Analysis of the income gap between women with and without disabilities beneficiaries of a microenterprise program in Chile Open Access

Globally, over one billion individuals live with some form of disability, with women representing a disproportionately higher share—19% compared to 12% of men. In low- and middle-income countries, women comprise up to 75% of the population with disability, making the intersection of gender and disability a critical factor in shaping experiences of poverty and social exclusion (UN Women, 2017, 2022). In Chile, disability is defined as any physical, mental, intellectual, sensory, or other health-related condition that restricts full and active societal participation due to contextual, attitudinal, and environmental barriers (Rozas Assael et al., 2023; SENADIS, 2015).

Employment plays a significant role in improving the well-being and autonomy of people with disabilities by reducing social costs and enhancing their independence (Pérez-Macías and Fernández-Fernández, 2021; Tussy and Bonino, 2019). However, people with disabilities have lower employment rates and higher rates of inactivity and unemployment compared to those without disabilities (Stoeveska, 2022). They often encounter additional challenges in the labor market, including physical and attitudinal barriers that limit their employment and income opportunities. In Chile, 17.6% of the population over 18 years old has some form of disability, which equates to 2,703,893 people (Rozas Assael et al., 2023). This gap in access to employment and income increases inequalities and social exclusion.

In this context, entrepreneurship emerges as a viable alternative for people with disabilities, as it allows them to tailor their activities to their needs and achieve greater autonomy. At a broader level, entrepreneurship is widely recognized as a catalyst for economic growth and job creation (Acs et al., 2018; Geldes et al., 2017; Munyo and Veiga, 2024). According to the Global Entrepreneurship Monitor (GEM), 582 million entrepreneurs and business owners worldwide significantly contribute to regional economic development (Hill et al., 2024).

However, despite its potential to drive economic and social development, entrepreneurship in Latin America faces important challenges, particularly in relation to informality, which results in low levels of formal employment and a significant gender wage gap (Maurizio, 2021; Munyo and Veiga, 2024; Ruiz-Martínez et al., 2021). In this context, a complex landscape emerges that necessitates a deep understanding of the underlying dynamics and their implications for those involved, particularly in countries like Chile, an emerging economy with high rates of entrepreneurship but also a high level of socioeconomic inequality (World Bank, 2024a).

While entrepreneurship research has increasingly explored disadvantaged groups, entrepreneurship led by persons with disabilities (PwD) remains underexplored (Saiful Bahry et al., 2023), particularly from an intersectional perspective that simultaneously considers gender and disability (Hidegh et al., 2023; Williams and Patterson, 2018). This gap is especially pronounced in Latin America, where entrepreneurship research is less developed (Lopez and Alvarez, 2018; Martinez et al., 2018) despite high entrepreneurial activity rates characterized by informality, low innovation, and weak institutional support—factors that disproportionately affect women (Ruiz-Martínez et al., 2021; Vaca Trigo, 2019).

This study examines the income gap between women entrepreneurs with and without disabilities among entrepreneurs in Chile's Valparaíso region. We employ ordinary least squares (OLS) regression analysis with sequential model specifications, followed by Propensity Score Matching (PSM) to ensure robustness and address potential selection biases. Methodologically, modeling income using these techniques testing whether observed disparities reflect disability status per se or are explained by structural factors such as sectoral concentration, business characteristics, and resource access. By employing linear regression models and propensity score matching, we isolate the specific effect of disability from confounding factors. The results offer a comprehensive view of disability-related income disparities in the context of female entrepreneurship and, in line with future research directions suggested by Strawser et al. (2021), contribute to identifying factors that allow us to understand how intersectionality operates in business contexts.

We present an analysis of the determinants of income among women entrepreneurs with disabilities participating in a targeted government support program. While existing literature documents disability penalties on business income (Sanga et al., 2023; Yang et al., 2022), our analysis shows that observed gaps could be related to socioeconomic characteristics such as economic sector, domestic responsibilities, and business experience. This finding suggests that apparent disability gaps are channeled through observable structural mechanisms, such as sectoral concentration and systematic differences in business characteristics, rather than representing direct discrimination or productivity differences attributable to disability itself. Consequently, interventions should consider the structural conditions through which disadvantage operates rather than focusing solely on disability status. This has important implications for how entrepreneurial disadvantage is conceptualized and measured in policy contexts. However, our dataset captures only a limited set of structural factors within a specific regional program, opening important questions for future research examining broader structural determinants and their effects across diverse entrepreneurial contexts.

