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The intergenerational production of health in South Korea Veenstra, Gerry; Jeong, B. G Sep 30, 2016

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1  Title  The intergenerational production of health in South Korea   Authors  Gerry Veenstra, Ph.D. Department of Sociology University of British Columbia, Canada Vancouver, Canada  Baekgeun Jeong, Ph.D. Department of Social and Preventive Medicine Gyeonsang National University, South Korea Jinju City, South Korea   Correspondence  Dr. Gerry Veenstra Department of Sociology The University of British Columbia 6303 N. W. Marine Drive Vancouver, BC, V6T 1Z1 Email: gerry.veenstra@ubc.ca    2  Abstract Informed by Bourdieu and Passeron’s theory of reproduction, we investigated relationships between the cultural and economic capitals of South Korean parents and the capitals and self-rated health of their adult children in cross-sectional survey data from the 2006 Korea Welfare Panel Study. Parental education and childhood economic circumstances manifested independent associations with the self-rated health of the adult respondents. Much of the connection between parental capitals and respondent self-rated health was explained by respondent education. Educational attainment and household income but not receipt of monetary inheritance from their parents were significantly associated with the self-rated health of the respondents. Lastly, parental educational attainment was associated with the self-rated health of the women but not the men over and above the capitals held by the respondents themselves. Our study illuminates the importance of the intergenerational transmission of multiple forms of capital in the production of good health for South Korean adults. In particular, we propose that wealthy and well-educated South Korean parents employ their capitals to develop math and linguistics skills in their children that facilitate good performance in the standardised university entrance exams, success in the labour and/or marriage markets and, ultimately, good self-rated health for their adult children.  Keywords: South Korea; health inequalities; life course processes; childhood circumstances; socioeconomic status; self-rated health   3  Introduction In Reproduction in Education, Society and Culture (1990 [1970]), Pierre Bourdieu and Jean-Claude Passeron argue that the educational system promulgates an essentially arbitrary cultural scheme which appears benign but is in actuality founded upon relations of power. Drawing upon the resources at their disposal, especially cultural capital (educational credentials and socially valued cultural talents and tastes) and economic capital (property and monetary assets), parents strategically cultivate valued cultural skills and talents in their children. These skills and talents facilitate achievement in the educational system, the acquisition of valued and valuable educational credentials and, ultimately, monetary success in the labour market. In other words, one of the primary means by which parents transmit capital to their children is through the development of cultural skills and talents which are valorised by the educational system. This process, a means of intergenerational capital transmission that is less straightforward and more risky but potentially more efficacious than monetary inheritance, functions to perpetuate socioeconomic inequalities and limit social mobility across generations. We propose that the intergenerational transmission of capitals is also implicated in health inequalities. Educational attainment, income and wealth are all strongly associated with health in contemporary societies (Adler & Newman, 2002; Braveman, 2010), and any social process that produces an unequal distribution of these socioeconomic resources is partly responsible for the health inequalities that accompany inequalities in resources. In particular, we suggest that the socioeconomic standing of parents can affect the health of their adult children over the long term via the children’s educational attainment and ensuing accumulation of financial resources in the labour and/or marriage markets. We test this hypothesis in the South Korean context by investigating whether and how the cultural and economic capitals of Korean parents are associated 4  with the cultural and economic capitals of their adult children and, ultimately, the adult children’s self-rated health. Originally applied to French society, Bourdieu and Passeron’s reproduction framework has since been subjected to the criticism that, while it may apply in France where the educational system is tightly linked to political and economic institutions, it may not have as much traction in societies that are