The Happiness Premium? Gender and Employment Sector Differences in Well-Being in China

The Happiness Premium? 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Gender and Employment Sector Differences in Well-Being in China Lin Xiu, Yufei Ren, Thomas Lange This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8020850/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines how employment sector and gender jointly shape happiness and subjective well-being (SWB) in China, focusing on life satisfaction, standard-of-living (SOL) satisfaction, and health. Using data from the China Household Income Project (CHIP), the analysis combines regression models and an endogenous switching framework to account for sectoral selection and returns effects. The results reveal a consistent public-sector happiness premium , with employees in the public sector reporting higher well-being than those in the private sector. However, the sources of this advantage differ by gender and well-being domain. For both men and women, higher life satisfaction in the public sector stems primarily from institutional returns rather than compositional differences in individual characteristics. Counterfactual analyses further show that private-sector workers would experience greater life satisfaction if employed in the public sector, underscoring the desirability of public employment in China’s labor market. For SOL satisfaction, the decomposition results differ by gender, indicating that the sources of the premium vary between men and women. Education, marriage, and job tenure are positively associated with well-being, while long working hours reduce satisfaction, especially in the public sector. The findings highlight how institutional context and gendered experiences jointly shape happiness, offering theoretical insights into the gender well-being paradox and practical implications for promoting gender equity and enhancing happiness. Happiness Well-being Gender Public vs. Private Sector Employment 1. Introduction Happiness and subjective well-being (SWB) are influenced not only by income and personality factors but also by the institutional and social environments in which people live and work. Classic theories of subjective well-being emphasize that happiness is not only determined by individual dispositions and material resources but also by the livability of society—the extent to which social institutions, norms, and governance structures enable individuals to lead satisfying and meaningful lives (Diener, 1984 ; Diener et al. 1999 ; Warr, 2007). From this perspective, well-being reflects the interplay between personal circumstances and the broader social and institutional context (Knight & Gunatilaka, 2024 ). In China, where market reforms and social change have reshaped employment relations and gender roles, understanding how institutional contexts affect happiness is both timely and essential. Gender differences in subjective well-being (SWB) have long intrigued scholars, particularly in countries undergoing economic and cultural transition. From a sociological and institutional perspective, gender shapes access to valued life domains—employment, family roles, and social participation—that underlie subjective well-being. The “female happiness paradox,” observed across many societies, captures the puzzling pattern that women tend to report higher life satisfaction but poorer mental health than men (Blanchflower & Bryson, 2024 ). Yet this paradox is not universal and may depend on the institutional and cultural contexts that condition gender roles and social expectations. China provides a distinctive case in this regard. Traditional Confucian norms emphasizing family responsibility coexist with rapid modernization, expanding education and labor market participation for women, and widening divides between public and private employment sectors (Zhou & Peng, 2018 ). These overlapping shifts create a complex terrain in which institutional arrangements and gendered expectations jointly shape experiences of happiness and well-being. Employment is a central life domain through which people experience happiness and subjective well-being. Work provides income, social identity, structure, and purpose, and its characteristics—such as job security, autonomy, workload, and social support—are key predictors of well-being (Warr, 2007). From an institutional perspective, employment systems reflect broader social arrangements that determine how work rewards are distributed and how individuals experience fairness, stability, and meaning in their occupations (Diener et al., 1999 ). In China, the contrast between public and private sector employment illustrates these institutional differences vividly. Public sector jobs are typically associated with greater job security, more generous benefits, predictable career progression, and lower work pressure, and thus are often believed to confer a “ happiness premium .” The intense competition for public employment reflects this perception: in 2024, over three million candidates applied for just 39,600 civil servant positions, translating to approximately 77 individuals competing for one spot (Du, 2023 ). This intense competition reflects the public sector’s enduring reputation for stability and prestige, particularly amid high youth unemployment rates of 18.8% among 16- to 24-year-olds (China Association of Social Security, 2024 ). Such institutional advantages suggest that public sector employment may influence not only material well-being but also broader life satisfaction and subjective well-being—potentially in different ways for men and women, given China’s persistent gendered patterns in work and family roles (Cooke & Xiao, 2024 ). Against this backdrop, this study asks: Does employment in the public sector enhance men’s and women’s happiness and well-being relative to the private sector? Are these differences primarily a result of selection—that is, who chooses public-sector employment—or do they reflect intrinsic institutional advantages of public employment, such as stability, benefits, or social prestige? Moreover, do the determinants and returns to well-being differ between men and women across employment sectors? Addressing these questions helps illuminate how gender and institutional contexts jointly shape happiness and subjective well-being in a rapidly changing society. This study makes several contributions to the growing literature on gender, employment, and well-being. First, it extends the understanding of gender differences in subjective well-being by examining both general and domain-specific measures of happiness and life satisfaction. While recent research finds that the historical “female happiness advantage” has reversed in several Western countries—with women now reporting lower well-being than men (Bryson & Blanchflower, 2024 )—evidence from China remains limited and mixed. For example, women on average report higher life satisfaction than men (Zhang et al., 2022 ), yet self-employed women are found to be less happy than their male counterparts (Xiu & Ren, 2022 ). By analyzing multiple indicators of well-being, this study provides a more nuanced and context-specific understanding of these patterns. Second, by accounting for potential selection into employment sectors, the study offers a more comprehensive examination of the so-called “public-sector happiness premium.” This approach allows us to distinguish between compositional effects—who works in each sector—and genuine institutional effects stemming from differences in working conditions, job security, and social recognition. Third, the study explores how demographic, family, and workplace characteristics influence happiness and well-being for men and women and whether the returns to these factors differ across sectors. Such an analysis reveals not only whether women and men experience happiness differently but also whether the sources of their happiness diverge in the public and private spheres. In sum, this study highlights how gender and employment sector—each reflecting distinct social and institutional contexts—jointly shape happiness and subjective well-being in China. Beyond advancing theoretical understanding, the findings carry policy relevance for promoting gender equity, improving employment quality, and enhancing overall life satisfaction in the Chinese labor market. Building on these considerations, the next section reviews two key strands of literature that inform this study. The first examines research on employment-sector differences in happiness—the so-called public-sector happiness premium —and explores how sectoral conditions and self-selection shape subjective well-being. The second reviews evidence on gendered patterns of happiness and well-being, highlighting how social roles, family responsibilities, and workplace structures contribute to differences between men and women. Together, these literatures provide the foundation for analyzing how gender and employment sector jointly influence happiness in China. 2. Literature Review 2.1 Employment Section and the Happiness Premium Public versus private sector employment has long been associated with differences in subjective well-being (SWB), reflecting variation in job security, benefits, autonomy, and perceived status. A substantial body of research finds that public sector employees often experience a happiness premium —higher levels of happiness and life satisfaction compared with those working in the private sector (Martín-García and Castro-Martín, 2013 ; Homberg, 2017 ; Fernández Puente & Sánchez-Sánchez, 2021 ). For instance, in Spain, employed women face higher opportunity costs when deciding to become mothers. However, women in public sector jobs often become mothers earlier than their counterparts in self-employment or the private sector, a trend attributed to the public sector's greater long-term stability and supportive policies for work-family balance in public employment (Martín-García and Castro-Martín, 2013 ). Homberg ( 2017 ) similarly finds that institutional differences between the public and private sectors significantly impact employee well-being, with public sector employees reporting higher happiness. Using data from the European Working Condition Survey across 19 European countries, Fernández Puente & Sánchez-Sánchez, ( 2021 ) show that public sector workers across the Eurozone are more satisfied than those in the private sector. Evidence from transition economies echoes these findings: in Ukraine, for instance, pre-war data show that public sector workers experienced a happiness premium associated with more extensive benefits and institutional protection (Danzer, 2019 ). An important issue is sector selection. Individuals who choose public or private employment often differ in characteristics—such as risk tolerance, career aspirations, or family priorities—that also shape happiness (Christofides & Pashardes, 2002 ; Özveren, 2016 ). Correcting for endogenous self-selection of workers into sectors is therefore essential to determine whether the observed happiness premium arises from employment conditions or from the individuals who select them (Danzer, 2019 ). Moreover, women and men may make this employment sector choice for different reasons. Women, for instance, might prioritize job roles that offer flexibility to balance work and family responsibilities (Thébaud, 2016 ), a feature more commonly found in public sector employment. Additionally, the unique challenges and barriers women face in the workplace, such as unequal access to resources and support, could differentially affect their happiness and well-being in public versus private sectors. In China, distinctions between public and private employment have deep historical roots in the danwei (work-unit) system that structured urban life during the planned-economy era. Public sector work units such as government offices and state-owned enterprises once monopolized access to scarce social resources and offered employees comprehensive benefits and high social prestige. Although market reforms have reduced their dominance, public sector jobs continue to provide greater job security, stable income, and superior welfare compared with the private sector (Hu, 2013 ; Xiao, Liu, & Ren, 2022 ). Studies show that public employees in China report higher levels of well-being (Cheng, 2014 ), yet it remains unclear whether these differences stem from workers’ characteristics, the institutional features of the sectors themselves, or both. Furthermore, little is known about whether this public-sector happiness premium varies by gender. The present study addresses this gap by examining the gendered nature of the public-sector happiness premium in China, while accounting for selection into employment sectors. 2.2 Gender and Subjective Well-Being Gender differences in happiness and SWB have long attracted scholarly attention (Haring et al., 1984 ; Blanchflower & Oswald, 2004; Blanchflower & Bryson, 2024 ). A robust literature documents the so-called female happiness paradox : women frequently report higher overall life satisfaction than men yet also exhibit higher levels of stress, anxiety, and depression (Becchetti & Conzo, 2022 ). This pattern—higher life satisfaction but poorer mental health among women—has been widely observed across countries and over time. Well-being is shaped by social status, family responsibilities and employment circumstances (Abreu et al., 2019 ), all of which tend to differ markedly for men and women. Since the first meta-analysis on gender and well-being over four decades ago (Haring et al., 1984 ), research has consistently found that women report higher levels of negative affect (depression, anxiety, poorer sleep) and lower satisfaction with specific life domains such as finances, marriage, and standard of living (Blanchflower & Bryson, 2024 ; Boerma et al., 2016 ), yet paradoxically express greater overall life satisfaction and happiness. Over time, however, gender gaps in happiness have shifted. In the United States, women’s happiness advantage observed in the 1970s and 1980s declined by the late 1990s (Blanchflower & Oswald, 2004). Using data from the General Social Survey (GSS, 1972–1998), they found that while women initially reported higher happiness levels than men, this pattern reversed as women’s reported happiness declined, narrowing the gender gap. Similar trends were observed in other datasets, such as the DDB Needham Lifestyle Surveys (1985–2005), which documented comparable declines in life satisfaction for both women and men (Stevenson & Wolfers, 2009 ). More recent cross-national evidence confirms this shift: in over a dozen Western countries, including the United States and the United Kingdom, women no longer report higher life satisfaction and are, on average, less happy than men (Blanchflower & Bryson, 2024 ). While women’s relative happiness has declined in many Western societies, evidence from China paints a more nuanced picture. Using data from the 2010–2018 China Family Panel Studies, Zhang et al. ( 2022 ) found that women generally report higher life satisfaction than men, though the gap narrows with age and follows a U-shaped pattern across the life course. Huang, Yi, and Clark ( 2023 ) observed no significant gender differences in happiness in urban areas but higher happiness among men in rural regions, suggesting that variations in gender norms and institutional settings contribute to divergent well-being outcomes. Marital and family dynamics also contribute to gendered well-being differences. Analyzing data from the 2006 Chinese General Social Survey (CGSS), Liu, Li, and Feldman ( 2013 ) found that marital dynamics significantly affect life satisfaction, with marital status being more significant for men and marital quality more important for women. Their findings highlight how family relationships, social expectations, and intergenerational support interact with gender to shape happiness in China. Consistent with this, Chen and Zhang ( 2024 ) show that gender stereotypes and social expectations continue to undermine women’s well-being, particularly among educated women who challenge traditional gender roles. Social and workplace environments further shape gendered experiences of happiness. Employment conditions, access to social capital, and perceived job security all play important roles in shaping wellbeing (Churchill & Mishra, 2017 ; Huang, Yi, & Clark, 2023 ). Work settings that provide stability, fair compensation, and supportive policies are consistently linked to higher life satisfaction and better health outcomes (Faragher, Cass, & Cooper, 2005 ). Such institutional features of the workplace are central to understanding differences in well-being across employment sectors. For example, van Dierendonck, Lv, and Xiu ( 2024 ) show that supportive and trust-based work environments enhance employees’ sense of meaningfulness and improve sleep quality, illustrating how positive organizational contexts can promote both psychological and physical well-being. Taken together, this body of research shows that gendered experiences of happiness are deeply intertwined with social structures, family responsibilities, and employment contexts. Men and women may evaluate their lives using similar criteria but experience distinct constraints and opportunities across these domains. In China, where public and private sectors differ markedly in job stability, benefits, and social prestige, these institutional divides may further shape gendered well-being outcomes. Understanding how gender and employment sector interact to influence happiness and subjective well-being provides the basis for this study’s analysis. 