Understanding Female Employment and Wages in Indonesia: The Importance of Accounting for Non-cognitive Skills

Understanding Female Employment and Wages in Indonesia: The Importance of Accounting for Non-cognitive Skills | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Understanding Female Employment and Wages in Indonesia: The Importance of Accounting for Non-cognitive Skills Seonkyung Choi, Huihui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748385/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Traditional female labour force participation (FLFP) models have predominantly assessed socio-demographic, cultural, and educational factors in relation to employment outcomes, with research focusing mainly on developed countries despite low FLFP rates in many developing and Muslim-majority nations. This study investigates how non-cognitive skills shape women’s labour market outcomes in Indonesia, a middle-income, Muslim-majority country where female labour force participation remains relatively stagnant despite rising educational attainment. Using data from the Indonesia Family Life Survey (IFLS5), we analyse how Big Five personality traits influence both labour force participation and wages for married and unmarried women. We address the methodological concern of sample selection bias in wage estimations by implementing Heckman’s two-step procedure. Our findings reveal that personality traits significantly affect labour market decisions differently across marital status: extraversion (+ 3.1%) and lower neuroticism (+ 1.8%) predict greater participation for married women, while conscientiousness (+ 7%) is the primary predictor for unmarried women. For wages, neuroticism shows a substantial negative effect (-33.7%) for unmarried women, with minimal personality effects observed for married women. Importantly, models omitting personality traits demonstrate inflated effects of education on labour force participation and wages, suggesting omitted variable bias in conventional analyses. These results emphasize that labour market outcomes are determined not only by formal education but also by behavioural traits that operate differently across life stages, highlighting the need for more nuanced educational and labour policies in developing contexts. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Female Labor Force Participation Rate of Return Big Five personality traits Heckman method Indonesia 1 Introduction Female labour force participation (FLFP) is widely recognized as a cornerstone of inclusive economic growth, gender equality, and women’s empowerment. Despite steady gains in educational attainment and gradual changes in gender norms, women in many developing countries continue to face persistent disadvantages in the labour market (Campbell, 2013). Indonesia offers a particularly compelling context for examining this issue. As the world’s largest Muslim-majority nation and a country undergoing rapid socioeconomic transformation, Indonesia has witnessed increasing educational attainment among women but has seen relatively stagnant FLFP rates, hovering around 53% in recent years World Bank (2020). This paradox, often referred to as the “Indonesian puzzle” (Schaner & Das, 2016), has raised important questions regarding the factors shaping women’s labour market outcomes. Traditional models of FLFP have long emphasized socio-demographic and economic determinants such as education, age, household composition, and regional disparities (Goldin, 1994; Klasen & Pieters, 2015). These frameworks typically assume that education determines productivity and therefore influences labour market participation and earnings. However, these analyses have primarily focused on developed countries, with relatively little empirical attention to developing nations like Indonesia, where labour decisions are shaped by diverse institutional, cultural, and religious factors. Moreover, while the role of cognitive skills and formal qualifications has been extensively studied, an emerging literature suggests that non-cognitive skills—including personality traits, attitudes, and behavioural tendencies—play a significant role in shaping labour market performance. These skills are frequently measured through the Big Five personality traits: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Research from both advanced and developing economies has increasingly highlighted the predictive power of such traits for labour market participation, earnings, and job stability (Borghans et al., 2008; Glewwe et al., 2017; Heckman & Kautz, 2012). Yet despite these advances, empirical studies on non-cognitive skills remain largely absent in non-Western contexts, particularly in Southeast Asia. This gap is particularly notable given recent methodological findings showing that omitting non-cognitive traits from empirical models may lead to overestimation of the returns to education. Fletcher (2013) and Collischon (2020), among others, show that non-cognitive traits are often correlated with educational attainment, and that failing to control for them can bias the estimated effects of schooling on labour outcomes. These findings raise the possibility that education may not act in isolation, but rather in tandem with personality traits that influence preferences, motivation, and resilience. In response to these gaps, this study examines how non-cognitive skills influence female labour market outcomes in Indonesia, with special attention to the role of marital status. Marriage in Indonesia often marks a significant shift in women’s social roles, caregiving responsibilities, and labour constraints, making it a critical dimension along which labour market behaviour may differ. Using data from the fifth wave of the Indonesia Family Life Survey (IFLS5), which includes detailed information on the Big Five personality traits, we assess how these traits influence both labour force participation and wage levels for married and unmarried women. By explicitly comparing estimates that do and do not control for personality, our study also offers empirical evidence on the extent to which education effects may be overstated in conventional models. Furthermore, we employ the Heckman two-step procedure to correct for sample selection bias in wage estimation—a particularly salient issue in female labour market analysis. Through this multidimensional approach, we aim to provide a more comprehensive understanding of how personality, education, and marital status interact to shape labour market outcomes for women in a complex and culturally diverse setting. This study contributes to the literature in three key ways. First, it expands the empirical scope of non-cognitive skill research by situating it in the context of a Muslim-majority developing country. Second, it highlights how marriage structures moderate the effects of personality traits, offering new insights into life-cycle dynamics in women’s employment. Third, it provides methodological value by demonstrating how the inclusion of non-cognitive traits alters key interpretations regarding education and labour force engagement. The findings hold important implications for policy, suggesting that interventions aiming to enhance women’s labour market outcomes may benefit from focusing not only on cognitive skills but also on fostering relevant personality traits—tailored to women’s social and life stage realities. 2 Background and Literature review 2.1 Women’s Labor Market Context in Indonesia Over the past decades, Indonesia has experienced significant socioeconomic changes, including improvements in female educational attainment and increased urbanization. However, FLFP remains relatively stagnant, hovering around 53%, and continues to lag far behind the 82% participation rate for men World Bank (2020). This persistent gap, despite improvements in human capital, has led scholars to question the adequacy of traditional labour supply models in explaining women’s economic participation in Indonesia. Prior research has pointed to several key determinants that influence FLFP in Indonesia. Education has consistently been shown to have a positive effect on women’s probability of participating in the labour market, particularly for those with upper secondary or higher levels of schooling (Keiichi & Masuma, 2007 ; Klasen et al., 2020 ). However, the strength of this relationship varies depending on life stage and marital status. For instance, Klasen et al. ( 2020 ) find that the positive returns to education in terms of labour force participation are more pronounced among unmarried women, while married women often face institutional and social constraints that offset the impact of their educational gains. Family structure and caregiving responsibilities have also been found to play a significant role. A number of studies have shown that the presence of young children negatively affects women’s labour participation, especially in contexts where affordable childcare is scarce and traditional gender roles are prevalent (Cameron et al., 2001 ; Gertler et al., 2014 ; Khandker, 1988 ). These effects are particularly acute for married women, whose labour supply decisions are often shaped by the wage level of their husband and their perceived household responsibilities. Empirical work by Schaner & Das ( 2016 ) highlights the “reservation wage” effect of husbands’ income, where higher male earnings reduce the incentives for women to enter the labour market. Intergenerational factors such as parental education and financial support have also been shown to influence young women’s labour force decisions. In households where parents provide ongoing financial support or have higher educational levels, unmarried women are more likely to delay labour market entry in favour of further education or informal caregiving roles (Choi et al., 2023 ). This aligns with findings from other developing countries, where familial expectations and extended support networks can alter the standard human capital–labour supply framework (Anker & Knowes, 1978 ; Youssef, 1971 ). Taken together, these studies suggest that women’s labour force participation in Indonesia cannot be fully understood through conventional economic variables alone. Instead, labour decisions are embedded in a broader socio-cultural system that includes marriage, fertility, family structure, and intergenerational support. While the role of formal education remains important, its impact is mediated by both household dynamics and gender norms—factors that vary considerably across regions and between married and unmarried women. 2.2 From Classical Labor Supply Models to Expanded Theories of Female Employment Classical models of female labour supply, rooted in neoclassical economics, have long conceptualized women’s employment decisions as outcomes of individual utility maximization, where the opportunity costs of time and wage offers are weighed against the utility derived from home production and caregiving (Becker, 1965 ; Mincer, 1962 ). While these foundational frameworks offer a useful starting point, they often prove insufficient in capturing the multi-layered social and institutional realities shaping women’s labour market behaviour in developing countries. A significant theoretical refinement emerged with Goldin's (1994) U-shaped hypothesis, which posits a non-linear relationship between economic development and FLFP. According to this model, FLFP initially declines as economies transition from agriculture to manufacturing, and rises again with the expansion of the service sector and increases in female education. While intuitive, this framework does not consistently hold across contexts. For instance, Gaddis & Klasen ( 2014 ) and Verick ( 2018 ) document numerous cases—including Indonesia—where educational attainment and economic growth fail to produce a corresponding rise in FLFP. This empirical disconnect points to the existence of additional social, institutional, and cultural constraints not adequately captured by structural economic models alone. In response, scholars have called for more comprehensive frameworks that integrate the role of gender norms, intra-household bargaining power, and institutional barriers. Kabeer ( 2012 ) highlights how women’s employment decisions are shaped within “structures of constraint”—persistent patterns of disadvantage rooted in patriarchy, limited state support, and unequal household roles. Klasen et al. ( 2020 ) further proposes an augmented labour supply model that incorporates these social constraints alongside economic factors. Likewise, Gunatilaka ( 2013 ) emphasizes the importance of recognizing labour supply decisions as often collective and negotiated within families, rather than strictly individual. These contributions reflect a broader shift in the theoretical understanding of women’s labour participation—one that moves beyond formal schooling and wages to include a wider array of influences such as caregiving responsibilities, social expectations, spousal earnings, extended family structures, and regional variation. As noted in various studies on Indonesia and other Southeast Asian countries, these context-specific factors critically mediate whether and how women participate in the labour market, even when they possess the requisite education and skills (Choi et al., 2023 ; Keiichi & Masuma, 2007 ; Klasen & Pieters, 2015 ; Schaner & Das, 2016 ). Recent theoretical work has added another layer of complexity by incorporating non-cognitive skills—including personality traits and socio-emotional capacities—into models of labour market behaviour.Heckman et al. ( 2006 ) presents a skill formation model where cognitive and non-cognitive skills jointly affect long-run economic outcomes.Bowles et al. ( 2001a ) identify three main channels through which personality traits influence labour market outcomes: direct productivity effects, sorting into appropriate jobs, and differential wage rewards based on trait-employer matching. These channels are increasingly relevant in developing countries, where informal employment, relational contracting, and weak enforcement of labour laws dominate (Bühler et al., 2020 ; Laajaj et al., 2019 ; Lee, 2009 ; Li et al., 2023 ). Importantly, ignoring such non-traditional variables may lead to biased interpretations of traditional ones—a point raised by Fletcher ( 2013 )and Collischon ( 2020 ), who argue that omitting non-cognitive traits from empirical models inflates the estimated effects of education on employment and earnings. In settings like Indonesia, where women’s labour decisions are simultaneously shaped by family expectations, access to information, and individual behavioural traits, recognizing omitted variables and household-level heterogeneity becomes analytically essential. These theoretical developments underline the importance of incorporating a broader set of individual, household, and social variables—including non-cognitive traits—into labour market analysis. The next section reviews the growing body of empirical literature that attempts to capture these dynamics in both developed and developing country contexts, with special attention to how non-cognitive skills interact with gender and socioeconomic constraints. 2.3 Empirical Evidence on Non-Cognitive Skills and Female Labor Outcomes A growing body of empirical research has examined the determinants of FLFP and wage outcomes in developing countries. These studies traditionally emphasize human capital indicators such as education and work experience, alongside household characteristics like marital status, number of children, and spousal income. For instance, Keiichi and Masuma ( 2007 ) and Klasen and Pieters ( 2015 ) find that education, urban residence, and smaller household size are associated with a higher likelihood of female labour participation in Indonesia and India, respectively. Yet even after controlling for these factors, large unexplained gaps remain, prompting scholars to explore the influence of behavioural and psychological characteristics—namely, non-cognitive skills. Recent empirical work has shown that personality traits, commonly operationalized using the Big Five framework, significantly influence labour market outcomes. Almlund et al. ( 2011 ) and Borghans et al. ( 2008 ) demonstrate that traits such as conscientiousness and emotional stability predict employment status, occupational choice, and earnings, even after accounting for education and cognitive ability. Fletcher ( 2013 ) provides compelling evidence that failure to control for non-cognitive skills results in upwardly biased estimates of the return to education. Using sibling fixed effects models in the U.S., he shows that, once personality is controlled, the effect of schooling on earnings is significantly reduced. This finding has direct relevance to the Indonesian context, where educational attainment is often correlated with personality traits due to selection into higher education. Empirical studies from Asia further confirm the importance of non-cognitive skills in shaping labour outcomes. Li et al. ( 2023 ) use data from China to show that gender differences in the Big Five traits help explain wage gaps, with women scoring higher in agreeableness and lower in neuroticism—traits that are negatively rewarded in most labour markets. Importantly, their analysis shows that the effects of non-cognitive traits are often comparable in magnitude to those of cognitive abilities. Using Vietnam data, Badiani-Magnusson et al. ( 2014 ) reports that non-cognitive skills, including personality traits, affect labour market outcomes, and that their influence can vary across the income distribution, particularly emphasizing that traits like emotional instability (related to neuroticism) may impose greater disadvantages at higher wage levels. Similarly, Collischon ( 2020 ) highlights that the wage premium for conscientiousness and the penalty for neuroticism vary across job sectors and gender, calling for heterogeneity-sensitive approaches. In the Southeast Asian context,Triggs & Urata ( 2020 ) emphasize that improving women’s labour participation in Indonesia requires attention not only to education and skills but also to institutional and behavioural constraints. They argue that traditional supply-side policies may fall short unless they account for individual characteristics that influence women's job preferences and resilience in navigating informal labour markets. This resonates with findings byGuerra et al. ( 2014 ) who show that, in developing countries, personality traits such as emotional stability and social competence have stronger labour market effects where informal employment dominates and formal credentialing is weak.Glewwe et al. ( 2017 ) further argue that non-cognitive skills are especially valuable in environments where job performance is not easily monitored, thus increasing the value of self-discipline and reliability. Beyond wage outcomes, several studies have investigated the link between personality traits and labour force participation decisions. Building on the earlier findings by Guerra et al. ( 2014 ), who demonstrate that non-cognitive traits can shape FLFP through their impact on expected wages, job search strategies, and bargaining power within households in developing countries, Cobb-Clark and Tan (2011) further emphasize that specific personality traits, such as extraversion and conscientiousness, are positively associated with occupational attainment and employment probabilities, while neuroticism tends to have a negative impact. However, few studies have explored how the effects of non-cognitive skills vary by marital status—a critical dimension in settings like Indonesia, where marriage significantly alters women’s labour market incentives and constraints. Some studies note that household dynamics, caregiving roles, and spousal income all interact with personality traits in complex ways (Choi et al., 2023 ; Schaner & Das, 2016 ), but empirical tests of such interactions remain rare. Moreover, while many existing studies examine either labour force participation or earnings, few analyse both outcomes within a unified framework that addresses sample selection bias. Significant gaps thus remain in the literature. First, much of the existing empirical evidence on non-cognitive skills and labour outcomes is concentrated in high-income or upper-middle-income countries, with relatively limited research focusing on Southeast Asia and Muslim-majority contexts such as Indonesia. Second, while numerous studies underscore the importance of personality traits, few explicitly examine the consequences of omitting these variables in estimating the effects of education or other traditional determinants—raising concerns about potential bias and misinterpretation of policy-relevant coefficients (Collischon, 2020 ; Fletcher, 2013 ). Third, although marriage is known to significantly restructure women’s roles and constraints, there is a notable lack of research that systematically compares how non-cognitive skills produce different effects for married and unmarried women within the same empirical setting. This study addresses these gaps by analysing the role of non-cognitive skills in shaping both labour force participation and wage outcomes among women in Indonesia. By explicitly controlling for personality traits and separating analyses by marital status, we offer new empirical insights into how behavioural traits interact with traditional determinants in a culturally complex labour market. In doing so, we contribute to both the methodological literature on omitted variable bias and the broader effort to understand heterogeneous pathways to female labour market integration in developing economies. 