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 are usually associated with lower average educational attainment and reduced levels of child health as measured by nutritional status, and mortality
Moreover, research findings from a number of studies on fertility in Vietnam showed that women's education has a negative influence on fertility For example, Nguyen (2001) found that women's education was an important factor helped to reduce the number of children born in Vietnam In addition, a wide range of empirical studies showed that raising level of education especially for women had important effect on fertility In their research in Sub-Sahara and Latin America, Jejeebhoy (1995) and Martin (1995) showed that the inverse relationship between education and fertility can be enhanced only after relatively high levels of education have been attained
Although many scholars found that education has a negative effect on fertility, there are still certain limitations in term of estimation Analysts employed statistical estimation techniques such as linear (Ordinary least squares- OLS) that are unsuitable for fertility data, which are based on counts (Long, 1997)
This paper examines the relationship between female schooling and fertility in Vietnam through data from Vietnam Demographic and Health Survey 2002 (VDHS 2002), focusing on Vietnamese ever-married women aged from 15-49 years old Findings from the study is expected to be used in monitoring the achievements of the government's population policies and programs in the years to come
Research objectives
The overall 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 to women's education.
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?
Hypotheses
The main research hypothesis concerning women's reproductive behavior to be addressed is that fertility is significantly influenced by women's education attainment.
Research Methodology
Data from Vietnam Demographic and Health Survey 2002 (VDHS2002) 1 is mainly used in this thesis DHS funded by the U.S Agency for International Development (USAID)-is a worldwide comprehensive survey on demographic, health and fertility indicators The VDHS 2002 was carried out in the framework of the activities of the Population and Family Health Project of the Committee for Population, Family and Children (previously the National Committee for Population and Family Planning) The VDHS 2002 was conducted by the General Statistical
1 Datasets and full explanation are available online from www.measuredhs.com i
Office (GSO) on behalf of the Population and Family Health Project of the Committee of Population, Family and Children Based on data set of VDHS2002, a model used in the analysis is the Poisson regression that estimates the likelihood that increasing women's schooling levels lower fertility in Vietnam.
Structure of the thesis
The thesis is organized in six chapters as follows:
Chapter one: Introduction The chapter introduces the research problem, research objectives, questions, hypothesis, and brief research methodology of the thesis
Chapter two: Literature review This chapter begins with the definitions and concepts of terms related to fertility Then, theoretical framework and empirical studies are reviewed
Chapter three: Research methodology The chapter presents source of data, sub-data set for the study, explanation of the relevant variables and estimation strategy
Chapter four: Socioeconomic context and respondent's profiles It presents the characteristics of respondent and the background of fertility in Vietnam based on VDHS2002 data In addition, it also provides descriptive statistics of fertility
Chapter five: Factors affect women's fertility in Vietnam The results of regression analysis, interpretation of coefficients and their marginal effects to fertility are shown
Chapter six: Conclusions and recommendations This chapter is to summarize the findings and conclude with some policy recommendations and research limitation
The objective of the study is to examine the effect of women's education on fertility Therefore, the definitions and concepts related to fertility, their measurements and determinants will be defined After that theoretical framework and empirical studies are also reviewed The final section is to summarize of the main point of literature review presented in the chapter.
Definitions, concepts related to fertility and its measures
In the jargon of demographers, there are two terms which are often used synonymously; in fact they are different from each other Fertility refers to a number of children born to women In the Multilingual Demographic Dictionary of the United Nations, fertility means the actual reproductive performance of women, whereas fecundity denotes the physical ability to reproduce
Some measures of fertility are cited in Tran (2001: 60), Nguyen (2001:7), VDHS 2002, as follows:
The most common measurement of fertility is the Total Fertility Rate (TFR) The TFR is the average number of children that would be born to a woman during her lifetime if she was to bear children at each age according to the prevailing age-specific fertility rates The TFR is obtained by summing the age- specific rates in a particular calendar year across all childbearing ages Therefore, the TFR shows a cross sectional picture of fertility and consists of values from many generations of women who are at different childbearing stages in any given year It is unaffected by the age and sex composition of the population and thus separate change in actual fertility It supposes that women don't die during the reproductive age, so it isn't influenced by mortality
An alternative measure of fertility is the Generational Fertility Rate The general fertility rate (GFR) which represents the annual number of births per 1,000 women in reproductive ages (15-49) Therefore it represents the actual number of births that a particular cohort of women experienced over their reproductive lifetime It is affected by the age distribution of women in childbearing age
The crude birth rate (CBR) which represents the annual number of births per 1,000 population The CBR was estimated using the birth history data in conjunction with the population data collected in the household schedule It is influenced by time and space, depending on many factors such as intensity of reproductive process, age and sex structure of population Consequently, CBR is just an indicator that approximately reflects the actual fertility rate
Age-specific fertility rates (ASFR) are calculated by dividing the number of births to women in a specific age group by the number of woman-years lived during a given period 2 • Age-specific fertility rates are useful in understanding the age