The paper is structured as follows: following this introduction, a literature review is presented, focusing on entrepreneurship and disability and its implications for women entrepreneurs in the context of this study. Next, the methodology section outlines the data used and describes the regression model employed. The results of the research are presented in the following section. Finally, the discussion of the results and the conclusions of the study are provided.

Entrepreneurship has garnered significant academic interest internationally as it is considered a driver of economic growth and social development in nations (Acs et al., 2018; Geldes et al., 2017; Munyo and Veiga, 2024), but entrepreneurship research has traditionally focused on studying entrepreneurial elites, who embody an idealized model —white, male, Western, and highly educated. This focus excludes marginalized populations relevant to the field (Hwang and Phillips, 2024) as well as other forms of entrepreneurship that contribute to wealth and job creation (Welter et al., 2017).

Martinez Dy (2020) argues that potentially disadvantaged groups in entrepreneurship —such as women and PwD— are positioned at different distances from the ideal entrepreneurial model, shaping a specific social positioning. This positioning is dynamic and depends on agency, social structure, and culture. While meritocracy influences agency, social structure encompasses systems of social relations, and culture includes belief systems where gender and other interrelated factors of discrimination play a significant role (Martinez Dy, 2020; Ozkazanc-Pan, 2022). This perspective supports understanding entrepreneurship as a relational, contextual, and gendered phenomenon (Ozkazanc-Pan, 2022; Welter, 2011, 2020; Welter et al., 2019).

Research on entrepreneurship and gender has documented the structural barriers faced by women entrepreneurs, such as limited access to credit, concentration in low-productivity sectors, underrepresentation in business networks, and the disproportionate burden of domestic and care responsibilities. As a result, women entrepreneurs tend to report lower income levels compared to men (Crane, 2021; Kevane et al., 2024). However, in general, literature works treats women as a homogeneous group, hindering the recognition of intragroup differences—particularly the specific situation of women with disabilities (Garcia et al., 2026; Harrison et al., 2020).

The intersectional perspective proposes analyzing how gender interacts with other social categories, such as disability, resulting in differentiated experiences of exclusion and unequal opportunities (Crenshaw, 1989; Henry and Marlow, 2014). This framework is essential for understanding the observed income gaps between women with and without disabilities in entrepreneurial contexts (Hechavarría et al., 2024). It also will help to identify structural mechanisms that influence both access to and performance in entrepreneurial activities.

While research has diversified to explore different aspects and disadvantaged groups, entrepreneurship led by PwD remains underexplored (Saiful Bahry et al., 2023), particularly from an intersectional perspective that simultaneously considers gender and disability (Hidegh et al., 2023; Williams and Patterson, 2018). In emerging economies, PwD face several impediments to accessing the labor market, and support from actors in the entrepreneurial ecosystem is crucial to address persistent structural barriers (Malhotra et al., 2024). From an intersectional gender perspective, evidence has been found that entrepreneurs with disabilities face these structural barriers, which are more significant for women than for men. Furthermore, greater development and support from public policy is needed (Sodhi and Dwivedi, 2024; Usman and Projo, 2021).

Kašperová (2021) studied groups of women entrepreneurs with disabilities in the United Kingdom, highlighting their strategies to gain legitimacy by making disability visible within their businesses. Along similar lines, Jammaers and Williams (2023) emphasize that low societal expectations in ableist contexts, dominant stereotypes, and distorted assumptions—such as the notion that PwD are only valuable if they can turn misfortune into success—establish disproportionately high standards for all PwD, a phenomenon known as the “achievement syndrome”.

In the Latin American and Caribbean (LAC) region, entrepreneurship research is less explored (Lopez and Alvarez, 2018; Martínez et al., 2018), despite the region's high rates of entrepreneurship. Informality, low innovation levels, and weak institutional articulation prevail. These characteristics disproportionately affect women, who face income gaps and barriers to accessing resources and networks. In this context, necessity-driven entrepreneurship predominates among women in disadvantaged sectors, reinforcing patterns of economic precarity and vulnerability (Ruiz-Martínez et al., 2021; Vaca Trigo, 2019). A recent study on the role of women in entrepreneurship and innovation in LAC highlights the importance of making visible both the gender gaps and the underlying reasons that explain them (Kuschel et al., 2022).