3 Data and Methodology 3.1 Data This study utilizes data from the Indonesian Family Life Survey (IFLS) to examine the relationship between non-cognitive skills and women’s labour market outcomes. The IFLS is a comprehensive longitudinal survey that collects detailed information at both individual and household levels, including data on personality traits, socioeconomic characteristics, labour force participation, earnings, education, and family relationships. Initially launched in 1993 (IFLS1), the survey has consistently tracked the same households across multiple waves, with subsequent rounds conducted in 1997, 2000, 2007, and 2014. We employ the fifth wave (IFLS5) collected in 2014, which provides particularly detailed information on respondents' Big Five personality traits alongside extensive data on labour market outcomes and socioeconomic characteristics. The IFLS5 is especially suitable for our research purposes as it includes a comprehensive personality assessment module that allows us to measure non-cognitive skills systematically. According to the World Bank (2020), female labour force participation in Indonesia has shown moderate improvement over recent decades, with the rate for women aged 15 and above exceeding 50% in 2008 and reaching 53% in 2019. Despite this progress, substantial gender disparities persist in both labour force participation and earnings. These patterns make Indonesia an ideal setting for investigating how non-cognitive skills influence women’s labour market performance. Our analytical sample consists of women between 15 and 55 years of age, with separate analyses conducted for married women (n = 4,399) and unmarried women (n = 288). This division by marital status allows us to explore how the relationships among personality traits, human capital, and labour market outcomes might differ based on women's family roles and responsibilities. While we recognize the substantial difference in sample sizes between these two groups, which may affect the precision of estimates for unmarried women, this approach enables us to capture important heterogeneity in labour market determinants. It is important to note that, in our sample, marital status strongly correlates with age: married women are significantly older (mean age 35.2 years) than unmarried women (mean age 20.1 years). This age difference means that our marital status comparison also largely captures a comparison between older and younger women, which may have implications for interpreting differences in education levels, labour market experience, and potential cohort effects in personality traits. We measure non-cognitive skills using the well-established Big Five personality framework, which encompasses five broad dimensions (OCEAN): Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (Costa & McCrae, 1992 ). The IFLS5 assesses each dimension through two questionnaire items, as shown in Table 1 , with respondents rating their agreement on a 5-point Likert scale from “strongly disagree” to “strongly agree.” For each personality dimension, we calculate the mean value of the corresponding items, resulting in scores ranging from 1 to 5, with higher values indicating stronger manifestation of that trait. While this abbreviated measurement approach is less comprehensive than longer inventories, previous research has validated such short measures for large-scale surveys (Gosling et al., 2003 ), and similar measures have been successfully employed in labour market studies across various cultural contexts (Guerra et al., 2014 ). Table 1 The Big Five Personality Traits and Their Measurement in IFLS5 Category Definition (Questionnaire Items) Openness to experience Is original, comes up with new ideas; Has an active imagination Conscientiousness Does a thorough job; Does things efficiently Extraversion Is talkative; Outgoing, sociable Agreeableness Has a forgiving nature; Is considerate and kind to almost everyone Neuroticism * Worries a lot; Gets nervous easily Note *: Unlike other personality traits where higher scores are generally considered positive for labour market outcomes, Neuroticism is typically considered a "reverse" trait, where lower scores are associated with better labour market outcomes such as higher wages and increased employment probability. In the psychology literature, the opposite of Neuroticism is often referred to as "Emotional Stability”. 3.2 Methodology We begin by estimating the impact of non-cognitive skills on women’s labour force participation using a probit model as specified in Eq. (1), $$\:{LMP}_{i}=1\left[\alpha\:+{\theta\:}_{n}\sum\:_{n=1}^{5}{BFP}_{n,i}+{\partial\:V}_{i}+{\beta\:}_{1}{age}_{i}\:+{{\beta\:}_{2}age}_{i}^{2}+{X}^{{\prime\:}}\gamma\:+{\mu\:}_{i}>0\right]\:\:\:\:\:\:\:\:\left(1\right)$$ where the dependent variable \(\:{LMP}_{i}\) represents labour market participation status (1 if participating, 0 if not) for female individual i . Our key explanatory variables are the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism \(\:{BFP}_{i}\) , with their effects captured by the coefficients \(\:{\theta\:}_{n}\) . We control for educational attainment as proxies for women’s potential productivity (Klasen et al. 2020 ). \(\:{V}_{i}\) captures the respondent’s education level, coded as 0 for below lower secondary education (reference category), and 1 through 4 for, respectively, general upper secondary, vocational upper secondary, post-secondary diploma, and a bachelor’s or above. \(\:{age}_{i}\:\) and its square are included to control for lifecycle effects on labour supply to capture potential non-linearity. Additional control variables in vector \(\:{X}^{{\prime\:}}\) include number of siblings (Choi et al., 2023 ), ethnicity indicators, and urban/rural residence, following established literature (Anker & Knowes, 1978 ; Choi et al., 2023 ; Youssef, 1971 ). For married women, we include controls for number of children and husband’s log wage, which prior research has identified as key determinants of married women’s labour supply (Gertler et al., 2014 ; Khandker, 1988 ; Klasen & Pieters, 2015 ). For unmarried women, we include father’s education level and a dummy for parental financial support. \(\:{\epsilon\:}_{i}\:\) is the error term. We estimate this model separately for married and unmarried women to allow for differential effects across these groups, reporting marginal effects for easier interpretation. It should be noted that \(\:{\mu\:}_{i}\) ​ is an error term assumed to be normally distributed. To analyse the effect of non-cognitive traits on labour market performance conditional on employment, we estimate a modified Mincerian earnings function as shown in Eq. (2): $$\:{WageRate}_{i}=\delta\:+{\pi\:}_{n}\sum\:_{n=1}^{5}{BFP}_{n,i}+\kappa\:{V}_{i}+{\rho\:}_{1}{age}_{i}\:+{{\rho\:}_{2}age}_{i}^{2}+{Z}^{{\prime\:}}\phi\:+{\epsilon\:}_{i}\:\:\:\:\:\:\:\:\:\left(2\right)$$ where \(\:{WageRate}_{i}\) represents the natural logarithm of hourly wages, allowing us to interpret coefficients approximately as percentage changes. The explanatory variables include the same personality traits, education, and age terms as in the participation model. Vector \(\:\text{Z}\) includes additional controls for siblings, ethnicity, occupational sector (government, private, or casual non-agricultural work), and urban/rural residence. A key methodological challenge in estimating women’s wage equations is potential sample selection bias, as wages are only observed for women who participate in the labour market.Heckman ( 1979 ) developed a two-step correction procedure to address this issue. In the first step, we estimate the probability of an individual’s decision to engage in wage work using a probit model. This selection equation includes all the variables in Eq. (2) (except occupational sector), as well as additional identifying variables that influence labour market participation but not wages directly. For married women, we include the husband's wage rate as an identifying variable. Theoretically, the husband’s wage level directly affects a woman’s reservation wage (the minimum wage at which she is willing to work), which in turn influences her decision to enter the labour market. Higher husband earnings typically increase the reservation wage, potentially reducing the likelihood of labour market participation through an income effect. For unmarried women, we include the father’s education level and a dummy variable indicating financial help from parents as identifying variables. These factors affect whether unmarried women choose to work and whether they engage in paid employment. Higher paternal education and financial support from parents may allow unmarried women to be more selective about employment or to pursue further education rather than immediately entering the labour force (Choi et al., 2023 ). The Heckman procedure uses different sets of variables in the wage equation (second stage) from those in the labour market participation equation (first stage). The identifying variables (husband’s wage for married women; father’s education and parental financial support for unmarried women) as exclusion restrictions functioning similarly to instrumental variables are included only in the first stage selection equation under the assumption that they influence labour market participation decisions but not wages directly. Consequently, these variables do not appear in the second-stage wage equation reported in Table 4 . Instead, their effects are captured through the Inverse Mills Ratio (IMR), which is calculated from the first-stage equation and included in the second-stage equation. The statistical significance of the IMR coefficient \(\:{\lambda\:}\) indicates whether selection bias is present. For married women, a significant IMR validates the use of Heckman results, while for unmarried women, an insignificant IMR suggests that OLS results may be more appropriate as selection bias is minimal. For comparison and robustness checks, we also present results from standard OLS estimation of Eq. (2). 3.3 Descriptive statistics This study measures women’s labour market outcomes using two primary dependent variables. First, labour market participation is a binary variable indicating whether an individual is economically active at the time of the survey, taking the value of 1 if participating and 0 otherwise. Second, wage level is measured as the natural logarithm of hourly earnings from employment, allowing for percentage change interpretations in our analysis. Non-cognitive skills are measured through the Big Five personality traits framework. Each trait is constructed as the average of responses to two items in the IFLS5 questionnaire, measured on a 5-point Likert scale (1= “strongly disagree” to 5= “strongly agree”). For human capital variables, educational attainment is categorized into five levels: Lower Secondary (reference category), Upper Secondary (general), Upper Secondary (vocational), Post-secondary diploma, and Bachelor's or higher academic qualification. In the Indonesian context, “Post-secondary diploma” refers to Diploma programs (D1-D4), which are 1–4 year vocational programs focusing on practical skills, while “Bachelor’s or above” includes academic degrees like Individual’s highest education is a bachelor’s degree or higher academic credential. This distinction reflects Indonesia’s dual-track higher education system separating vocational and academic pathways. Demographic characteristics include age and its squared term to capture non-linear effects of age on labour market outcomes. Number of siblings measures the total number of siblings, which may influence early-life resource allocation and experiences. Urban residence is a binary variable taking the value of 1 if residing in an urban area and 0 if rural. Ethnicity is categorized as Javanese, Sundanese (two major ethnic groups in Indonesia), and Other ethnicities. Marriage-specific variables differ between groups. For married women, we include number of children and husband’s hourly wage (in natural logarithm). For unmarried women, we include father’s educational level (Lower Secondary, Upper Secondary general, Upper Secondary vocational, Post-secondary diploma, Bachelor’s or above) and financial support from parents (1 if receiving support, 0 otherwise). Occupational sector is categorized into three groups: government worker, private sector worker, and casual worker not in agriculture, which are used to control for industry-specific wage differentials in the wage analysis. Table 2 presents descriptive statistics for the key variables in our analysis, separated by marital status. The labour force participation rate is 43.9% for married women and 38.5% for unmarried women. For personality traits, unmarried women show slightly higher average scores for Openness to Experience (3.684 vs. 3.536 for married women), Extraversion (3.793 vs. 3.683), and Neuroticism (3.328 vs. 3.103), while married women score higher on Conscientiousness (3.972 vs. 3.859) and Agreeableness (4.122 vs. 4.071). There are notable differences in educational attainment between the two groups. Among married women, the majority (51.5%) have lower secondary education as their highest level, followed by general upper secondary (17.6%) and bachelor’s degree or above (13.0%). In contrast, unmarried women have significantly higher educational attainment, with 45.5% holding a bachelor’s degree or higher, followed by vocational upper secondary (18.4%) and general upper secondary (17.0%). This education gap primarily reflects both the considerable age difference between the groups (married women average 35 years versus 20 years for unmarried women) and the generational improvements in female educational access in Indonesia over recent decades. Similarly, married women come from larger families (3.9 average siblings) compared to younger unmarried women (2.8) and tend to be less urban (65%) than unmarried women (86%). For those with wage income, the mean logarithm of hourly wages is 8.947 for married women and 9.182 for unmarried women, indicating higher average hourly earnings for unmarried women. Regarding occupation, private sector employment predominates in both groups (62.2% of married women and 81.1% of unmarried women), with government employment more common among married women (19.7% vs. 8.1%) and casual non-agricultural work accounting for 18.6% of married women and 10.8% of unmarried women in the labour force. Table 2 Descriptive Statistics by Marital Status (Mean and Standard deviation Values) Married Women Unmarried Women Obs. Mean SD Obs. Mean SD Labor force participation 4399 0.439 0.496 288 0.385 0.488 Big Five Personality traits (OCEAN) : Openness to experience 4399 3.536 0.782 288 3.684 0.632 Conscientiousness 4399 3.972 0.592 288 3.859 0.559 Extraversion 4399 3.683 0.689 288 3.793 0.684 Agreeableness 4399 4.122 0.536 288 4.071 0.550 Neuroticism 4399 3.103 0.899 288 3.328 0.895 Education attainments : Lower Secondary 4399 0.515 0.500 288 0.069 0.255 Upper Secondary (general) 4399 0.176 0.381 288 0.170 0.376 Upper Secondary (vocational) 4399 0.129 0.336 288 0.184 0.388 Post-secondary diploma 4399 0.049 0.216 288 0.122 0.327 Bachelor’s and above 4399 0.130 0.336 288 0.455 0.499 Demographic characteristics : Age 4399 35.2 8.7 288 20.056 3.318 Age 2 4399 1312 647 288 413.194 146.039 Number of Siblings 4399 3.935 2.268 288 2.767 1.772 Urban 4399 0.648 0.478 288 0.858 0.350 Javanese 4399 0.444 0.497 288 0.403 0.491 Sundanese 4399 0.133 0.340 288 0.087 0.282 Other Ethnicity 4399 0.423 0.494 288 0.510 0.501 Number of Children 4399 1.234 1.235 288 Husband’s Hourly wage (log) 4399 3.581 4.622 288 Parent financial support 288 0.847 0.360 Father’s educational attainment : Lower Secondary 288 0.472 0.500 Upper Secondary (general) 288 0.267 0.443 Upper Secondary (vocational) 288 0.059 0.236 Post-secondary diploma 288 0.031 0.174 Bachelor’s or above 288 0.170 0.376 Wage and employment (for employed women only) : Hourly wage (IDR) 1202 13508 16273 69 14067 9982 Hourly wage (log) 1202 8.947 1.171 69 9.182 1.085 Occupational sectors : Government worker 1931 0.192 0.394 111 0.081 0.274 Private worker agriculture 1931 0.622 0.485 111 0.811 0.393 Casual worker 1931 0.186 0.389 111 0.108 0.312 Note: 1) Obs = number of Observations. SD = Standard deviation. The Big Five personality traits are measured on a 5-point scale where 1 = "strongly disagree" and 5 = "strongly agree" with trait-descriptive statements. Hourly wages are in Indonesian Rupiah (IDR). The sample is restricted to women aged 15–55 years. 2) In Indonesia, "Post-secondary diploma" refers to Diploma programs (D1-D3), which are 1–3 year vocational programs with flexible entry ages and varied duration, focusing on practical skills rather than academic theory. "Bachelor or above" corresponds to conventional higher education (Sarjana S1, S2, and S3 degrees), requiring entrance examinations and following standardized academic curricula comparable to international university degrees. This distinction represents Indonesia's dual-track higher education system of vocational versus academic pathways ( Choi et al 2023 ). Among employed women, we observe that, despite lower labour force participation rates, unmarried women who work earn slightly higher wages on average than their married counterparts (average log hourly wage of 9.182 versus 8.947). Occupational patterns also differ, with private sector employment predominating in both groups but more common among unmarried women (81.1% versus 62.2%), while government employment is more prevalent among married women (19.7% versus 8.1%). We acknowledge the potential endogeneity between personality traits and labour market experiences. While we treat personality traits as predetermined characteristics in our analysis, following standard practice in the literature (Almlund et al., 2011 ), we recognize that labour market experiences may also shape personality development. The cross-sectional nature of our study prevents us from establishing causal relationships; rather, we aim to identify associations that can enhance understanding of how non-cognitive skills relate to labour market outcomes in the Indonesian context. Appendix Table 7 provides additional descriptive statistics specifically for the subsample of women with wage employment used in our wage analysis. These statistics allow for direct comparison of characteristics between wage-earning married and unmarried women. When restricting our analysis to wage earners, the sample size decreases substantially—from 4,399 to 1,202 for married women and from 288 to just 69 for unmarried women. This significant reduction reflects a common characteristic of developing economies like Indonesia, where despite increasing overall female labour force participation, the proportion of women in formal wage employment remains relatively low, with many women working in informal, family, or agricultural sectors without formal wages. This pattern is consistent with findings by Schaner and Das ( 2016 ) regarding the structure of female employment in developing countries. The substantial sample size reduction, particularly for unmarried women, further exacerbates statistical power limitations and potentially affects the precision of our wage analysis. The substantial sample size reduction represents a significant limitation of our study, particularly for the analysis of unmarried women’s wages, where the final sample of just 69 observations may compromise statistical power and the reliability of our estimates. The table also reveals that both groups of employed women have somewhat different personality trait distributions compared to the full sample, highlighting the importance of accounting for selection into employment when analysing wage determinants. 4 Results and Discussion We present the results of the effects of non-cognitive skills on women’s labour market outcomes in Tables 3 through 5 . To examine the influence of the Big Five personality traits on labour force participation and wages, each table compares results estimated with and without the inclusion of personality variables. Table 3 reports the marginal effects from Probit models for labour force participation, based on coefficient estimates presented in Appendix Table 6. Table 4 displays the second-stage results from the Heckman two-step procedure, and Table 5 shows the OLS estimates of women’s log hourly wages. We discuss the key findings from these models in detail below. 