characterised by less influential educational systems. In South Korea, however, the educational system is extremely rigid in its structure and streams students in a manner that strongly affects the nature of their participation in the labour market (Lee & Brinton, 1996; Sorensen, 1994). Parents closely monitor their children’s progress at school and many provide private tutoring for their children to prepare them for the highly competitive university entrance exams (Dawson, 2010). Indeed, South Korean society places such a high priority on university degrees and consequently the university entrance examination that there are few other legitimate avenues for success in that country (Wollam, 1992). Strong connections between family socioeconomic histories, university credentials, marital prospects and occupational careers (Byun & Kim, 2010; Chung, 1994; Lee & Brinton, 1996) make South Korea an especially appropriate context in which to apply Bourdieu and Passeron’s theory of reproduction to socioeconomic health inequalities. The nature of the cultural skills and talents that are fostered by middle- and upper-class parents and valorised by educational systems is another issue of contestation in the sociological literature. Bourdieu and Passeron (1990 [1970]) stressed the importance of the cultivation of highbrow talents and skills, including linguistic skills, in middle- and upper-class families for promulgating intergenerational inequality. Other researchers, however, have found little evidence that highbrow culture mediates associations between parental capitals and the performance of 5  children in schools (De Graaf et al., 2000; DiMaggio, 1982; Katsillis & Rubinson, 1990; Milne & Aurini, 2015), indicating that the role of highbrow culture in these processes may be overstated. Rather, these scholars have identified the importance of “concerted cultivation” on the part of middle- and upper-class parents, namely, the active teaching and modelling of problem solving, critical thinking and negotiation skills that comply with schools’ standards of behaviour (Lareau, 2002; Lareau, 2011; Milne & Aurini, 2015). The qualities of the cultural skills and talents that are fostered by higher class parents and valorised by the education system in South Korea have yet to be illuminated, but we suspect that skills that facilitate success in the university entrance examinations are particularly salient in this regard. Although numerous studies have uncovered associations between socioeconomic status and health for adult Koreans (Hanibuchi et al., 2010; Jung-Choi et al., 2011; Khang et al., 2004), the degree to which parental capitals are antecedent to these associations has not yet been investigated. In addition, while a small number of studies have uncovered associations between the socioeconomic status of Korean parents and the health of their young children (Ahn et al., 2012; Kwon & Ku, 2010; Park & Jang, 2013; Yoon et al., 2007), whether parental capitals shape the capitals and then the health of their adult children remains unexamined. We use nationally representative survey data from the Korean Welfare Panel Study to investigate the effects of processes of intergenerational capital transmission on adult self-rated health. Utilising indicators of both parental and personal educational attainment and economic circumstances as well as a unique measure of financial inheritance from parents to children enables us to examine multiple capital transmission pathways. Our study adds to the growing literature on socioeconomic health inequalities in South Korea by uncovering some of the effects of the intergenerational transmission of capitals on the health of South Korean adults. 6  Methods Survey sample We use publicly available data from the 2006 wave of the Korea Welfare Panel Study which was conducted by the Korean Institute of Social and Health Affairs with a response rate of 71.3%. The publicly available nature of these data precludes the need for formal ethical review according to the policies of the Research Ethics Board at the University of British Columbia. We restricted our investigation to survey respondents (n = 13,645) who were twenty-five years of age or older at the time of the survey in order to produce relatively stable measures of educational attainment. We further restricted our analyses to the 5,981 men and 7,045 women (95.5% of the entire sample) who provided valid information for all of the variables used in our study. Table 1 describes some of the characteristics of our working samples.  