In this regional context, Chile stands out with one of the highest rates of entrepreneurial activity (Pinkovetskaia et al., 2020). Studies such as Cuberes and Teignier (2022) show that women-led firms tend to be smaller and less productive. Ruiz-Martínez and Quiroz-Rojas (2022) found that informality in entrepreneurship is strongly influenced by gender roles and weak institutional coordination. Moreover, the intersection of gender and other factors, such as disability, produces specific forms of exclusion that have been insufficiently explored in regional entrepreneurship studies (Byrne and Giuliani, 2025; Flores-Novelo et al., 2021; Ruiz-Martínez, 2023).

Finally, empirical evidence suggests that many PwD pursue entrepreneurship not solely for financial gains but also for non-pecuniary reasons such as autonomy and self-determination, especially in contexts where labor market discrimination remains pervasive (Chung and Yu, 2022; Queruel et al., 2023).

Disability is a complex, diverse, and dynamic field (World Health Organization, 2011). However, several studies indicate that entrepreneurship support programs rarely adopt an intersectional perspective that considers gender and disability simultaneously (Cukier et al., 2022; UN Women, 2022; Sodhi and Dwivedi, 2024). This absence limits their scope and effectiveness.

Multiple studies point to systemic limitations in the design, implementation, and monitoring of these programs, which constrain their impact on income generation and business sustainability for women with disabilities (Lawton, 2023; Mendoza, 2021). These shortcomings often stem from a failure to address the real and intersecting barriers faced by women with disabilities—ranging from infrastructural and social to financial and technological obstacles.

In diverse contexts such as India, Australia, and Bangladesh, qualitative studies highlight the importance of education, family support, individual resilience, and policy access as key factors in fostering entrepreneurship among PwD (Miller and Le Breton-Miller, 2017; Saxena and Pandya, 2018; Darcy et al., 2020; Dhar and Farzana, 2017).

In European welfare states, policies supporting labor market inclusion for PwD are aimed at enabling autonomous living. These policies typically focus on four dimensions: economic assistance, labor support, shared responsibility for social and labor integration, and measures for job-person fit. However, entrepreneurship support remains limited, as stability and job security are prioritized (Rosell and Belzunegui, 2020).

In Chile, various public policies supporting disability have been implemented (Gobierno de Chile, 2024). While measures such as quotas for hiring PwD that promote labor inclusion, there are no inclusive programs targeting entrepreneurship or women. A mapping study of 69 entrepreneurship support programs for women in Chile found no evidence of an intersectional approach (Araya et al., 2022). As Martinez Dy (2020) argues, the social positioning of “women entrepreneurs with disabilities” presents unique needs distinct from those of the two groups separately, requiring specific measures to overcome the systemic barriers they face, which appear to be unaddressed.

PwD faces systemic disadvantages in both the labor market and entrepreneurship. Previous studies show a significant income gap associated with disability, partially explained by limited access to finance, training, infrastructure, and markets (OECD and European Commission, 2023b; World Bank, 2024b). These structural barriers constrain business performance and reduce prospects for economic independence.

Sanga et al. (2023) reports that PwD earns significantly lower income from self-employment, and such income represents a smaller share of total household earnings. Yang et al. (2022) emphasize that factors such as age at the onset of disability, unmet adaptation needs, and gender significantly influence entrepreneurial income.

Further studies highlight specific structural barriers: lower levels of formal education, widespread social stigma, and discriminatory practices in labor and financial markets (OECD and European Commission, 2023a). These barriers hinder not only the start-up process but also the consolidation and growth of entrepreneurial ventures.

Restricted access to business training, financing mechanisms, and mentoring programs further limits the income-generating potential of women entrepreneurs with disabilities (Mendoza, 2021; Lawton, 2023). These limitations are compounded by physical, technological, and social constraints, all of which obstruct full participation in entrepreneurial ecosystems (Sanga et al., 2023; Yang et al., 2022; Tiasakul et al., 2024). Collectively, these factors help identify the structural barriers many women with disabilities face in achieving sustainable income levels through entrepreneurship.

Gan et al. (2022), in a study of digital women entrepreneurs with disabilities, found that those who gained access to digital platforms experienced income growth. However, they also observed that the type of business, digital exclusion, and initial socioeconomic conditions contributed to important disparities. This is consistent with findings by Cukier et al. (2022), who emphasize that the intersection of gender, disability, and socioeconomic status influences opportunity structures, working conditions, and business outcomes.