2 Numerators for the age-specific fertility rates were obtained by classifying births during the 5-year period prior to the survey into standard five-year age groups, according to the mother's age at the time of birth and summing Den~minators fo.r the r~tes were the numbe~ of person-years lived by all women in each five~ year age group dunng the penod Smce only ever-rnamed women were interviewed in the VDHS, it was pattern of fertility In an ever-married sample of women such as in the VDHS, the calculation of all-women fertility rates makes the implicit assumption that no births occurred among women who have never married
Children ever born (CEB) are the average number of live births that women has had during her lifetime It is derived from data gathered by censuses or surveys
Different from the macro measures of fertility mentioned above, which refer
Theoretical framework, empirical studies related to determinants of fertility
Theoretical framework
Van de Kaa (1996) organizes the literature on fertility written in the second half of the twentieth century and classifies them according to their main "narrative", that means what story a piece of demographic literature is talking about His work provides a convenient and clear picture of the different schools of thought reflected necessary to inflate the number of person-years lived by ever-married women by factors representing the proportion of women who were ever-married in each age group These factors were calculated from the data collected in the household schedule Never-married women were presumed not to have given birth In Vietnam, few births occur outside of marriage so that any underestimation of fertility from this source is negligible in various researches on fertility Table 2.1 shows a summary of the different narratives and their specific themes
• Table 2.1: Narratives on the determinants of fertility
Narratives S_Qecific Themes of Research
Classical: Initial Narrative Explanations for "what people all know about the way things go in this world"
• Social progress and desire for mobility
• Modernization process and the demographic transition theory
Proximate determinants and mortality decline
• Eleven intermediate fertility variables which affect the exposure to the risk of conception
• Exposure factors, deliberate marital control and natural marital control
• Falling infant and child mortality
Economic Application of microeconomic theories
• Consumer choice demand for children theory
• Demand-supply oriented, combining biological
Innovation, Diffusion, and Ideational and Cultural Change
Path-Dependency and Institutional Change and sociological elements, and later fertility regulation and cost
Changing function of the family and the value of children
• Pre-transitional societies favored early marriage and high fertility
• Social conditions determining intergenerational wealth flow
• Macro-analytic framework that includes the socio-cultural context, education, occupation and location
• Non-economic value of children: Social and psychological
Innovation and diffusion of ideas and practices
• Spread of the practice of fertility regulation
• Effects of changes in value systems
Fertility variables and determinants being influenced by clusters of behavioral rules
Source ofbasic data: Van de Kaa (1996)
At least six themes have been identified, namely, the classical narratives revolving around the initial explanations of demographic changes, the biological and technological narratives on the proximate determinants of fertility, the economic narratives using microeconomic theories, the social narratives about the family and society, the narratives on innovation and diffusion of ideas and practices and the narratives of path-dependency behavior and institutional changes
The narratives involving economic models on fertility are largely microeconomic in approach The two main groups of studies include demand- oriented consumer choice theories and the demand-supply analytical framework
One usual argument in economic models uses the concept of opportunity cost of childcare to argue that there is a negative relationship between parental education and fertility: more educated parents, who are usually working parents, give up some income-earning opportunities when they devote more time to childcare, so they would rather have fewer children If such behavior indeed happens, this is the so- called dominant substitution effect It may further be said that the income earned from devoting more time working can be used to provide for better quality childcare to fewer children An alternative picture is that a positive relationship between education and fertility is also possible This is the case of a dominant income effect, wherein the more educated and presumably working parents will be earning an income enough to afford raising more children Which of these scenarios apply to a given society depends on empirical testing
The theoretical framework for modeling fertility has mainly exposed by Becker He argued that fertility is determined by the interaction between quantity and quality of children, which are separate argument in the utility function The quality - quantity approach developed by Becker (1960) and Becker and Lewis
(1973) emphasizes that there is likely a substitution effect from quantity to quality of children with rising family income The key feature in Becker analysis is that the shadow prices of children with respect to their number (the cost of an additional child, holding their quality constant) is greater the higher their quality is Similarly, the shadow price of children with respect to their quality (the cost of a unit increase in quality, holding number constant) is greater, the greater the number of children
To illustrate this reasoning, the following simple utility function was specified:
Where n is the number of children, q is their quality and y IS the rate of consumption of all other commodities The budget constraint is
I is the full income, 7r is the price of nq and 1r)' is the price of y The first order conditions optimization are:
Un = Aq7r =f.pn Uq= Amr=f.pq Uy=Azy=f.py (3)
The important point is that the shadow price of children respect to number (pn) is positively related to q, the level of quality, and the shadow price with respect to quality (pq) is positively related to n, the number children Quality has a major effect on the resource constraint because the cost of an additional child depends on its quality, while the full cost of higher quality children depends on their number
The economic interpretation is that an increase in quality is more expensive if there are more children because the increase has to apply to more units Similarly, an increase in quantity is more expensive if the children are of higher quality, because higher quality children cost more A simple modification of constraint in (2) is:
The interaction between the quantity and quality of children in the non-linear budget equation and also in the utility function has several paradoxical implications on fertility Explicitly a rise in income could reduce the demand for children if higher income greatly increases the education and other training of children The reason is that higher expenditures on training increase the variable cost of children, and could dominate the increased demand due to the income effect (Becker 1960, Willis 1973, Becker and Lewis 1973) An increase in quality per child implies an increase in costs raising a child, which decreases fertility For more technical details see Becker and Lewis 1973 The improvement of the women human capital level following a high achievement enrollment, increase the time cost and notably the price of the service offered to bearing and rearing children Henceforth, the burden of raising child increase and so the desire to have an additional child decreaseso
Beside that, the rise of the mother's time value encourages her to participate intensively in the labor market This improvement of the well being empowers her to actively participate in the decision making within the family in favor of fertility decrease, Schultz (1990) The fertility is determined by female wage and family income, which are assumed to measure the time cost of raising children and earning potential Increases in the value of female time by female wage increase tend to increase the children cost A negative effect on fertility is so expected since the opportunity cost of having children increases Therefore, fertility decision is taken in putting in balance the advantage and cost of an additional birth The increase in the wife's wage enhances of course the family income but it will also increase the opportunity cost of child bearing and rearing The change of women's wage and family income present two fold an income and a price effect The effect of change is depending of the magnitude of setting income and substitution effects Becker argues that the substitution effect would be larger than the income effect, referring to his assumption that the income elasticity of demand for child quality is larger then the income elasticity of demand for child quantity Henceforth, higher family income would lead fewer children and high quality per child The net effect of income on fertility depends on the relative strength of the income effect to the substitution effect Also, an asymmetric effect on fertility can be observed with a positive wage's change resulting by an improvement of the level of the men's human capital or by the rise of his market wage rate that increases the family's income Improvement of human capital and the education development provides to woman as man a multiple choice on their lifestyle and the autonomy in the marital timing decision Education development lets woman to delays her marriage age
Hence educated woman doesn't use the total fertility period and so has few children in comparison to the less educated one
Becker's work belongs to the so-called economic sub-discipline of household or new home economics, which uses the household production concept Household economic modeling was used as early as 1957 by Leibenstein and in the later years has found its way into various aspects of family life Van de Kaa ( 1996) considers the common features of the new home economics studies to include the use of a time constraint especially for women, the household stock of human and physical capital, and the lifecycle conditions influencing labor market training, migration, marriage, children and retirement savings
In addition, a prominent approach, which is associated with the "New Household Economics," begins from the proposition that members of the household unit seek to maximize income In this formulation, it is assumed that women and men respond to economic incentive structures Accordingly, the theory predicts that once education is provided as a public good and becomes widespread for women and men, an increase in education leads to a decline in fertility, all things being equal The putative mechanism for this effect is the opportunity costs associated with caring for children as education increases As women acquire skill sets useful in the marketplace with higher levels of educational attainment, they tend to command a higher wage, increasing the value of their time To the degree that education raises women's and men's earning potential in the labor market, the theory also implies that education reduces the incentive to attempt to use fertility as a mechanism to increase family production and income While recognizing the micro-economic influences of education, complementary approaches suggest that the link between education and fertility is more complex A theoretically eclectic framework developed by Castro Martin and Juarez hypothesizes that education may depress fertility rates for a number of reasons, including improved literacy and cognitive skills that increase the likelihood of interaction between women and public health institutions; improved knowledge of the biology of reproduction (which raises the potential efficacy of contraceptive use); and changes in attitudes that that raise the likelihood of using contraceptives
Van de Kaa (1996, p 410) highlights the following important findings in the demand-oriented models: there is a strong interaction between the quantity and quality of children although the two are not close substitutes, and the demand for children is highly responsive to their price This means that parents often consider the kind of life they can offer to their children when they make decisions about family size, and this includes the costs of childcare But Van de Kaa also exposes a potential weakness in this model, i.e., it may be hard to apply to less developed countries because time may not be a real constraint to numerous low-income households
Later recognizing that the demand-oriented model obviously focused only on one side of the story and did not dwell on the supply side, economists like Easterlin
(1975) started to pursue a demand-supply framework on fertility Nevertheless Easterlin (1975, p 54) commended the important contributions of the household production model He cited that: The model clarifies the concept of full income for analyzing fertility decisions; reduces the conceptual confusion between cost of children and expenditures per child and that rising incomes may even increase the demand for both quantity and quality of children; and explicitly recognizes both the competition between children and economic goods for parental time and the value of that time to each parent In the basic model of Easterlin (1975, p-57), the determinants of fertility work through three main variables- demand and supply of children and the costs of fertility regulation - and their immediate determinants
These main determinants in relation with regulation cost are explained in detail as follows:
(a) The demand for children (Cd) if fertility regulation were costless: The determinants are income, price of children and the subjective preference for children relative to other goods;
(b) The potential supply of children (Cn) if no conscious effort were made to control fertility: The determinants are natural fertility and the survival prospects of a baby to adulthood; and
(c) The cost of fertility regulation: Fertility regulation, in this case, refers to the individual's desire or action to control the factors affecting childbirth The determinants are the attitudes and the time and money needed to learn and use the techniques of regulation
Empirical studies related to effects of women's education on fertility
There are a number of studies focused the determinants of fertility m from VHLSS or VDHS to analyze fertility determinants via descriptive statistics method Nguyen (2001) with data from VHLSS 1997-1998 explored socioeconomic determinants of fertility in Vietnam The study investigated the effects of factors including women's age, schooling, occupation, religion, area of residence, marital status, household income, and infrastructure condition on fertility Similarly, Shapiro (1996) who also observed the relationship between fertility and women's employment, education and family planning in Vietnam through data from VDHS
1998 and Population census in 1999 The research ofNguyen-Dinh (1997) using a microeconomic model, a demand-supply framework, on the 1988 Vietnam Demographic and Health Survey, which covered 4,172 ever-married women in their childbearing ages, 15-49, to study the socioeconomic determinants of fertility Two important results relevant to the current research are (I) that paternal education significantly lowers household fertility levels in Vietnam and (2) this negative effect of education is not based on the opportunity cost, but on attitudinal and/or information effects that influence preferences for children Beside socioeconomic determinants, Le et al (1999) used data in the period 1993-1998 to analyze proximate determinants of fertility in Vietnam such as age of marriage, contraceptive use, abortion and infecundability etc
Ainsworth (1989) used the model of demand for children of Becker and OLS technique to find out the effect of women's education and household income on fertility in Cote d'Ivoire Cochrane (1979) argues that earlier economists such as Malthus and his successors have proposed theories about why more education is inversely related to fertility However, the relationship between education and fertility is more complex than suggested Though the underlying pattern most commonly known shows a negative relationship, there are instances where positive relationships at very low and very high levels of schooling have been found
Bledsoe et al (1999) suggested that understanding the nature and strength of the relationship between education and fertility remains a central challenge both for researchers seeking to explain demographic and social changes and for policy makers who must decide on the allocation of scarce public resources According to Martin and Juarez (1995, pp 53), education is a "source" of knowledge transmission, "vehicle" of socioeconomic advancement, and a "transformer" of attitudes In the contemporary world, any development depends on the effective transmission of new information As a source of knowledge transmission, Martin and Juarez showed that schooling imparts literacy skills, which enable people to process a wide range of information and arouse cognitive change that shape individuals interaction with their surrounding environment As a vehicle of socioeconomic development, the authors hypothesized that education not only enhances cognitive abilities, but also it opens up economic opportunities and social mobility In the contemporary world, education credentials open the door for formal employment and for sorting individuals into the hierarchy of occupations
Educated women are more likely to exercise the "quality-quantity trade-off' of their children Most of these women are likely to see the benefit of their schooling; they may develop higher aspirations for their own children's schooling
It is obvious that as the number of children increases, familial resources available to an individual child decrease Restricting the number of children is the best solution in order to have better educated children and more familial resources per child It would be advantageous for a woman to have fewer children that she can afford to pay for the tuition and other related fees associated with schooling, hence the trade- off between quality and quantity of children
Besides education, a large number of variables also pose effect to fertility
For example, Bongaarts et a/ (1984) consider two groups of variables: socioeconomic variables and proximate variables Socioeconomic variables include education, social, cultural, economic, and health variables whereas proximate variables include biological and behavioral variables such as contraception and age of a woman Davis and Blake (1956), Bongaarts and Potter (1983) hypothesize that in order for the socioeconomic variables to affect fertility, they must operate through proximate determinants
Another factor is the age at first marriage Women who get married early have a longer period where the likelihood of an additional child is greater An additional reason for including age at first marriage is that this variable might partly control for a woman's "social status," since high income and highly educated individuals are likely to get married late Other fertility studies, such as Kiernan
(1989), and Santos Silva and Covas (2000) find a negative effect of a late marriage on the probability of having children
Cultural traits such as son preference and number of siblings are important to explain fertility behavior in a traditional society such as Vietnam, therefore, they deserves to be looked in detail Khan and Khanum (2000) found that sons are generally preferred over daughters owing to a complex interplay of economic and socio-cultural factors Hank and Hans-Peter (2000) suggest that son preference is embedded in cultural and religious traditions and community norms as well as economical factors, shaping individual attitudes and behavior In most developing countries where women are economically and socially dependent on men, male offspring are presumed to have greater economic net utility than female offspring
The argument is that sons can help to provide old age support to their parents This is particularly important in most developing countries where there is no other form of old-age security Hank and Hans-Kohler (2002) suggest that sex preferences for children might have implications for a couple's fertility behavior, where parents who desire one or more children of a certain sex should tend to have larger families than would otherwise be the case Studies by Duncan et a! (1965), Axinn et a!