3. Methodology 3.1. Sample This study uses data from the 2013 wave of the Chinese Household Income Project (CHIP) survey, the most recently available wave of the project. Widely used in academic scholarship, CHIP is considered a foundational dataset for understanding China’s economic development and income distribution during recent decades. It gathered information on household and work details from households across 14 provinces in China. This survey was conducted as a part of an international collaborative research effort focused on income and inequality in China, involving both Chinese and international scholars. The data was obtained through the China Institute for Income Distribution. Our analysis focused on urban households and individuals working at least 30 hours per week, including employees from both the public and private sectors. The sample consists of 3,580 individuals employed in the public sector (42%) and 4,878 in the private sector (58%). Public sector workers include those employed by the government, state agencies, and state-owned enterprises. Private sector workers are defined as those employed outside the public sector. Well-being is measured using three variables, including self-rated health, satisfaction with the Standard of Living (SOL), and overall life satisfaction based on three questions in the survey regarding respondents’ satisfaction with their health, SOL and overall life satisfaction. The self-rated health variable is derived from a question asking respondents to compare their health to that of the average person in their own age group, using a five-point scale from “very poor” to “very good.” The standard of living (SOL) satisfaction variable is measured by the question, “How do you rate your standard of living compared to others?” with responses ranging on a five-point scale from “much lower” to “much higher.” Overall life satisfaction is assessed based on the question, “Considering all aspects of life, are you happy?” with options on a five-point scale from “not happy at all” to “very happy.” Single-item satisfaction measures, such as life satisfaction, are widely used in the well-being literature and are shown to have high validity and reliability comparable to multi-item scales (Cheung & Lucas, 2014 ; Kibler et al., 2019 ). Control variables include age, gender, education levels, marital status, the presence of children and elders in the household, Chinese Communist Party (CCP) membership, Han ethnicity, job tenure, weekly working hours, industry, and geographical regions. 3.2. Descriptive Statistics and Methodology Table 1 provides the descriptive statistics of the sample by sectors. Table 2 provides a comparison of self-reported health, SOL satisfaction, and life satisfaction by gender across the two sectors. The results show that both men and women in the public sector have slightly better health scores, higher SOL satisfaction, and greater overall life satisfaction compared to their private sector counterparts. Table 1 Summary Statistics by Employment Sector Public Sector (N = 3,580) Private Sector (N = 4,878) Mean S.D. Mean S.D. Health (max. 5) 4.161 0.747 4.119 0.748 Satisfaction with standard of living (SOL) 2.936 0.740 2.806 0.796 Overall Life Satisfaction 3.879 0.778 3.691 0.787 Female 0.388 0.487 0.437 0.497 Age 42.000 9.254 41.021 10.038 Education (reference = High School and less) Vocational School 0.125 0.331 0.113 0.317 2-years College 0.256 0.437 0.155 0.362 University and Above 0.345 0.476 0.094 0.292 Han Ethnicity 0.959 0.199 0.953 0.212 CCP Membership 0.389 0.488 0.099 0.299 Job Tenure 17.274 10.543 9.495 8.340 Child Under 7 in the Household 0.148 0.355 0.163 0.369 Child 7–18 in the Household 0.368 0.482 0.365 0.481 Senior 66–75 in the Household 0.057 0.231 0.063 0.242 Senior above 75 in the Household 0.038 0.191 0.043 0.203 Married 0.888 0.315 0.854 0.353 Weekly Working Hours 43.501 7.719 49.298 11.540 Father Public Sector 0.455 0.498 0.220 0.414 Mother Public Sector 0.238 0.426 0.114 0.318 Note: N = SOL and Life satisfaction questions are in a separate questionnaire with additional missing values. For SOL, the Public Sector N = 3,350 and the Private Sector N = 4,510. For life satisfaction, the Public Sector N = 3,516 and the Private Sector N = 4,777. Table 2 Comparison of Well-being Variables by Gender and Employment Sector Pooled Sample Public Sector Private Sector Difference Mean S.D. Mean S.D. Diff t-value Health (max. 5) 4.161 0.747 4.118 0.748 0.043*** 2.612 SOL Satisfaction 2.936 0.740 2.806 0.796 0.130*** 7.372 Life Satisfaction 3.879 0.778 3.691 0.788 0.188*** 10.849 Female Sample Public Sector Private Sector Difference Mean S.D. Mean S.D. Diff t-value Health (max. 5) 4.163 0.744 4.118 0.729 0.044* 1.803 SOL Satisfaction 2.947 0.731 2.812 0.787 0.135*** 4.944 Life Satisfaction 3.902 0.771 3.697 0.790 0.205*** 7.528 Male Sample Public Sector Private Sector Difference Mean S.D. Mean S.D. Diff t-value Health (max. 5) 4.160 0.748 4.119 0.763 0.041* 1.898 SOL Satisfaction 2.929 0.746 2.802 0.802 0.127*** 5.517 Life Satisfaction 3.865 0.782 3.686 0.785 0.179*** 7.914 Note. *<0.10, **<0.05 ***<0.01. In the following, we first estimate regression models separately for the public and private sectors. We then employ an Endogenous Switching Regression (ESR) model to jointly model sector selection (public vs. private) and well-being outcomes. Endogenous Switching Regression (ESR) model applies full-information maximum likelihood (FIML) to simultaneously fit both the sector selection (public sector vs. private sector) model and the well-being equation model. This approach helps address the potential endogeneity issue that may exist when variables that affect employment type selection (public sector vs. private sector) also affect employment outcomes. Some unobserved characteristics that influence the probability of choosing a particular sector of employment could also influence individuals’ well-being once they are employed in that sector. Neglecting these selectivity effects is likely to bias the estimate of relative wellbeing in both the public and private sector (Lokshin and Sajaia, 2004 ). The Endogenous Switching model corrects for the selection bias in the sectoral wellbeing estimates. Following previous literature on sector selection (e.g. Xiu & Gunderson, 2021 ), two instrumental variables are used to identify the sector selection: father’s public sector employment and mother’s public sector employment. This Endogenous Switching approach allows estimation of selection-corrected well-being outcomes for public- and private-sector workers, as well as decomposition into endowment and returns effects using counterfactual predictions. The overall sectoral difference in well-being can be expressed as the sum of endowment and returns effects: $$\:\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{E}\text{f}\text{f}\text{e}\text{c}\text{t}=E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)$$ 1 Endowment Effect = \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)\) (2) Returns Effect = \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) (3) Where, \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)\) is the expected wellbeing in the public sector for the individuals who currently employed in the public sector, \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) is the expected wellbeing in the private sector for the individuals who currently employed in the private sector, and \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)\) is the expected value of wellbeing if the private sector employees were selected into the public sector. The endowment effect showed the well-being difference between the private sector employees and the counterfactual well-being that private sector employees would have if they were in the public sector. In contrast, the returns effect captures the change in well-being if private sector employees had selected into public sector and shows whether switching from private to public sector would have an impact on their well-being. The analysis was conducted using STATA 18.5 . 4. Results Table 3 presents the well-being regressions on the three well-being outcomes in the two employment sectors. In the private sector, the factors that influence the wellbeing are generally consistent. College education, being married and longer job tenure are associated with better health, higher standard-of-living (SOL) satisfaction and life satisfaction. There is no statistically significant gender difference in any of the well-being outcomes when individual, family and work characteristics are controlled. In contrast, in the public sector, when these factors are accounted for, women have lower levels of reported health, and higher levels of life satisfaction, consistent with the gender well-being paradox. Table 3 Well-Being Regressions for Public- and Private-Sector Employees Public Sector Private Sector Health SOL Satisfaction Life Satisfaction Health SOL Satisfaction Life Satisfaction Female -0.045* 0.044 0.060** -0.009 0.028 0.015 (-1.702) (1.584) (2.136) (-0.421) (1.119) (0.629) Age -0.012 -0.033** -0.028** -0.012 -0.029*** -0.031*** (-0.937) (-2.448) (-2.043) (-1.250) (-2.796) (-3.083) Age Squared -0.000 0.000*** 0.000* -0.000 0.000*** 0.000** (-0.168) (2.737) (1.717) (-0.722) (2.652) (2.433) 2-year College 0.024 0.138*** 0.109*** 0.044 0.063* 0.017 (0.727) (4.076) (3.124) (1.425) (1.788) (0.500) University and above 0.083** 0.182*** 0.128*** 0.124*** 0.142*** 0.172*** (2.354) (4.979) (3.451) (3.064) (3.125) (3.991) Han Ethnicity -0.025 -0.039 -0.052 0.067 0.016 0.035 (-0.393) (-0.589) (-0.761) (1.313) (0.284) (0.636) CCP Party Member -0.020 0.061** 0.054* 0.024 0.117*** 0.098** (-0.725) (2.087) (1.829) (0.654) (2.877) (2.526) Job Tenure 0.000 0.005*** 0.002 0.003** 0.005*** 0.004*** (0.209) (2.898) (1.177) (2.424) (3.037) (2.858) Child < 7 in Household 0.020 0.097** 0.022 0.008 0.031 0.017 (0.501) (2.343) (0.526) (0.255) (0.904) (0.525) Child 7–18 in Household 0.047 0.052 -0.021 0.036 -0.025 0.022 (1.529) (1.604) (-0.636) (1.470) (-0.898) (0.838) Senior 66–75 in the Household -0.068 0.087 0.017 0.046 0.070 0.073 (-1.257) (1.563) (0.304) (1.059) (1.409) (1.523) Senior > 75 in the Household -0.033 -0.021 -0.094 -0.014 0.049 0.001 (-0.511) (-0.316) (-1.355) (-0.265) (0.830) (0.026) Married 0.038 0.002 0.284*** 0.116*** 0.196*** 0.327*** (0.767) (0.030) (5.376) (3.055) (4.562) (8.035) Weekly Working Hours -0.001 -0.004*** -0.007*** -0.000 0.002* 0.000 (-0.616) (-2.594) (-3.988) (-0.279) (1.658) (0.472) Industries Yes Yes Yes Yes Yes Yes Provinces Yes Yes Yes Yes Yes Yes Constant 4.524*** 3.313*** 4.253*** 4.371*** 2.860*** 4.070*** (15.654) (11.144) (13.850) (22.789) (13.170) (19.726) R2 0.078 0.057 0.061 0.075 0.040 0.059 N 3580 3350 3516 4878 4510 4777 Note: *<0.10, **<0.05 ***<0.01 Next, we run regressions by sector and by gender to examine the different mechanisms through which one’s well-being is influenced by individual, family and work characteristics. As shown in Table 4 , education emerges as a consistent positive determinant of well-being, particularly in the public sector. Among men in the public sector, both two-year college and university education significantly improve SOL and life satisfaction. Women in the public sector experience similar benefits from higher education, though the effects are more limited to SOL satisfaction. In the private sector, the positive effects of higher education on well-being are concentrated among men, while the coefficients for women are not statistically significant. Marital status is found to have a strong and consistent effect across most groups. Being married is positively associated with life satisfaction for both men and women in all sectors, consistent with prior evidence of a marriage premium in subjective well-being. The effects are particularly pronounced for public-sector men (β = 0.39, p < 0.01) and somewhat smaller for women in the same sector (β = 0.19, p < 0.05). Marriage also contributes to higher self-rated health and SOL satisfaction in the private sector, but these associations are weaker or absent in the public sector, possibly reflecting stronger institutional supports that buffer single employees’ well-being. Job tenure is positively related to well-being in most models, particularly in the private sector, where it significantly predicts all three outcomes for men and two for women. In the public sector, tenure matters mainly for SOL satisfaction, indicating that long-term stability and experience contribute to perceptions of material well-being more than to overall life satisfaction. Working hours show contrasting associations across sectors and genders. In the public sector, longer working hours are linked to lower SOL and life satisfaction, especially for men (β = − 0.005, p < 0.05 for SOL; β = − 0.008, p < 0.01 for life satisfaction). These negative effects are less pronounced in the private sector and, for men, even reversed in SOL satisfaction, possibly reflecting differing expectations about workload and rewards. Age effects follow a partial U-shaped pattern. Among public-sector men, age is negatively associated with SOL satisfaction (β = − 0.045, p < 0.01) but becomes positive at older ages through the squared term, indicating a turning point in midlife. Similar curvature is observed for women in the private sector, suggesting that life satisfaction reaches its lowest point around the late 40s to early 50s, consistent with prior findings in the Chinese context (Zhang et al., 2022 ). Household composition variables, such as the presence of children or elderly members, show no consistent or statistically significant associations with well-being once employment and individual characteristics are controlled. Table 4 Well-Being Regressions by Employment Sector and Gender Public Sector Private Sector Male Female Male Female M1 Health M2 SOL Sat. M3 Life Sat. M4 Health M5 SOL Sat. M6 Life Sat. M7 Health M8 SOL Sat. M9 Life Sat. M10 Health M11 SOL Sat. M12 Life Sat. Age -0.013 -0.045*** -0.033* -0.032 -0.030 -0.029 -0.008 -0.021 -0.008 -0.017 -0.049*** -0.060*** (-0.762) (-2.606) (-1.856) (-1.353) (-1.227) (-1.179) (-0.612) (-1.522) (-0.639) (-1.212) (-2.982) (-3.813) Age Squared -0.000 0.001*** 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 -0.000 0.001*** 0.001*** (-0.169) (2.768) (1.503) (0.875) (1.372) (1.029) (-0.868) (1.375) (0.288) (-0.067) (2.912) (3.180) 2-year College 0.028 0.144*** 0.130*** 0.020 0.125** 0.071 0.050 0.087* 0.075* 0.027 0.028 -0.063 (0.666) (3.335) (2.955) (0.369) (2.230) (1.223) (1.175) (1.799) (1.667) (0.582) (0.542) (-1.241) Univ. & above 0.092** 0.199*** 0.160*** 0.064 0.150** 0.055 0.174*** 0.164*** 0.223*** 0.042 0.104 0.090 (2.059) (4.251) (3.381) (1.085) (2.471) (0.887) (3.139) (2.633) (3.834) (0.702) (1.530) (1.360) Han Ethnicity 0.013 -0.067 -0.035 -0.091 0.008 -0.090 0.055 -0.047 0.045 0.082 0.092 0.028 (0.153) (-0.784) (-0.395) (-0.906) (0.075) (-0.831) (0.749) (-0.561) (0.585) (1.147) (1.140) (0.358) CCP -0.016 0.060 0.006 -0.026 0.073 0.147*** 0.063 0.107** 0.087* -0.045 0.138** 0.107 (-0.449) (1.618) (0.162) (-0.554) (1.515) (2.956) (1.362) (2.059) (1.799) (-0.744) (2.044) (1.620) Job Tenure -0.000 0.004* 0.004 0.002 0.007** -0.000 0.004** 0.005** 0.004** 0.002 0.005** 0.004* (-0.089) (1.894) (1.644) (0.725) (2.454) (-0.054) (2.503) (2.321) (2.126) (0.840) (2.014) (1.845) Child < 7 in Household 0.008 0.075 0.014 0.041 0.133** 0.032 -0.013 0.035 0.016 0.044 0.023 0.018 (0.164) (1.406) (0.258) (0.638) (1.981) (0.461) (-0.320) (0.772) (0.364) (0.902) (0.418) (0.343) Child 7–18 in Household 0.050 0.016 -0.046 0.064 0.113** 0.021 0.046 -0.060 0.012 0.019 0.015 0.019 (1.252) (0.392) (-1.087) (1.256) (2.171) (0.390) (1.357) (-1.554) (0.332) (0.527) (0.362) (0.491) Senior 66–75 in the Household -0.123* 0.122* -0.006 0.007 0.046 0.063 0.030 0.074 0.082 0.070 0.079 0.070 (-1.746) (1.652) (-0.082) (0.077) (0.535) (0.701) (0.514) (1.137) (1.323) (1.031) (1.002) (0.922) Senior > 75 in the Household -0.005 -0.018 -0.159* -0.100 -0.020 0.034 -0.047 0.034 0.002 0.031 0.067 -0.007 (-0.064) (-0.208) (-1.837) (-0.925) (-0.177) (0.289) (-0.670) (0.437) (0.024) (0.393) (0.755) (-0.086) Married 0.016 0.053 0.393*** 0.085 -0.029 0.192** 