Finally, Saiful Bahry et al. (2023) identifies additional constraints that affect the economic performance of entrepreneurs with disabilities, such as high costs associated with impairment, social exclusion, health risks, and lack of targeted support systems. These findings reinforce the argument that lower performance levels are not the result of individual capacity, but rather the consequence of systemic and structural inequalities embedded in the broader socio-economic context.

Based on this theoretical foundation and empirical evidence, we expect to observe income disadvantages for women entrepreneurs with disabilities. However, following the structural barriers perspective, these income differences should be largely explained by observable characteristics—sectoral concentration, business factors (formalization, years in business), household characteristics, and differential resource access—rather than disability status per se. This expectation aligns with arguments that lower performance reflects “systemic and structural inequalities rather than individual capacity” (Saiful Bahry et al., 2023).

The following empirical analysis tests these expectations by examining income differences while controlling for comprehensive individual, household, business, and contextual factors.

The Microenterprise Support Program is a nationwide Chilean government initiative aimed at promoting entrepreneurship among the most vulnerable 40% of the population. It is implemented through regional offices and offers technical assistance, business training, and access to microcredit for emerging entrepreneurs.

Participants receive individualized mentoring over a period of 12–18 months, covering business plan development, financial literacy, and market linkage support. Importantly, the program adopts an inclusive approach, actively encouraging participation of individuals with disabilities through partnerships with disability organizations and accessible registration processes.

This study focuses exclusively on women entrepreneurs from the Valparaíso Region, in order to ensure homogeneity and comparability across cases. Accordingly, the final analytical sample consists of 1,658 women entrepreneurs who participated in the program between 2013 and 2016. All participants belonged to the lowest 40% of the income distribution, according to Chile's Socioeconomic Characterization Framework. Among them, 125 women (7.54%) reported having a disability, while 1,533 (92.46%) did not. All income observations are positive, allowing for the direct application of a log-linear model without the need for adjustments for zero-income values.

The descriptive analysis reveals that women with disabilities tend to work longer hours but earn less, both in monthly and hourly terms. However, they exhibit higher levels of formalization and savings, suggesting the presence of distinct financial strategies. Table 1 summarizes these characteristics.

The comprehensive descriptive statistics presented in Table 1 reveal several important patterns. Despite similar sectoral distributions, women with disabilities work longer hours yet earn less per hour and monthly. Interestingly, they show higher formalization rates and higher savings, suggesting different financial management strategies. The higher unemployment rate among women with disabilities (23 vs. 18%) contrasts with their greater business formalization, indicating potential barriers in transitioning from unemployment to formal entrepreneurship. These patterns inform our analytical approach and highlight the complexity of factors influencing entrepreneurial outcomes.

We applied a 40% missing data threshold for variable exclusion, following established guidelines in social science research (Bennett, 2001). This threshold balances data quality with sample size preservation, particularly important when studying vulnerable populations where missing data patterns may not be random (Rubin, 2008; Schafer and Graham, 2002). Variables exceeding this threshold were excluded, while those with acceptable data quality were retained through listwise deletion for complete case analysis. Detailed information on missing data patterns and variable selection criteria is provided in the Appendix.

This study aims to analyze income disparities between women entrepreneurs with and without disabilities who participated in a public entrepreneurship support program. Specifically, it investigates whether disability status is associated with lower hourly income, controlling for individual, household, business, and contextual characteristics.

The dependent variable is the income per hour of work, calculated as monthly sales revenue divided by total monthly hours worked (self-reported). This measure allows for a fairer comparison of entrepreneurial performance across disability groups, since it adjusts for time allocation and work intensity. All income values are standardized to 2016 U.S. dollars, controlling for inflation over the period.

The analysis incorporates multiple explanatory variables grounded in economic and sociological theory.

1. Disability status (key variable of interest): Binary indicator (1 = disability) capturing the hypothesized penalty documented in disability-entrepreneurship literature.

2. Entrepreneur and household characteristics: Age (life-cycle effects), head-of-household status (economic responsibility and motivation), and household size (family labor/support networks) control for demographic heterogeneity linked to earnings capacity.

3. Business characteristics: Formalization (market access, regulatory compliance) and years in business (accumulated experience) capture firm-level mechanisms through which disability may operate.

4. Economic sector: Eighteen sector dummies proxy for structural barriers and gendered segregation in entrepreneurial ecosystems.