4.1 Non-cognitive Skills and Labor Market Participation The results in Table 3 reveal distinct patterns in the determinants of labour market participation between married and unmarried women. For married women (column 1), the labour market participation rates of those with a post-secondary diploma and those with a bachelor’s degree or above) are respectively 22.7% and 41.6% higher than those of women whose highest level of education is lower secondary education. However, no significant difference was observed between married women with lower secondary education and those with upper secondary general or vocational education. After controlling for the Big Five personality traits (column 2), the effect of education level on female labour force participation remains, although the coefficient becomes slightly smaller. This aligns with the theoretical framework proposed by Bowles et al. ( 2001b ), suggesting interaction between personality traits and human capital in determining labour market outcomes. Notably, only the results for Extraversion and Neuroticism were statistically significant. The stronger the Extraversion traits of a married woman, the higher her probability of labour market participation, with an increase of 3.1%. Conversely, the stronger the Neuroticism traits, the lower her probability of labour market participation, with a decrease of 1.8%. These findings are consistent with those of Gertler et al. ( 2014 ), supporting the theory that extraverted personality traits enhance networking capabilities and job search efficiency, positively influencing labour market participation. The negative effect of Neuroticism may be related to difficulties in stress management and forming social relationships in the workplace, which could present additional barriers for married women who must navigate multiple roles between household and employment spheres. For unmarried women (column 3), the labour market participation rates of those with upper secondary general education (17.2%), post-secondary diploma (41.4%) and bachelor’s or above (47.8%) are lower than that of women whose highest level of education is lower secondary education. This pattern aligns with the “education investment and delayed labour market entry” phenomenon observed by Schaner & Das ( 2016 ) in Indonesia. When controlling for the Big Five personality traits (column 4), only Conscientiousness showed statistical significance, with stronger Conscientiousness traits increasing the probability of labour market participation by 7%. The pattern of higher education levels being associated with lower probability of labour market participation among unmarried women is noteworthy. The descriptive statistics in Table 2 show that the average age of the unmarried female group is 20 years old. This suggests that unmarried women at this age may choose to continue their studies rather than enter the workforce at a young age. This finding is consistent with overall statistics for Indonesia, which show that the employment rate of women aged 15 to 24 is lower than that of women aged 24 to 60, and that women aged 15 to 24 generally have a higher level of education than women aged 24 to 60. This pattern reflects what Triggs and Urata ( 2020 ) identified as a strategy among young Indonesian women to pursue long-term labour market outcomes through human capital accumulation. Moreover, the differential impact of personality traits by marital status supports the theoretical framework proposed by Gunatilaka ( 2013 ) and Kabeer ( 2012 ) that women’s labour decisions are made within “structures of constraint.” For married women, household roles and positions may make extraverted personality traits particularly helpful in overcoming labour market barriers, while unmarried women, with relatively fewer family responsibilities and greater autonomy, may find conscientiousness more instrumental. Table 3 Marginal effects of Probit estimation for labour market participation VARIABLES Married Women Unmarried Women (1) (2) (3) (4) Extraversion 0.031*** 0.002 [0.011] [0.030] Conscientiousness 0.021 0.070** [0.013] [0.033] Openness to experience 0.002 -0.034 [0.010] [0.033] Agreeableness 0.005 -0.012 [0.015] [0.035] Neuroticism -0.018** -0.010 [0.008] [0.020] Educational level (reference group: below lower secondary high school) High school at upper secondary (general) -0.027 -0.031 -0.172** -0.173** [0.020] [0.020] [0.067] [0.068] High school at upper secondary (vocational) -0.016 -0.021 -0.117 -0.123* [0.023] [0.023] [0.072] [0.073] Post-secondary diploma 0.227*** 0.218*** -0.414*** -0.421*** [0.035] [0.036] [0.091] [0.087] Bachelor’s or above 0.416*** 0.404*** -0.478*** -0.473*** [0.020] [0.021] [0.069] [0.068] Age 0.031*** 0.030*** 0.142 0.130 [0.006] [0.006] [0.086] [0.090] Age 2 -0.000*** -0.000*** -0.001 -0.001 [0.000] [0.000] [0.002] [0.002] Urban 0.031** 0.030** 0.013 0.002 [0.015] [0.015] [0.047] [0.046] No. of Siblings -0.001 -0.001 0.006 0.007 [0.003] [0.003] [0.012] [0.012] No. of Children 0.005 0.006 [0.007] [0.007] Husband’s Hourly wage (log) 0.003* 0.003* [0.002] [0.002] Ethnicity (reference group: Javanese) Sundanese -0.074*** -0.070*** -0.146** -0.145* [0.022] [0.022] [0.073] [0.079] Other Ethnicity -0.069*** -0.066*** -0.054 -0.064 [0.015] [0.015] [0.041] [0.040] Father’s Highest education level completed for Unmarried women High school at upper secondary (general) -0.066 -0.054 [0.049] [0.048] High school at upper secondary (vocational) -0.136** -0.133** [0.061] [0.058] Post-secondary diploma 0.212 0.235* [0.130] [0.135] Bachelor’s or above -0.058 -0.036 [0.058] [0.058] Parent financial support -0.116* -0.110* [0.067] [0.063] Observations 4,399 4,399 288 288 Robust standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1 4.2 Non-cognitive Skills and Wage Determination Tables 4 and 5 present, respectively, the results of the Heckman procedure estimation and OLS estimation for women’s hourly wages. We first use the estimated value of the Inverse Mills Ratio (IMR) to assess whether the Heckman model can correct the problem of sample selection bias. In the analysis of the married women group (columns 1 and 2 of Table 4 ), the estimated values of the IMR are statistically significant, suggesting that the Heckman procedure can alleviate the biased estimates of explanatory variables caused by sample selection bias. This is consistent with Heckman's (1979) theoretical predictions and particularly important in contexts like Indonesia where family structures and cultural norms strongly influence women’s labour market participation. In the sample estimation for married women, after controlling for job section and age (column 1 of Table 4 ), differences in education level do not statistically affect hourly wage rates. This aligns with findings by Fletcher ( 2013 ) and Collischon ( 2020 ), suggesting that the effects of education may be overstated when non-cognitive skills are not controlled for. In column 2, after adding the variables for the Big Five personality traits, only the coefficient for married women with upper secondary general education is statistically significant at the 10% level, with their wage rate being 26.8% higher than that of women whose highest education level is lower secondary education. However, the Big Five personality traits do not show statistical significance. Similarly, In the analysis of unmarried women, we find that the Inverse Mills Ratio (IMR) in Table 4 (columns 3 and 4) is statistically insignificant. This lack of significance indicates that there is no detectable sample selection bias for this group, suggesting that unobserved factors affecting labour market participation are not significantly correlated with factors influencing wages. Therefore, the OLS estimates in Table 5 are more appropriate for interpreting the wage determinants for unmarried women. Looking at these OLS results in column 3 of Table 5 , after controlling for variables such as occupation and age, we find that the difference in education level does not statistically affect wage levels. After adding the control variables for the Big Five personality traits (column 4), the estimated values for education levels remain statistically insignificant. Among the Big Five personality traits, only the coefficient for Neuroticism is statistically significant at the 5% level, with stronger Neuroticism traits decreasing hourly wage rates by 33.7%. This finding is consistent with patterns observed byLi et al. ( 2023 ) in China andBadiani-Magnusson et al. ( 2014 ) in Vietnam, where Neuroticism was found to have substantial negative wage effects in East and Southeast Asian labour markets. This may be particularly relevant in the service sectors and in professional occupations where emotional regulation, stress management, and interpersonal skills are important. As suggested by Glewwe et al. ( 2017 ), non-cognitive skills are especially valuable in environments where work performance is not easily monitored, and in such contexts, high levels of Neuroticism may serve as a negative signal to employers. In the analysis of wage rates overall, regardless of whether the Big Five personality traits are controlled for, the difference in educational level between married and unmarried women does not significantly affect the hourly wage rate (except for the coefficient of married women with upper secondary general education in column 2 of Table 4 ). In this analysis, we controlled for occupation and age, suggesting that among peers in the same occupation, the return on education in terms of wages is almost non-existent. This aligns with the theory proposed by Borghans et al. ( 2008 ) that non-cognitive skills may play an important role in explaining wage inequality among individuals with the same level of education. In the sample analysis of unmarried women, we confirmed that among peers with the same education level working in the same occupational sector, a strong personality trait of Neuroticism negatively impacts wage growth. This suggests that improving women's non-cognitive abilities in the labour market—at least by alleviating Neuroticism—could help increase their wage returns. However, it is important to note that there are multiple dimensions by which labour market outcomes should be evaluated. In addition to wages, factors such as opportunities for vocational training and career advancement also play a significant role. These findings have significant implications for policy design aimed at enhancing female labour market outcomes in Indonesia. Human capital development policies should focus not only on formal education but also on developing relevant non-cognitive skills, particularly enhancing Extraversion and reducing Neuroticism for married women, and developing Conscientiousness for unmarried women. As suggested by Klasen et al. ( 2020 ) and Kabeer ( 2012 ), differentiated approaches based on marital status are necessary, recognizing that women’s labour market participation occurs within “structures of constraint.” Furthermore, our research demonstrates that estimates of female wage determinants may be distorted if sample selection bias is not considered, highlighting the importance of incorporating such methodological considerations into policy evaluation and design. Table 4 Heckman correction estimation for women’s hourly wages (log) VARIABLES Married Women Unmarried Women (Probit-OLS) (Probit-OLS) (1) (2) (3) (4) Extraversion -0.123 0.117 [0.0795] [0.245] Conscientiousness -0.0158 -0.150 [0.0900] [0.258] Openness to experience -0.0509 0.00498 [0.0630] [0.215] Agreeableness 0.0149 0.261 [0.0984] [0.295] Neuroticism 0.00168 -0.303** [0.0573] [0.145] Educational level (reference group: below lower secondary high school) High school at upper secondary (general) 0.244 0.268* 0.154 0.321 [0.149] [0.141] [0.565] [0.661] High school at upper secondary (vocational) 0.165 0.172 0.370 0.527 [0.182] [0.171] [0.429] [0.500] Post-secondary diploma -0.445 -0.380 0.567 0.573 [0.433] [0.407] [0.623] [0.642] Bachelor’s or above -0.699 -0.601 0.747 0.644 [0.502] [0.470] [0.738] [0.764] Age -0.193** -0.182** 0.374 0.459 [0.0775] [0.0737] [0.406] [0.395] Age 2 0.00239** 0.00226** -0.00658 -0.00736 [0.000938] [0.000893] [0.00626] [0.00625] No. of Siblings 0.00115 0.000628 -0.0319 -0.0558 [0.0210] [0.0201] [0.0687] [0.0707] No. of Children 0.0644 0.0660 [0.0512] [0.0492] Urban -0.0526 -0.0291 -0.500 -0.530 [0.182] [0.175] [0.377] [0.362] Job Sector (reference group: Casual worker not in agriculture) Private worker -0.563*** -0.574*** 0.348 0.113 [0.115] [0.110] [0.446] [0.450] Government worker -1.500*** -1.536*** -0.346 -0.668 [0.158] [0.153] [0.641] [0.653] Ethnicity (reference group: Javanese) Sundanese 0.168 0.161 0.139 0.313 [0.149] [0.141] [0.435] [0.458] Other Ethnicity 0.463*** 0.425*** -0.435 -0.424 [0.163] [0.153] [0.382] [0.384] IMR -2.121*** -2.024*** -0.276 -0.00349 [0.720] [0.701] [0.688] [0.647] Constant 15.42*** 15.71*** 4.312 2.907 [2.405] [2.460] [6.157] [6.284] Observations 4,399 4,399 288 288 Wald Chi2 148.7*** 167.7*** 15.16 24.29 Standard errors in brackets *** p < 0.01, ** p < 0.05, * p < 0.1 Table 5 OLS estimation for women’s hourly wages (log) VARIABLES Married Women Unmarried Women (1) (2) (3) (4) Extraversion -0.0178 -0.0535 [0.0455] [0.188] Conscientiousness 0.0601 -0.0776 [0.0566] [0.297] Openness to experience -0.0322 0.0466 [0.0405] [0.182] Agreeableness -0.0287 0.421 [0.0618] [0.282] Neuroticism -0.0754** -0.337** [0.0325] [0.142] High school at upper secondary (general) 0.381*** 0.374*** 0.329 0.670 [0.0876] [0.0883] [0.584] [0.735] High school at upper secondary (vocational) 0.421*** 0.393*** 0.532 0.882 [0.0920] [0.0932] [0.510] [0.585] Post-secondary diploma 0.647*** 0.621*** 0.560 0.640 [0.112] [0.114] [0.527] [0.556] Bachelor’s or above 0.668*** 0.649*** 0.615 0.820 [0.0970] [0.0987] [0.476] [0.520] Age -0.0148 -0.0169 0.534*** 0.537*** [0.0284] [0.0285] [0.172] [0.199] Age 2 0.000366 0.000381 -0.00941*** -0.00932*** [0.000368] [0.000369] [0.00294] [0.00343] No. of Siblings -0.00337 -0.00328 -0.0635 -0.0965 [0.0126] [0.0125] [0.0630] [0.0703] No. of Children 0.00661 0.0103 [0.0292] [0.0296] Urban 0.341*** 0.342*** -0.338 -0.348 [0.0734] [0.0736] [0.459] [0.450] Job Sector (reference group: Casual worker not in agriculture) Private worker agriculture -0.565*** -0.575*** 0.0317 -0.0889 [0.0922] [0.0927] [0.482] [0.493] Government worker -1.498*** -1.528*** -0.705 -0.795 [0.140] [0.140] [0.750] [0.757] Ethnicity (reference group: Javanese) Sundanese 0.0385 0.0530 0.244 0.421 [0.0988] [0.0982] [0.405] [0.409] Other ethnicity 0.100 0.0954 -0.474 -0.239 [0.0647] [0.0652] [0.460] [0.437] Father’s educational level (reference group: below lower secondary high school) High school at upper secondary (general) 0.115 0.369 [0.342] [0.374] High school at upper secondary (vocational) -0.104 -0.353 [0.878] [0.954] Post-secondary diploma 0.550** 0.927** [0.265] [0.426] Bachelor’s or above 0.838 0.678 [0.545] [0.514] Parent financial support -0.951*** -0.949*** [0.262] [0.289] Husband’s Hourly wage (log) 0.0319*** 0.0320*** [0.00606] [0.00608] Constant 8.718*** 9.074*** 2.701 2.200 [0.533] [0.585] [2.406] [3.710] Observations 1,202 1,202 69 69 R-squared 0.317 0.321 0.411 0.468 Standard errors in brackets *** p < 0.01, ** p < 0.05, * p < 0.1 5 Conclusion This study has examined the role of non-cognitive skills in determining female labour market outcomes in Indonesia, focusing specifically on the relationship among the Big Five personality traits, employment status, and wages across different marital statuses. Our findings offer several important contributions to the literature on female labour force participation in developing countries. First, our results confirm that non-cognitive skills significantly influence women's labour market decisions, though these effects differ markedly between married and unmarried women. For married women, Extraversion enhances labour market participation by 3.1%, while Neuroticism reduces it by 1.8%. In contrast, unmarried women's participation is most strongly influenced by Conscientiousness, which increases their likelihood of employment by 7%. These distinct patterns highlight how personality traits interact differently with the social roles and constraints associated with marital status in the Indonesian context. Second, our wage analysis revealed that while non-cognitive skills have limited direct effects on married women’s earnings, Neuroticism significantly reduces hourly wages (by 33.7%) for unmarried women. This finding is consistent with previous research suggesting that emotional stability is particularly valuable in labor markets where job performance requires interpersonal skills and emotional regulation. The absence of strong wage effects for married women may reflect their concentration in occupational sectors where non-cognitive skills are less directly rewarded or where other factors like household responsibilities constrain occupational choices. Third, our methodology comparing models with and without personality trait controls demonstrates that conventional analyses may misattribute some effects to education that are actually explained by non-cognitive factors. When personality traits are included in our models, the estimated returns to education are slightly reduced, suggesting that traditional labour market analyses that omit these variables may overstate the direct effects of formal qualifications. The application of Heckman’s two-step procedure proved essential for the analysis of married women’s wages, as evidenced by the statistically significant Inverse Mills Ratio, confirming the importance of addressing sample selection bias when studying female employment outcomes. This methodological approach allowed us to generate more accurate estimates of wage determinants by accounting for the non-random selection of women into the labour force. From a policy perspective, our findings suggest that interventions aimed at enhancing women’s labour market outcomes in Indonesia should consider both cognitive and non-cognitive dimensions. While education remains a critical pathway to improved employment prospects, particularly for married women, programs that foster relevant personality traits—such as reducing Neuroticism and enhancing Conscientiousness or Extraversion—may yield additional benefits. This could include targeted training programs that develop social skills, emotional regulation, and self-discipline alongside technical abilities. The contrasting results for married and unmarried women also highlight the importance of life-stage-specific approaches to labour market policy. For unmarried women, who are often younger and at earlier career stages, the negative association between higher education and labour force participation reflects their continued investment in human capital. Policies that support work-study arrangements or provide incentives for early career development might better address their specific needs and constraints. In conclusion, this study contributes to a more nuanced understanding of female labour market dynamics in Indonesia by highlighting the often-overlooked role of non-cognitive skills. By demonstrating how personality traits differently affect married and unmarried women’s employment decisions and earnings, we provide evidence that conventional human capital frameworks should be augmented to include behavioural dimensions. Such an expanded approach has the potential to enhance both analytical accuracy and policy effectiveness in addressing persistent gender disparities in labour force participation and earnings. The limitations of this study include the relatively small sample size for unmarried women and the cross-sectional nature of our analysis, which prevents us from establishing causal relationships or tracking how personality traits interact with labour outcomes over time. Future research should explore the heterogeneous impacts of non-cognitive skills across income levels and occupational sectors, as suggested by Collischon ( 2020 ) and Badiani-Magnusson et al. ( 2014 ), possibly using quantile regression approaches with larger samples. Declarations Funding This work was supported by the Japan Society for the Promotion of Science (JSPS KAKENHI), Grant Numbers JP23KK0225 and JP24K16631 . Author Contribution Seonkyung Choi: Conceptualization; Investigation; Methodology (shared); Writing—original draft (Data and Methods review); Writing—review & editing; Project administration; Corresponding author.Huihui Li: Formal analysis; Data curation; Methodology (Data and Methods); Writing—original draft (primarily Data and Methods). Data Availability This study analysed publicly available secondary data from the Indonesian Family Life Survey (IFLS), provided by RAND Social and Economic Well-Being: [https://www.rand.org/health/surveys/FLS/IFLS.html](https:/www.rand.org/health/surveys/FLS/IFLS.html)No new data were generated during this study. References Almlund, M., Lee Duckworth, A., Heckman, J. J., & Kautz, T. D. (2011). Personality Psychology and Economics . http://www.nber.org/data-appendix/w16822 Anker, R., & Knowes, J. C. (1978). A Micro Analysis of Female Labor Force Participation in Kenya (62). https://webapps.ilo.org/public/libdoc/ilo/1978/78B09_28.pdf Badiani-Magnusson, R. C., Bodewig, C., Macdonald, K. A. D., Newhouse, D. L., & Rutkowski, J. J. (2014). Skilling up Vietnam : preparing the workforce for a modern market economy . https://documents.worldbank.org/en/publication/documents-reports/documentdetail/283651468321297015/skilling-up-vietnam-preparing-the-workforce-for-a-modern-market-economy Becker, G. S. (1965). A Theory of the Allocation of Time. The Economic Journal , 75 (299), 493–517. https://doi.org/10.2307/2228949 Borghans, L., Meijers, H., & Ter Weel, B. (2008). The Role of Non-cognitive Skills in Explaining Cognitive Test Scores. Economic Inquiry , 46 (1), 2–12. https://doi.org/10.1111/j.1465-7295.2007.00073.x