0.116** 0.202*** 0.332*** 0.108** 0.193*** 0.324*** (0.228) (0.723) (5.268) (1.186) (-0.399) (2.510) (2.142) (3.333) (5.843) (1.998) (3.134) (5.454) Weekly Working Hours -0.002 -0.005** -0.008*** 0.000 -0.003 -0.006* 0.001 0.003** 0.001 -0.002* 0.000 -0.000 (-0.797) (-2.470) (-3.676) (0.093) (-1.038) (-1.854) (0.967) (2.008) (0.633) (-1.714) (0.105) (-0.162) Constant 4.861*** 3.571*** 4.281*** 4.420*** 3.193*** 4.433*** 4.219*** 2.724*** 3.571*** 4.618*** 3.277*** 4.810*** (12.950) (9.215) (10.758) (8.779) (6.153) (8.256) (16.162) (9.251) (12.996) (15.460) (9.643) (14.553) R2 0.083 0.060 0.073 0.091 0.074 0.068 0.086 0.043 0.068 0.075 0.054 0.065 N 2191 2058 2152 1385 1289 1361 2746 2532 2683 2132 1978 2094 Note: Industries and provinces are controlled in all regressions in this table. *<0.10, **<0.05 ***<0.01. Overall, Table 4 indicates that the determinants of well-being differ systematically by gender and employment sector. Education, marriage, and job tenure consistently predict higher well-being, while long working hours reduce satisfaction primarily in the public sector. These patterns underscore how institutional features of employment—stability, workload norms, and sectoral expectations—interact with gendered social roles to shape health, material satisfaction, and overall happiness. Then, we conducted the endogenous switching model analysis. The findings are shown in Table 5 . Panel A of Table 5 presents the decomposition results for self-rated health. The estimated total gap—the overall health difference between public- and private-sector employees—is positive and statistically significant for both women (0.045, p < 0.01) and men (0.041, p < 0.01). This indicates that, on average, public sector men and women have better health than those in the private sector after controlling for individual, family, and work characteristics, as well as selection into the types of employment. For both men and women, the endowment effect is positive and statistically significant (0.243 for women; 0.079 for men), suggesting that public-sector workers possess more favorable characteristics related to health—such as education, job stability, and income security—than private-sector workers. These compositional differences explain a substantial portion of the observed health advantage in the public sector. In contrast, the returns effect is negative for both women (–0.198, p < 0.01) and men (–0.038, p < 0.01). This implies that if private-sector workers were employed in the public sector but experienced the same “returns structure” as public-sector workers, their expected health would actually be lower. In other words, the way characteristics translate into health outcomes appears less favorable within the public sector once individual endowments are held constant. Taken together, the results indicate that the public-sector health premium is largely driven by differences in worker characteristics rather than by superior sectoral conditions. Table 5 Well-Being Differences between Public and Private Sector Employment: Endowments and Returns Effects from Endogenous Switching Model Panel A (Health). Estimated Total Gap Endowment Effect Returns Effect \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)\) \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) Females 0.045*** 0.243*** -0.198*** (0.006) (0.007) (0.003) Males 0.041*** 0.079*** -0.038*** (0.003) (0.006) (0.002) Panel B (SOL Satisfaction). Estimated Total Gap Endowment Effect Returns Effect \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)\) \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) Females 0.137*** 0.099*** 0.039*** (0.005) (0.003) (0.003) Males 0.128*** -0.009*** 0.137*** (0.004) (0.004) (0.003) Panel C (Life Satisfaction). Estimated Total Gap Endowment Effect Returns Effect \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) \(\:E\left({Y}_{pub}|{X}_{pub},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)\) \(\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pub}\right)-\:E\left({Y}_{pri}|{X}_{pri},\:{\beta\:}_{pri}\right)\) Females 0.203*** -0.401*** 0.604*** (0.003) (0.006) (0.004) Males 0.180*** -0.554*** 0.734*** (0.005) (0.006) (0.004) Note: *<0.10, **<0.05 ***<0.01 Panel B of Table 5 reports the decomposition results for satisfaction with standard of living (SOL). The total gap between public- and private-sector workers is positive and statistically significant for both women (0.137, p < 0.01) and men (0.128, p < 0.01), indicating that employees in the public sector report higher SOL satisfaction on average than those in the private sector, even after accounting for observed characteristics and selection into sectors. For women, both the endowment effect (0.099, p < 0.01) and returns effect (0.039, p < 0.01) are positive and significant. This pattern suggests that the public-sector advantage in women’s SOL satisfaction stems from both more favorable characteristics (e.g., higher education, job security, or institutional benefits) and more rewarding returns to these characteristics. In other words, women not only enter the public sector with characteristics conducive to material well-being but also benefit more from them once employed in that sector—perhaps reflecting better alignment between women’s expectations, social protection policies, and workplace conditions. For men, the decomposition reveals a different pattern. The endowment effect is small and negative (–0.009, p < 0.01) and the returns effect is large and positive (0.137, p < 0.01), showing that private sector men’s SOL satisfaction would be higher than that of current public sector men, and higher than their current SOL satisfaction if they had selected into the public sector. Panel C of Table 5 presents the decomposition results for overall life satisfaction. The estimated total gap between public- and private-sector workers is positive and significant for both women (0.203, p < 0.01) and men (0.180, p < 0.01), confirming that public-sector employees report higher overall life satisfaction than those in the private sector after accounting for selection effects and observable characteristics. For women, the decomposition shows a large and negative endowment effect (–0.401, p < 0.01) alongside a strong positive returns effect (0.604, p < 0.01). This indicates that women in the private sector possess characteristics that, if evaluated under the public-sector structure, would predict higher life satisfaction. However, the large positive returns effect suggests that the institutional environment of the public sector substantially enhances the translation of individual and job characteristics into higher subjective well-being. In other words, once employed in the public sector, women derive markedly greater happiness from similar attributes than they would in the private sector. For men, the pattern is broadly similar but slightly stronger. In other words, the results show that, for both men and women, private sector individuals’ life satisfaction would be higher than that of current public sector individuals, and higher than their current satisfaction if they had selected into the public sector. This finding is consistent with the notion of a public-sector happiness premium and aligns with the intense competition for public-sector positions in China. In summary, the findings reveal significant differences in well-being between public- and private-sector employees, with a consistent public-sector advantage across health, standard-of-living satisfaction, and life satisfaction. However, the sources of this advantage differ by gender and by well-being indicators. Self-reported Health differences are largely explained by compositional factors—public-sector workers’ more favorable endowments—whereas SOL and life satisfaction advantages are primarily driven by institutional returns, particularly in the public sector. Both men and women gain more in subjective well-being when their attributes are situated in the public-sector context, although the magnitudes differ. These results confirm that the public-sector “happiness premium” is not merely a product of worker selection, but also of the institutional environment that shapes how individual and job characteristics translate into well-being. 5. Discussion This study examines gendered well-being outcomes across public and private employment sectors in China, focusing on how institutional context and gender jointly shape subjective well-being across domains of health, standard of living (SOL) satisfaction, and overall life satisfaction. The results reveal several key patterns. First, public sector employment is generally associated with better well-being outcomes compared to the private sector. Both men and women in the public sector report better health, higher SOL satisfaction, and greater life satisfaction, after controlling for selection effects and other relevant characteristics. However, the benefits of public sector employment vary significantly by gender and well-being domain. Second, the endogenous switching model highlights the distinct roles of endowment and returns effects. The public-sector happiness premium is driven primarily by institutional returns rather than by differences in worker characteristics. For both men and women, the public-sector context enhances how individual and job attributes translate into subjective well-being, particularly in standard-of-living and life satisfaction. Notably, the counterfactual results show that if private-sector employees were employed in the public sector, their expected life satisfaction would exceed both their current levels and those of their counterparts in the public sector, underscoring the perceived desirability and well-being advantages associated with public employment. Lastly, education, marriage, and job tenure are consistently associated with higher well-being, while longer working hours reduce satisfaction—especially in the public sector. These patterns suggest that institutional factors such as job stability, workload expectations, and sectoral norms interact with gendered life circumstances to shape subjective well-being in China’s labor market. Taken together, the evidence indicates that the public-sector happiness premium is shaped not only by institutional advantages but also by gendered experiences and selection mechanisms. 5.1 Theoretical Implications The findings of this study contribute to the growing literature on gender and subjective well-being by situating happiness within China’s institutional and labor market context. It extends prior research on the “female happiness paradox” (Blanchflower & Bryson, 2024 ) by showing that gendered differences in happiness vary systematically across employment sectors, reflecting both selection mechanisms and institutional effects. First, this study reinforces the importance of sectoral context in understanding well-being disparities (Haring et al., 1984 ; Huang, Yi, & Clark, 2023 ). Public-sector employment—characterized by greater job stability, benefits, and social protection—is linked to higher satisfaction with life and standard of living. Yet, the health advantage observed for public-sector workers appears largely compositional, highlighting that not all well-being domains respond equally to institutional conditions. Second, the gendered patterns observed—such as women’s relatively higher life satisfaction and better health in the public sector—add nuance to the gender well-being paradox (Blanchflower & Bryson, 2024 ). These findings suggest that institutional settings offering greater stability and protection may particularly enhance women’s well-being, even as gendered expectations shape how they evaluate satisfaction across life domains. This pattern aligns with theories of gendered trade-offs, where women may derive greater happiness from secure and balanced work environments, even when facing other constraints or pressures. By incorporating an endogenous switching model, this study highlights how subjective well-being differences reflect both who enters particular employment sectors and how individual attributes are rewarded within them. This methodological approach advances the well-being literature by linking micro-level gendered experiences to macro-level institutional structures, emphasizing that happiness is not merely an individual outcome but also a product of broader employment systems and social norms. 5.2 Policy Implications The findings carry several important policy implications for promoting gender equity and enhancing well-being outcomes in China’s labor market. First, the evidence of a public-sector happiness premium—particularly in life and standard-of-living satisfaction—highlights the institutional advantages of stable employment, social protection, and predictable workloads. Yet, access to such positions is highly competitive, reinforcing existing inequalities. Expanding social protections, benefits, and work-family supports in the private sector could help narrow this well-being gap and improve overall life satisfaction across the workforce. Second, while public-sector workers enjoy higher well-being, the results also indicate that women in the private sector face persistent disadvantages that affect both their health and happiness. Policies that strengthen workplace health initiatives, promote gender-sensitive management practices, and expand access to flexible work arrangements would mitigate these disparities. For instance, promoting family-friendly workplace policies—such as parental leave, childcare support, and flexible work schedules—could help both men and women balance work and family responsibilities more effectively. Finally, the positive returns effects observed for women’s life and SOL satisfaction suggest that expanding women’s access to public-sector opportunities could yield substantial well-being gains. Ensuring transparent hiring practices, equal opportunity programs, and institutional pathways for women’s career development would help translate employment stability into broader happiness benefits. 5.3 Limitations and Future Research Despite its contributions, this study has several limitations that suggest directions for future research. First, the analysis relies on cross-sectional data from the 2013 China Household Income Project (CHIP). While this dataset provides rich information on employment and well-being, its design limits the ability to infer causal relationships or capture changes over time. Future studies using longitudinal or more recent nationally representative data could better identify dynamic patterns of happiness and well-being across sectors. Second, although the endogenous switching model accounts for selection bias between public- and private-sector employment, unobserved factors—such as personality traits, social networks, or subjective job motivations—may still influence both sector choice and well-being outcomes. Incorporating psychological and attitudinal variables would provide a more comprehensive understanding of the mechanisms linking employment and happiness. Third, the present study focuses primarily on gender differences, yet other intersecting social dimensions—such as class, region, or age cohort—may further shape well-being in China’s rapidly transforming labor market. Future research could adopt an intersectional approach to explore how multiple identities interact with institutional structures to influence subjective well-being. Finally, while this study emphasizes individual outcomes, future work could examine the organizational and policy-level factors that create or reduce well-being disparities across sectors. Comparative analyses across countries or institutional regimes would further clarify how governance quality, labor policies, and cultural norms shape the public-sector happiness premium. The use of CHIP 2013 data represents a limitation, as it may not capture more recent labor-market developments and post-pandemic structural shifts. Nevertheless, the dataset remains one of the most comprehensive and methodologically rigorous sources on Chinese employment and individuals’ well-being. Its breadth and representativeness provide a strong foundation for analyzing sectoral and gendered differences that continue to hold theoretical and policy relevance. Future research could build on these findings using newer data to explore how evolving labor-market conditions and institutional reforms shape contemporary patterns of happiness and well-being. 5.4 Conclusion In conclusion, this study demonstrates that gender and employment sector jointly shape subjective well-being in China. Public-sector employment confers a consistent happiness advantage, but the sources of this advantage differ across life domains and between men and women. Institutional environments play a central role in amplifying or constraining the well-being returns to individual and job characteristics. By addressing sectoral disparities, strengthening private-sector protections, and advancing gender-sensitive labor policies, policymakers can foster greater equity and happiness in the workforce. 