Survey measures In regards to cultural capital, respondents were asked about their mother and father’s levels of educational attainment from which we created a single variable assessing parental highest level of education. We also assessed the cultural capital of the survey respondents themselves in the form of personal highest level of educational attainment. Both variables distinguished between seven levels of education: did not complete elementary school, completed elementary school, completed middle school, completed high school, completed technical college, completed university and completed graduate school. We combined the elementary and middle school categories and the university and graduate school categories in our regression modelling for reasons of sample size. 7  To assess parental economic capital, respondents were asked “What were your economic living conditions during childhood (1–18 years of age)?” with response categories ‘very poor,’ ‘poor,’ ‘average,’ ‘rich’ and ‘very rich.’ We combined the latter two categories for reasons of sample size. The current household incomes of respondents were also calculated from a series of questions assessing income from multiple sources including employment, investments, pensions, social insurance, etc. Assessed in 10,000,000 won units, this right-skewed continuous variable ranged from a low of zero to a high of 30.1. We used the natural logarithm of this variable in our regression models. Respondents were also asked “Have you ever received an inheritance or donation from your parents?” to which they could reply ‘yes’ or ‘no.’ Most respondents would understand this question to be referring to inheritances of land, houses or money when a parent passes away or substantial gifts when a child marries and leaves home, buys a new house or apartment, starts a new business or has accrued large debts. It would not normally pertain to small monetary gifts, funding a child’s education or paying for a wedding ceremony or honeymoon. Lastly, respondents were asked to describe their overall state of health by choosing one of the following response options: ‘very healthy,’ ‘healthy,’ ‘moderately healthy,’ ‘unhealthy’ or ‘very unhealthy.’ We dichotomised this variable for use in our models by assigning 1 = unhealthy or very unhealthy (24.1% of men and 35.3% of women) and 0 = moderately healthy, healthy or very healthy.  Statistical methods We created a series of binary logistic regression models on the dichotomised self-rated health variable separately for men (Table 2) and women (Table 3). The first model in each set describes 8  associations between self-rated health and both parental education and childhood economic circumstances while controlling for age, gender and marital status. The second model adds personal education to the first model, the third model adds receipt of inheritance money to the second model and the fourth model adds household income to the third model. This sequence of models enables us to identify independent associations between each of parental cultural capital and economic capital and self-rated health and then investigate the degree to which various forms of capital held by the respondents are potentially mediating factors. However, the problem of residual variance in logistic regression means that changes in regression coefficients across nested models can reflect changes in the scaling of the dependent variable (Mood, 2009). Accordingly, we also applied the KHB method of decomposing effects in non-linear probability models (Karlson et al., 2011; Karlson & Holm, 2011) via the khb command in Stata (Kohler et al., 2011) when investigating mediation. In all of our models we applied the master weight variable provided with the Korea Welfare Panel Study in order to produce estimates which are representative of the adult South Korean population.  Results Model 1 in Table 2 indicates that parental education and childhood economic circumstances manifest independent and statistically significant associations with the likelihood of reporting ill health for men. The odds ratio comparing uneducated parents to parents with a university degree is modest at 1.80 (95% CI=1.06-3.03). The odds ratio comparing parents who were very poor to parents who were rich or very rich is also modest at 1.44 (95% CI=1.04-1.99). The corresponding odds ratios for women (Model 1 in Table 3) are 3.49 (95% CI=2.20-5.54) and 1.55 (95% CI=1.15-2.08), respectively. These results indicate that the cultural and economic capitals of parents are 9  independent correlates of adult self-rated health for both men and women. We calculated predicted probabilities from Model 1 using the margins (atmeans) command in Stata to address the problem of residual variance that prevents direct comparisons of odds ratios for men and women (Mood, 2010). Predicted probabilities of 7.99% (95% CI=4.41-11.56) and 6.32% (95% CI=3.72-8.92) for university educated parents and 13.49% (95% CI=11.48-15.49) and 