The effect of ưomen''''s education on fertility in Vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTIUTTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM- NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE EFFECT OF WOMEN'S EDUCATION ON FERTILITY IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By LE HOANG THIEN KIM r Academic Supervisor: DR NGUYEN HUU DUNG - ··,, CERTIFICATION I certify that the substance of this thesis has not already been submitted for any degree and is not being current submitted for any other degree I certify that to the best of my knowledge any help received in preparing this thesis and all sources used, have been acknowledged in this thesis I LE HOANG THIEN KIM • ACKNOWLEDGEMENT Firstly, I would like to thank my academic supervisor Dr Nguyen Huu Dung for his valuable advice, comments and making reference materials available to me Particularly, thanks to these worthy instructions and kindly help from him, I can complete the research , I greatly appreciate Mr Truong Thanh Vu for his technique assistance and valuable comments to the study Many thanks are respectfully sent to my parents, my husband who are always encourage and sympathize with me I would like to thank to all teachers and staffs of the Vietnam- Netherlands programme at University of Economics HCM Finally, I am indebted to Measure DHS Office - ICF Macro, especially Bridgette James- data archive administrator for their assistance and permission to access VDHS 2002 data so that I can complete my thesis " 11 TABLE OF CONTENTS CERTIFICATION i A CKN0 WLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES AND TABLES vi ABBREVIATIONS vii ABSTRACT viii CHAPTER ONE: INTRODUCTION ! 1.1 Problem Statement 1.2 Research objectives 1.3 Research questions 1.4 Hypotheses 1.5 Research Methodology 1.6 Structure of the thesis CHAPTER TWO: LITERATURE REVIEW 2.1 Definitions, concepts related to fertility and its measures 2.2 Theoretical framework, empirical studies related to determinants of fertility • 2.2.1 Theoretical framework 2.2.1.1 Household demand model 12 2.2.1.2 Demand- supply framework 16 2.2.2 Empirical studies related to effects of women's education on fertility 20 2.3 Summary 25 CHAPTER THREE: RESEARCH METHODOLOGY 26 3.1 Structure of the VDHS 2002 26 111 3.2 Data set 27 3.3 Model specification 28 3.4 Description of variables in the model 29 3.4.1 Dependent variable 29 3.4.2 Independent variables 29 3.5 Estimation strategy 34 3.5.1 Poisson regression model (PRM) 35 3.5.2 Factor change in E(yJx) 36 3.5.3 Percent change in E(yJx) 37 3.6 Chapter summary 37 CHAPTER FOUR: SOCIOECONOMIC CONTEXT AND PROFILES OF WOMEN'S FERTILITY 39 4.1 Geography and economy 39 4.2 Population and family planning policies and programs 41 4.2.1 Population 41 4.2.2 Family Planning Policies and Programs 43 4.3 General characteristics of women's fertility 44 4.4 Differentials in education level of women 46 4.5 Family planning message 47 4.6 Children ever born 48 Chapter summary 49 " CHAPTER FIVE: FACTORS AFFECT WOMEN'S FERTILITY IN VIETNAM 50 5.1 Empirical model 50 5.2 Estimation results 50 5.3 Summary 55 CHAPTER SIX: CONCLUSION AND RECOMMENDATIONS 56 IV 6.1 Conclusions 56 6.2 Recommendations 57 6.3 Further Research 58 REFERENCES APPENDIX v LIST OF FIGURES AND TABLES Table 2.1: Narratives on the determinants offertility Table 2.2: Intervening variables in Cochrane's model on education and fertility 19 Table 4.1: Basic demographic indicators .42 Table 4.2: Distribution of ever-married women by background characteristics (%), Vietnam 2002 45 Table 4.3: Level of education of ever-married women, Vietnam 2002 (%) .46 Table 4.4: Exposure to family planning messages on radio and television (%) .47 Table 4.5: Children ever born by ever-married women aged 15-49, classified by place of residence and education level .48 Table 5.1: Poisson Regression Results- Fertility model 63 Figure 2.1: Key variables and interrelations in a variant of Easterlin 's supply demand model 19 VI ABBREVIATIONS ASFR Age-specific fertility rate CBR Crude birth rate CEB Children ever born GFR General Fertility rate GSO General Statistical Office PRM Poisson regression model TFR Total fertility rate US AID U.S Agency for International Development VDHS Vietnam Demographic and Health Survey / Vll ABSTRACT There are numerous studies indicate that women's education plays an important role in number of children ever born This thesis aims to explore the effect of women's education on fertility in Vietnam by using the 2002 Vietnam Demographic and Health Survey Given the characteristics of observed fertility pattern, the study applied a count data