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Applied Research in Quality of Life , 17 (4), 2311–2348. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8020850","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550568332,"identity":"d2f715b7-0385-4e4e-9b42-bd5de4eddd9f","order_by":0,"name":"Lin Xiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACPgbGBoYPbFAeDzFa2IBaGGeQqIWBgZmHNC0SyW3SNmUMiWtnNzA+eNtGjBaeg23SOecYErfdOcBsOJcoLeyNbdK5bUAtNxLYpHmJ0sLM2CZtCdHC/ps4LSBbGKG2MBOnhedgs2XPOQnjbTcSmyXnnCNCC79E+sMbP8psZLfdSD744U0ZEVqgQAKIgclgFIyCUTAKRgGVAACzAS/c7/wyswAAAABJRU5ErkJggg==","orcid":"","institution":"University of Minnesota, Duluth","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xiu","suffix":""},{"id":550568333,"identity":"91be103b-7722-4123-94d3-b7e3e2bf1e69","order_by":1,"name":"Yufei Ren","email":"","orcid":"","institution":"University of Minnesota, Duluth","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Ren","suffix":""},{"id":550568334,"identity":"ea0f8db9-a79f-4eac-9ecc-aa5fcfec93dd","order_by":2,"name":"Thomas Lange","email":"","orcid":"","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Lange","suffix":""}],"badges":[],"createdAt":"2025-11-03 15:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8020850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8020850/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97138383,"identity":"4d34e8eb-fca2-43e4-b2eb-384dd3fb3b0b","added_by":"auto","created_at":"2025-12-01 09:58:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89936,"visible":true,"origin":"","legend":"","description":"","filename":"HappinessPremiumNov2025v.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/4c18d4505524a1930304f982.docx"},{"id":97017624,"identity":"baccd3c5-b769-4bf5-896e-d4e6deb704b2","added_by":"auto","created_at":"2025-11-28 17:36:57","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5247,"visible":true,"origin":"","legend":"","description":"","filename":"e0b01812711f482a8685012452ef76a7.json","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/c68a21be543a14915d3a23f0.json"},{"id":97017625,"identity":"54a3480f-e9ee-4ad8-b3bc-11622d9c7dcb","added_by":"auto","created_at":"2025-11-28 17:36:57","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193135,"visible":true,"origin":"","legend":"","description":"","filename":"e0b01812711f482a8685012452ef76a71enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/003ace5cc9981edc755ef777.xml"},{"id":97017627,"identity":"b1d81c48-8bfe-4e9d-8f0c-a99eb91be8fe","added_by":"auto","created_at":"2025-11-28 17:36:57","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191388,"visible":true,"origin":"","legend":"","description":"","filename":"e0b01812711f482a8685012452ef76a71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/3d13b8080ce03fb7c26bab2f.xml"},{"id":97017628,"identity":"e463038f-770b-4073-804c-4ab3dc1a61fd","added_by":"auto","created_at":"2025-11-28 17:36:57","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205320,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/7188af9cba93bcc3aa74bc9c.html"},{"id":109068489,"identity":"c8d557c8-e233-415c-82b4-e4a293f35792","added_by":"auto","created_at":"2026-05-12 10:12:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":926124,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8020850/v1/1b5d35fb-a26b-4822-b114-735ab9fd6df5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Happiness Premium? Gender and Employment Sector Differences in Well-Being in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHappiness and subjective well-being (SWB) are influenced not only by income and personality factors but also by the institutional and social environments in which people live and work. Classic theories of subjective well-being emphasize that happiness is not only determined by individual dispositions and material resources but also by the livability of society\u0026mdash;the extent to which social institutions, norms, and governance structures enable individuals to lead satisfying and meaningful lives (Diener, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Diener et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Warr, 2007). From this perspective, well-being reflects the interplay between personal circumstances and the broader social and institutional context (Knight \u0026amp; Gunatilaka, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In China, where market reforms and social change have reshaped employment relations and gender roles, understanding how institutional contexts affect happiness is both timely and essential.\u003c/p\u003e\u003cp\u003eGender differences in subjective well-being (SWB) have long intrigued scholars, particularly in countries undergoing economic and cultural transition. From a sociological and institutional perspective, gender shapes access to valued life domains\u0026mdash;employment, family roles, and social participation\u0026mdash;that underlie subjective well-being. The \u0026ldquo;female happiness paradox,\u0026rdquo; observed across many societies, captures the puzzling pattern that women tend to report higher life satisfaction but poorer mental health than men (Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yet this paradox is not universal and may depend on the institutional and cultural contexts that condition gender roles and social expectations. China provides a distinctive case in this regard. Traditional Confucian norms emphasizing family responsibility coexist with rapid modernization, expanding education and labor market participation for women, and widening divides between public and private employment sectors (Zhou \u0026amp; Peng, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These overlapping shifts create a complex terrain in which institutional arrangements and gendered expectations jointly shape experiences of happiness and well-being.\u003c/p\u003e\u003cp\u003eEmployment is a central life domain through which people experience happiness and subjective well-being. Work provides income, social identity, structure, and purpose, and its characteristics\u0026mdash;such as job security, autonomy, workload, and social support\u0026mdash;are key predictors of well-being (Warr, 2007). From an institutional perspective, employment systems reflect broader social arrangements that determine how work rewards are distributed and how individuals experience fairness, stability, and meaning in their occupations (Diener et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In China, the contrast between public and private sector employment illustrates these institutional differences vividly. Public sector jobs are typically associated with greater job security, more generous benefits, predictable career progression, and lower work pressure, and thus are often believed to confer a \u0026ldquo;\u003cem\u003ehappiness premium\u003c/em\u003e.\u0026rdquo; The intense competition for public employment reflects this perception: in 2024, over three million candidates applied for just 39,600 civil servant positions, translating to approximately 77 individuals competing for one spot (Du, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This intense competition reflects the public sector\u0026rsquo;s enduring reputation for stability and prestige, particularly amid high youth unemployment rates of 18.8% among 16- to 24-year-olds (China Association of Social Security, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such institutional advantages suggest that public sector employment may influence not only material well-being but also broader life satisfaction and subjective well-being\u0026mdash;potentially in different ways for men and women, given China\u0026rsquo;s persistent gendered patterns in work and family roles (Cooke \u0026amp; Xiao, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, this study asks: Does employment in the public sector enhance men\u0026rsquo;s and women\u0026rsquo;s happiness and well-being relative to the private sector? Are these differences primarily a result of selection\u0026mdash;that is, who chooses public-sector employment\u0026mdash;or do they reflect intrinsic institutional advantages of public employment, such as stability, benefits, or social prestige? Moreover, do the determinants and returns to well-being differ between men and women across employment sectors? Addressing these questions helps illuminate how gender and institutional contexts jointly shape happiness and subjective well-being in a rapidly changing society.\u003c/p\u003e\u003cp\u003eThis study makes several contributions to the growing literature on gender, employment, and well-being. First, it extends the understanding of gender differences in subjective well-being by examining both general and domain-specific measures of happiness and life satisfaction. While recent research finds that the historical \u0026ldquo;female happiness advantage\u0026rdquo; has reversed in several Western countries\u0026mdash;with women now reporting lower well-being than men (Bryson \u0026amp; Blanchflower, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026mdash;evidence from China remains limited and mixed. For example, women on average report higher life satisfaction than men (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), yet self-employed women are found to be less happy than their male counterparts (Xiu \u0026amp; Ren, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By analyzing multiple indicators of well-being, this study provides a more nuanced and context-specific understanding of these patterns. Second, by accounting for potential selection into employment sectors, the study offers a more comprehensive examination of the so-called \u0026ldquo;public-sector happiness premium.\u0026rdquo; This approach allows us to distinguish between compositional effects\u0026mdash;who works in each sector\u0026mdash;and genuine institutional effects stemming from differences in working conditions, job security, and social recognition. Third, the study explores how demographic, family, and workplace characteristics influence happiness and well-being for men and women and whether the returns to these factors differ across sectors. Such an analysis reveals not only whether women and men experience happiness differently but also whether the sources of their happiness diverge in the public and private spheres.\u003c/p\u003e\u003cp\u003eIn sum, this study highlights how gender and employment sector\u0026mdash;each reflecting distinct social and institutional contexts\u0026mdash;jointly shape happiness and subjective well-being in China. Beyond advancing theoretical understanding, the findings carry policy relevance for promoting gender equity, improving employment quality, and enhancing overall life satisfaction in the Chinese labor market.\u003c/p\u003e\u003cp\u003eBuilding on these considerations, the next section reviews two key strands of literature that inform this study. The first examines research on employment-sector differences in happiness\u0026mdash;the so-called \u003cem\u003epublic-sector happiness premium\u003c/em\u003e\u0026mdash;and explores how sectoral conditions and self-selection shape subjective well-being. The second reviews evidence on gendered patterns of happiness and well-being, highlighting how social roles, family responsibilities, and workplace structures contribute to differences between men and women. Together, these literatures provide the foundation for analyzing how gender and employment sector jointly influence happiness in China.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Employment Section and the Happiness Premium\u003c/h2\u003e\u003cp\u003ePublic versus private sector employment has long been associated with differences in subjective well-being (SWB), reflecting variation in job security, benefits, autonomy, and perceived status. A substantial body of research finds that public sector employees often experience a \u003cem\u003ehappiness premium\u003c/em\u003e\u0026mdash;higher levels of happiness and life satisfaction compared with those working in the private sector (Mart\u0026iacute;n-Garc\u0026iacute;a and Castro-Mart\u0026iacute;n, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Homberg, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fern\u0026aacute;ndez Puente \u0026amp; S\u0026aacute;nchez-S\u0026aacute;nchez, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, in Spain, employed women face higher opportunity costs when deciding to become mothers. However, women in public sector jobs often become mothers earlier than their counterparts in self-employment or the private sector, a trend attributed to the public sector's greater long-term stability and supportive policies for work-family balance in public employment (Mart\u0026iacute;n-Garc\u0026iacute;a and Castro-Mart\u0026iacute;n, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Homberg (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) similarly finds that institutional differences between the public and private sectors significantly impact employee well-being, with public sector employees reporting higher happiness. Using data from the European Working Condition Survey across 19 European countries, Fern\u0026aacute;ndez Puente \u0026amp; S\u0026aacute;nchez-S\u0026aacute;nchez, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that public sector workers across the Eurozone are more satisfied than those in the private sector. Evidence from transition economies echoes these findings: in Ukraine, for instance, pre-war data show that public sector workers experienced a happiness premium associated with more extensive benefits and institutional protection (Danzer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn important issue is sector selection. Individuals who choose public or private employment often differ in characteristics\u0026mdash;such as risk tolerance, career aspirations, or family priorities\u0026mdash;that also shape happiness (Christofides \u0026amp; Pashardes, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; \u0026Ouml;zveren, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Correcting for endogenous self-selection of workers into sectors is therefore essential to determine whether the observed happiness premium arises from employment conditions or from the individuals who select them (Danzer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, women and men may make this employment sector choice for different reasons. Women, for instance, might prioritize job roles that offer flexibility to balance work and family responsibilities (Th\u0026eacute;baud, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a feature more commonly found in public sector employment. Additionally, the unique challenges and barriers women face in the workplace, such as unequal access to resources and support, could differentially affect their happiness and well-being in public versus private sectors.\u003c/p\u003e\u003cp\u003eIn China, distinctions between public and private employment have deep historical roots in the \u003cem\u003edanwei\u003c/em\u003e (work-unit) system that structured urban life during the planned-economy era. Public sector work units such as government offices and state-owned enterprises once monopolized access to scarce social resources and offered employees comprehensive benefits and high social prestige. Although market reforms have reduced their dominance, public sector jobs continue to provide greater job security, stable income, and superior welfare compared with the private sector (Hu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Xiao, Liu, \u0026amp; Ren, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies show that public employees in China report higher levels of well-being (Cheng, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), yet it remains unclear whether these differences stem from workers\u0026rsquo; characteristics, the institutional features of the sectors themselves, or both. Furthermore, little is known about whether this public-sector happiness premium varies by gender. The present study addresses this gap by examining the gendered nature of the public-sector happiness premium in China, while accounting for selection into employment sectors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Gender and Subjective Well-Being\u003c/h2\u003e\u003cp\u003eGender differences in happiness and SWB have long attracted scholarly attention (Haring et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Blanchflower \u0026amp; Oswald, 2004; Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A robust literature documents the so-called \u003cem\u003efemale happiness paradox\u003c/em\u003e: women frequently report higher overall life satisfaction than men yet also exhibit higher levels of stress, anxiety, and depression (Becchetti \u0026amp; Conzo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This pattern\u0026mdash;higher life satisfaction but poorer mental health among women\u0026mdash;has been widely observed across countries and over time. Well-being is shaped by social status, family responsibilities and employment circumstances (Abreu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), all of which tend to differ markedly for men and women. Since the first meta-analysis on gender and well-being over four decades ago (Haring et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), research has consistently found that women report higher levels of negative affect (depression, anxiety, poorer sleep) and lower satisfaction with specific life domains such as finances, marriage, and standard of living (Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Boerma et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), yet paradoxically express greater overall life satisfaction and happiness.