5. Geographic and temporal controls: Municipality and year fixed effects net out local market conditions and macroeconomic shocks affecting income trajectories.

Education level was not included as a covariate due to data limitations. Previous studies characterizing similar programs show that only 5% of entrepreneurs have technical or higher education levels, with significantly higher incomes only at these levels (Scapini et al., 2024), indicating a relatively homogeneous educational profile among participants from the most vulnerable population sectors.

The main outcome variable is hourly income, analyzed in relation to disability status. Before conducting group comparisons, we rigorously tested the distributional assumptions of the income variables. The Shapiro–Wilk test indicated significant deviations from normality (p < 0.001; see Appendix, Table A1), consistent with the positive skew commonly observed in entrepreneurial earnings. Consequently, we employed the non-parametric Mann–Whitney U test, which is robust to non-normal distributions and unequal group sizes. The analysis revealed statistically significant differences for both monthly income (W = 77,306.5, p < 0.001) and hourly income (W = 84,629.5, p = 0.029). To assess the magnitude of these disparities, we calculated the Hodges–Lehmann (HL) median difference estimator, which indicated a location shift of –USD 59.10 for monthly income and –USD 1.32 for hourly income (see Appendix, Table A2). These findings confirm that the observed income gaps are statistically robust and not driven by distributional irregularities.

Our analytical strategy is developed in three complementary stages to examine income disparities associated with disability: (1) Descriptive analysis documents raw differences between groups in income and other characteristics (Table 1); (2) OLS regression isolates the effect of disability by progressively conditioning on control variables, allowing us to assess whether observed gaps reflect disability status or other observable differences; (3) Propensity Score Matching (PSM) provides robustness by creating matched comparison groups, addressing concerns about distributional differences and potential selection bias.

Together, these approaches can provide consistent evidence on whether disability status independently predicts entrepreneurial income after accounting for observable characteristics.

Following this preliminary analysis, a log-linear regression model to examine the relationship between disability status and hourly income was designed. The model is specified as follows:

Where:

1. log (y): represents the natural logarithm of the dependent variable, hourly income.

2. Disability: Dummy variable “Disability” (1 = disabled; 0 = non-disabled) captures the effect

3. ε: captures unobserved factors affecting hourly income

This log-linear model (1) is appropriate for this data for several reasons. First, the logarithmic transformation of income addresses the non-normality of the income distribution, improving model suitability for estimation. Second, since PwD tend to work fewer hours than those without disabilities, we use the hourly wage per hour worked as the dependent variable, to make a proper comparison between the wages of the two groups (Ananian and Dellaferrera, 2024). Third, the dummy variable for disability status allows for a straightforward comparison between the two groups: individuals with and without disabilities.

The model selection process follows a sequential approach to isolate the effect of disability status while controlling for potential confounders. This step-wise methodology allows us to assess how the disability coefficient changes as additional controls are introduced, helping to identify which factors explain the observed income differences.

We estimated four sequential models with increasing complexity:

1. Model 1 includes only the disability variable as the base specification.

2. Model 2 adds entrepreneur and household characteristics: age, household size, and head of household status.

3. Model 3 incorporates business characteristics (formalization status, years in business) and economic sector controls.

4. Model 4 includes municipality and year fixed effects to control for unobserved spatial and temporal heterogeneity.

For Models 2–4, Breusch-Pagan tests indicated heteroscedasticity, so robust standard errors were applied (detailed test results in Appendix).

Due to their natural structure, the data exhibit an unbalanced distribution between classes. Therefore, a propensity score matching (PSM) analysis is added to the models to mitigate the possibility of drawing erroneous conclusions and to verify the consistency of the results through a fairer and more balanced comparison of treatment and non-treatment groups.

The inclusion of PSM aims to strengthen the causal interpretation of the relationship between disability status and income. While OLS regression provides unbiased estimates under the assumption of random assignment, in observational data such as ours, individuals with and without disabilities may differ systematically in observable characteristics that also affect income (e.g. age, household composition, or business experience). This selection bias can distort the estimated effect of disability.

PSM mitigates this problem by constructing a matched comparison group of women without disabilities who are statistically similar to those with disabilities across all observed covariates. In this sense, PSM complements the OLS models by reducing bias due to non-random sample composition, providing a robustness check for the regression estimates. Furthermore, PSM addresses potential model dependence in OLS by relying less on functional form assumptions, focusing instead on the comparison of units with similar propensity scores.