model, namely, Poisson regression to examine the effects of women's education and other determinants on fertility The major finding of the study is that women's education poses a strong effect to reduce children born in Vietnam The higher the educated women, the lower the expected number of children Similarly, education level of husband or partners also influence the change in the number of children Other determinants of importance in the study show that the higher the age and age of giving first birth, the lower the number of children ever born Public program and knowledge such as of ovulatory cycle and family planning positively help reducing fertility Women live in the rural areas still have a higher number of children than that of women in the urban areas Recommendation for public policies and women health governance in Vietnam should focus more on the education for low-educated women, improving related knowledge of family planning, especially in the rural areas vm CHAPTER ONE INTRODUCTION 1.1 Problem Statement Population growth and socioeconomic development are an important issue to Vietnamese policy makers and development planners Vietnam has clearly made significant progress in slowing its rapidly population growth The decline in fertility has been one of the most important demographic changes in recent years The key element behind the change in population in Vietnam is considered as a result at fertility level Many policies to reduce population growth received increasing attention of the government and efforts to extend coverage of birth control services In January 1993, the Communist Party Central Committee for the first time approved a resolution on population and family planning The resolution proposed the objective of "applying small-sized family," and recommended that "each family should have one or two children" in order to lower fertility and stabilize population At the micro level, high population growth leads to a more serious issue of poverty Poorer families, especially women bear the burden of a large number of children with fewer resources per child, further adding to the spiral of poverty Low levels of income among the poorer families with many children leads to inadequate food availability, which perpetuates malnutrition, which in tum accelerates high levels of infant mortality Studies by Ernst and Angst (1983), King (1985) have widely reviewed the relationship between family size, education and the health of children Among poorer families, beyond a certain family size, additional children statistically significant at the five percent level Only two variables, namely, hus_ edprimar and hus_ edsecond are not statistically significant The results show that women's education significantly reduces the number of children born per women All the measures of women's education are strongly significant and the coefficients signs are negative If a woman has only primary education, the expected number of children born decreases by a factor of 0.837, holding all other variables constant If a woman has secondary education, the expected number of children born decreases by a factor of 0.768, and if a woman has higher education, the expected number of children born decrease by a factor of 0.700, holding all other variables constant For a standard deviation increase in women education , the number of children born decreases by a factor of 0.924, 0.880 and 0.934 for primary, secondary and higher education respectively, holding all variables constant Another interpretation of the result is that for a woman holding primary, secondary and higher education, the expected number of children born decreases by 16 percent, 23 percent, 30 percent respectively (see Table A3 in the appendix) The results are consistent with previous studies [Ainsworth et a/.(1996), Martin and Juarez (1995)] However, the negative magnitude of the coefficient of women with higher education is higher than women's primary and secondary schoolings Thus, the negative effect of women's education on fertility gets larger and larger with the increase of education levels The knowledge of ovulatory cycle (know) is also strongly significant to lower number of children born per woman The coefficient on know variable is 51 - - negative that means the knowledge of ovulatory cycle increases, the number of children per woman will decrease The effect of this coefficient on the expected number of children shows that if a woman has knowledge of ovulatory cycle, the expected number of children born decreases by a factor of 0.958, holding all other variables constant In other words, the expected number of children will decrease by 4.2 percent