Bowles, S., Gintis, H., & Osborne, M. (2001a). Incentive-Enhancing Preferences: Personality, Behavior, and Earnings. The American Economic Review , 91 (2), 155–158. https://doi.org/10.1257/aer.91.2.155 Bowles, S., Gintis, H., & Osborne, M. (2001b). The Determinants of Earnings: A Behavioral Approach. Journal of Economic Literature , 39 (4), 1137–1176. https://doi.org/10.1257/jel.39.4.1137 Bühler, D., Sharma, R., & Stein, W. (2020). Occupational Attainment and Earnings in Southeast Asia: The Role of Non-cognitive Skills. Labour Economics , 67 . https://doi.org/10.1016/j.labeco.2020.101913 Cameron, L. A., Dowling, J. M., & Worswick, C. (2001). Education and labor market participation of women in Asia: Evidence from five countries. Economic Development and Cultural Change , 49 (3), 459–477. https://doi.org/10.1086/452511 Campbell, D. (2013). The labour market in developing countries (S. Cazes & S. Verick, Eds.). ILO. Choi, S., Li, H., & Ogawa, K. (2023). Upper secondary vocational education and decent work in Indonesia: A gender comparison. International Journal of Educational Development , 101 . https://doi.org/10.1016/j.ijedudev.2023.102833 Collischon, M. (2020). The Returns to Personality Traits Across the Wage Distribution. Labour , 34 (1), 48–79. https://doi.org/10.1111/labr.12165 Costa, P. T., & McCrae, R. R. (1992). Four ways five factors are not basic. Personality and Individual Differences , 13 (6), 667–673. https://doi.org/10.1016/0191-8869(92)90237-J Fletcher, J. M. (2013). The effects of personality traits on adult labor market outcomes: Evidence from siblings. Journal of Economic Behavior and Organization , 89 , 122–135. https://doi.org/10.1016/j.jebo.2013.02.004 Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labor force participation: Journal of Population Economics , 27 (3), 639–681. https://doi.org/10.1007/s00148-013-0488-2 Gertler, P., Heckman, J., Pinto, R., Zanolini, A., Vermeersch, C., Walker, S., Chang, S. M., & Grantham-McGregor, S. (2014). Labor market returns to an early childhood stimulation intervention in Jamaica. In Science (Vol. 344, Issue 6187). https://doi.org/10.1126/science.1251178 Glewwe, P., Huang, Q., & Park, A. (2017). Cognitive skills, noncognitive skills, and school-to-work transitions in rural China. Journal of Economic Behavior & Organization , 134 , 141–164. https://doi.org/10.1016/j.jebo.2016.12.009 Goldin, C. (1994). The U-Shaped Female Labor Force Function in Economic Development and Economic History (4707). https://doi.org/10.3386/w4707 Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality , 37 (6), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1 Guerra, N., Modecki, K., & Cunningham, W. (2014). Developing Social-Emotional Skills for the Labor Market: The PRACTICE Model (WPS1723; Policy Research Working Paper). https://researchportal.murdoch.edu.au/esploro/outputs/report/Developing-Social-Emotional-Skills-for-the-Labor/991005541392607891 Gunatilaka, R. (2013). To work or not to work? Factors holding women back from market work in Sri Lanka (ILO Asia-Pacific Working Paper Series). https://www.researchgate.net/profile/Ramani-Gunatilaka/publication/287198803_To_work_or_not_to_work_Factors_holding_women_back_from_market_work_in_Sri_Lanka/links/5672a01508ae3aa2fcf0caa7/To-work-or-not-to-work-Factors-holding-women-back-from-market-work-in-Sri-Lanka.pdf Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica: Journal of the Econometric Society , 47 (1), 153–161. http’//www.jstor.org/journals/econosoc.html. Heckman, J. J., & Kautz, T. (2012). Hard evidence on soft skills. Labour Economics , 19 (4), 451–464. https://doi.org/10.1016/j.labeco.2012.05.014 Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior. Journal of Labor Economics , 24 (3), 411–482. https://doi.org/10.1086/504455 Kabeer, N. (2012). Women’s economic empowerment and inclusive growth: labour markets and enterprise development (2012/1). https://www.womenindisplacement.org/sites/g/files/tmzbdl1471/files/2020-10/Womens%20Economic%20Empowerment%20and%20Inclusive%20Growth.pdf Keiichi, O., & Masuma, A. (2007). female labor force participation in Indonesia. Journal of International Cooperation Studies , 14 (3), 71–108. https://doi.org/10.24546/00520272 Khandker, S. R. (1988). Determinants of Women’s Time Allocation in Rural Bangladesh. Economic Development and Cultural Change , 37 (1), 111–126. https://doi.org/10.1086/451710 Klasen, S., Le, T. T. N., Pieters, J., & Santos Silva, M. (2020). What Drives Female Labour Force Participation? Comparable Micro-level Evidence from Eight Developing and Emerging Economies. Journal of Development Studies , 1–26. https://doi.org/10.1080/00220388.2020.1790533 Klasen, S., & Pieters, J. (2015). What Explains the Stagnation of Female Labor Force Participation in Urban India? The World Bank Economic Review , 29 (3), 449–478. https://doi.org/10.1093/wber/lhv003 Laajaj, R., Macours, K., Pinzon Hernandez, D. A., Arias, O., Gosling, S. D., Potter, J., Rubio-Codina, M., & Vakis, R. (2019). Challenges to capture the big five personality traits in non-WEIRD populations. Science Advances , 5 (7), 1–13. https://doi.org/10.1126/sciadv.aaw5226 Lee, J. (2009). Non-cognitive characteristics and academic achievement in Southeast Asian countries (223; OECD Education Working Papers). https://doi.org/10.1787/c3626e2f-en Li, H., Chen, C., & Zhang, Z. (2023). Are gender differences related to non-cognitive abilities? ——Evidence from China. Journal of the Asia Pacific Economy , 28 (4), 1560–1579. https://doi.org/10.1080/13547860.2021.1982483 Mincer, J. (1962). Labor Force Participation of Married Women: A Study of Labor Supply. National Bureau of Economic Research , 63–105. http://www.nber.org/books/univ62-2 Schaner, S., & Das, S. (2016). Female Labor Force Participation in Asia: Indonesia Country Study . http://ssrn.com/abstract=2737842https://ssrn.com/abstract=2737842 Triggs, A., & Urata, S. (2020). Achieving Inclusive Growth in The Asia Pacific (1st ed.). Australian National University Press. https://doi.org/10.22459/AIGAP.2020 Verick, S. (2018). The Puzzles and Contradictions of the Indian Labour Market: What Will the Future of Work Look Like? (11376). https://hdl.handle.net/10419/177180 World Bank Group, Kementerian PPN/Bappenas, & Australian Government. (2020). Indonesia’s Occupational Employment Outlook - 2020 Technical Report . 1–97. https://www.bappenas.go.id/files/4916/2304/8835/Occupational_Employment_Outlook_TREnglish.pdf Youssef, N. H. (1971). Social structure and the female labor force: The case of women workers in muslim Middle Eastern countries. Demography , 8 (4), 427–439. https://doi.org/10.2307/2060680 Additional Declarations No competing interests reported. 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Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACHgY2BgYDmwQgycDMYAAXZyOkJY1kLQyHExjAWogB8j2Hnz34UHA+j0/68OHPBQXb5BkkEhg//GDgy8OlxeBsm7nhDIPbxWx8aWnSQIZhg0QCs2QPA1sxTi38PGzSPAa3E9t4eMyYgQzG/TcSGKSBfklswOWwfrCWcyAtxp+BWuxBtvzGp4XhbA9IywGQFgOwdUAtbHhtMThzzExyhkEyUAtbGkhLcgPPwzbLHgPcfpHvSX4m8eGPXeL8HubDn3n+3LZtYE8+fONHxTGcIYYNMAKdZHAsgRQtYFBDupZRMApGwSgYrgAAt9VK1IWPihwAAAAASUVORK5CYII=","orcid":"","institution":"Hiroshima University","correspondingAuthor":true,"prefix":"","firstName":"Seonkyung","middleName":"","lastName":"Choi","suffix":""},{"id":543723701,"identity":"ff7e541c-b9c9-4470-8b91-435c59fadbf3","order_by":1,"name":"Huihui Li","email":"","orcid":"","institution":"Kindai University","correspondingAuthor":false,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-09-30 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08:06:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28645,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7748385/v1/6d5b5480f3088efac018d0d2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding Female Employment and Wages in Indonesia: The Importance of Accounting for Non-cognitive Skills","fulltext":[{"header":"1\tIntroduction","content":"\u003cp\u003eFemale labour force participation (FLFP) is widely recognized as a cornerstone of inclusive economic growth, gender equality, and women\u0026rsquo;s empowerment. Despite steady gains in educational attainment and gradual changes in gender norms, women in many developing countries continue to face persistent disadvantages in the labour market (Campbell, 2013). Indonesia offers a particularly compelling context for examining this issue. As the world\u0026rsquo;s largest Muslim-majority nation and a country undergoing rapid socioeconomic transformation, Indonesia has witnessed increasing educational attainment among women but has seen relatively stagnant FLFP rates, hovering around 53% in recent years World Bank (2020). This paradox, often referred to as the \u0026ldquo;Indonesian puzzle\u0026rdquo; (Schaner \u0026amp; Das, 2016), has raised important questions regarding the factors shaping women\u0026rsquo;s labour market outcomes.\u003c/p\u003e\n\u003cp\u003eTraditional models of FLFP have long emphasized socio-demographic and economic determinants such as education, age, household composition, and regional disparities \u0026nbsp;(Goldin, 1994; Klasen \u0026amp; Pieters, 2015). These frameworks typically assume that education determines productivity and therefore influences labour market participation and earnings. However, these analyses have primarily focused on developed countries, with relatively little empirical attention to developing nations like Indonesia, where labour decisions are shaped by diverse institutional, cultural, and religious factors.\u003c/p\u003e\n\u003cp\u003eMoreover, while the role of cognitive skills and formal qualifications has been extensively studied, an emerging literature suggests that non-cognitive skills\u0026mdash;including personality traits, attitudes, and behavioural tendencies\u0026mdash;play a significant role in shaping labour market performance. These skills are frequently measured through the Big Five personality traits: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Research from both advanced and developing economies has increasingly highlighted the predictive power of such traits for labour market participation, earnings, and job stability (Borghans et al., 2008; Glewwe et al., 2017; Heckman \u0026amp; Kautz, 2012). Yet despite these advances, empirical studies on non-cognitive skills remain largely absent in non-Western contexts, particularly in Southeast Asia.\u003c/p\u003e\n\u003cp\u003eThis gap is particularly notable given recent methodological findings showing that omitting non-cognitive traits from empirical models may lead to overestimation of the returns to education. Fletcher (2013) and Collischon (2020), among others, show that non-cognitive traits are often correlated with educational attainment, and that failing to control for them can bias the estimated effects of schooling on labour outcomes. These findings raise the possibility that education may not act in isolation, but rather in tandem with personality traits that influence preferences, motivation, and resilience.\u003c/p\u003e\n\u003cp\u003eIn response to these gaps, this study examines how non-cognitive skills influence female labour market outcomes in Indonesia, with special attention to the role of marital status. Marriage in Indonesia often marks a significant shift in women\u0026rsquo;s social roles, caregiving responsibilities, and labour constraints, making it a critical dimension along which labour market behaviour may differ. Using data from the fifth wave of the Indonesia Family Life Survey (IFLS5), which includes detailed information on the Big Five personality traits, we assess how these traits influence both labour force participation and wage levels for married and unmarried women.\u003c/p\u003e\n\u003cp\u003eBy explicitly comparing estimates that do and do not control for personality, our study also offers empirical evidence on the extent to which education effects may be overstated in conventional models. Furthermore, we employ the Heckman two-step procedure to correct for sample selection bias in wage estimation\u0026mdash;a particularly salient issue in female labour market analysis. Through this multidimensional approach, we aim to provide a more comprehensive understanding of how personality, education, and marital status interact to shape labour market outcomes for women in a complex and culturally diverse setting.\u003c/p\u003e\n\u003cp\u003eThis study contributes to the literature in three key ways. First, it expands the empirical scope of non-cognitive skill research by situating it in the context of a Muslim-majority developing country. Second, it highlights how marriage structures moderate the effects of personality traits, offering new insights into life-cycle dynamics in women\u0026rsquo;s employment. Third, it provides methodological value by demonstrating how the inclusion of non-cognitive traits alters key interpretations regarding education and labour force engagement. The findings hold important implications for policy, suggesting that interventions aiming to enhance women\u0026rsquo;s labour market outcomes may benefit from focusing not only on cognitive skills but also on fostering relevant personality traits\u0026mdash;tailored to women\u0026rsquo;s social and life stage realities.\u003c/p\u003e"},{"header":"2 Background and Literature review","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Women\u0026rsquo;s Labor Market Context in Indonesia\u003c/h2\u003e\u003cp\u003eOver the past decades, Indonesia has experienced significant socioeconomic changes, including improvements in female educational attainment and increased urbanization. However, FLFP remains relatively stagnant, hovering around 53%, and continues to lag far behind the 82% participation rate for men World Bank (2020). This persistent gap, despite improvements in human capital, has led scholars to question the adequacy of traditional labour supply models in explaining women\u0026rsquo;s economic participation in Indonesia.\u003c/p\u003e\u003cp\u003ePrior research has pointed to several key determinants that influence FLFP in Indonesia. Education has consistently been shown to have a positive effect on women\u0026rsquo;s probability of participating in the labour market, particularly for those with upper secondary or higher levels of schooling (Keiichi \u0026amp; Masuma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Klasen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the strength of this relationship varies depending on life stage and marital status. For instance, Klasen et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that the positive returns to education in terms of labour force participation are more pronounced among unmarried women, while married women often face institutional and social constraints that offset the impact of their educational gains.\u003c/p\u003e\u003cp\u003eFamily structure and caregiving responsibilities have also been found to play a significant role. A number of studies have shown that the presence of young children negatively affects women\u0026rsquo;s labour participation, especially in contexts where affordable childcare is scarce and traditional gender roles are prevalent (Cameron et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gertler et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khandker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). These effects are particularly acute for married women, whose labour supply decisions are often shaped by the wage level of their husband and their perceived household responsibilities. Empirical work by Schaner \u0026amp; Das (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlights the \u0026ldquo;reservation wage\u0026rdquo; effect of husbands\u0026rsquo; income, where higher male earnings reduce the incentives for women to enter the labour market.\u003c/p\u003e\u003cp\u003eIntergenerational factors such as parental education and financial support have also been shown to influence young women\u0026rsquo;s labour force decisions. In households where parents provide ongoing financial support or have higher educational levels, unmarried women are more likely to delay labour market entry in favour of further education or informal caregiving roles (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This aligns with findings from other developing countries, where familial expectations and extended support networks can alter the standard human capital\u0026ndash;labour supply framework (Anker \u0026amp; Knowes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Youssef, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1971\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTaken together, these studies suggest that women\u0026rsquo;s labour force participation in Indonesia cannot be fully understood through conventional economic variables alone. Instead, labour decisions are embedded in a broader socio-cultural system that includes marriage, fertility, family structure, and intergenerational support. While the role of formal education remains important, its impact is mediated by both household dynamics and gender norms\u0026mdash;factors that vary considerably across regions and between married and unmarried women.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 From Classical Labor Supply Models to Expanded Theories of Female Employment\u003c/h2\u003e\u003cp\u003eClassical models of female labour supply, rooted in neoclassical economics, have long conceptualized women\u0026rsquo;s employment decisions as outcomes of individual utility maximization, where the opportunity costs of time and wage offers are weighed against the utility derived from home production and caregiving (Becker, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Mincer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). While these foundational frameworks offer a useful starting point, they often prove insufficient in capturing the multi-layered social and institutional realities shaping women\u0026rsquo;s labour market behaviour in developing countries.\u003c/p\u003e\u003cp\u003eA significant theoretical refinement emerged with Goldin's (1994) U-shaped hypothesis, which posits a non-linear relationship between economic development and FLFP. According to this model, FLFP initially declines as economies transition from agriculture to manufacturing, and rises again with the expansion of the service sector and increases in female education. While intuitive, this framework does not consistently hold across contexts. For instance, Gaddis \u0026amp; Klasen (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Verick (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) document numerous cases\u0026mdash;including Indonesia\u0026mdash;where educational attainment and economic growth fail to produce a corresponding rise in FLFP. This empirical disconnect points to the existence of additional social, institutional, and cultural constraints not adequately captured by structural economic models alone.\u003c/p\u003e\u003cp\u003eIn response, scholars have called for more comprehensive frameworks that integrate the role of gender norms, intra-household bargaining power, and institutional barriers. Kabeer (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highlights how women\u0026rsquo;s employment decisions are shaped within \u0026ldquo;structures of constraint\u0026rdquo;\u0026mdash;persistent patterns of disadvantage rooted in patriarchy, limited state support, and unequal household roles. Klasen et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) further proposes an augmented labour supply model that incorporates these social constraints alongside economic factors. Likewise, Gunatilaka (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) emphasizes the importance of recognizing labour supply decisions as often collective and negotiated within families, rather than strictly individual.\u003c/p\u003e\u003cp\u003eThese contributions reflect a broader shift in the theoretical understanding of women\u0026rsquo;s labour participation\u0026mdash;one that moves beyond formal schooling and wages to include a wider array of influences such as caregiving responsibilities, social expectations, spousal earnings, extended family structures, and regional variation. As noted in various studies on Indonesia and other Southeast Asian countries, these context-specific factors critically mediate whether and how women participate in the labour market, even when they possess the requisite education and skills (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Keiichi \u0026amp; Masuma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Klasen \u0026amp; Pieters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schaner \u0026amp; Das, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent theoretical work has added another layer of complexity by incorporating non-cognitive skills\u0026mdash;including personality traits and socio-emotional capacities\u0026mdash;into models of labour market behaviour.Heckman et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) presents a skill formation model where cognitive and non-cognitive skills jointly affect long-run economic outcomes.Bowles et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001a\u003c/span\u003e) identify three main channels through which personality traits influence labour market outcomes: direct productivity effects, sorting into appropriate jobs, and differential wage rewards based on trait-employer matching. These channels are increasingly relevant in developing countries, where informal employment, relational contracting, and weak enforcement of labour laws dominate (B\u0026uuml;hler et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Laajaj et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImportantly, ignoring such non-traditional variables may lead to biased interpretations of traditional ones\u0026mdash;a point raised by Fletcher (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)and Collischon (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who argue that omitting non-cognitive traits from empirical models inflates the estimated effects of education on employment and earnings. In settings like Indonesia, where women\u0026rsquo;s labour decisions are simultaneously shaped by family expectations, access to information, and individual behavioural traits, recognizing omitted variables and household-level heterogeneity becomes analytically essential.