By combining both approaches, the analysis ensures that the estimated income gap is not merely driven by observable differences in demographic or business characteristics but reflects the residual effect associated with disability status.

The results of all OLS regression models are presented in Table 2. In the initial specification (Model 1), disability status shows a negative and statistically significant effect on hourly income, with a coefficient of −0.1333 (p = 0.042). This indicates that entrepreneurs with disabilities earn approximately 12.5% less per hour than those without disabilities. Tests for heteroscedasticity confirmed appropriate model specification (see Appendix for details).

Our analytical approach prioritizes identifying income disparities between groups rather than maximizing explanatory power. The sequential modeling strategy isolates the effect of disability by progressively incorporating control variables, providing a robust assessment of whether income gaps persist after accounting for observable characteristics.

As shown in Table 2, the negative association between disability and income becomes statistically insignificant in subsequent models as controls for demographic and business characteristics are added. This suggests that much of the income variation is explained by other factors, though the persistent negative coefficient indicates that income disparities related to disability may still exist and could be detected with larger and more balanced datasets.

Among the control variables, being the head of household increases income by approximately 8.5–10.2%, while age shows a negative association with earnings. Interestingly, larger household size correlates with higher income, highlighting the potential role of family structure in economic outcomes. Regarding business characteristics, years in operation positively affect income, reflecting the value of experience. In contrast, formalization shows weak and inconsistent effects, suggesting that becoming a formal business does not always lead to higher earnings. Sectoral analysis reveals substantial differences across industries: fishing, leather, and wood manufacturing show income penalties, while tourism initially exhibits a positive income effect.

The overall explanatory power of the model remains moderate (R² = 18.8% in Model 4), which is expected given the focus on isolating disability's impact rather than maximizing predictive accuracy. While demographic and business factors explain most observed disparities, the remaining negative coefficient for disability suggests persistent structural inequalities that warrant further study.

Demographic and business factors account for most of the variation in income, reducing the relative influence of disability status. Nevertheless, the persistent negative coefficient, despite losing statistical significance in the full models, suggests that structural income disparities associated with disability may still exist. These effects could become statistically detectable in analyses using larger and more balanced samples, potentially revealing smaller yet meaningful differences.

Propensity scores are estimated via a logit model using the same covariates as Model 3, and we employ one-to-one nearest-neighbor matching to estimate the Average Treatment Effect on the Treated (ATT).

The ATT compares the mean outcomes of women with disabilities to matched women without disabilities with similar observable characteristics.

Table 3 shows that matching improves covariate balance, with mean standardized bias dropping from 12.3% to 5.3%, confirming the validity of the matched comparisons.

The PSM results presented in Table 4 indicate that, after applying propensity score matching, the income difference between women entrepreneurs with and without disabilities becomes statistically insignificant (ATT = −0.053, t = −0.57). This finding reinforces the regression results, suggesting that once individuals are compared on a similar basis—controlling for demographic and business characteristics—the apparent income gap associated with disability largely disappears. In other words, the lower earnings observed before matching can be attributed to differences in observable factors rather than to disability status itself. The matching procedure successfully retained 124 out of 125 individuals with disabilities within the region of common support, confirming strong overlap and comparability between the treated and control groups.

The matching procedure successfully includes 124 of 125 individuals with disabilities within the widespread support region.

This study examines the income gap between women entrepreneurs with and without disabilities. Initial analysis revealed that entrepreneurs with disabilities earn approximately 13% less per hour than their counterparts without disabilities, a difference significant at the 5% level. However, this disparity becomes statistically insignificant after controlling household characteristics, business attributes, and economic sectors. Propensity Score Matching (PSM) confirms these findings, showing no significant income difference when comparing women with similar observable characteristics.

Although the disability coefficient becomes statistically insignificant after controlling for observable characteristics, this does not imply that both groups experience equal economic outcomes. The persistence of a negative, albeit non-significant, coefficient suggests that structural inequalities—such as sectoral concentration, unequal access to finance, or limited network integration—continue to influence entrepreneurial performance. Hence, the absence of statistical significance should be interpreted as evidence of structural convergence within the sample rather than true equality of opportunity.