(1994) have found a direct relationship between the number of children born to a family and the number of children within the couple's (husband and/or wife) family
In other words, a couple from larger families is more likely to mimic the sexual behavior of their parents hence breeding intergenerational inheritance of family
Another important determinant of fertility decision is the contraceptive knowledge and application Access to information about and the actual use of several birth control measures and medical facilities services can be critical in slowing population growth in low-income countries The use of contraceptive may help in avoiding pregnancies for woman who want to limit her birth, to space birth or to avoid bearing child This being so, it can be expected that differences in access to, and/or the use of birth control will explain the fertility variations The use might differ between rural communities and urban centers Therefore variable indicating whether the couple grew up in a rural or urban area is included in explaining fertility decision.
Summary
This chapter presents literature review of the theories of fertility, fertility determinants and empirical studies on fertility Fertility is measured by total number children ever born per woman is of interest in the study From literature review, a group of explanatory variables including women's education, age, knowledge of ovulatory, family planning, her partner's education, type of place of residence, age at first birth will be examined empirically After exploring and analyzing many previous studies, the household demand approach is used as a core of fertility model in my research and Poisson regression model The framework will be specified to apply for estimation the effects of women's education on fertility in Vietnam
This chapter deals with the methodological aspects of this study It presents the source of data, structure of VDHS 2002, model specification, variables of interest, and strategy estimation Firstly, structure of VDHS 2002 and data set are presented Secondly, the suggested model will be specified with specific variables
Dependent and independent variables, their definition, expected signs also shown
Then, estimation method is described in detail.
Structure of the VDHS 2002
The sample unit for the VDHS 2002 was based on that used in the VDHS
1997, which in tum was a subsample of the 1996 Multi-Round Demographic Survey (MRS), a semi-annual survey of about 243,000 households undertaken regularly by GSO The MRS sample consisted of 1,590 sample areas known as enumeration areas (EAs) spread throughout the 53 provinces/cities of Vietnam, with
30 EAs in each province On average, an EA comprises about 150 households For the VDHS 1997, a subsample of205 EAs was selected, with 26 households in each urban EA and 39 households for each rural EA A total of 7,150 households were selected for the survey The VDHS 1997 was designed to provide separate estimates for the whole country, urban and rural areas, for 18 project provinces and the remaining non project provinces as well
Because the main objective of the VDHS 2002 was to measure change in reproductive health indicators over the five years since the VDHS 1997, the sample design for the VDHS 2002 was as similar as possible to that of the VDHS 1997
Although it would have been ideal to have returned to the same households or at least the same sample points as were selected for the VDHS 1997, several factors made this undesirable Revisiting the same households would have held the sample artificially rigid over time and would not allow for newly formed households This would have conflicted with the other major survey objective, which was to provide up-to-date, representative data for the whole of Vietnam Revisiting the same sample points that were covered in 1997 was complicated by the fact that the country had conducted a population census in 1999, which allowed for a more representative sample frame
In order to balance the two mam objectives of measuring change and providing representative data, it was decided to select enumeration areas from the
1999 Population Census, but to cover the same communes that were sampled in the VDHS 1997 and attempt to obtain a sample point as close as possible to that selected in 1997 Consequently, the VDHS 2002 sample also consisted of 205 sample points and reflects the over-sampling in the 20 provinces that fall in the World Bank-supported Population and Family Health Project The sample was designed to produce about 7,000 completed household interviews and 5,665 completed interviews with ever-married women age 15-49.
Data set
As stated, the data in this study is extracted from Vietnam Demographic and Health Survey 2002 of married women aged 15-49 The VDHS 2002 is a nationally representative sample survey of 5,665 ever married women age 15-49 selected from
205 sample points throughout Vietnam It was conducted by the General Statistical Office (GSO) on behalf of the Population and Family Health Project of the Committee of Population, Family and Children Fieldwork took place from October to December 2002 The Demographic and Health Surveys division of ORC Macro in Calverton Maryland provided technical assistance to the project through several visits and through e-mails However, due to missing values for some variables in the data set, only 5,381 observations ever married women have completed information in the model
For our analysis, a limitation of the survey is its lack of information on household income However, as in most developing countries, such information is difficult to collect and unreliable Perhaps it is for these reasons that no questions about money income or wages were asked Instead, women were asked only if their husbands had a stable source of income as a proxy for household income.