\u003c/p\u003e\u003cp\u003eThese theoretical developments underline the importance of incorporating a broader set of individual, household, and social variables\u0026mdash;including non-cognitive traits\u0026mdash;into labour market analysis. The next section reviews the growing body of empirical literature that attempts to capture these dynamics in both developed and developing country contexts, with special attention to how non-cognitive skills interact with gender and socioeconomic constraints.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Empirical Evidence on Non-Cognitive Skills and Female Labor Outcomes\u003c/h2\u003e\u003cp\u003eA growing body of empirical research has examined the determinants of FLFP and wage outcomes in developing countries. These studies traditionally emphasize human capital indicators such as education and work experience, alongside household characteristics like marital status, number of children, and spousal income. For instance, Keiichi and Masuma (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Klasen and Pieters (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) find that education, urban residence, and smaller household size are associated with a higher likelihood of female labour participation in Indonesia and India, respectively. Yet even after controlling for these factors, large unexplained gaps remain, prompting scholars to explore the influence of behavioural and psychological characteristics\u0026mdash;namely, non-cognitive skills.\u003c/p\u003e\u003cp\u003eRecent empirical work has shown that personality traits, commonly operationalized using the Big Five framework, significantly influence labour market outcomes. Almlund et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Borghans et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) demonstrate that traits such as conscientiousness and emotional stability predict employment status, occupational choice, and earnings, even after accounting for education and cognitive ability. Fletcher (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) provides compelling evidence that failure to control for non-cognitive skills results in upwardly biased estimates of the return to education. Using sibling fixed effects models in the U.S., he shows that, once personality is controlled, the effect of schooling on earnings is significantly reduced. This finding has direct relevance to the Indonesian context, where educational attainment is often correlated with personality traits due to selection into higher education. Empirical studies from Asia further confirm the importance of non-cognitive skills in shaping labour outcomes. Li et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) use data from China to show that gender differences in the Big Five traits help explain wage gaps, with women scoring higher in agreeableness and lower in neuroticism\u0026mdash;traits that are negatively rewarded in most labour markets. Importantly, their analysis shows that the effects of non-cognitive traits are often comparable in magnitude to those of cognitive abilities. Using Vietnam data, Badiani-Magnusson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reports that non-cognitive skills, including personality traits, affect labour market outcomes, and that their influence can vary across the income distribution, particularly emphasizing that traits like emotional instability (related to neuroticism) may impose greater disadvantages at higher wage levels. Similarly, Collischon (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlights that the wage premium for conscientiousness and the penalty for neuroticism vary across job sectors and gender, calling for heterogeneity-sensitive approaches.\u003c/p\u003e\u003cp\u003eIn the Southeast Asian context,Triggs \u0026amp; Urata (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasize that improving women\u0026rsquo;s labour participation in Indonesia requires attention not only to education and skills but also to institutional and behavioural constraints. They argue that traditional supply-side policies may fall short unless they account for individual characteristics that influence women's job preferences and resilience in navigating informal labour markets. This resonates with findings byGuerra et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who show that, in developing countries, personality traits such as emotional stability and social competence have stronger labour market effects where informal employment dominates and formal credentialing is weak.Glewwe et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) further argue that non-cognitive skills are especially valuable in environments where job performance is not easily monitored, thus increasing the value of self-discipline and reliability.\u003c/p\u003e\u003cp\u003eBeyond wage outcomes, several studies have investigated the link between personality traits and labour force participation decisions. Building on the earlier findings by Guerra et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who demonstrate that non-cognitive traits can shape FLFP through their impact on expected wages, job search strategies, and bargaining power within households in developing countries, Cobb-Clark and Tan (2011) further emphasize that specific personality traits, such as extraversion and conscientiousness, are positively associated with occupational attainment and employment probabilities, while neuroticism tends to have a negative impact.\u003c/p\u003e\u003cp\u003eHowever, few studies have explored how the effects of non-cognitive skills vary by marital status\u0026mdash;a critical dimension in settings like Indonesia, where marriage significantly alters women\u0026rsquo;s labour market incentives and constraints. Some studies note that household dynamics, caregiving roles, and spousal income all interact with personality traits in complex ways (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schaner \u0026amp; Das, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but empirical tests of such interactions remain rare. Moreover, while many existing studies examine either labour force participation or earnings, few analyse both outcomes within a unified framework that addresses sample selection bias.\u003c/p\u003e\u003cp\u003eSignificant gaps thus remain in the literature. First, much of the existing empirical evidence on non-cognitive skills and labour outcomes is concentrated in high-income or upper-middle-income countries, with relatively limited research focusing on Southeast Asia and Muslim-majority contexts such as Indonesia. Second, while numerous studies underscore the importance of personality traits, few explicitly examine the consequences of omitting these variables in estimating the effects of education or other traditional determinants\u0026mdash;raising concerns about potential bias and misinterpretation of policy-relevant coefficients (Collischon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fletcher, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Third, although marriage is known to significantly restructure women\u0026rsquo;s roles and constraints, there is a notable lack of research that systematically compares how non-cognitive skills produce different effects for married and unmarried women within the same empirical setting. This study addresses these gaps by analysing the role of non-cognitive skills in shaping both labour force participation and wage outcomes among women in Indonesia. By explicitly controlling for personality traits and separating analyses by marital status, we offer new empirical insights into how behavioural traits interact with traditional determinants in a culturally complex labour market. In doing so, we contribute to both the methodological literature on omitted variable bias and the broader effort to understand heterogeneous pathways to female labour market integration in developing economies.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Data and Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data\u003c/h2\u003e\u003cp\u003eThis study utilizes data from the Indonesian Family Life Survey (IFLS) to examine the relationship between non-cognitive skills and women\u0026rsquo;s labour market outcomes. The IFLS is a comprehensive longitudinal survey that collects detailed information at both individual and household levels, including data on personality traits, socioeconomic characteristics, labour force participation, earnings, education, and family relationships. Initially launched in 1993 (IFLS1), the survey has consistently tracked the same households across multiple waves, with subsequent rounds conducted in 1997, 2000, 2007, and 2014. We employ the fifth wave (IFLS5) collected in 2014, which provides particularly detailed information on respondents' Big Five personality traits alongside extensive data on labour market outcomes and socioeconomic characteristics. The IFLS5 is especially suitable for our research purposes as it includes a comprehensive personality assessment module that allows us to measure non-cognitive skills systematically.\u003c/p\u003e\u003cp\u003eAccording to the World Bank (2020), female labour force participation in Indonesia has shown moderate improvement over recent decades, with the rate for women aged 15 and above exceeding 50% in 2008 and reaching 53% in 2019. Despite this progress, substantial gender disparities persist in both labour force participation and earnings. These patterns make Indonesia an ideal setting for investigating how non-cognitive skills influence women\u0026rsquo;s labour market performance. Our analytical sample consists of women between 15 and 55 years of age, with separate analyses conducted for married women (n\u0026thinsp;=\u0026thinsp;4,399) and unmarried women (n\u0026thinsp;=\u0026thinsp;288). This division by marital status allows us to explore how the relationships among personality traits, human capital, and labour market outcomes might differ based on women's family roles and responsibilities. While we recognize the substantial difference in sample sizes between these two groups, which may affect the precision of estimates for unmarried women, this approach enables us to capture important heterogeneity in labour market determinants. It is important to note that, in our sample, marital status strongly correlates with age: married women are significantly older (mean age 35.2 years) than unmarried women (mean age 20.1 years). This age difference means that our marital status comparison also largely captures a comparison between older and younger women, which may have implications for interpreting differences in education levels, labour market experience, and potential cohort effects in personality traits.\u003c/p\u003e\u003cp\u003eWe measure non-cognitive skills using the well-established Big Five personality framework, which encompasses five broad dimensions (OCEAN): Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (Costa \u0026amp; McCrae, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The IFLS5 assesses each dimension through two questionnaire items, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with respondents rating their agreement on a 5-point Likert scale from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree.\u0026rdquo; For each personality dimension, we calculate the mean value of the corresponding items, resulting in scores ranging from 1 to 5, with higher values indicating stronger manifestation of that trait. While this abbreviated measurement approach is less comprehensive than longer inventories, previous research has validated such short measures for large-scale surveys (Gosling et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and similar measures have been successfully employed in labour market studies across various cultural contexts (Guerra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Big Five Personality Traits and Their Measurement in IFLS5\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition (Questionnaire Items)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpenness to experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIs original, comes up with new ideas; Has an active imagination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes a thorough job; Does things efficiently\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIs talkative; Outgoing, sociable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgreeableness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHas a forgiving nature; Is considerate and kind to almost everyone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroticism\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorries a lot; Gets nervous easily\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote *: Unlike other personality traits where higher scores are generally considered positive for labour market outcomes, Neuroticism is typically considered a \"reverse\" trait, where lower scores are associated with better labour market outcomes such as higher wages and increased employment probability. In the psychology literature, the opposite of Neuroticism is often referred to as \"Emotional Stability\u0026rdquo;.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Methodology\u003c/h2\u003e\u003cp\u003eWe begin by estimating the impact of non-cognitive skills on women\u0026rsquo;s labour force participation using a probit model as specified in Eq.\u0026nbsp;(1),\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{LMP}_{i}=1\\left[\\alpha\\:+{\\theta\\:}_{n}\\sum\\:_{n=1}^{5}{BFP}_{n,i}+{\\partial\\:V}_{i}+{\\beta\\:}_{1}{age}_{i}\\:+{{\\beta\\:}_{2}age}_{i}^{2}+{X}^{{\\prime\\:}}\\gamma\\:+{\\mu\\:}_{i}\u0026gt;0\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere the dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{LMP}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents labour market participation status (1 if participating, 0 if not) for female individual \u003cem\u003ei\u003c/em\u003e. Our key explanatory variables are the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BFP}_{i}\\)\u003c/span\u003e\u003c/span\u003e, with their effects captured by the coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{n}\\)\u003c/span\u003e\u003c/span\u003e. We control for educational attainment as proxies for women\u0026rsquo;s potential productivity (Klasen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{i}\\)\u003c/span\u003e\u003c/span\u003e captures the respondent\u0026rsquo;s education level, coded as 0 for below lower secondary education (reference category), and 1 through 4 for, respectively, general upper secondary, vocational upper secondary, post-secondary diploma, and a bachelor\u0026rsquo;s or above. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{age}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e and its square are included to control for lifecycle effects on labour supply to capture potential non-linearity. Additional control variables in vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e include number of siblings (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ethnicity indicators, and urban/rural residence, following established literature (Anker \u0026amp; Knowes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Youssef, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). For married women, we include controls for number of children and husband\u0026rsquo;s log wage, which prior research has identified as key determinants of married women\u0026rsquo;s labour supply (Gertler et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khandker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Klasen \u0026amp; Pieters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For unmarried women, we include father\u0026rsquo;s education level and a dummy for parental financial support. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the error term. We estimate this model separately for married and unmarried women to allow for differential effects across these groups, reporting marginal effects for easier interpretation. It should be noted that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ is an error term assumed to be normally distributed.\u003c/p\u003e\u003cp\u003eTo analyse the effect of non-cognitive traits on labour market performance conditional on employment, we estimate a modified Mincerian earnings function as shown in Eq.\u0026nbsp;(2):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{WageRate}_{i}=\\delta\\:+{\\pi\\:}_{n}\\sum\\:_{n=1}^{5}{BFP}_{n,i}+\\kappa\\:{V}_{i}+{\\rho\\:}_{1}{age}_{i}\\:+{{\\rho\\:}_{2}age}_{i}^{2}+{Z}^{{\\prime\\:}}\\phi\\:+{\\epsilon\\:}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{WageRate}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the natural logarithm of hourly wages, allowing us to interpret coefficients approximately as percentage changes. The explanatory variables include the same personality traits, education, and age terms as in the participation model. Vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Z}\\)\u003c/span\u003e\u003c/span\u003e includes additional controls for siblings, ethnicity, occupational sector (government, private, or casual non-agricultural work), and urban/rural residence.\u003c/p\u003e\u003cp\u003eA key methodological challenge in estimating women\u0026rsquo;s wage equations is potential sample selection bias, as wages are only observed for women who participate in the labour market.Heckman (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) developed a two-step correction procedure to address this issue. In the first step, we estimate the probability of an individual\u0026rsquo;s decision to engage in wage work using a probit model. This selection equation includes all the variables in Eq.\u0026nbsp;(2) (except occupational sector), as well as additional identifying variables that influence labour market participation but not wages directly. For married women, we include the husband's wage rate as an identifying variable. Theoretically, the husband\u0026rsquo;s wage level directly affects a woman\u0026rsquo;s reservation wage (the minimum wage at which she is willing to work), which in turn influences her decision to enter the labour market. Higher husband earnings typically increase the reservation wage, potentially reducing the likelihood of labour market participation through an income effect. For unmarried women, we include the father\u0026rsquo;s education level and a dummy variable indicating financial help from parents as identifying variables. These factors affect whether unmarried women choose to work and whether they engage in paid employment. Higher paternal education and financial support from parents may allow unmarried women to be more selective about employment or to pursue further education rather than immediately entering the labour force (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Heckman procedure uses different sets of variables in the wage equation (second stage) from those in the labour market participation equation (first stage). The identifying variables (husband\u0026rsquo;s wage for married women; father\u0026rsquo;s education and parental financial support for unmarried women) as exclusion restrictions functioning similarly to instrumental variables are included only in the first stage selection equation under the assumption that they influence labour market participation decisions but not wages directly. Consequently, these variables do not appear in the second-stage wage equation reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Instead, their effects are captured through the Inverse Mills Ratio (IMR), which is calculated from the first-stage equation and included in the second-stage equation. The statistical significance of the IMR coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}\\)\u003c/span\u003e\u003c/span\u003e indicates whether selection bias is present. For married women, a significant IMR validates the use of Heckman results, while for unmarried women, an insignificant IMR suggests that OLS results may be more appropriate as selection bias is minimal. For comparison and robustness checks, we also present results from standard OLS estimation of Eq.