These results suggest that observed income disparities are not directly attributable to disability status per se, but rather to other individual, household, and business characteristics. While existing literature documents significant disability penalties in entrepreneurial income (Sanga et al., 2023; Yang et al., 2022; Tiasakul et al., 2024), our analysis reveals that when controlling relevant characteristics, these gaps become statistically insignificant.

Our findings highlight the importance of several key determinants of entrepreneurial income. Being head of household consistently shows a positive effect (8.54–10.17% increase), which could reflect greater economic responsibility and motivation. Age shows a negative relationship with income across all models, while larger household size is associated with higher income, potentially indicating family support or expanded customer networks. Business experience (years in business) also significantly predicts higher earnings, emphasizing the value of accumulated expertise.

Economic sector differences are particularly pronounced, with sectors like fishing, furniture, and leather showing substantial income penalties compared to the reference category, while tourism showed initial premium effects. These patterns point to the importance of addressing structural barriers—such as access to financing, support networks, and markets—that may disproportionately affect women with disabilities through sectoral concentration (Mendoza, 2021; Lawton, 2023; OECD and European Commission, 2023a; World Bank, 2024b).

The results do not show significant differences in income levels between women with and without disabilities operating within the same economic sectors. These findings suggest that the observed wage gap may be attributed to structural factors, such as the overrepresentation of women with disabilities in sectors traditionally associated with lower income levels, rather than to disability status per se.

Given the relevance of the findings, future research would benefit from scaling this study to include a nationally representative dataset. Expanding the analysis beyond the Valparaíso region would allow for broader generalization of the results and a more comprehensive understanding of the income disparities faced by women entrepreneurs with disabilities across different territories. Incorporating national-level data would also make it possible to explore regional heterogeneities, assess the impact of local policies, and inform more inclusive strategies that address the structural barriers affecting this population throughout the country.

Entrepreneurship among people with disabilities can generate benefits not only for the entrepreneurs themselves but also for their communities, fostering both economic and social development. Previous studies (Bhardwaj et al., 2023; Jammaers and Williams, 2023; Kašperová, 2021; Martin and Honig, 2020; Sajjad and Talat, 2024) highlight the relationship between entrepreneurship within vulnerable groups and overall economic performance. In this context, the development and implementation of public policies that promote the inclusion of PwD in the business sector are essential.

From a policy perspective, these results underline the need to strengthen inclusive entrepreneurship programs through an intersectional approach. Public initiatives should move beyond categorical inclusion of PwD and instead target the structural factors that perpetuate disadvantage—such as financial exclusion, gendered caregiving roles, and regional disparities. Integrating intersectional evaluation criteria into policy design would allow for better monitoring of how gender and disability jointly shape access to resources and business sustainability.

This study examined the income gap between women entrepreneurs with and without disabilities in Chile's Valparaíso region. While initial analysis revealed a significant income disadvantage for women with disabilities, this gap becomes statistically insignificant after controlling for household, business, and sectoral characteristics, as confirmed through propensity score matching analysis.

These results contribute to the growing literature on entrepreneurship by providing empirical evidence that apparent income disparities between women with and without disabilities may be explained by other observable characteristics rather than disability status per se. Importantly, the finding of non-significant income differences should not be interpreted as equality, but rather as an indication that underlying structural disparities may persist beneath the observed economic outcomes. These latent inequalities underscore the importance of contextual and intersectional approaches in both research and policymaking. From a policy perspective, this study highlights the need to strengthen inclusive entrepreneurship programs through an intersectional approach. Public initiatives should move beyond categorical inclusion of PwD and instead target the structural factors that perpetuate disadvantage—such as financial exclusion, gendered caregiving roles, and regional disparities. Integrating intersectional evaluation criteria into policy design would allow for better monitoring of how gender and disability jointly shape access to resources and business sustainability. The findings support an intersectional analytical approach, demonstrating the importance of examining multiple factors simultaneously when studying entrepreneurial outcomes (Martinez Dy, 2020; Ozkazanc-Pan, 2022). Furthermore, this study contributes to the robustness of entrepreneurship research by including entrepreneurs from marginalized groups and employing rigorous methods to isolate causal effects (Hwang and Phillips, 2024; Welter et al., 2017; Williams and Patterson, 2018).

The implications of these findings are both socially and economically significant. The absence of a statistically significant income gap, once observable characteristics are controlled for, highlights that inequality may be embedded in structural conditions rather than individual attributes. This finding reinforces the idea that social and institutional barriers—rather than disability status alone—influence entrepreneurial outcomes. Therefore, inclusive policy design should address these underlying mechanisms by ensuring equitable access to training, capital, and market opportunities for women with disabilities.