\u003c/p\u003e\u003cp\u003eOver time, however, gender gaps in happiness have shifted. In the United States, women\u0026rsquo;s happiness advantage observed in the 1970s and 1980s declined by the late 1990s (Blanchflower \u0026amp; Oswald, 2004). Using data from the General Social Survey (GSS, 1972\u0026ndash;1998), they found that while women initially reported higher happiness levels than men, this pattern reversed as women\u0026rsquo;s reported happiness declined, narrowing the gender gap. Similar trends were observed in other datasets, such as the DDB Needham Lifestyle Surveys (1985\u0026ndash;2005), which documented comparable declines in life satisfaction for both women and men (Stevenson \u0026amp; Wolfers, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). More recent cross-national evidence confirms this shift: in over a dozen Western countries, including the United States and the United Kingdom, women no longer report higher life satisfaction and are, on average, less happy than men (Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile women\u0026rsquo;s relative happiness has declined in many Western societies, evidence from China paints a more nuanced picture. Using data from the 2010\u0026ndash;2018 China Family Panel Studies, Zhang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that women generally report higher life satisfaction than men, though the gap narrows with age and follows a U-shaped pattern across the life course. Huang, Yi, and Clark (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed no significant gender differences in happiness in urban areas but higher happiness among men in rural regions, suggesting that variations in gender norms and institutional settings contribute to divergent well-being outcomes. Marital and family dynamics also contribute to gendered well-being differences. Analyzing data from the 2006 Chinese General Social Survey (CGSS), Liu, Li, and Feldman (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that marital dynamics significantly affect life satisfaction, with marital status being more significant for men and marital quality more important for women. Their findings highlight how family relationships, social expectations, and intergenerational support interact with gender to shape happiness in China. Consistent with this, Chen and Zhang (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that gender stereotypes and social expectations continue to undermine women\u0026rsquo;s well-being, particularly among educated women who challenge traditional gender roles.\u003c/p\u003e\u003cp\u003eSocial and workplace environments further shape gendered experiences of happiness. Employment conditions, access to social capital, and perceived job security all play important roles in shaping wellbeing (Churchill \u0026amp; Mishra, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang, Yi, \u0026amp; Clark, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Work settings that provide stability, fair compensation, and supportive policies are consistently linked to higher life satisfaction and better health outcomes (Faragher, Cass, \u0026amp; Cooper, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Such institutional features of the workplace are central to understanding differences in well-being across employment sectors. For example, van Dierendonck, Lv, and Xiu (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that supportive and trust-based work environments enhance employees\u0026rsquo; sense of meaningfulness and improve sleep quality, illustrating how positive organizational contexts can promote both psychological and physical well-being.\u003c/p\u003e\u003cp\u003eTaken together, this body of research shows that gendered experiences of happiness are deeply intertwined with social structures, family responsibilities, and employment contexts. Men and women may evaluate their lives using similar criteria but experience distinct constraints and opportunities across these domains. In China, where public and private sectors differ markedly in job stability, benefits, and social prestige, these institutional divides may further shape gendered well-being outcomes. Understanding how gender and employment sector interact to influence happiness and subjective well-being provides the basis for this study\u0026rsquo;s analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Sample\u003c/h2\u003e\u003cp\u003eThis study uses data from the 2013 wave of the \u003cem\u003eChinese Household Income Project\u003c/em\u003e (CHIP) survey, the most recently available wave of the project. Widely used in academic scholarship, CHIP is considered a foundational dataset for understanding China\u0026rsquo;s economic development and income distribution during recent decades. It gathered information on household and work details from households across 14 provinces in China. This survey was conducted as a part of an international collaborative research effort focused on income and inequality in China, involving both Chinese and international scholars. The data was obtained through the China Institute for Income Distribution. Our analysis focused on urban households and individuals working at least 30 hours per week, including employees from both the public and private sectors. The sample consists of 3,580 individuals employed in the public sector (42%) and 4,878 in the private sector (58%). Public sector workers include those employed by the government, state agencies, and state-owned enterprises. Private sector workers are defined as those employed outside the public sector.\u003c/p\u003e\u003cp\u003eWell-being is measured using three variables, including self-rated health, satisfaction with the Standard of Living (SOL), and overall life satisfaction based on three questions in the survey regarding respondents\u0026rsquo; satisfaction with their health, SOL and overall life satisfaction. The self-rated health variable is derived from a question asking respondents to compare their health to that of the average person in their own age group, using a five-point scale from \u0026ldquo;very poor\u0026rdquo; to \u0026ldquo;very good.\u0026rdquo; The standard of living (SOL) satisfaction variable is measured by the question, \u0026ldquo;How do you rate your standard of living compared to others?\u0026rdquo; with responses ranging on a five-point scale from \u0026ldquo;much lower\u0026rdquo; to \u0026ldquo;much higher.\u0026rdquo; Overall life satisfaction is assessed based on the question, \u0026ldquo;Considering all aspects of life, are you happy?\u0026rdquo; with options on a five-point scale from \u0026ldquo;not happy at all\u0026rdquo; to \u0026ldquo;very happy.\u0026rdquo; Single-item satisfaction measures, such as life satisfaction, are widely used in the well-being literature and are shown to have high validity and reliability comparable to multi-item scales (Cheung \u0026amp; Lucas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kibler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eControl variables include age, gender, education levels, marital status, the presence of children and elders in the household, Chinese Communist Party (CCP) membership, Han ethnicity, job tenure, weekly working hours, industry, and geographical regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Descriptive Statistics and Methodology\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the descriptive statistics of the sample by sectors. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a comparison of self-reported health, SOL satisfaction, and life satisfaction by gender across the two sectors. The results show that both men and women in the public sector have slightly better health scores, higher SOL satisfaction, and greater overall life satisfaction compared to their private sector counterparts.\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\u003eSummary Statistics by Employment Sector\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePublic Sector (N\u0026thinsp;=\u0026thinsp;3,580)\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\u003ePrivate Sector (N\u0026thinsp;=\u0026thinsp;4,878)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\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\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth (max. 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSatisfaction with standard of living (SOL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall Life Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\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\u003e42.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e10.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eEducation (reference\u0026thinsp;=\u0026thinsp;High School and less)\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-years College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity and Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCP Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob Tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e8.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild Under 7 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild 7\u0026ndash;18 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior 66\u0026ndash;75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior above 75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeekly Working Hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e11.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFather Public Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother Public Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: N\u0026thinsp;=\u0026thinsp;SOL and Life satisfaction questions are in a separate questionnaire with additional missing values. For SOL, the Public Sector N\u0026thinsp;=\u0026thinsp;3,350 and the Private Sector N\u0026thinsp;=\u0026thinsp;4,510. For life satisfaction, the Public Sector N\u0026thinsp;=\u0026thinsp;3,516 and the Private Sector N\u0026thinsp;=\u0026thinsp;4,777.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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\u003eComparison of Well-being Variables by Gender and Employment Sector\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePooled Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\u003ePublic Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePrivate Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth (max. 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.043***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOL Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.130***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.188***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\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\u003cb\u003eFemale Sample\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePublic Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePrivate Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth (max. 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOL Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.135***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eMale Sample\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePublic Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePrivate Sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\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\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth (max. 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.041*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOL Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.127***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.179***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote. *\u0026lt;0.10, **\u0026lt;0.05 ***\u0026lt;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the following, we first estimate regression models separately for the public and private sectors. We then employ an Endogenous Switching Regression (ESR) model to jointly model sector selection (public vs. private) and well-being outcomes. Endogenous Switching Regression (ESR) model applies full-information maximum likelihood (FIML) to simultaneously fit both the sector selection (public sector vs. private sector) model and the well-being equation model. This approach helps address the potential endogeneity issue that may exist when variables that affect employment type selection (public sector vs. private sector) also affect employment outcomes. Some unobserved characteristics that influence the probability of choosing a particular sector of employment could also influence individuals\u0026rsquo; well-being once they are employed in that sector. Neglecting these selectivity effects is likely to bias the estimate of relative wellbeing in both the public and private sector (Lokshin and Sajaia, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The Endogenous Switching model corrects for the selection bias in the sectoral wellbeing estimates.\u003c/p\u003e\u003cp\u003eFollowing previous literature on sector selection (e.g. Xiu \u0026amp; Gunderson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), two instrumental variables are used to identify the sector selection: father\u0026rsquo;s public sector employment and mother\u0026rsquo;s public sector employment. This Endogenous Switching approach allows estimation of selection-corrected well-being outcomes for public- and private-sector workers, as well as decomposition into endowment and returns effects using counterfactual predictions.\u003c/p\u003e\u003cp\u003eThe overall sectoral difference in well-being can be expressed as the sum of endowment and returns effects:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{E}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t}=E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEndowment Effect = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e\u003cp\u003eReturns Effect = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the expected wellbeing in the public sector for the individuals who currently employed in the public sector, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the expected wellbeing in the private sector for the individuals who currently employed in the private sector, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the expected value of wellbeing if the private sector employees were selected into the public sector. The endowment effect showed the well-being difference between the private sector employees and the counterfactual well-being that private sector employees would have if they were in the public sector. In contrast, the returns effect captures the change in well-being if private sector employees had selected into public sector and shows whether switching from private to public sector would have an impact on their well-being. The analysis was conducted using \u003cem\u003eSTATA 18.5\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the well-being regressions on the three well-being outcomes in the two employment sectors. In the private sector, the factors that influence the wellbeing are generally consistent. College education, being married and longer job tenure are associated with better health, higher standard-of-living (SOL) satisfaction and life satisfaction. There is no statistically significant gender difference in any of the well-being outcomes when individual, family and work characteristics are controlled. In contrast, in the public sector, when these factors are accounted for, women have lower levels of reported health, and higher levels of life satisfaction, consistent with the gender well-being paradox.