19.05% (95% CI=16.61-21.49) for uneducated parents, among men and women, respectively, indicate that parental education is more strongly related to self-rated health among women than among men. Model 2 in Table 2 indicates that personal education has a strong association with self-rated health among men, with the comparison between the least and best educated respondents producing a large odds ratio of 4.41 (95% CI=2.97-6.57). The corresponding odds ratio for women is also large at 5.89 (95% CI=3.60-9.64). Sizeable declines in the odds ratios for parental education and childhood economic circumstances from Model 1 to Model 2 in both tables suggest that much of the association between parental resources and the self-rated health of the respondents is explained by the educational attainment of the respondents. For men, the KHB decomposition indicates that 64.9% of the association between parental education (most versus least educated) and self-rated health is caught up in the educational attainment of the respondents; for women this percentage is 47.4%. Parental education retains a statistically significant association with self-rated health over and above the educational attainment of the respondents themselves for women but not for men. These models indicate that parental and personal educational credentials manifest independent and interconnected associations with the self-rated health of adult women. Among adult men, by contrast, parental educational credentials are not associated with self-rated health over and above personal educational credentials. 10  In regards to economic capital, the receipt of inheritance or donation from parents has no independent association with the self-rated health of respondents over and above the other capitals in the model (Model 3 in Table 2 and Model 3 in Table 3). However, Model 4 indicates that household income has a strong association with self-rated health for men (OR=0.38, 95% CI=0.31-0.47) and for women (OR=0.47, 95% CI=0.40-0.55). The declines in effect size for respondent education from Model 3 to Model 4 in both tables suggest that some of the association between respondent education and self-rated health is explained by respondent income. For men, the KHB decomposition indicates that 34.6% of the association between respondent education (most versus least educated) and self-rated health is caught up in the incomes of the respondents; for women this percentage is 28.4%. These results indicate that the cultural and economic capitals of the respondents themselves manifest independent and interconnected associations with self-rated health. Finally, Model 4 in Table 2 indicates that, for men, the capitals held by the parents during childhood socialisation manifest little to no association with the self-rated health of the respondents over and above the capitals held by the respondents themselves. This suggests that associations between parental capitals and self-rated health are almost entirely entangled with the accumulation of the men’s own stores of capitals. For women, however, parental educational attainment retains an association with self-rated health above and beyond the capitals held by the women themselves, suggesting that associations between parental capitals and self-rated health are partly attributable to factors other than the economic and cultural capitals held by the women in adulthood.    11  Discussion Personal education and household income are both positively and strongly related to self-rated health in our representative sample of South Korean adults. This is unsurprising in light of previous Korean research which has established strong relationships between socio-economic status and various health indicators (Jung-Choi et al., 2011; Khang et al., 2004) including self-rated health (Hanibuchi et al., 2010; Kim & Ruger, 2010). Strong links between the education system, labour market opportunities, marital prospects and social status in South Korea (Chung, 1994; Lee & Brinton, 1996; Sorensen, 1994) provide the backdrop to our finding that, for both men and women, some of the association between education and self-rated health is attributable to household income. The persistence of associations between education and self-rated health after controlling for income suggests that additional factors, perhaps including health-related practices such as exercise, moderate alcohol consumption and smoking (Ross & Wu, 1995; Sander, 1999), may be applicable as well. The persistence of an association between income and self-rated health after controlling for education suggests that material factors, perhaps pertaining to the quality of health care, nutrition or housing (Adler & Newman, 2001; Shibuya et al., 2002; Braveman, 2010), may also be germane.  