Moreover, the analysis reveals that entrepreneurship income is influenced by a range of sociodemographic factors, including age, household size, head of household status, and business characteristics such as years in business and economic sector. These findings suggest that policies targeting entrepreneurial support should consider the multifaceted nature of income determinants rather than focusing solely on disability status.

In conclusion, this study demonstrates the value of rigorous analytical approaches that account for multiple factors when examining entrepreneurial outcomes among marginalized populations. Future research and policy development would benefit from adopting comprehensive frameworks that consider the complex interplay of individual, household, and business characteristics in shaping entrepreneurial success.

While the study provides valuable insights, it is restricted to women entrepreneurs from a specific region and program in Chile. Yet, this result is especially relevant given the context of a well-established national entrepreneurship support program, which offers continuous and personalized advice across all regions of the country. Extending this analysis to more diverse groups of entrepreneurs and in less controlled or protected contexts could show greater differences in this gap, which would be consistent with the indicators reviewed in the literature and current government studies.

Our models show relatively low explanatory power, with the highest R² reaching 18.81% in Model 4. However, this limitation should be interpreted within the context of our research objectives. This study focuses specifically on isolating the effect of disability status on entrepreneurial income rather than building a comprehensive predictive model of income variation. The low R² is therefore not surprising, as disability status—being a demographic characteristic—would not be expected to be a primary driver of business income compared to direct business factors such as sector specialization, product/service quality, market positioning, customer base, or operational efficiency. The key finding that disability effects become insignificant when controlling observable characteristics remains robust regardless of overall model explanatory power. Nevertheless, the substantial unexplained variation suggests the presence of unobserved factors that may influence entrepreneurial income, such as individual motivation, social networks, market conditions, or specific business capabilities not captured in our dataset.

It is important to note that the absence of educational variables as control variables constitutes a limitation of this study, since several studies highlight that human capital—particularly formal education—is a significant determinant of entrepreneurial performance (Unger et al., 2011). Therefore, the results presented here should be interpreted as an initial comparative approximation rather than a fully specified model. It is possible that the observed income gap is partially influenced by differences in formal training or access to specialized education between the two groups. Nevertheless, evidence from similar program evaluations indicates that only about 5% of participants possess technical or higher education credentials (Scapini et al., 2024), suggesting a relatively homogeneous educational profile that may mitigate this potential source of bias. Future research should incorporate educational attainment into multivariate analyses to more precisely isolate the specific contribution of disability status from other human capital factors. Despite this limitation, the findings provide valuable preliminary evidence on income disparities, laying the groundwork for more comprehensive models that account for educational and other human capital dimensions.

A further limitation is that the study relies on the available dataset, which does not include information on the type or severity of participants' disabilities. This represents a significant constraint on the scope of the findings, as it limits the ability to assess whether different forms or levels of disability are associated with distinct entrepreneurial outcomes.

Additionally, the relatively small sample size (N = 1,658) and unbalanced distribution between groups (125 with disabilities vs. 1,533 without disabilities) may limit the statistical power to detect smaller but meaningful effects. The persistent negative coefficient for disability across models, despite becoming statistically insignificant, suggests that studies with larger, more balanced samples might reveal effects that our analysis lacks sufficient power to identify.

Future research should adopt a comprehensive approach to evaluating the factors influencing entrepreneurial success, incorporating detailed business-specific variables such as product/service differentiation, market reach, operational efficiency, and customer satisfaction alongside demographic characteristics. While initial descriptive differences may exist between disability groups, our analysis demonstrates that these differences become statistically insignificant when accounting for relevant observable characteristics, suggesting that apparent gaps may reflect differential access to resources or opportunities rather than inherent productivity differences.

Moreover, longitudinal data and comparative studies across regions or countries would help to assess the broader applicability of these findings. Finally, qualitative research could provide deeper insights into the lived experiences of women entrepreneurs with disabilities and the specific mechanisms underlying resource access and business development processes, particularly focusing on how structural factors may create differential pathways to entrepreneurial success.

Future research should further examine whether the apparent absence of statistical difference conceals structural inequalities that are not captured by observable variables. Mixed-method approaches could provide deeper insights into how gender, disability, and institutional environments interact to shape entrepreneurial trajectories.

The supplementary material for this article can be found online.