\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\u003eWell-Being Regressions for Public- and Private-Sector Employees\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\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePublic Sector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003ePrivate Sector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\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\" colname=\"c2\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSOL Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eSOL Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.045*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.060**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.015\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(-1.702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.584)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(2.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(-0.421)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(1.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.629)\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-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.033**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.028**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e-0.029***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e-0.031***\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(-0.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.448)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-2.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(-1.250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(-2.796)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(-3.083)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Squared\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\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.000**\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(-0.168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.737)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1.717)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(-0.722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(2.652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(2.433)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-year College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.109***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.063*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.017\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(0.727)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(3.124)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(1.425)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(1.788)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.500)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.083**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.182***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.128***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.124***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.142***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.172***\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(2.354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.979)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(3.451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(3.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(3.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(3.991)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.035\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(-0.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-0.761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(1.313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(0.284)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.636)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCP Party Member\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.061**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.054*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.117***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.098**\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(-0.725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(0.654)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(2.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(2.526)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob Tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.004***\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(0.209)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.898)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1.177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(2.424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(3.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(2.858)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026thinsp;\u0026lt;\u0026thinsp;7 in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.008\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\u003e0.017\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(0.501)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.526)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(0.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(0.904)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.525)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild 7\u0026ndash;18 in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.022\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(1.529)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.604)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-0.636)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(1.470)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(-0.898)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.838)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior 66\u0026ndash;75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.073\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(-1.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.304)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(1.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(1.409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(1.523)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior\u0026thinsp;\u0026gt;\u0026thinsp;75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.033\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\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.001\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(-0.511)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-1.355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(-0.265)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(0.830)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.284***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.116***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.196***\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(5.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(3.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(4.562)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(8.035)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeekly Working Hours\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-0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.007***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.000\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(-0.616)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.594)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-3.988)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(-0.279)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(1.658)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(0.472)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvinces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003eYes\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\u003e4.524***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.313***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.253***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e4.371***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.860***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e4.070***\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\u003cp\u003e(15.654)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(11.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(13.850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e(22.789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e(13.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e(19.726)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e4878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e4777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"16\"\u003eNote: *\u0026lt;0.10, **\u0026lt;0.05 ***\u0026lt;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNext, we run regressions by sector and by gender to examine the different mechanisms through which one\u0026rsquo;s well-being is influenced by individual, family and work characteristics. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, education emerges as a consistent positive determinant of well-being, particularly in the public sector. Among men in the public sector, both two-year college and university education significantly improve SOL and life satisfaction. Women in the public sector experience similar benefits from higher education, though the effects are more limited to SOL satisfaction. In the private sector, the positive effects of higher education on well-being are concentrated among men, while the coefficients for women are not statistically significant. Marital status is found to have a strong and consistent effect across most groups. Being married is positively associated with life satisfaction for both men and women in all sectors, consistent with prior evidence of a marriage premium in subjective well-being. The effects are particularly pronounced for public-sector men (β\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and somewhat smaller for women in the same sector (β\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Marriage also contributes to higher self-rated health and SOL satisfaction in the private sector, but these associations are weaker or absent in the public sector, possibly reflecting stronger institutional supports that buffer single employees\u0026rsquo; well-being. Job tenure is positively related to well-being in most models, particularly in the private sector, where it significantly predicts all three outcomes for men and two for women. In the public sector, tenure matters mainly for SOL satisfaction, indicating that long-term stability and experience contribute to perceptions of material well-being more than to overall life satisfaction. Working hours show contrasting associations across sectors and genders. In the public sector, longer working hours are linked to lower SOL and life satisfaction, especially for men (β = \u0026minus;\u0026thinsp;0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for SOL; β = \u0026minus;\u0026thinsp;0.008, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for life satisfaction). These negative effects are less pronounced in the private sector and, for men, even reversed in SOL satisfaction, possibly reflecting differing expectations about workload and rewards. Age effects follow a partial U-shaped pattern. Among public-sector men, age is negatively associated with SOL satisfaction (β = \u0026minus;\u0026thinsp;0.045, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but becomes positive at older ages through the squared term, indicating a turning point in midlife. Similar curvature is observed for women in the private sector, suggesting that life satisfaction reaches its lowest point around the late 40s to early 50s, consistent with prior findings in the Chinese context (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Household composition variables, such as the presence of children or elderly members, show no consistent or statistically significant associations with well-being once employment and individual characteristics are controlled.\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\u003eWell-Being Regressions by Employment Sector and Gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"33\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c32\" colnum=\"32\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c33\" colnum=\"33\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e\u003cp\u003ePublic Sector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"16\" nameend=\"c31\" namest=\"c16\"\u003e\u003cp\u003ePrivate Sector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c33\" namest=\"c32\"\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=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c22\" namest=\"c15\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c32\" namest=\"c25\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c33\" namest=\"c33\"\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\u003eM1 Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM2 SOL Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eM3 Life Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM4 Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eM5 SOL Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eM6 Life Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003eM7 Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003eM8 SOL Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u003cp\u003eM9 Life Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c26\" namest=\"c22\"\u003e\u003cp\u003eM10 Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c29\" namest=\"c27\"\u003e\u003cp\u003eM11 SOL Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c30\"\u003e\u003cp\u003eM12 Life Sat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c33\" namest=\"c31\"\u003e\u0026nbsp;\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-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.045***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.033*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e-0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e-0.060***\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(-0.762)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.606)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-1.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(-1.353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(-1.227)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(-1.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(-0.612)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(-1.522)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(-0.639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(-1.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(-2.982)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(-3.813)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Squared\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\u003e0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.001***\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(-0.169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.768)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(1.372)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(1.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(-0.868)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(1.375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(-0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(2.912)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(3.180)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-year College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.144***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.130***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.125**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.087*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.075*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e-0.063\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(0.666)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.335)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(2.955)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.369)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(2.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(1.223)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(1.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(1.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(1.667)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.582)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(0.542)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(-1.241)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniv. \u0026amp; above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.092**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.199***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.160***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.150**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.174***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.164***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.223***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.090\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(2.