But personal education and household income are not randomly distributed in Korean society: parental capitals and processes of socialisation during childhood are key to the acquisition and accumulation of capitals by adults. Linking parental capitals (education and economic circumstances) and personal capitals (education, household income and receipt of monetary inheritance) in a suppositional multilink causal chain, we find that a significant portion of the association between parental capitals generally writ and self-rated health is explained by the education of the adult children. This suggests that expending capital in pursuit of quality and 12  prestigious education for the children of South Korean pays off in the health of their adult children. In other words, the intergenerational transmission of capitals via the education system appears to be an important process in the production of good health among adults in South Korea. How does this occur, that is, which specific cultural skills and talents of children fostered by parents during childhood socialisation are implicated in this process? Because of the centrally important gatekeeper function of the university entrance examinations in South Korean society, those skills which pertain to the cornerstones of the standardised exams are centrally important parts of the intergenerational capital transmission process in this nation, these being Korean language skills, English language skills and math skills (Kim & Lee, 2002). In regards to Korean language skills, the linguistic capital of South Korean parents is likely directly transferred to their children during processes of childhood socialisation, a more or less concerted cultivation of linguistic proficiency in the interactions of everyday life. In regards to fostering math and especially English language skills in children, however, the concerted application of economic capital is likely of paramount importance (Lee, 2009; Park, 2009). One prohibitively expensive option for cultivating English language skills is to enrol the children in school in countries such as the United States, Canada, Australia, New Zealand, the United Kingdom or the Philippines. A less expensive option, though one that is still beyond the reach of many Korean parents, is to engage private tutoring for the children. More common are private classes in the evenings and weekends which are much less expensive than private tutoring. This suggests that the economic capital of South Korean parents is being converted into valuable cultural capital in their children via these and related venues for developing their children’s math and English skills in particular. We also find that the intergenerational transmission of capitals and self-rated health is to a degree gendered. In particular, parental education is a stronger predictor of self-rated health among 13  women than among men, an association that is fully explained by the cultural and economic capitals of the men but not the women. It is not entirely obvious to us why this should be the case. Table 1 indicates that the men in our sample are, on average, better educated than the women. Further scrutiny of our data indicates that, although the associations between parental capitals and personal education are of similar strength for men and women, men with poor or very poor parents are much more likely than women in similar circumstances to possess a high school diploma or university degree (results not shown). Taken together these findings are consistent with the observation that, “if a child is to be educated, preference is given to the son” (Chung, 1994: 504). In addition, women who attain higher education degrees typically work in lower status occupations than similarly educated men (Chung, 1994). These factors make it relatively difficult for South Korean women to achieve high social status. This in turn may make parental social status, another important source of social standing in Korean society, more important for the wellbeing of women than men. The main limitations of our study also serve to identify avenues of future research in this area. First, our measures of educational attainment, albeit standard in the health inequalities literature, are less sophisticated than we would like. In particular, in light of close ties between credentials from prestigious universities and desirable occupations in South Korea it would be helpful to know the names or level of prestige of the universities from which degrees are obtained. Second, our measures of parental economic capital and monetary inheritance are especially weak. More objective measures of childhood economic circumstances that do not rely on subjective, retrospective judgements by adult children, as well as objective information