\u0026nbsp;(2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Descriptive statistics\u003c/h2\u003e\u003cp\u003eThis study measures women\u0026rsquo;s labour market outcomes using two primary dependent variables. First, labour market participation is a binary variable indicating whether an individual is economically active at the time of the survey, taking the value of 1 if participating and 0 otherwise. Second, wage level is measured as the natural logarithm of hourly earnings from employment, allowing for percentage change interpretations in our analysis. Non-cognitive skills are measured through the Big Five personality traits framework. Each trait is constructed as the average of responses to two items in the IFLS5 questionnaire, measured on a 5-point Likert scale (1= \u0026ldquo;strongly disagree\u0026rdquo; to 5= \u0026ldquo;strongly agree\u0026rdquo;). For human capital variables, educational attainment is categorized into five levels: Lower Secondary (reference category), Upper Secondary (general), Upper Secondary (vocational), Post-secondary diploma, and Bachelor's or higher academic qualification. In the Indonesian context, \u0026ldquo;Post-secondary diploma\u0026rdquo; refers to Diploma programs (D1-D4), which are 1\u0026ndash;4 year vocational programs focusing on practical skills, while \u0026ldquo;Bachelor\u0026rsquo;s or above\u0026rdquo; includes academic degrees like Individual\u0026rsquo;s highest education is a bachelor\u0026rsquo;s degree or higher academic credential. This distinction reflects Indonesia\u0026rsquo;s dual-track higher education system separating vocational and academic pathways. Demographic characteristics include age and its squared term to capture non-linear effects of age on labour market outcomes. Number of siblings measures the total number of siblings, which may influence early-life resource allocation and experiences. Urban residence is a binary variable taking the value of 1 if residing in an urban area and 0 if rural. Ethnicity is categorized as Javanese, Sundanese (two major ethnic groups in Indonesia), and Other ethnicities. Marriage-specific variables differ between groups. For married women, we include number of children and husband\u0026rsquo;s hourly wage (in natural logarithm). For unmarried women, we include father\u0026rsquo;s educational level (Lower Secondary, Upper Secondary general, Upper Secondary vocational, Post-secondary diploma, Bachelor\u0026rsquo;s or above) and financial support from parents (1 if receiving support, 0 otherwise). Occupational sector is categorized into three groups: government worker, private sector worker, and casual worker not in agriculture, which are used to control for industry-specific wage differentials in the wage analysis.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for the key variables in our analysis, separated by marital status. The labour force participation rate is 43.9% for married women and 38.5% for unmarried women. For personality traits, unmarried women show slightly higher average scores for Openness to Experience (3.684 vs. 3.536 for married women), Extraversion (3.793 vs. 3.683), and Neuroticism (3.328 vs. 3.103), while married women score higher on Conscientiousness (3.972 vs. 3.859) and Agreeableness (4.122 vs. 4.071). There are notable differences in educational attainment between the two groups. Among married women, the majority (51.5%) have lower secondary education as their highest level, followed by general upper secondary (17.6%) and bachelor\u0026rsquo;s degree or above (13.0%). In contrast, unmarried women have significantly higher educational attainment, with 45.5% holding a bachelor\u0026rsquo;s degree or higher, followed by vocational upper secondary (18.4%) and general upper secondary (17.0%). This education gap primarily reflects both the considerable age difference between the groups (married women average 35 years versus 20 years for unmarried women) and the generational improvements in female educational access in Indonesia over recent decades. Similarly, married women come from larger families (3.9 average siblings) compared to younger unmarried women (2.8) and tend to be less urban (65%) than unmarried women (86%). For those with wage income, the mean logarithm of hourly wages is 8.947 for married women and 9.182 for unmarried women, indicating higher average hourly earnings for unmarried women. Regarding occupation, private sector employment predominates in both groups (62.2% of married women and 81.1% of unmarried women), with government employment more common among married women (19.7% vs. 8.1%) and casual non-agricultural work accounting for 18.6% of married women and 10.8% of unmarried women in the labour force.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics by Marital Status (Mean and Standard deviation Values)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eMarried Women\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c14\" namest=\"c8\"\u003e\u003cp\u003eUnmarried Women\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor force participation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBig Five Personality traits (OCEAN)\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOpenness to experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eConscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eExtraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAgreeableness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e4.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNeuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducation attainments\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLower Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUpper Secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUpper Secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDemographic characteristics\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e20.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e3.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e413.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e146.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNumber of Siblings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e2.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e1.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eJavanese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSundanese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOther Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNumber of Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHusband\u0026rsquo;s Hourly wage (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e4.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParent financial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFather\u0026rsquo;s educational attainment\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLower Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUpper Secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUpper Secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWage and employment (for employed women only)\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHourly wage (IDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e13508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e16273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e14067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e9982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHourly wage (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e8.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e1.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e9.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e1.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOccupational sectors\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGovernment worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrivate worker agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCasual worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003cem\u003eNote: 1) Obs\u0026thinsp;=\u0026thinsp;number of Observations. SD\u0026thinsp;=\u0026thinsp;Standard deviation. The Big Five personality traits are measured on a 5-point scale where 1 = \"strongly disagree\" and 5 = \"strongly agree\" with trait-descriptive statements. Hourly wages are in Indonesian Rupiah (IDR). The sample is restricted to women aged 15\u0026ndash;55 years. 2) In Indonesia, \"Post-secondary diploma\" refers to Diploma programs (D1-D3), which are 1\u0026ndash;3 year vocational programs with flexible entry ages and varied duration, focusing on practical skills rather than academic theory. \"Bachelor or above\" corresponds to conventional higher education (Sarjana S1, S2, and S3 degrees), requiring entrance examinations and following standardized academic curricula comparable to international university degrees. This distinction represents Indonesia's dual-track higher education system of vocational versus academic pathways (\u003c/em\u003eChoi et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong employed women, we observe that, despite lower labour force participation rates, unmarried women who work earn slightly higher wages on average than their married counterparts (average log hourly wage of 9.182 versus 8.947). Occupational patterns also differ, with private sector employment predominating in both groups but more common among unmarried women (81.1% versus 62.2%), while government employment is more prevalent among married women (19.7% versus 8.1%). We acknowledge the potential endogeneity between personality traits and labour market experiences. While we treat personality traits as predetermined characteristics in our analysis, following standard practice in the literature (Almlund et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we recognize that labour market experiences may also shape personality development. The cross-sectional nature of our study prevents us from establishing causal relationships; rather, we aim to identify associations that can enhance understanding of how non-cognitive skills relate to labour market outcomes in the Indonesian context.\u003c/p\u003e\u003cp\u003eAppendix Table\u0026nbsp;7 provides additional descriptive statistics specifically for the subsample of women with wage employment used in our wage analysis. These statistics allow for direct comparison of characteristics between wage-earning married and unmarried women. When restricting our analysis to wage earners, the sample size decreases substantially\u0026mdash;from 4,399 to 1,202 for married women and from 288 to just 69 for unmarried women. This significant reduction reflects a common characteristic of developing economies like Indonesia, where despite increasing overall female labour force participation, the proportion of women in formal wage employment remains relatively low, with many women working in informal, family, or agricultural sectors without formal wages. This pattern is consistent with findings by Schaner and Das (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) regarding the structure of female employment in developing countries. The substantial sample size reduction, particularly for unmarried women, further exacerbates statistical power limitations and potentially affects the precision of our wage analysis. The substantial sample size reduction represents a significant limitation of our study, particularly for the analysis of unmarried women\u0026rsquo;s wages, where the final sample of just 69 observations may compromise statistical power and the reliability of our estimates. The table also reveals that both groups of employed women have somewhat different personality trait distributions compared to the full sample, highlighting the importance of accounting for selection into employment when analysing wage determinants.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cp\u003eWe present the results of the effects of non-cognitive skills on women\u0026rsquo;s labour market outcomes in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e through \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. To examine the influence of the Big Five personality traits on labour force participation and wages, each table compares results estimated with and without the inclusion of personality variables. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the marginal effects from Probit models for labour force participation, based on coefficient estimates presented in Appendix Table\u0026nbsp;6. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the second-stage results from the Heckman two-step procedure, and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the OLS estimates of women\u0026rsquo;s log hourly wages. We discuss the key findings from these models in detail below.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Non-cognitive Skills and Labor Market Participation\u003c/h2\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal distinct patterns in the determinants of labour market participation between married and unmarried women. For married women (column 1), the labour market participation rates of those with a post-secondary diploma and those with a bachelor\u0026rsquo;s degree or above) are respectively 22.7% and 41.6% higher than those of women whose highest level of education is lower secondary education. However, no significant difference was observed between married women with lower secondary education and those with upper secondary general or vocational education.\u003c/p\u003e\u003cp\u003eAfter controlling for the Big Five personality traits (column 2), the effect of education level on female labour force participation remains, although the coefficient becomes slightly smaller. This aligns with the theoretical framework proposed by Bowles et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001b\u003c/span\u003e), suggesting interaction between personality traits and human capital in determining labour market outcomes. Notably, only the results for Extraversion and Neuroticism were statistically significant. The stronger the Extraversion traits of a married woman, the higher her probability of labour market participation, with an increase of 3.1%. Conversely, the stronger the Neuroticism traits, the lower her probability of labour market participation, with a decrease of 1.8%. These findings are consistent with those of Gertler et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), supporting the theory that extraverted personality traits enhance networking capabilities and job search efficiency, positively influencing labour market participation. The negative effect of Neuroticism may be related to difficulties in stress management and forming social relationships in the workplace, which could present additional barriers for married women who must navigate multiple roles between household and employment spheres.\u003c/p\u003e\u003cp\u003eFor unmarried women (column 3), the labour market participation rates of those with upper secondary general education (17.2%), post-secondary diploma (41.4%) and bachelor\u0026rsquo;s or above (47.8%) are lower than that of women whose highest level of education is lower secondary education. This pattern aligns with the \u0026ldquo;education investment and delayed labour market entry\u0026rdquo; phenomenon observed by Schaner \u0026amp; Das (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Indonesia. When controlling for the Big Five personality traits (column 4), only Conscientiousness showed statistical significance, with stronger Conscientiousness traits increasing the probability of labour market participation by 7%. The pattern of higher education levels being associated with lower probability of labour market participation among unmarried women is noteworthy. The descriptive statistics in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that the average age of the unmarried female group is 20 years old. This suggests that unmarried women at this age may choose to continue their studies rather than enter the workforce at a young age. This finding is consistent with overall statistics for Indonesia, which show that the employment rate of women aged 15 to 24 is lower than that of women aged 24 to 60, and that women aged 15 to 24 generally have a higher level of education than women aged 24 to 60. This pattern reflects what Triggs and Urata (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified as a strategy among young Indonesian women to pursue long-term labour market outcomes through human capital accumulation.\u003c/p\u003e\u003cp\u003eMoreover, the differential impact of personality traits by marital status supports the theoretical framework proposed by Gunatilaka (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Kabeer (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that women\u0026rsquo;s labour decisions are made within \u0026ldquo;structures of constraint.\u0026rdquo; For married women, household roles and positions may make extraverted personality traits particularly helpful in overcoming labour market barriers, while unmarried women, with relatively fewer family responsibilities and greater autonomy, may find conscientiousness more instrumental.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMarginal effects of Probit estimation for labour market participation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMarried Women\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eUnmarried Women\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.011]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.030]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.070**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.013]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.033]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpenness to experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.010]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.033]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgreeableness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.035]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.020]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational level (reference group: below lower secondary high school)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.172**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.173**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.020]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.020]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.067]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.068]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.123*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.023]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.023]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.072]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.073]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.227***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.218***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.414***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.421***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.035]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.036]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.091]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.087]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.416***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.404***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.478***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.473***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.020]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.021]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.069]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.068]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.006]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.006]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.086]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.090]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.000]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.000]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.002]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.002]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.047]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.046]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Siblings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.003]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.003]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.012]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.012]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.007]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.007]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHusband\u0026rsquo;s Hourly wage (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.002]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.002]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEthnicity (reference group: Javanese)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSundanese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.074***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.070***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.146**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.145*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.022]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.022]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.073]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.079]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.069***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.066***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.041]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.040]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFather\u0026rsquo;s Highest education level completed for Unmarried women\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.049]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.048]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.136**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.133**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.058]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.235*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.130]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.135]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.058]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.058]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent financial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.116*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.110*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.067]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.063]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eRobust standard errors in brackets. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Non-cognitive Skills and Wage Determination\u003c/h2\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present, respectively, the results of the Heckman procedure estimation and OLS estimation for women\u0026rsquo;s hourly wages. We first use the estimated value of the Inverse Mills Ratio (IMR) to assess whether the Heckman model can correct the problem of sample selection bias. In the analysis of the married women group (columns 1 and 2 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the estimated values of the IMR are statistically significant, suggesting that the Heckman procedure can alleviate the biased estimates of explanatory variables caused by sample selection bias. This is consistent with Heckman's (1979) theoretical predictions and particularly important in contexts like Indonesia where family structures and cultural norms strongly influence women\u0026rsquo;s labour market participation.\u003c/p\u003e\u003cp\u003eIn the sample estimation for married women, after controlling for job section and age (column 1 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), differences in education level do not statistically affect hourly wage rates. This aligns with findings by Fletcher (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Collischon (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), suggesting that the effects of education may be overstated when non-cognitive skills are not controlled for. In column 2, after adding the variables for the Big Five personality traits, only the coefficient for married women with upper secondary general education is statistically significant at the 10% level, with their wage rate being 26.8% higher than that of women whose highest education level is lower secondary education. However, the Big Five personality traits do not show statistical significance.\u003c/p\u003e\u003cp\u003eSimilarly, In the analysis of unmarried women, we find that the Inverse Mills Ratio (IMR) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (columns 3 and 4) is statistically insignificant. This lack of significance indicates that there is no detectable sample selection bias for this group, suggesting that unobserved factors affecting labour market participation are not significantly correlated with factors influencing wages. Therefore, the OLS estimates in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are more appropriate for interpreting the wage determinants for unmarried women. Looking at these OLS results in column 3 of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, after controlling for variables such as occupation and age, we find that the difference in education level does not statistically affect wage levels. After adding the control variables for the Big Five personality traits (column 4), the estimated values for education levels remain statistically insignificant. Among the Big Five personality traits, only the coefficient for Neuroticism is statistically significant at the 5% level, with stronger Neuroticism traits decreasing hourly wage rates by 33.7%. This finding is consistent with patterns observed byLi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in China andBadiani-Magnusson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in Vietnam, where Neuroticism was found to have substantial negative wage effects in East and Southeast Asian labour markets. This may be particularly relevant in the service sectors and in professional occupations where emotional regulation, stress management, and interpersonal skills are important. As suggested by Glewwe et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), non-cognitive skills are especially valuable in environments where work performance is not easily monitored, and in such contexts, high levels of Neuroticism may serve as a negative signal to employers.\u003c/p\u003e\u003cp\u003eIn the analysis of wage rates overall, regardless of whether the Big Five personality traits are controlled for, the difference in educational level between married and unmarried women does not significantly affect the hourly wage rate (except for the coefficient of married women with upper secondary general education in column 2 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this analysis, we controlled for occupation and age, suggesting that among peers in the same occupation, the return on education in terms of wages is almost non-existent. This aligns with the theory proposed by Borghans et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) that non-cognitive skills may play an important role in explaining wage inequality among individuals with the same level of education.\u003c/p\u003e\u003cp\u003eIn the sample analysis of unmarried women, we confirmed that among peers with the same education level working in the same occupational sector, a strong personality trait of Neuroticism negatively impacts wage growth. This suggests that improving women's non-cognitive abilities in the labour market\u0026mdash;at least by alleviating Neuroticism\u0026mdash;could help increase their wage returns. However, it is important to note that there are multiple dimensions by which labour market outcomes should be evaluated. In addition to wages, factors such as opportunities for vocational training and career advancement also play a significant role. These findings have significant implications for policy design aimed at enhancing female labour market outcomes in Indonesia. Human capital development policies should focus not only on formal education but also on developing relevant non-cognitive skills, particularly enhancing Extraversion and reducing Neuroticism for married women, and developing Conscientiousness for unmarried women. As suggested by Klasen et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Kabeer (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), differentiated approaches based on marital status are necessary, recognizing that women\u0026rsquo;s labour market participation occurs within \u0026ldquo;structures of constraint.\u0026rdquo; Furthermore, our research demonstrates that estimates of female wage determinants may be distorted if sample selection bias is not considered, highlighting the importance of incorporating such methodological considerations into policy evaluation and design.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeckman correction estimation for women\u0026rsquo;s hourly wages (log)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMarried Women\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eUnmarried Women\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(Probit-OLS)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(Probit-OLS)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0795]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.245]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0900]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.258]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpenness to experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0630]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.215]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgreeableness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0984]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.295]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.303**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0573]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.145]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational level (reference group: below lower secondary high school)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school at upper secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.149]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.141]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.565]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.661]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school at upper secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.182]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.171]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.429]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.500]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.433]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.407]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.623]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.642]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.502]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.470]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.738]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.764]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.193**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.182**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0775]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0737]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.406]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.395]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00239**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00226**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.000938]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.000893]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.00626]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.00625]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Siblings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0210]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0201]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.0687]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.0707]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0512]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0492]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.530\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.182]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.175]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.377]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.362]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJob Sector (reference group: Casual worker not in agriculture)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.563***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.574***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.115]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.110]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.446]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.450]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.500***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.536***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.158]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.153]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.641]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.653]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEthnicity (reference group: Javanese)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSundanese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.149]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.141]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.435]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.458]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.463***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.425***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.163]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.153]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.382]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.384]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.121***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.720]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.701]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.688]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.647]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.42***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.71***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[2.405]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[2.460]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[6.157]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[6.284]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald Chi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.7***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167.7***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eStandard errors in brackets *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOLS estimation for women\u0026rsquo;s hourly wages (log)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMarried Women\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnmarried Women\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0455]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.188]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0566]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.297]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpenness to experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0405]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.182]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgreeableness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0618]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.282]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0754**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.337**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0325]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.142]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school at upper secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.381***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.374***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0876]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0883]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.584]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.735]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school at upper secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.421***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.393***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0920]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0932]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.510]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.585]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.647***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.621***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.112]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.114]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.527]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.556]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.668***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.649***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0970]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0987]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.476]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.520]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.534***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.537***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0284]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0285]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.172]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.199]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00941***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00932***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.000368]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.000369]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.00294]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.00343]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Siblings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0126]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0125]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.0630]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.0703]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0292]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0296]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.341***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.342***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0734]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0736]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.459]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.450]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJob Sector (reference group: Casual worker not in agriculture)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate worker agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.565***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.575***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0922]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0927]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.482]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.493]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.498***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.528***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.140]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.140]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.750]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.757]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEthnicity (reference group: Javanese)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSundanese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0988]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0982]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.405]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.409]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.0647]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0652]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.460]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.437]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFather\u0026rsquo;s educational level (reference group: below lower secondary high school)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (general)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.342]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.374]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh school at upper secondary (vocational)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.878]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.954]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePost-secondary diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.550**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.927**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.265]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.426]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.545]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.514]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent financial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.951***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.949***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.262]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.289]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHusband\u0026rsquo;s Hourly wage (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0319***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0320***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.00606]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.00608]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.718***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.074***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.533]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.585]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[2.406]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[3.710]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eStandard errors in brackets *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study has examined the role of non-cognitive skills in determining female labour market outcomes in Indonesia, focusing specifically on the relationship among the Big Five personality traits, employment status, and wages across different marital statuses. Our findings offer several important contributions to the literature on female labour force participation in developing countries.