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(3.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(1.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(2.471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(0.887)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(3.139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(2.633)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(3.834)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(1.530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(1.360)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.028\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(0.153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-0.395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(-0.906)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(-0.831)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(0.749)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(-0.561)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.585)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(1.147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(1.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(0.358)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCP\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\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.147***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.107**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.087*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e-0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.138**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.107\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(-0.449)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(-0.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(1.515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(2.956)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(1.362)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(2.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(1.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(-0.744)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(2.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(1.620)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob Tenure\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\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.007**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.004*\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(-0.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(1.644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(2.454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(-0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(2.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(2.321)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(2.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(2.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(1.845)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChild\u0026thinsp;\u0026lt;\u0026thinsp;7 in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.133**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.406)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.638)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(1.981)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(0.461)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(-0.320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(0.772)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.902)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(0.418)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(0.343)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChild 7\u0026ndash;18 in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.113**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.252)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.392)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-1.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(1.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(2.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(0.390)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(1.357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(-1.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.527)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(0.362)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(0.491)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSenior 66\u0026ndash;75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.123*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.122*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.746)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(0.701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(0.514)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(1.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(1.323)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(1.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(0.922)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSenior\u0026thinsp;\u0026gt;\u0026thinsp;75 in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.159*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-1.837)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(-0.925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(-0.177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(-0.670)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(0.437)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(0.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(0.755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(-0.086)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.393***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.192**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.116**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.202***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.332***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.108**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.193***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.324***\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(0.228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(5.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(1.186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(-0.399)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(2.510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(2.142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(3.333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(5.843)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(1.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(3.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(5.454)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeekly Working Hours\u003c/p\u003e\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-0.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.008***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e-0.006*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e-0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.470)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(-3.676)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(-1.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(-1.854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(0.967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(2.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(0.633)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(-1.714)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(0.105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(-0.162)\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\u003e4.861***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.571***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.281***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e4.420***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.193***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e4.433***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e4.219***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e2.724***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e3.571***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e4.618***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e3.277***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e4.810***\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(12.950)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.215)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(10.758)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(8.779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(6.153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e(8.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e(16.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e(9.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e(12.996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e(15.460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(9.643)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e(14.553)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e1361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u003cp\u003e2746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e2532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c23\" namest=\"c21\"\u003e\u003cp\u003e2683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e\u003cp\u003e2132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c28\"\u003e\u003cp\u003e1978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c33\" namest=\"c29\"\u003e\u003cp\u003e2094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"33\"\u003eNote: Industries and provinces are controlled in all regressions in this table. *\u0026lt;0.10, **\u0026lt;0.05 ***\u0026lt;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOverall, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that the determinants of well-being differ systematically by gender and employment sector. Education, marriage, and job tenure consistently predict higher well-being, while long working hours reduce satisfaction primarily in the public sector. These patterns underscore how institutional features of employment\u0026mdash;stability, workload norms, and sectoral expectations\u0026mdash;interact with gendered social roles to shape health, material satisfaction, and overall happiness.\u003c/p\u003e\u003cp\u003eThen, we conducted the endogenous switching model analysis. The findings are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the decomposition results for self-rated health. The estimated total gap\u0026mdash;the overall health difference between public- and private-sector employees\u0026mdash;is positive and statistically significant for both women (0.045, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and men (0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that, on average, public sector men and women have better health than those in the private sector after controlling for individual, family, and work characteristics, as well as selection into the types of employment. For both men and women, the endowment effect is positive and statistically significant (0.243 for women; 0.079 for men), suggesting that public-sector workers possess more favorable characteristics related to health\u0026mdash;such as education, job stability, and income security\u0026mdash;than private-sector workers. These compositional differences explain a substantial portion of the observed health advantage in the public sector. In contrast, the returns effect is negative for both women (\u0026ndash;0.198, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and men (\u0026ndash;0.038, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This implies that if private-sector workers were employed in the public sector but experienced the same \u0026ldquo;returns structure\u0026rdquo; as public-sector workers, their expected health would actually be lower. In other words, the way characteristics translate into health outcomes appears less favorable within the public sector once individual endowments are held constant. Taken together, the results indicate that the public-sector health premium is largely driven by differences in worker characteristics rather than by superior sectoral conditions.\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\u003eWell-Being Differences between Public and Private Sector Employment: Endowments and Returns Effects from Endogenous Switching Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePanel A (Health).\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\u003eEstimated Total Gap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEndowment Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReturns Effect\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\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.243***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.198***\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(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.079***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.038***\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(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\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=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePanel B (SOL Satisfaction).\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\u003eEstimated Total Gap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEndowment Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReturns Effect\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\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.137***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.099***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039***\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(0.005)\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\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.009***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.137***\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(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\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=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePanel C (Life Satisfaction).\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\u003eEstimated Total Gap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEndowment Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReturns Effect\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\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pub}|{X}_{pub},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pub}\\right)-\\:E\\left({Y}_{pri}|{X}_{pri},\\:{\\beta\\:}_{pri}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.203***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.401***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.604***\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(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.180***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.554***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.734***\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(0.005)\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\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *\u0026lt;0.10, **\u0026lt;0.05 ***\u0026lt;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePanel B of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the decomposition results for satisfaction with standard of living (SOL). The total gap between public- and private-sector workers is positive and statistically significant for both women (0.137, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and men (0.128, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that employees in the public sector report higher SOL satisfaction on average than those in the private sector, even after accounting for observed characteristics and selection into sectors. For women, both the endowment effect (0.099, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and returns effect (0.039, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) are positive and significant. This pattern suggests that the public-sector advantage in women\u0026rsquo;s SOL satisfaction stems from both more favorable characteristics (e.g., higher education, job security, or institutional benefits) and more rewarding returns to these characteristics. In other words, women not only enter the public sector with characteristics conducive to material well-being but also benefit more from them once employed in that sector\u0026mdash;perhaps reflecting better alignment between women\u0026rsquo;s expectations, social protection policies, and workplace conditions. For men, the decomposition reveals a different pattern. The endowment effect is small and negative (\u0026ndash;0.009, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the returns effect is large and positive (0.137, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), showing that private sector men\u0026rsquo;s SOL satisfaction would be higher than that of current public sector men, and higher than their current SOL satisfaction if they had selected into the public sector.