The effect of womens 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 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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 ... objective of the study is to examine 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... 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. .. regarding to women's education 1.3 Research questions Based on the research objectives, the paper will find out the answer of the following question: Does women's schooling affect on fertility in Vietnam?