This is an important finding because women may control reproductive behavior better when they have better knowledge of ovulatory cycle Family planning programs are very important in explaining a country's fertility rate and are expected to have a negative effect on fertility The variable plan is significant and has expected sign The marginal effect of the coefficient indicates that if a woman heard from local family planning program, the expected number of children born decreases by a factor of 0.961 or 3.9 percent, holding all other variables constant We can explain for this result as local family planning program was launched in 1993 by the government and it is interested to reduce large family size in Vietnam The coefficient of place is negative and significant at one percent level, denoting a negative relationship between women who live in urban areas and number children ever born per women The marginal probability suggests if a woman live in rural area, the expected number of children born decreases by a factor of0.818, holding all other variables constant This is supported by Ainsworth et al (1996), who determined that urban women have lower fertility than rural women The result is also consistent with Adelman (1963), who found out that 52 "socioeconomic phenomena associated with the urbanization process tend to reduce birth rates in the long run" The variable age_birth has a negative coefficient and is significant at one percent level The result shows that if the age at first birth is late, the expected number of children born decreases by a factor of 0.9437, holding all other variables constant The variables hus_ edprimar, hus_ edsecond are not statistically significant, only hus_ eduhigher is significant If a husband has a higher education, the expected number of children born decreases by a factor of 0.885 or 11.5 percent, holding all other variables constant Higher educated men tend to have fewer children because they may understand the sacrifice and bearing those women have to when they have pregnancy Finally, the coefficients and marginal change for age groups also have expected effects Numbers of children ever born increases as women become older The finding is similar to descriptive analysis in Chapter four Table 5.1 Poisson Regression Results- Fertility model Dependent variable (fert) is the number of children born per woman Variables Parameters Robust Exponent Perentage Standard error of beta change in Exp(b) dependent variable edprimar -0.178* 0.025 53 0.837 -16.3 • edsecond -0.264* 0.026 0.768 -23.2 eduhigher -0.356* 0.038 0.700 -30.0 know -0.043* 0.013 0.958 -4.2 plan -0.040** 0.018 0.961 -3.9 age_birth -0.058* 0.002 0.944 -5.6 place -0.201 * 0.013 0.818 -18.2 -0.006 0.030 0.994 -0.6 -0.038 0.030 0.963 -3.7 -0.122* 0.039 0.885 -11.5 age2024 0.395* 0.033 1.484 48.4 age2529 0.809* 0.031 2.247 124.7 age3034 1.109* 0.030 3.033 203.3 age3539 1.354* 0.031 3.872 287.2 age4044 1.565* 0.031 4.780 378.0 age4549 1.721 * 0.032 5.594 459.4 cons 1.324* 0.048 hus_ edprimar hus edsecond hus_ eduhigher Notes: n=5381; * denoted significant at 1% level **denoted significant at 5% level exp(b) = factor change in expected count for unit increase in X exp(b*SD of X)= change in expected count for SD increase in X SDofX = standard deviation of X 54 5.3 Summary The chapter presented and analyzed the research findings on effect of women education on fertility in Vietnam using the data set from VDHS2002 A reducedform equation is estimated using Poisson regression The study reveals some important findings First, women education has a strong negative effect on fertility as expected The number of children born increases by age of women and women who live in rural areas have more children than women living in the urban From these findings, some recommendations will be suggested in the next chapter 55 CHAPTER SIX CONCLUSION AND RECOMMENDATIONS 6.1 Conclusions This study has examined the effects of women's education and other factors on fertility in Vietnam It finds strong support for negative correlation between women's education and fertility The findings answer very important question of how women's education, which rarely addressed issues relevant to reproductive and contraception behavior, influences fertility According to Becker et a! (1992) women's education raises their labor participation which in tum raises their earnings, "and hence greater investment in market-oriented skills" which increases women's time value The empirical results are consistent with previous results on household fertility decision in the literature review and corroborate the importance of women's education in