Model specification
The empirical model for analysis of fertility regresses a number of children ever born to each woman on a set of independent variables that are assumed to be exogenous to fertility decision but that influence either the demand or supply of children This reduced-form model of fertility "determinants" can be written as:
Fert = {1 0 + {J 1 edprimar+ {J 2 edsecond + {J 3 eduhigher+ {J 4 know+ {J 5 plan + {J 6 age_mar + {J 7place + {J 8hus_edprimar + {J 9hus_edsecond + {J 10hus_eduhigher + {J 11age2024 + {J 12 age2529 + {J 13 age3034 + /J14age3539 + /J15age4044 + /J16age4549 + p.1
Bj ({3 0 to {3 16): estimated parameters and J L: Random disturbance term
The equation describes the relationship between women fertility and its determinants The left-hand side (Fert) of equation is dependent variable and the right side is independent variables.
Description of variables in the model
Dependent variable
The measure for fertility is total number of children ever born during the respondent's lifetime The fertility measure is thus a type of count data Long ( 1997, pp 217) has noted that, " use of the linear regression model for count data can result in inefficient, inconsistent and biased estimates." Therefore, we estimate the model using Poisson regression instead of OLS Simulation studies have demonstrated that robust standard errors control successfully for heterogeneity or over-dispersion in data sets estimated with Poisson regression (J Boher and R.J
Independent variables
They consist of socioeconomic and proximate variables that affect to fertility behavior of women in Vietnam They are respectively: women's education, knowledge of the ovulatory cycle, family planning, age at first birth, place of residence of women, partner's education, and women's age group Among socioeconomic variables, women's education is the major independent variable in the study
In fertility studies, the common approach in measunng the independent variables is by considering questions asked during the survey regarding if the level of educational attainment or the highest level of education, the type of residence, etc The VDHS 2002 provided data for a wide range of information on population, women and children health, fertility, family planning, maternal, mortality, nutrition
Therefore, we can easily extracted necessary data to use in the model
The table below presents how these proximate and socio-economic variables are measured and drawn from VDHS 2002 and their expected signs
Variables Notations of Definition and Code in Expected
Variables Measurement data of sign
Fertility Fert Total number of children v201 + ever born per woman
Women's edprimar = 1 if a woman has v106 - education primary education level level = 0 otherwise edsecond = 1 if a woman has - secondary education level
= 0 otherwise edhigher = 1 if a woman has - higher education level
Knowledge know Knowledge of the v217 - of the ovulatory cycle indicates ovulatory when during her cycle monthly cycle the respondent thinks a woman has the greatest chance ofbecoming pregnant
= 1 if a woman has knwoledge of ovulatory cycle
Family plan = 1 if a woman ever v384a, - planning heard of a local family v384b knowledge planning from tv or radio
Age groups age2024 Dummy variables v013 + age2529 Current age in 5 - year groups age3034 age3539 age4044
Age at first age_birth Age of the respondent at v212 - birth first birth
Type of place place Type of place of v102 - of residence residence where the respondent was interviewed as either urban or rural
= 1 if a woman lives urban area
The current or most v701 - recent husband or partner's highest level of education attended Hus _ edprimar = 1 if husband or partner has pnmary education level
Husband or = 0 otherwise partner's education Hus edsecond - = 1 if husband or partner - level has secondary education level
Hus _ edhigher = 1 if husband or partner - has higher education level
•!• Women's schooling is classified in the study in three distinct dummy variables: primary education level (edprimar), secondary (edsecontf) and higher education (edhigher) No education is served as a reference base It is expected that women's schooling will have a negative coefficient Theoretically, given the opportunity costs of childrearing (which is time intensive), the utility of the woman will be maximized by reducing the number of children to reproduce and spend more time in other earnings activities
•!• The knowledge of ovulatory cycle (know) may also affect the probability of a woman to have fewer children This is especially important in developing countries such as Vietnam where the number of unwanted births is very high partly because of women not knowing their reproductive cycle The variable know was measured by asking a woman at what time during her menstrual cycle she is likely to get pregnant It is expected that the estimated coefficient will have a negative
•!• Family planning knowledge (plan): The variable plan is used in this study to see the influence of family planning knowledge on number of children born per a woman The variable is measured by asking women if they ever heard a local family planning program broadcasting in Vietnam It is expected that the variable will have a negative sign indicating that knowledge of family planning decreasing the number of children a woman may have
•!• Age of women (agr): The probability for a woman to have many children also depends on her age Age of women were asked during the survey then they are grouped for analysis Following the research of Mensch et al (1996) dichotomous variables for the respondent's age groups are constructed in six dummy variable as: age20-24, age 25-29, age30-34, age35-39, age40-44, and age45-49) The age group 15-19 is served as a baseline
•!• Age of first birth (age_ birth): The age at which a female gives birth to her first child is important The lower the age at first birth is the higher risk of more number of births This variable is expected to have a negative sign, showing that age of first birth influences to children ever born
•!• Type of place of residence: The variable for urbanization (place) is equal to 1 if a woman lives in the urban areas The coefficient on the variable place is expected to be negative suggesting that women who live in urban areas will have fewer children than their counterparts in the rural areas
•!• Husband or partner's schooling is classified m three groups of independent variables: pnmary education level (Hus_edprimar), secondary (Hus_edsecond) and higher education (Hus_edhigher) No education is served as a baseline It is expected that husband/partner's schooling will have a negative influence to fertility of his women.
