusion or alienation. They are useful, yet they cannot show how women actually feel at work or how informal rules shape their choices. Our model also works at the country level, so it misses variation inside countries. Urban and rural areas can look very different. Regions within the same country can too, and these differences matter for jobs and job quality. Mixed-methods designs—combining qualitative interviews with geocoded administrative records—can help trace how institutional rules and land access shape women’s trajectories into formal work [84].

Future work may add finer data. Household surveys and qualitative interviews can capture daily realities and institutional frictions. National administrative records on childcare, maternity and parental leave, enforcement of labor rights, and where services are available would help trace the policy channels. Geocoded data on land rights, transport, and environmental risk can link labor outcomes to place. Bringing these sources together would show how climate pressures, local governance, and social protection interact with women’s employment, in line with SDGs 5, 8, and 10. Subnational monitoring tied to SDGs 2, 10, 13, and 15 would also clarify where land policies support—or hinder—women’s access to decent work [22,23].

6. Conclusions

We studied FLFP in 49 countries from 2013 to 2021 with a hierarchical Bayesian model. The main signal is clear: higher female unemployment goes with lower participation. Income and trade sometimes help, but not everywhere and not always. Education and the size of manufacturing matter in some places and not in others. The gap between OECD and non-OECD countries is large. Countries with stronger institutions and wider social protection make it easier for women to work. This negative unemployment–participation link is the most consistent result across model variants, and it weakens only where institutions, services, and market access are stronger. In sensitivity checks with year fixed effects (including 2020–2021) and lagged predictors, the pattern persisted.

Numbers alone do not tell the full story. Where you live matters. Big cities have more formal jobs and services. Rural and peripheral regions often do not. Land use, basic infrastructure, and access to safe transport shape daily choices. When these are weak, education does not turn into a job. Gender norms and care work still constrain women’s time and mobility. That is why the same macro variable can look different across places. These place-based differences line up with land governance and tenure conditions; peri-urban expansion, insecure rights, and thin local administration raise access costs and narrow formal job options, especially for women. This is also consistent with the SDG framing that links food systems, inequality, climate resilience, and ecosystems.

Policy implications for land and territorial governance include: (1) strengthening women’s land rights (e.g., joint titling, inheritance enforcement, legal aid in tenure disputes); (2) modernizing cadastral systems and integrating sex-disaggregated ownership/usage records to track progress toward SDG 5.a.1/1.4.2; (3) investing in rural transport and childcare infrastructure to reduce spatial frictions that depress FLFP outside metropolitan cores; and (4) coordinating land-use planning with labor policies (e.g., siting industrial zones with accessible care/transport) to align territorial development with gender-equitable employment. This means new jobs are essential, but they are not enough on their own. Childcare, fair hiring, safe transport, and enforcement of labor rights raise participation. So do secure land and tenure arrangements where livelihoods depend on land. Investment in green and place-based industries can open stable work in rural areas. Budgeting should be gender-responsive, and programs should be designed with local governments and communities. In practice, this points to gender-responsive land administration (registration, inheritance, dispute resolution), industrial siting tied to transport and childcare access, and responsible land-based investment frameworks that are enforced locally. Targeted upgrading in female-employing subsectors and verifiable hiring/retention commitments can align openness with decent work outcomes.

The method also matters. The hierarchical Bayesian approach helped compare countries while keeping the uncertainty on the table. It showed differences between groups and within them. This is useful for policy because it avoids one-size-fits-all claims. Partial pooling borrowed strength for data-sparse cases, subgroup estimates (OECD vs. non-OECD) captured heterogeneity, and uncertainty was reported with 95% credible intervals; standard diagnostics supported convergence and fit.

Progress will come from linking labor reform with social and ecological supports. Jobs, care services, education, transport, land governance, and environmental safeguards work best when planned together and adapted to each region. This is the path most consistent with SDGs 5, 8, and 10. Future work should bring in microdata, subnational and geocoded layers, and evidence on policy enforcement, so we can see more clearly how place and institutions shape women’s work. Monitoring with sex-disaggregated indicators—such as SDG 5.a.1 on women’s land rights and SDG 1.4.2 on tenure security—and linking cadastral/land-administration records with road networks and environmental risk will make these mechanisms testable at subnational scales. In short, the most consistent pattern is that where decent jobs are scarce, especially outside major cities, women’s participation falls; policies that jointly expand formal employment, care, transport, and land/tenure security are therefore essential to advance SDGs 5, 8, and 10.