family fertility decision The research has used Poisson regression model based on count data The results show that estimated coefficients of all variables on number of children ever born have the expected sign and both women's education and husbands' higher education have statistically significant effects on number of children born Among the socio-economic variables in the model, women's education is found to be the most powerful predictor of fertility decline Weeks (2005) who claims that an increase in education is strongly associated with rational decisionmaking, encourages the diffusion of an innovation such as fertility limitation, offers 56 to people a view of a world that expands their horizon beyond the boundaries of traditional society to re-evaluate the role of women in society The findings also show that women's education pose significant effect to the number of children born Results from Poisson regression indicate that women holding higher education , residence in urban areas has significant negative effect on fertility Higher educated women should be able to implement their fertility preferences including their higher degree of autonomy in reproductive decisionmaking as well as cooperation from their husbands The educated women are likely to live in urban areas and they have lower fertility due to access of having family planning services and other modernization effects Hence, the government should take necessary steps to improve the family planning services in the rural areas 6.2 Recommendations From the result, I emphasize the importance of improving access to education as a way of empowering women and creating more health and family planning framework, especially in rural areas where the effects of education are stronger We find that education has a strong effect for a woman to decide the number of children she has Therefore, investment to women's education must be the objective of fertility reduction To execute this purpose, there are some recommendations drawn from this study as follows: The government should put more efforts in promoting women's education, expanding family programs The promotion of women's education will empower 57 women to make individual decisions and increase their participation in employment where they are exposed to ideas and attitudes to desire smaller family • Providing the employment and earning opportunities for educated young women Besides, the government should have regulations protect women from gender inequality in all aspects of society, beginning with childhood 6.3 Further Research The findings of the study give rise to implication for future research on • fertility in Vietnam might be extended First, exogenous measures of the quality of education are not among those available in the individual VDHS data set The unavailability of that information forces this study and previous study to use either • year of schooling or education levels to capture the effect of education on fertility The quality of education depends on a variety of factors including a country's education system Additional research needs to take a closer look at how different education quality behaves differently in explaining fertility in developing countries where there is more diversity in the quality of education Moreover, VDHS also does not collect on income or household resources So we are difficult to find the effect of income and fertility supporting the qualityquantity theory The association between household income and number of children born per woman is probably the most important economic explanation of decreasing fertility rate Researchers should conduct a survey for themselves or modified data to their study purpose 58 REFERENCES Adelman, Irma June (1963) An econometric analysis of population growth The • American Economic Review, 53(3), pp 314-339 Ainsworth 1989 Socioeconomic Determiants of Fertility in Co6te d'Ivoire Living Standard Measurement Study, Working Paper No.53 Washington DC: The World Bank Ainsworth, Martha & Beegle, Kathleen & Nyamete, Andrew, 1996 "The Impact of Women's Schooling on Fertility and Contraceptive Use: A Study of Fourteen SubSaharan African Countries," World Bank Economic Review, Oxford University Press, vol 10(1), pages 85-122, January Axinn, William G, Marin E Clarkberg, and Arland Thornton February (1994) Family influences on family size preferences Demography, 31(1) pp 65-79 • Becker Gary.S 1960 An Economic Analysis of Fertility in Demographic and Economic Changes in Developed Countries, NBER, Princeton University Press, Princeton, NJ Becker and Lewis H Gregg 1973 "On the Interaction Between the Quantity and Quality of