\u003c/p\u003e\u003cp\u003eFirst, our results confirm that non-cognitive skills significantly influence women's labour market decisions, though these effects differ markedly between married and unmarried women. For married women, Extraversion enhances labour market participation by 3.1%, while Neuroticism reduces it by 1.8%. In contrast, unmarried women's participation is most strongly influenced by Conscientiousness, which increases their likelihood of employment by 7%. These distinct patterns highlight how personality traits interact differently with the social roles and constraints associated with marital status in the Indonesian context.\u003c/p\u003e\u003cp\u003eSecond, our wage analysis revealed that while non-cognitive skills have limited direct effects on married women\u0026rsquo;s earnings, Neuroticism significantly reduces hourly wages (by 33.7%) for unmarried women. This finding is consistent with previous research suggesting that emotional stability is particularly valuable in labor markets where job performance requires interpersonal skills and emotional regulation. The absence of strong wage effects for married women may reflect their concentration in occupational sectors where non-cognitive skills are less directly rewarded or where other factors like household responsibilities constrain occupational choices.\u003c/p\u003e\u003cp\u003eThird, our methodology comparing models with and without personality trait controls demonstrates that conventional analyses may misattribute some effects to education that are actually explained by non-cognitive factors. When personality traits are included in our models, the estimated returns to education are slightly reduced, suggesting that traditional labour market analyses that omit these variables may overstate the direct effects of formal qualifications.\u003c/p\u003e\u003cp\u003eThe application of Heckman\u0026rsquo;s two-step procedure proved essential for the analysis of married women\u0026rsquo;s wages, as evidenced by the statistically significant Inverse Mills Ratio, confirming the importance of addressing sample selection bias when studying female employment outcomes. This methodological approach allowed us to generate more accurate estimates of wage determinants by accounting for the non-random selection of women into the labour force.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, our findings suggest that interventions aimed at enhancing women\u0026rsquo;s labour market outcomes in Indonesia should consider both cognitive and non-cognitive dimensions. While education remains a critical pathway to improved employment prospects, particularly for married women, programs that foster relevant personality traits\u0026mdash;such as reducing Neuroticism and enhancing Conscientiousness or Extraversion\u0026mdash;may yield additional benefits. This could include targeted training programs that develop social skills, emotional regulation, and self-discipline alongside technical abilities. The contrasting results for married and unmarried women also highlight the importance of life-stage-specific approaches to labour market policy. For unmarried women, who are often younger and at earlier career stages, the negative association between higher education and labour force participation reflects their continued investment in human capital. Policies that support work-study arrangements or provide incentives for early career development might better address their specific needs and constraints.\u003c/p\u003e\u003cp\u003eIn conclusion, this study contributes to a more nuanced understanding of female labour market dynamics in Indonesia by highlighting the often-overlooked role of non-cognitive skills. By demonstrating how personality traits differently affect married and unmarried women\u0026rsquo;s employment decisions and earnings, we provide evidence that conventional human capital frameworks should be augmented to include behavioural dimensions. Such an expanded approach has the potential to enhance both analytical accuracy and policy effectiveness in addressing persistent gender disparities in labour force participation and earnings.\u003c/p\u003e\u003cp\u003eThe limitations of this study include the relatively small sample size for unmarried women and the cross-sectional nature of our analysis, which prevents us from establishing causal relationships or tracking how personality traits interact with labour outcomes over time. Future research should explore the heterogeneous impacts of non-cognitive skills across income levels and occupational sectors, as suggested by Collischon (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Badiani-Magnusson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), possibly using quantile regression approaches with larger samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS KAKENHI), Grant Numbers \u003cb\u003eJP23KK0225\u003c/b\u003e and \u003cb\u003eJP24K16631\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSeonkyung Choi: Conceptualization; Investigation; Methodology (shared); Writing\u0026mdash;original draft (Data and Methods review); Writing\u0026mdash;review \u0026amp; editing; Project administration; Corresponding author.Huihui Li: Formal analysis; Data curation; Methodology (Data and Methods); Writing\u0026mdash;original draft (primarily Data and Methods).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study analysed publicly available secondary data from the Indonesian Family Life Survey (IFLS), provided by RAND Social and Economic Well-Being: [https://www.rand.org/health/surveys/FLS/IFLS.html](https:/www.rand.org/health/surveys/FLS/IFLS.html)No new data were generated during this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmlund, M., Lee Duckworth, A., Heckman, J. J., \u0026amp; Kautz, T. D. (2011). \u003cem\u003ePersonality Psychology and Economics\u003c/em\u003e. http://www.nber.org/data-appendix/w16822\u003c/li\u003e\n\u003cli\u003eAnker, R., \u0026amp; Knowes, J. C. (1978). \u003cem\u003eA Micro Analysis of Female Labor Force Participation in Kenya\u003c/em\u003e (62). https://webapps.ilo.org/public/libdoc/ilo/1978/78B09_28.pdf\u003c/li\u003e\n\u003cli\u003eBadiani-Magnusson, R. C., Bodewig, C., Macdonald, K. A. D., Newhouse, D. L., \u0026amp; Rutkowski, J. J. (2014). \u003cem\u003eSkilling up Vietnam : preparing the workforce for a modern market economy\u003c/em\u003e. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/283651468321297015/skilling-up-vietnam-preparing-the-workforce-for-a-modern-market-economy\u003c/li\u003e\n\u003cli\u003eBecker, G. S. (1965). A Theory of the Allocation of Time. \u003cem\u003eThe Economic Journal\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(299), 493\u0026ndash;517. https://doi.org/10.2307/2228949\u003c/li\u003e\n\u003cli\u003eBorghans, L., Meijers, H., \u0026amp; Ter Weel, B. (2008). The Role of Non-cognitive Skills in Explaining Cognitive Test Scores. \u003cem\u003eEconomic Inquiry\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(1), 2\u0026ndash;12. https://doi.org/10.1111/j.1465-7295.2007.00073.x\u003c/li\u003e\n\u003cli\u003eBowles, S., Gintis, H., \u0026amp; Osborne, M. (2001a). Incentive-Enhancing Preferences: Personality, Behavior, and Earnings. \u003cem\u003eThe American Economic Review\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(2), 155\u0026ndash;158. https://doi.org/10.1257/aer.91.2.155\u003c/li\u003e\n\u003cli\u003eBowles, S., Gintis, H., \u0026amp; Osborne, M. (2001b). The Determinants of Earnings: A Behavioral Approach. \u003cem\u003eJournal of Economic Literature\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(4), 1137\u0026ndash;1176. https://doi.org/10.1257/jel.39.4.1137\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;hler, D., Sharma, R., \u0026amp; Stein, W. (2020). Occupational Attainment and Earnings in Southeast Asia: The Role of Non-cognitive Skills. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e. https://doi.org/10.1016/j.labeco.2020.101913\u003c/li\u003e\n\u003cli\u003eCameron, L. A., Dowling, J. M., \u0026amp; Worswick, C. (2001). Education and labor market participation of women in Asia: Evidence from five countries. \u003cem\u003eEconomic Development and Cultural Change\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(3), 459\u0026ndash;477. https://doi.org/10.1086/452511\u003c/li\u003e\n\u003cli\u003eCampbell, D. (2013). \u003cem\u003eThe labour market in developing countries\u003c/em\u003e (S. Cazes \u0026amp; S. Verick, Eds.). ILO.\u003c/li\u003e\n\u003cli\u003eChoi, S., Li, H., \u0026amp; Ogawa, K. (2023). Upper secondary vocational education and decent work in Indonesia: A gender comparison. \u003cem\u003eInternational Journal of Educational Development\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e. https://doi.org/10.1016/j.ijedudev.2023.102833\u003c/li\u003e\n\u003cli\u003eCollischon, M. (2020). The Returns to Personality Traits Across the Wage Distribution. \u003cem\u003eLabour\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 48\u0026ndash;79. https://doi.org/10.1111/labr.12165\u003c/li\u003e\n\u003cli\u003eCosta, P. T., \u0026amp; McCrae, R. R. (1992). Four ways five factors are not basic. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 667\u0026ndash;673. https://doi.org/10.1016/0191-8869(92)90237-J\u003c/li\u003e\n\u003cli\u003eFletcher, J. M. (2013). The effects of personality traits on adult labor market outcomes: Evidence from siblings. \u003cem\u003eJournal of Economic Behavior and Organization\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e, 122\u0026ndash;135. https://doi.org/10.1016/j.jebo.2013.02.004\u003c/li\u003e\n\u003cli\u003eGaddis, I., \u0026amp; Klasen, S. (2014). Economic development, structural change, and women\u0026rsquo;s labor force participation: \u003cem\u003eJournal of Population Economics\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(3), 639\u0026ndash;681. https://doi.org/10.1007/s00148-013-0488-2\u003c/li\u003e\n\u003cli\u003eGertler, P., Heckman, J., Pinto, R., Zanolini, A., Vermeersch, C., Walker, S., Chang, S. M., \u0026amp; Grantham-McGregor, S. (2014). Labor market returns to an early childhood stimulation intervention in Jamaica. In \u003cem\u003eScience\u003c/em\u003e (Vol. 344, Issue 6187). https://doi.org/10.1126/science.1251178\u003c/li\u003e\n\u003cli\u003eGlewwe, P., Huang, Q., \u0026amp; Park, A. (2017). Cognitive skills, noncognitive skills, and school-to-work transitions in rural China. \u003cem\u003eJournal of Economic Behavior \u0026amp; Organization\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e, 141\u0026ndash;164. https://doi.org/10.1016/j.jebo.2016.12.009\u003c/li\u003e\n\u003cli\u003eGoldin, C. (1994). \u003cem\u003eThe U-Shaped Female Labor Force Function in Economic Development and Economic History\u003c/em\u003e (4707). https://doi.org/10.3386/w4707\u003c/li\u003e\n\u003cli\u003eGosling, S. D., Rentfrow, P. J., \u0026amp; Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. \u003cem\u003eJournal of Research in Personality\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(6), 504\u0026ndash;528. https://doi.org/10.1016/S0092-6566(03)00046-1\u003c/li\u003e\n\u003cli\u003eGuerra, N., Modecki, K., \u0026amp; Cunningham, W. (2014). \u003cem\u003eDeveloping Social-Emotional Skills for the Labor Market: The PRACTICE Model\u003c/em\u003e (WPS1723; Policy Research Working Paper). https://researchportal.murdoch.edu.au/esploro/outputs/report/Developing-Social-Emotional-Skills-for-the-Labor/991005541392607891\u003c/li\u003e\n\u003cli\u003eGunatilaka, R. (2013). \u003cem\u003eTo work or not to work? Factors holding women back from market work in Sri Lanka\u003c/em\u003e (ILO Asia-Pacific Working Paper Series). https://www.researchgate.net/profile/Ramani-Gunatilaka/publication/287198803_To_work_or_not_to_work_Factors_holding_women_back_from_market_work_in_Sri_Lanka/links/5672a01508ae3aa2fcf0caa7/To-work-or-not-to-work-Factors-holding-women-back-from-market-work-in-Sri-Lanka.pdf\u003c/li\u003e\n\u003cli\u003eHeckman, J. J. (1979). Sample Selection Bias as a Specification Error. \u003cem\u003eEconometrica: Journal of the Econometric Society\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(1), 153\u0026ndash;161. http\u0026rsquo;//www.jstor.org/journals/econosoc.html.\u003c/li\u003e\n\u003cli\u003eHeckman, J. J., \u0026amp; Kautz, T. (2012). Hard evidence on soft skills. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 451\u0026ndash;464. https://doi.org/10.1016/j.labeco.2012.05.014\u003c/li\u003e\n\u003cli\u003eHeckman, J. J., Stixrud, J., \u0026amp; Urzua, S. (2006). The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior. \u003cem\u003eJournal of Labor Economics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 411\u0026ndash;482. https://doi.org/10.1086/504455\u003c/li\u003e\n\u003cli\u003eKabeer, N. (2012). \u003cem\u003eWomen\u0026rsquo;s economic empowerment and inclusive growth: labour markets and enterprise development\u003c/em\u003e (2012/1). https://www.womenindisplacement.org/sites/g/files/tmzbdl1471/files/2020-10/Womens%20Economic%20Empowerment%20and%20Inclusive%20Growth.pdf\u003c/li\u003e\n\u003cli\u003eKeiichi, O., \u0026amp; Masuma, A. (2007). female labor force participation in Indonesia. \u003cem\u003eJournal of International Cooperation Studies\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 71\u0026ndash;108. https://doi.org/10.24546/00520272\u003c/li\u003e\n\u003cli\u003eKhandker, S. R. (1988). Determinants of Women\u0026rsquo;s Time Allocation in Rural Bangladesh. \u003cem\u003eEconomic Development and Cultural Change\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 111\u0026ndash;126. https://doi.org/10.1086/451710\u003c/li\u003e\n\u003cli\u003eKlasen, S., Le, T. T. N., Pieters, J., \u0026amp; Santos Silva, M. (2020). What Drives Female Labour Force Participation? Comparable Micro-level Evidence from Eight Developing and Emerging Economies. \u003cem\u003eJournal of Development Studies\u003c/em\u003e, 1\u0026ndash;26. https://doi.org/10.1080/00220388.2020.1790533\u003c/li\u003e\n\u003cli\u003eKlasen, S., \u0026amp; Pieters, J. (2015). What Explains the Stagnation of Female Labor Force Participation in Urban India? \u003cem\u003eThe World Bank Economic Review\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 449\u0026ndash;478. https://doi.org/10.1093/wber/lhv003\u003c/li\u003e\n\u003cli\u003eLaajaj, R., Macours, K., Pinzon Hernandez, D. A., Arias, O., Gosling, S. D., Potter, J., Rubio-Codina, M., \u0026amp; Vakis, R. (2019). Challenges to capture the big five personality traits in non-WEIRD populations. \u003cem\u003eScience Advances\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(7), 1\u0026ndash;13. https://doi.org/10.1126/sciadv.aaw5226\u003c/li\u003e\n\u003cli\u003eLee, J. (2009). \u003cem\u003eNon-cognitive characteristics and academic achievement in Southeast Asian countries\u003c/em\u003e (223; OECD Education Working Papers). https://doi.org/10.1787/c3626e2f-en\u003c/li\u003e\n\u003cli\u003eLi, H., Chen, C., \u0026amp; Zhang, Z. (2023). Are gender differences related to non-cognitive abilities? \u0026mdash;\u0026mdash;Evidence from China. \u003cem\u003eJournal of the Asia Pacific Economy\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(4), 1560\u0026ndash;1579. https://doi.org/10.1080/13547860.2021.1982483\u003c/li\u003e\n\u003cli\u003eMincer, J. (1962). Labor Force Participation of Married Women: A Study of Labor Supply. \u003cem\u003eNational Bureau of Economic Research\u003c/em\u003e, 63\u0026ndash;105. http://www.nber.org/books/univ62-2\u003c/li\u003e\n\u003cli\u003eSchaner, S., \u0026amp; Das, S. (2016). \u003cem\u003eFemale Labor Force Participation in Asia: Indonesia Country Study\u003c/em\u003e. http://ssrn.com/abstract=2737842https://ssrn.com/abstract=2737842\u003c/li\u003e\n\u003cli\u003eTriggs, A., \u0026amp; Urata, S. (2020). \u003cem\u003eAchieving Inclusive Growth in The Asia Pacific\u003c/em\u003e (1st ed.). Australian National University Press. https://doi.org/10.22459/AIGAP.2020\u003c/li\u003e\n\u003cli\u003eVerick, S. (2018). \u003cem\u003eThe Puzzles and Contradictions of the Indian Labour Market: What Will the Future of Work Look Like?\u003c/em\u003e (11376). https://hdl.handle.net/10419/177180\u003c/li\u003e\n\u003cli\u003eWorld Bank Group, Kementerian PPN/Bappenas, \u0026amp; Australian Government. (2020). \u003cem\u003eIndonesia\u0026rsquo;s Occupational Employment Outlook - 2020 Technical Report\u003c/em\u003e. 1\u0026ndash;97. https://www.bappenas.go.id/files/4916/2304/8835/Occupational_Employment_Outlook_TREnglish.pdf\u003c/li\u003e\n\u003cli\u003eYoussef, N. H. (1971). Social structure and the female labor force: The case of women workers in muslim Middle Eastern countries. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 427\u0026ndash;439. https://doi.org/10.2307/2060680\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Female Labor Force Participation, Rate of Return, Big Five personality traits, Heckman method, Indonesia","lastPublishedDoi":"10.21203/rs.3.rs-7748385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7748385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional female labour force participation (FLFP) models have predominantly assessed socio-demographic, cultural, and educational factors in relation to employment outcomes, with research focusing mainly on developed countries despite low FLFP rates in many developing and Muslim-majority nations. This study investigates how non-cognitive skills shape women\u0026rsquo;s labour market outcomes in Indonesia, a middle-income, Muslim-majority country where female labour force participation remains relatively stagnant despite rising educational attainment. Using data from the Indonesia Family Life Survey (IFLS5), we analyse how Big Five personality traits influence both labour force participation and wages for married and unmarried women. We address the methodological concern of sample selection bias in wage estimations by implementing Heckman\u0026rsquo;s two-step procedure. Our findings reveal that personality traits significantly affect labour market decisions differently across marital status: extraversion (+\u0026thinsp;3.1%) and lower neuroticism (+\u0026thinsp;1.8%) predict greater participation for married women, while conscientiousness (+\u0026thinsp;7%) is the primary predictor for unmarried women. For wages, neuroticism shows a substantial negative effect (-33.7%) for unmarried women, with minimal personality effects observed for married women. Importantly, models omitting personality traits demonstrate inflated effects of education on labour force participation and wages, suggesting omitted variable bias in conventional analyses. These results emphasize that labour market outcomes are determined not only by formal education but also by behavioural traits that operate differently across life stages, highlighting the need for more nuanced educational and labour policies in developing contexts.\u003c/p\u003e","manuscriptTitle":"Understanding Female Employment and Wages in Indonesia: The Importance of Accounting for Non-cognitive Skills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 08:06:37","doi":"10.21203/rs.3.rs-7748385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T04:41:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T03:16:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T02:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198554891852524296399697035925486077860","date":"2026-03-01T15:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103674726196643585459189121759728907686","date":"2026-02-28T14:04:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313499810833889514445517998046502117147","date":"2025-11-12T06:50:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T03:57:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-23T10:53:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T13:17:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T12:14:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-30T07:13:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"851e02ea-d577-401a-bd1b-089a1298e1dc","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":57846825,"name":"Health sciences/Health care"},{"id":57846826,"name":"Biological sciences/Psychology"},{"id":57846827,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-02T04:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 08:06:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7748385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7748385","identity":"rs-7748385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}