Estimation strategy
Poisson regression model (PRM)
Long and Freeze (1997) found that Poisson distribution is fundamental to understanding regression model for count data In general, the distribution can be understood as follows:
Let y be a random variable indicating the number of children ever born If y has a Poisson distribution, then: e-llflY Pr(yiJl)= , y y = 0,1,2,
Where Jl > 0 is the sole parameter defining the distribution
Four characteristics of Poisson distribution that are important for understanding regression models for counts:
1 Jl is the mean of the distribution As Jl mcreases, the mass of the distribution shifts to the right
2 Jl IS also the vanance Thus, var(y) = Jl, which is known as equidispersion In real data, many count variables have a variance greater than the mean, which is called overdispersion
3 As J l increases, the probability of a zero count decreases For many count variables, there are more observed zeros than predicted by the Poisson distribution
4 As J l increases, the Poisson distribution approximates a normal distribution
The Poisson regression model (PRM) extends the Poisson distribution by allowing each observation to have a different value of J L The PRM assumes that the observed count for observation i is drawn from Poisson distribution with mean J li , where J li is estimated from observed characteristics This is sometimes referred to as incorporating observed heterogeneity and leads to structural equation:
The PRM is appropriate for modeling fertility because it accounts for the nature of dependent variable by using an exponential distribution.
Factor change in E(yJx)
The estimated parameters can be used to examine the changes in J l for changes in the independent variables by using the rate (i.e factor change, marginal change or predicted)
According to Long and Freeze (1997), the most common method of interpretation is the factor change in the rate If we define E(y I x, rJ as the expected count for a given x where we explicitly note the value of~ and define E(y
I x, ~ + l>) as the expected count after increasing ~by () units, then
Factor change can be interpreted as: for a unit change in ~.the expected count changes by a factor of exp(~), holding all other variables constant
Standardized factor change: for a standard deviation change in ~ the expected count changes by a factor of exp(,Bk x sk), holding all other variables constant.
Percent change in E(yJx)
According to Long and Freeze (1997), the percent change in the expected count for a () unit change in xk , holding other variables constant, can be computed as:
Chapter summary 3 7
In this chapter, model specification, measurement of variables and estimation method are presented The definition of dependent and independent variables, their notation and expected signs of parameter are also explained and summarized The main source of data are drawn from VDHS 2002 of survey 5,665
- - - - ever married women aged 15-49 However, only 5,381 observations have complete information is used in the regression model Finally, Poisson regression model will be applied to find out the effects of women's education on fertility in Vietnam
Effects of dependent variables on the fertility is explained via factor changes
SOCIOECONOMIC CONTEXT AND PROFILES OF WOMEN'S
FERTILITY This chapter provides a descriptive summary of the background characteristics of respondents sampled in the Vietnam Demographic and Health Survey of 2002 The characteristics examined include respectively socioeconomic factors and proximate determinants such as: education, type of place of residence, employment, contraceptive use etc These characteristics are chosen because fertility level could be determined by them and helped better understanding the results of empirical study.
