The effect of ưomens education on fertility in vietnam (2)

UNIVERSIT Y OF ECONOMIC S HO CHI MINH CITY VIETNAM INSTIUTTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEYELOPMENT 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 Academic Supervisor: DR NGUYEN HUU DUNG •' HO CHI MINH CITY, NOVEMBER 2010 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 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 TABLE OF CONTENTS CERTIFICATION i ACKNOWLEDGEMEN TABLE OF CONTENTS iii LIST OF FIGURES AND TABLES vi ABBREVIATION ABSTRA CBAPTER 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 TRREE: RESEARCH METHODOLOGY 26 3.1 Structure of the VDHS 2002 26 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(y|x) 36 3.5.3 Percent change in E(y|x) 37 3.6 Chapter summary 37 CHAPTER FOUR: SOCIOECONOMIC CONTEXT AND PROFILES OF WOMEN’S FERTILITY 39 CRAPTER FIYE: 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 6.1 Conclusions 56 6.2 Recommendations 57 6.3 Further Research 58 REFERENCES APPENDIX LIST OF FIGURES AND TABLES Table 2.1: Narratives on the determinants offertility Table 2.2: Intervening variables in Cochrane ’smodel 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 4.3i Level of education ofever-married women, Vietnam 2002 (º ) 46 Table 4.4: Exposure tofamily planning messages on radio and television (° ) 47 Table 4.5: Children ever born by ever-married women aged 15-49, classified by place ofresidence 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 ABBREVIATIONS ' ASFR Age-specific fertility rate CBR Crude birth rate CEB Children ever born GFR General Fertility rate GSO General Statistical Office Poisson TFR regression model Total USAID fertility rate VDHS U.S Agency for International Development Vietnam Demographic and Health Survey 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 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 turn 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 ‹z/.(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 (wow) is also strongly significant to lower number of children born per woman The coefficient on /‹ziow 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.fJ58, 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 ‹:ycle 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 p/‹zn 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 too reduce large family size in Vietnam The coefficient of p/‹z ce 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 of 0.818, holding all other variables constant This is supported by Ainsworth et at (1996), who determined that urban women have lowei 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 0.958 -4.2 0.018 0.961 -3.9 0.002 0.944 -5.6 0.818 -18.2 0.994 -0.6 0.963 -3.7 -0.043* -0.040** plan age birth -0.201* place hus edprimar hus_edsecond hus_eduhigher 0.030 006 038 ›22* 0.039 0.885 age2024 0.395* 0.033 1.484 48.4 age2529 0.809* 0.031 2.247 124.7 age3034 1.109* 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 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 negalive 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 liviny 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 ef at (1992) women’s education raises their labor participation which in turn 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 estimatcd 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 ofeducation 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 quality- quantity 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, Irina June (1963) An econometric analysis of population growth The American Economic Review, 53(3), pp 314-339 Ainsworth 1989 Socioeconomic Determiants of Fertility in CD6te d’Ivoire Living Standard Measurement Study, Working Paper No.53 Washington DC: The World Bank Ainsworth, Martha & Beegle, Kathleen & Nyamete, Andrew, 11996 "The Impact of Women's Schooling on Fertility and Contraceptive Use: A Study of Fourteen Sub- Saharan 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 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-8268.5945 fert | Coef Number of obs = 5381 Wald chi2(16) = 8222.69 Prob > chi2 = 0.0000 Pseudo R2 - 0.1297 Robust Std Err [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 -.0174l26 plan | -.0401104 0179302 -2.24 0.025 -.075253 -.0049679 age_birth | -.0579904 0015693 -36.95 0.000 -.0610663 -.0549146 place | -.2013016 0l3336 -15.09 0.000 -.2274397 -.1751636 hus_edprimar | -.0056176 0303507 -0.19 0.853 -.065l038 0538687 hus_edsecond | -.03811l4 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 1.049595 1.169541 age3539 | 1.353828 0310706 43.57 0.000 1.292931 1.414726 age4044 | 1.564484 0313031 49.98 0.000 1.503131 1.625837 age4549 | 1.721734 0318651 54.03 0.000 1.65928 1.784189 _cons | 1.324691 0481697 27.50 0.000 1.23028 1.419102 A2 Factor change listcoef, help poisson (N=5381): Factor Change in Expected Count Observed SD: 1.5210555 fert | b z P>|z| e’b e’bStdX SDofX edprimar | -0.17783 -7.151 0.000 0.8371 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 0.9871 0.3240 age_birth | -0.05799 -36.952 0.000 0.9437 0.8116 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 hus_eduhig-r | -0.12205 -3.115 0.002 0.8851 0.9729 0.46 35 2 55 age2024 | 0.39458 12.059 0.000 1.4838 1.1144 74 age2529 | 0.80945 26.317 0.000 2.2467 1.3579 0.3780 age3034 | 1.10957 36.261 0.000 3.0330 1.5476 393 age3539 | 1.35383 43.573 0.000 3.8722 1.7295 04 age4044 | 1.56448 49.979 0.000 4.7802 1.8549 94 age4549 | 1.72173 54.032 0.000 5.5942 1.8475 35 65 b = raw coefficient z = z-score for test of b=0 P>|z| = p-value for z-test e’b = exp(b) = factor change in expected count for unit increase in X e’bStdX = exp(b*SD of X) = change in expected count for SD increase in X SDofX = standard deviation of X 64 A3 Percent change listcoef, percent help poisson (N=5381): Percentage Change in Expected Count Observed SD: 1.5210555 b fert SDofX edprimar | -0.17783 edsecond | -0.26364 -7.151 0.000 -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 -16.3 -7.6 0.4463 | 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 1.3S383 573 0.000 287.2 73 0 04 age2024 age4 4 | 1.56448 49.979 378.0 85.5 0.3949 age4 54 | 1.72173 54.032 0.000 459.4 84.8 0.3565 b = raw coefficient z = z - score for test of b-0 P>|z| = 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 ... 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. .. the effect of women’s education on fertility in Vietnam Specifically, the objective of the thesis is to measure the likelihood of controlling fertility regarding to women’s education 1.3 Research... modification of constraint in (2) is: R= npn+qpq+ypy= I+nqr (4) The interaction between the quantity and quality of children in the nonlinear budget equation and also in the utility function has