\u003c/p\u003e\u003cp\u003ePanel C of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the decomposition results for overall life satisfaction. The estimated total gap between public- and private-sector workers is positive and significant for both women (0.203, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and men (0.180, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming that public-sector employees report higher overall life satisfaction than those in the private sector after accounting for selection effects and observable characteristics. For women, the decomposition shows a large and negative endowment effect (\u0026ndash;0.401, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) alongside a strong positive returns effect (0.604, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that women in the private sector possess characteristics that, if evaluated under the public-sector structure, would predict higher life satisfaction. However, the large positive returns effect suggests that the institutional environment of the public sector substantially enhances the translation of individual and job characteristics into higher subjective well-being. In other words, once employed in the public sector, women derive markedly greater happiness from similar attributes than they would in the private sector. For men, the pattern is broadly similar but slightly stronger. In other words, the results show that, for both men and women, private sector individuals\u0026rsquo; life satisfaction would be higher than that of current public sector individuals, and higher than their current satisfaction if they had selected into the public sector. This finding is consistent with the notion of a \u003cem\u003epublic-sector happiness premium\u003c/em\u003e and aligns with the intense competition for public-sector positions in China.\u003c/p\u003e\u003cp\u003eIn summary, the findings reveal significant differences in well-being between public- and private-sector employees, with a consistent public-sector advantage across health, standard-of-living satisfaction, and life satisfaction. However, the sources of this advantage differ by gender and by well-being indicators. Self-reported Health differences are largely explained by compositional factors\u0026mdash;public-sector workers\u0026rsquo; more favorable endowments\u0026mdash;whereas SOL and life satisfaction advantages are primarily driven by institutional returns, particularly in the public sector. Both men and women gain more in subjective well-being when their attributes are situated in the public-sector context, although the magnitudes differ. These results confirm that the public-sector \u0026ldquo;happiness premium\u0026rdquo; is not merely a product of worker selection, but also of the institutional environment that shapes how individual and job characteristics translate into well-being.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examines gendered well-being outcomes across public and private employment sectors in China, focusing on how institutional context and gender jointly shape subjective well-being across domains of health, standard of living (SOL) satisfaction, and overall life satisfaction. The results reveal several key patterns. First, public sector employment is generally associated with better well-being outcomes compared to the private sector. Both men and women in the public sector report better health, higher SOL satisfaction, and greater life satisfaction, after controlling for selection effects and other relevant characteristics. However, the benefits of public sector employment vary significantly by gender and well-being domain.\u003c/p\u003e\u003cp\u003eSecond, the endogenous switching model highlights the distinct roles of endowment and returns effects. The public-sector happiness premium is driven primarily by institutional returns rather than by differences in worker characteristics. For both men and women, the public-sector context enhances how individual and job attributes translate into subjective well-being, particularly in standard-of-living and life satisfaction. Notably, the counterfactual results show that if private-sector employees were employed in the public sector, their expected life satisfaction would exceed both their current levels and those of their counterparts in the public sector, underscoring the perceived desirability and well-being advantages associated with public employment.\u003c/p\u003e\u003cp\u003eLastly, education, marriage, and job tenure are consistently associated with higher well-being, while longer working hours reduce satisfaction\u0026mdash;especially in the public sector. These patterns suggest that institutional factors such as job stability, workload expectations, and sectoral norms interact with gendered life circumstances to shape subjective well-being in China\u0026rsquo;s labor market. Taken together, the evidence indicates that the \u003cem\u003epublic-sector happiness premium\u003c/em\u003e is shaped not only by institutional advantages but also by gendered experiences and selection mechanisms.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Theoretical Implications\u003c/h2\u003e\u003cp\u003eThe findings of this study contribute to the growing literature on gender and subjective well-being by situating happiness within China\u0026rsquo;s institutional and labor market context. It extends prior research on the \u0026ldquo;female happiness paradox\u0026rdquo; (Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) by showing that gendered differences in happiness vary systematically across employment sectors, reflecting both selection mechanisms and institutional effects.\u003c/p\u003e\u003cp\u003eFirst, this study reinforces the importance of sectoral context in understanding well-being disparities (Haring et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Huang, Yi, \u0026amp; Clark, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Public-sector employment\u0026mdash;characterized by greater job stability, benefits, and social protection\u0026mdash;is linked to higher satisfaction with life and standard of living. Yet, the health advantage observed for public-sector workers appears largely compositional, highlighting that not all well-being domains respond equally to institutional conditions.\u003c/p\u003e\u003cp\u003eSecond, the gendered patterns observed\u0026mdash;such as women\u0026rsquo;s relatively higher life satisfaction and better health in the public sector\u0026mdash;add nuance to the gender well-being paradox (Blanchflower \u0026amp; Bryson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest that institutional settings offering greater stability and protection may particularly enhance women\u0026rsquo;s well-being, even as gendered expectations shape how they evaluate satisfaction across life domains. This pattern aligns with theories of gendered trade-offs, where women may derive greater happiness from secure and balanced work environments, even when facing other constraints or pressures.\u003c/p\u003e\u003cp\u003eBy incorporating an endogenous switching model, this study highlights how subjective well-being differences reflect both who enters particular employment sectors and how individual attributes are rewarded within them. This methodological approach advances the well-being literature by linking micro-level gendered experiences to macro-level institutional structures, emphasizing that happiness is not merely an individual outcome but also a product of broader employment systems and social norms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Policy Implications\u003c/h2\u003e\u003cp\u003eThe findings carry several important policy implications for promoting gender equity and enhancing well-being outcomes in China\u0026rsquo;s labor market. First, the evidence of a public-sector happiness premium\u0026mdash;particularly in life and standard-of-living satisfaction\u0026mdash;highlights the institutional advantages of stable employment, social protection, and predictable workloads. Yet, access to such positions is highly competitive, reinforcing existing inequalities. Expanding social protections, benefits, and work-family supports in the private sector could help narrow this well-being gap and improve overall life satisfaction across the workforce. Second, while public-sector workers enjoy higher well-being, the results also indicate that women in the private sector face persistent disadvantages that affect both their health and happiness. Policies that strengthen workplace health initiatives, promote gender-sensitive management practices, and expand access to flexible work arrangements would mitigate these disparities. For instance, promoting family-friendly workplace policies\u0026mdash;such as parental leave, childcare support, and flexible work schedules\u0026mdash;could help both men and women balance work and family responsibilities more effectively. Finally, the positive returns effects observed for women\u0026rsquo;s life and SOL satisfaction suggest that expanding women\u0026rsquo;s access to public-sector opportunities could yield substantial well-being gains. Ensuring transparent hiring practices, equal opportunity programs, and institutional pathways for women\u0026rsquo;s career development would help translate employment stability into broader happiness benefits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Limitations and Future Research\u003c/h2\u003e\u003cp\u003eDespite its contributions, this study has several limitations that suggest directions for future research. First, the analysis relies on cross-sectional data from the 2013 China Household Income Project (CHIP). While this dataset provides rich information on employment and well-being, its design limits the ability to infer causal relationships or capture changes over time. Future studies using longitudinal or more recent nationally representative data could better identify dynamic patterns of happiness and well-being across sectors.\u003c/p\u003e\u003cp\u003eSecond, although the endogenous switching model accounts for selection bias between public- and private-sector employment, unobserved factors\u0026mdash;such as personality traits, social networks, or subjective job motivations\u0026mdash;may still influence both sector choice and well-being outcomes. Incorporating psychological and attitudinal variables would provide a more comprehensive understanding of the mechanisms linking employment and happiness.\u003c/p\u003e\u003cp\u003eThird, the present study focuses primarily on gender differences, yet other intersecting social dimensions\u0026mdash;such as class, region, or age cohort\u0026mdash;may further shape well-being in China\u0026rsquo;s rapidly transforming labor market. Future research could adopt an intersectional approach to explore how multiple identities interact with institutional structures to influence subjective well-being.\u003c/p\u003e\u003cp\u003eFinally, while this study emphasizes individual outcomes, future work could examine the organizational and policy-level factors that create or reduce well-being disparities across sectors. Comparative analyses across countries or institutional regimes would further clarify how governance quality, labor policies, and cultural norms shape the public-sector happiness premium. The use of CHIP 2013 data represents a limitation, as it may not capture more recent labor-market developments and post-pandemic structural shifts. Nevertheless, the dataset remains one of the most comprehensive and methodologically rigorous sources on Chinese employment and individuals\u0026rsquo; well-being. Its breadth and representativeness provide a strong foundation for analyzing sectoral and gendered differences that continue to hold theoretical and policy relevance. Future research could build on these findings using newer data to explore how evolving labor-market conditions and institutional reforms shape contemporary patterns of happiness and well-being.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Conclusion\u003c/h2\u003e\u003cp\u003eIn conclusion, this study demonstrates that gender and employment sector jointly shape subjective well-being in China. Public-sector employment confers a consistent happiness advantage, but the sources of this advantage differ across life domains and between men and women. Institutional environments play a central role in amplifying or constraining the well-being returns to individual and job characteristics. By addressing sectoral disparities, strengthening private-sector protections, and advancing gender-sensitive labor policies, policymakers can foster greater equity and happiness in the workforce. These findings highlight that happiness is not only a personal pursuit but also a reflection of the institutional contexts in which people live and work.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.X. and Y.R. conceptualized the study and wrote the main manuscript text. T.L. contributed to the theoretical framing and refinement of arguments. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eInformation about Chinese Household Income Project (CHIP) 2013 is available via University of Michigan\u0026rsquo;s Inter-university Consortium for Political and Social Research (ICPSR) and Beijing Normal University \u0026mdash; China Institute for Income Distribution (CIID).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbreu, M., Oner, O., Brouwer, A., \u0026amp; van Leeuwen, E. (2019). Well-being effects of self-employment: A spatial inquiry. \u003cem\u003eJournal of Business Venturing\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 589\u0026ndash;607.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecchetti, L., \u0026amp; Conzo, G. (2022). 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Life satisfaction in China, 2010\u0026ndash;2018: trends and unique determinants. \u003cem\u003eApplied Research in Quality of Life\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), 2311\u0026ndash;2348.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Happiness, Well-being, Gender, Public vs. Private Sector Employment","lastPublishedDoi":"10.21203/rs.3.rs-8020850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8020850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how employment sector and gender jointly shape happiness and subjective well-being (SWB) in China, focusing on life satisfaction, standard-of-living (SOL) satisfaction, and health. Using data from the China Household Income Project (CHIP), the analysis combines regression models and an endogenous switching framework to account for sectoral selection and returns effects. The results reveal a consistent \u003cem\u003epublic-sector happiness premium\u003c/em\u003e, with employees in the public sector reporting higher well-being than those in the private sector. However, the sources of this advantage differ by gender and well-being domain. For both men and women, higher life satisfaction in the public sector stems primarily from institutional returns rather than compositional differences in individual characteristics. Counterfactual analyses further show that private-sector workers would experience greater life satisfaction if employed in the public sector, underscoring the desirability of public employment in China\u0026rsquo;s labor market. For SOL satisfaction, the decomposition results differ by gender, indicating that the sources of the premium vary between men and women. Education, marriage, and job tenure are positively associated with well-being, while long working hours reduce satisfaction, especially in the public sector. The findings highlight how institutional context and gendered experiences jointly shape happiness, offering theoretical insights into the gender well-being paradox and practical implications for promoting gender equity and enhancing happiness.\u003c/p\u003e","manuscriptTitle":"The Happiness Premium? Gender and Employment Sector Differences in Well-Being in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 17:36:52","doi":"10.21203/rs.3.rs-8020850/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88e2be60-f4f8-4259-b6e0-696f9c8a9112","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-12T09:14:32+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T09:28:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 17:36:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8020850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8020850","identity":"rs-8020850","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}