on the timing and magnitude of monetary inheritances, would be welcome in this area of inquiry. Third, it would be useful to have information on the actual cultural talents and skills of the children. As it stands we 14  can only speculate about the nature of the cultural skills and talents presumably valorised by the educational system that undergird the intergenerational processes alluded to here. Finally, our data are observational and cross-sectional and as a consequence causality cannot be confidently ascertained. In particular, we could not control for childhood health which may confound aspects of the causal storyline presented here. Longitudinal studies that traverse generations and life courses and the application of econometric techniques such as instrumental variable analysis would provide the kind of explanatory power that is currently lacking in this area of inquiry. In conclusion, we find no evidence that self-rated health is shaped by the intergenerational transmission of economic capital via monetary inheritance. However, in concert with general tenets of Bourdieu and Passeron’s theory of reproduction and recent developments in cultural capital theory, we do find reason to believe that South Korean parents employ their cultural and economic capitals to develop math and linguistic skills in their children that facilitate admission to university, subsequent success in the labour and/or marriage markets and, ultimately, good self-rated health for their adult children. The general outline of this storyline is consistent with, but not identical to, similar research from other national contexts. For example, a Finnish study found that parental education had identical associations with self-rated health for women and men, childhood economic circumstances had stronger associations with self-rated health among women than among men, and the association between parental education and self-rated health was entirely explained by personal education for women and men (Laaksonen et al., 2005). A study utilising British birth cohort data found that social class position at birth retained a significant association with self-rated health even after controlling for social class position at ages 16, 23 and 33 (Power et al., 1999). Notably, the moderating role of gender evident in our study was not replicated in the Finnish and British studies while the persistent role of parental education in adult self-rated health 15  was not replicated in the Finnish one. These contextual specificities suggest that the Confucian underpinnings of Korean society, predicated upon patriarchal relations between men and women and reverence towards elders and ancestors, play a role in shaping the effects of the intergenerational transmission of capitals on the health of the residents of the Republic of Korea.   16  References Adler N. E. & Newman K. (2002). Socioeconomic disparities in health: Pathways and policies. Health Affairs, 21, 2, 60-76. Ahn, J., Jung, Y. H., Shin, S. H., Lee, J., Sohn, J. A., Lee, J. A., Choi, C. W., Kim, E., Kim, H., Kim, B. & Choi, J. (2012). The associations of parental education level and employment status on the risks of low birth weight. Journal of the Korean Society of Neonatology, 19, 262-268. Bourdieu, P. & Passeron, J. (1990 [1970]). Reproduction in education, society and culture. Second edition. London: Sage Publications. Braveman, P. (2010). Socioeconomic disparities in health in the US: What the patterns tell us. 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Sander W. (1999). Cognitive ability, schooling, and the demand for alcohol by young adults. Education Economics, 7, 53- 66. Shibuya K., Hashimoto H. & Yano E. (2002). Individual income, income distribution, and self-rated health in Japan: Cross-sectional analysis of a nationally representative sample. BMJ, 324, 1–5. Sorensen, C. W. (1994). Success and education. Comparative Education Review, 38, 1, 10-35. Wollam, J. (1992). Equality versus excellence – the South Korean dilemma in gifted education. Roeper Review, 14, 4, 212-217. 20  Yoon, T., Kim, J., Yu, S., Kim, J., Lee, Y., Hong, Y. & Lee, S. (2007). The relationship between parental socioeconomic position and childhood mortality. Health and Medical Sociology, 20, 29-46.   