Children" Journal ofPolitical Economy, vol 81 :279··299 Gary S Murphy Becker & Kevin M., 1992 "The Division of Labor, Coordination Costs, and Knowledge," University of Chicago- George G Stigler Center for Study of Economy and State 79, Chicago - Center for Study of Economy and State Bledsoe, Caroline H., Jennifer A Johnson-Kuhn, and John G Haaga (Eds.) 1999 Critical perspectives on schooling and fertility in the developing world National Washington, D.C: Academy Press Bongaarts J and G.R Potter, 1983 Fertility, Biology and Behavior: An Analysis of the Proximate determinants, Academic press, New York Bongaarts, J., Frank, 0., and Lesthaeghe, R 1984 The proximate eterminants of fertility in Sub-Saharan Africa Population and Development Review,10(3) pp.511537 59 Bongaarts, J 1993 The supply-demand framework for the determinants of fertility: An alternative implementation Population Studies 47, no.3: 437-456 Cameron and Johansson 1997 Count Data Regression Using series Expansions with Application , Journal of Applied Econometrics, vol 12: 203-224 Cochrane, Susan H 1979 Fertility and education: What we really know? Baltimore:Johns Hopkins University Press Davis K., Blake J 1956 Social Structure and Fertility: An Analytical Framework, Economic Development and Cultural Change, 4(4), 211-235 Duncan, Dudley Otis, Ronald Freedman, J Michael Cobble and Doris P Slesinger 1965 Marital fertility and size of family of orientation Demography, 2, pp 508515 Easterlin, Richard A., 1975 An Economic Framework for Fertility Analysis Studies In Family Planning, Vol 6, No.3 (Mar 1975), pp.54-63 Ernst, C., and J Angst 1983 Birth order: Its influence on personality New York: Springer-Verlag Gujarati, D.N.2003 Basic Econometrics New York: Mc-Graw-Hill/Irwin Hank, Karsten and Kohler Hans-Peter May 2002 Gender preference for children revisited: New evidence from Germany MPIDR WORKING PAPER WP 2002-017 (http://www demogr.mpg.de) J Boher and R.J Cook 2004 "Implications of Misspecification Among Robust Tests for Recurrent Events." Working Paper, Department of Statistics and Actuarial Science, University ofWaterloo, 17-18,23 Jain A.K.et al 1981 The effect of female education on fertility: A simple explanation, Demography, Vo/.18, No.4, Nov 577-595 Khan, M Asaduzzaman and Parveen A Khanum September 2000 Influence of son preference on contraceptive use in Bangladesh Asia-Pacific Population Journal, 15(3), pp 43-56 i King, E.M 1985 Consequences of population pressure in the family welfare.Background paper prepared for the Working Group on Population Growth 60 and Economic Development, Committee on Population, National Research Council, Washington, D.C Long, J Scott 1997 Regression Models for Categorical and Limited Dependent Variables Thousand Oaks, California: SAGE Publications, Inc Martin, C., and Juarez, F 1995 The impact of women's education on fertility in Latin America: Searching for explanations International Family Planning Perspectives, 21(2), pp 52-57+80 Mensch, Arends-Kuenning, and Jain 1996 "The Impact of the Quality of Family Planning Services and Contraceptive use in Peru." National Committee for Population and Family Planning 2003 Demographic and Health Survey 2002, Ha noi Nguyen-Dinh, H 1997 A socioeconomic analysis of the determinants of fertility: The case of Vietnam Journal ofPopulation Economics 10, No.3: 251-271 • Nguyen T.T.Huyen 2001 Determinants of fertility in Vietnam MDE Thesis Ha n01 Phuong Thi Thu Huong and Carl Haub 2003 An overview of population and development in Vietnam Available at: http://www.prb.org/Articles/2003/AnOverviewofPopulationandDevelopmentinViet nam.aspx [accessed 12 May 2009] Retherford, R & Choe, M.K Statistical Models for Causal Analysis, John Willey &Sons Inc Santos Silva J.M.C and Covas Francisco 2000 A Modified Hurdle Model for completed Fertility Journal of Population Economics, vol.13: 173-188 Schultz Paul.T 1990 Women.s Changing Participation in the Labor Force: A World Perspective Economic Development and Cultural Change, vol.38: 457- 488 and Zeng 1995 Fertilty of Rural China., Journal of Population Economics, vol.8: 329-350 Sobel ME and Arminger G 1992 .Modeling Household Fertility Decisions: A Nonlinear Simultaneous Probit Model , Journal of the American Statistical Association, vol.87: 38-47 61 Sevilla, Jaypee 2007 Fertility and relative cohort size Available at: http://www.framtidsstudier.se/filebank/files/20071005$123330$fil$no7STOtzOGE5 7T07TmPI.pdf, [accessed 12 May 2009] Tran K.D.2001 Human Resource Development, Coursebook HCMC: University of Economics UNFP A Vietnam 2008, Vietnam population 2007 Ha noi: Luck House Graphics LTD Van de Kaa, D.J (1996) Anchored