21   Table 1. Characteristics of the survey sample (un-weighted data)   Men Women Variable Categories n % n %  Age less than 40 1932 32.30 2022 28.70 40 to 59 2175 36.37 2344 33.27 60 and older 1874 31.33 2679 38.03  Marital status married 4609 77.06 4631 65.73 bereaved 161 2.69 1518 21.55 separated/divorced 280 4.68 362 5.14 single 931 15.57 534 7.58  Highest parental education did not complete elementary school 2383 39.84 3082 43.75 elementary school 1686 28.19 1773 25.17 middle school 735 12.29 858 12.18 high school 827 13.83 930 13.20 technical college 71 1.19 93 1.32 university 248 4.15 280 3.97 graduate school 31 0.52 29 0.41  Economic conditions in childhood very poor 602 10.07 605 8.59 poor 2088 34.91 2090 29.67 average 2639 44.12 3319 47.11 rich 596 9.96 932 13.32 very rich 56 0.94 99 1.41  Educational attainment did not complete elementary school 445 7.44 1553 22.04 elementary school 944 15.78 1421 20.17 middle school 692 11.57 821 11.65 high school 2216 37.05 2021 28.69 technical college 417 6.97 398 5.65 university 1103 18.44 772 10.96 graduate school 164 2.74 59 0.84  Received inheritance yes 1526 25.51 1056 14.99 no 4455 74.49 5989 85.01  Household income < 1000 1313 21.95 2096 29.75 1000-1999 1342 22.44 1559 22.13 2000-2999 1124 18.79 1169 16.59 3000-3999 771 12.89 788 11.19 4000-4999 557 9.31 548 7.78 5000-5999 287 4.80 285 4.05 6000-6999 223 3.73 241 3.42 7000-7999 161 2.69 154 2.19 8000-8999 78 1.30 72 1.02 9000-999 38 0.64 39 0.55 >10000 87 1.45 94 1.33   22   Table 1. continued   Men Women Variable Categories n % n %  Self-rated health very healthy 1245 20.82 1015 14.41 healthy 2510 41.97 2527 35.87 moderately healthy 786 13.14 1017 14.44 unhealthy 1085 18.14 1906 27.05 very unhealthy 355 5.94 580 8.23   23   Table 2. Binary logistic regression models on unhealthy or very unhealthy self-rated health among men (n=5,981; weighted data)   Model 1 Model 2 Model 3 Model 4 Variable Categories OR 95% CI OR 95% CI OR 95% CI OR 95% CI Highest parental education did not complete elementary school 1.796 1.063--3.032 1.205 0.705--2.058 1.204 0.705--2.057 1.011 0.581--1.759 elementary or middle school 1.333 0.799--2.223 1.066 0.636--1.788 1.066 0.635--1.787 0.929 0.544--1.587 high school 1.274 0.729--2.226 1.175 0.672--2.055 1.176 0.672--2.055 1.061 0.594--1.893 technical college 1.261 0.548--2.900 1.273 0.555--2.919 1.272 0.554--2.917 1.325 0.562--3.123 university (reference) 1.000  1.000  1.000  1.000  Economic conditions in childhood very poor 1.438 1.040--1.990 1.071 0.765--1.501 1.074 0.766--1.506 1.142 0.809--1.611 poor 1.102 0.839--1.449 0.923 0.697--1.223 0.924 0.697--1.226 0.984 0.741--1.305 average 0.859 0.656--1.127 0.827 0.629--1.088 0.828 0.630--1.089 0.846 0.654--1.114 rich or very rich (reference) 1.000  1.000  1.000  1.000  Educational attainment did not complete elementary school   4.413 2.965--6.567 4.414 2.965--6.572 2.759 1.844--4.127 elementary or middle school   2.808 2.083--3.786 2.809 2.083--3.789 2.011 1.479--2.734 high school   1.617 1.226--2.132 1.618 1.226--2.134 1.328 0.998--1.767 technical college   1.432 0.898--2.284 1.431 0.898--2.282 1.253 0.780--2.011 university (reference)   1.000  1.000  1.000  Received inheritance yes     1.011 0.832--1.229 1.004 0.823--1.225 no (reference)     1.000  1.000  Log household income …       0.382 0.314--0.465  Chi-square (p)   753.53 (<0.001)  826.58 (<0.001)  827.83 (<0.001)  864.51 (<0.001)  Each model controls for age, age squared and marital status.    24   Table 3. Binary logistic regression models on unhealthy or very unhealthy self-rated health among women (n=7,045; weighted data)    Model 1 Model 2 Model 3 Model 4 Variable Categories OR 95% CI OR 95% CI OR 95% CI OR 95% CI Highest parental education did not complete elementary school 3.488 2.198--5.535 1.947 1.187--3.194 1.924 1.172--3.158 1.884 1.137--3.123 elementary or middle school 2.733 1.730--4.317 1.901 1.170--3.091 1.881 1.156--3.059 1.865 1.135--3.063 high school 1.823 1.098--3.026 1.528 0.903--2.586 1.515 0.894--2.568 1.499 0.875--2.568 technical college 2.295 1.076--4.894 2.298 1.063--4.969 2.282 1.056--4.937 2.184 1.007--4.736 university (reference) 1.000  1.000  1.000  1.000  Economic conditions in childhood very poor 1.548 1.154--2.076 1.190 0.877--1.613 1.171 0.862--1.591 1.135 0.833--1.548 poor 1.164 0.933--1.453 0.977 0.776--1.229 0.967 0.768--1.218 1.024 0.811--1.293 average 0.867 0.699-1.077 0.820 0.656--1.024 0.811 0.648--1.015 0.851 0.678--1.068 rich or very rich (reference) 1.000  1.000  1.000  1.000  Educational attainment did not complete elementary school   5.889 3.596--9.642 5.792 3.527--9.511 3.660 2.198--6.094 elementary or middle school   3.618 2.308--5.670 3.575 2.276--5.614 2.549 1.607--4.041 high school   2.013 1.299--3.118 2.000 1.290--3.101 1.611 1.033--2.514 technical college   1.772 0.895--3.526 1.771 0.890--3.5224 1.542 0.780--3.047 university (reference)   1.000  1.000  1.000  Received inheritance yes     0.877 0.696--1.105 0.869 0.689--1.096 no (reference)     1.000  1.000  Log household income …       0.469 0.399--0.550  Chi-square (p)   1043.87 (<0.001)  1095.06 (<0.001)  1093.53 (<0.001)  1253.59 (<0.001)  Each model controls for age, age squared and marital status.   

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