narratives: The story and findings of half a century of research into the determinants of fertility Population Studies, 50: 389432 Vietnam Demographic and Health Survey 2002 • " Weeks, J.R 2005 Population, an introduction to concepts and issues, ninth edition, Wadsworth, USA, 675 p Weinberger, M.B 1987 The relationship between women's education and fertility: selected findings from the World Fertility Surveys, international family planning perspectives 1: 35-46 Willis Robert 1973 A New Approach to the Economic Theory of Fertility Behavior Journal ofPolitical Economy, vol 81: S279-S288 Winkelmann R 1997 Econometric Analysis of Count Dat and (ed.), Springer Verlag, Berlin Heidelberg, New York World Fertility Report 2003 Department of Economic and Social Affairs, Population Division, United Nations '!' 62 APPENDIX Al Poisson regression global xlist edprimar edsecond eduhigher know plan age_birth place hus_edprimar hus_edsecond hus_eduhigher age2024 age2529 age3034 age3539 age4044 age4549 **** Hoi quy Poisson poisson fert $xlist, vce(robust) Iteration 0: log pseudolikelihood -8268.6616 Iteration 1: log pseudolikelihood -8268.5945 Iteration 2: log pseudolikelihood -8268.5945 Poisson regression Log pseudolikelihood = -8268.5945 • \ ! Number of obs 5381 Wald chi2(16) 8222.69 Prob > chi2 0.0000 Pseudo R2 0.1297 fert I I Coef Robust Std Err z P>lzl [95% Conf Interval] -+ edprimar -.1778293 0248673 -7.15 0.000 -.2265684 -.1290903 edsecond -.263644 0259178 -10.17 0.000 -.3144418 -.2128461 eduhigher -.3565376 0377163 -9.45 0.000 -.4304602 -.282615 know -.0431927 0131534 -3.28 0.001 - 0689728 -.0174126 plan -.0401104 0179302 -2.24 0.025 -.075253 -.0049679 age_birth -.0579904 0015693 -36.95 0.000 -.0610663 - 0549146 place -.2013016 013336 -15.09 0.000 -.2274397 -.1751636 hus_edprimar -.0056176 0303507 -0.19 0.853 -.0651038 0538687 hus edsecond -.0381114 0305449 -1.25 0.212 -.0979783 0217555 hus_eduhig-r -.12205 0391771 -3.12 0.002 -.1988357 -.0452644 age2024 3945823 0327207 12.06 0.000 3304508 4587138 age2529 8094472 0307576 26.32 0.000 7491634 8697311 age3034 1.109568 0305992 36.26 0.000 049595 1.169541 age3539 353828 0310706 43.57 0.000 1.292931 1.414726 age4044 564484 0313031 49.98 0.000 503131 625837 age4549 721734 0318651 54.03 0.000 65928 784189 cons 1.324691 0481697 27.50 0.000 1.23028 419102 63 A2 Factor change ' listcoef, help poisson (N=5381) : Factor Change in Expected Count Observed SD: 1.5210555 -fert I b z P>lzl e b e bStdX SDofX -+ edprimar -0.17783 -7.151 0.000 8371 0.9237 0.4463 edsecond -0.26364 -10.172 0.000 0.7682 0.8802 0.4838 eduhigher -0.35654 -9.453 0.000 0.7001 0.9343 0.1905 know -0.04319 -3.284 0.001 0.9577 0.9814 0.4346 plan -0.04011 -2.237 0.025 0.9607 9871 0.3240 age_birth -0.05799 -36.952 0.000 0.9437 8116 3.5997 place -0.20130 -15.095 0.000 0.8177 0.9189 0.4203 hus _edprimar -0.00562 -0.185 0.853 0.9944 0.9977 0.4105 hus edsecond -0.03811 -1.248 0.212 0.9626 0.9825 0.4635 hus_eduhig-r -0.12205 -3 115 0.002 0.8851 0.9729 0.2255 age2024 0.39458 12.059 0.000 1.4838 1.1144 0.2746 age2529 0.80945 26.317 0.000 2.2467 1.3579 0.3780 age3034 1.10957 36.261 0.000 3.0330 5476 0.3936 age3539 35383 43.573 0.000 8722 1.7295 0.4047 age4044 56448 49.979 0.000 4.7802 1.8549 3949 age4549 72173 54.032 0.000 5.5942 8475 0.3565 b raw coefficient z z-score for test of b=O P>lzl e b e bStdX SDofX p-value for z-test exp(b) = factor change in expected count for unit increase in X exp(b*SD of X) = change in expected count for SD increase in X standard deviation of X 64 A3 Percent change listcoef, percent help poisson (N=5381) : Percentage Change in Expected Count Observed SD: 1.5210555 -b z % %StdX SDofX fert I P>lzl -+ • edprimar -0.17783 -7.151 0.000 -16.3 -7.6 0.4463 edsecond -0.26364 -10.172 0.000 -23.2 -12.0 0.4838 eduhigher -0.35654 -9.453 0.000 -30.0 -6.6 0.1905 know -0.04319 -3.284 0.001 -4.2 -1.9 0.4346 plan -0.04011 -2.237 0.025 -3.9 -1.3 0.3240 age_birth -0.05799 -36.952 0.000 -5.6 -18.8 3.5997 place -0.20130 -15.095 0.000 -18.2 -8.1 0.4203 hus _edprimar -0.00562 -0.185 0.853 -0.6 -0.2 0.4105 hus - edsecond -0.03811 -1.248 0.212 -3.7 -1.8 0.4635 hus_eduhig-r -0.12205 -3.115 0.002 -11.5 -2.7 0.2255 age2024 0.39458 12.059 0.000 48.4 11.4 0.2746 age2529 0.80945 26.317 0.000 124.7 35.8 0.3780 age3034 1.10957 36.261 0.000 203.3 54.8 0.3936 age3539 1.35383 43.573 0.000 287.2 73.0 0.4047 age4044 56448 49.979 0.000 378.0 85.5 3949 age4549 72173 54.032 0.000 459.4 84.8 0.3565 -b raw coefficient z z-score for test of b=O P>lzl % p-value for z-test percent change in expected count for unit increase in X %StdX percent change in expected count for SD increase in X SDofX standard deviation of X 65