Geography and economy
The Socialist Republic of Vietnam is located in Southeast Asia bordering the Peoples Republic of China to the North, the Peoples Democratic Republic of Laos and the Kingdom of Cambodia to the west, and the Pacific Ocean to the east With a coastline of thousands of kilometers from north to south, Vietnam has a land area of 330,000 square kilometers and a sea area of one million square kilometers There are thousands of small and large islands, some of which are isolated, while others form archipelagos in the East Sea Vietnam lies in the hot region of the tropics The climate is monsoon and subtropical in the North, which has four distinct seasons
The southern provinces experience two seasons, a rainy season and a dry season
Some provinces in the center of the country are characterized by the 'hot wind' influence in summer caused by the Truong Son mountain range in the west adjacent to Laos Vietnam includes tropical rain forests, hills and mountains, and fertile agricultural land Mountains, highland and forests cover about 80 percent of Vietnam's land area These areas have low agricultural productivity The Red River Delta in the North and the MeKong River Delta in the South provide the main source of food for the whole country The country is divided into 61 provinces and cities directly belonging to the central government There are three administrative levels in Vietnam: provinces, districts, and communes At present, there are 600 administrative units at district level (districts, urban districts, cities belonging to provinces, and towns) and about 11,000 administrative units at commune level or equivalent (ward, town, hamlet)
In the period of 1954-1975, the economy in North Vietnam was centrally planned and based mainly on agriculture There were only two socialist sectors in the economy, the state sector and the cooperative sector From 1975 to 1980, after the unification of the North and the South, the centrally planned model was applied in the South, pursuant to the second five-year plan (1976-1980) In the period 1981-
1985, the contractual system was improved, with contractual quotas being given to working groups and individuals in agricultural co-operatives In 1986-1991, Vietnam implemented institutional reforms with a market orientation and endeavored to stabilize the economy In the 6th Assembly, the Vietnamese Communist Party recognized the existence of the private sector and established a policy of eliminating subsidies In the period 1991- 1995, Vietnam accelerated economic reforms and built up "the multi-sector economy operating along market mechanisms with state management and a socialist orientation." The period since
1995 has been characterized by a marked effort at reform and development The structure of gross output in 2002 is as follows: agriculture-forestry-aquaculture sector (23.0 percent); industry and construction sector (38.5 percent); and service sector (38.5 percent)- (VDHS 2002 report)
Population and family planning policies and programs
Population
The major source of demographic data in Vietnam is the population census
Since unification in 1975, there have been three national population censuses, carried out in 1979, 1989, and 1999 Additional population data have been collected in nationwide demographic sample surveys and other related surveys
Some demographic indicators from the two most recent censuses are shown in Table 4.1 According to the 1999 census, Vietnam's population grew at the rate of 1.7 percent annually, a decline from 2.1 percent as of the 1989 census The total population in 2002 was estimated to be around 79.7 million people Thus, the population growth rate in the period 1999-2002 continued to decline The births per woman in the census 1989 were 3.8, however it reduced sharply to 2.3 births per woman in the census 1999 For ten years, total fertility rate of Vietnam has a sharply decline
Table 4.1 Basic demographic indicators Selected demographic indicators, Vietnam
Sex ratio (number of men per 100 women) 94 96
Crude birth rate (of oo) 30.0 19.9
Crude death rate (of oo) 8.0 5.6
Total fertility rate (births per woman) 3.8 2.3 a Compared with the 1979 census Source: VDHS2002 report
Family Planning Policies and Programs
The Democratic Republic of Vietnam in the North was among the first developing countries to adopt a policy to reduce the population growth rate (VDHS
2002 report) As early as 1961, spurred by the results of the 1960 population census in the North, the government of the Democratic Republic of Vietnam promulgated a decree to encourage married couples to restrict family size and space births to reduce population growth The policy was motivated by pressure on cultivated land and chronic food shortages in the North, as well as by the related desire to improve women's and children's welfare, being part of the strategy to enhance labor productivity to meet the needs of the struggle for independence and reunification of the country In the South of Vietnam, prior to unification, the standing government did not promote family planning until the U.S Agency for International Development encouraged it to do so in 1971 Nevertheless, the family planning program in the South remained incomplete until the end of the war After unification, the policies to reduce population growth received increasing attention of the government and efforts to extend coverage of birth control services throughout the country gained the highest priority A series of government decisions and decrees in late 1988 showed the formal approval at the national level of a policy advocating a family norm of one to two children The National Health Law approved by the National Assembly on 30 June 1989 legalized the principle of freedom for couples in choosing family planning practices It emphasized that individuals must be free to choose the family planning method they wished and stated that "all acts of preventing or forcing the implementation of family planning are prohibited." In January 1993, the Communist Party Central Committee for the first time approved a resolution on population and family planning In a strong statement, they identified excessive population growth as contributing to a wide range of social, economic, and ecological problems 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
The Strategy in Population and Family Planning to the Year 2000, the Strategy in
Population for the Period 2001-2010, the Strategy in Reproductive Health for the
Period 2001-2010, and the State Law on Population launched by the National Assembly's Standing Committee are comprehensive and official plans to guide efforts to implement the above resolution.
General characteristics of women's fertility
Figures in the table show both the actual (unweighted) and weighted number of women interviewed Weighting is necessary to compensate for differences in the selection probabilities and the response rate Because the sample design was not proportional, but included over-sampling in certain areas, weighting is required to make data reflect the actual proportional distribution in Vietnam
Table 4.2 Distribution of ever-married women by background characteristics(%),
Background characteristics of the respondents
Compl.higher secondary or more 16.8 953 956
Table 4.2 shows that the population has a young age structure as 28.5 percent of respondents represented women aged 15-29 at the time of the survey Six percent of women have no education, 17 percent of women have some primary education,
28 percent have completed primary, 32 percent have completed lower secondary and close to 17 percent have completed higher secondary education The majority (80.9%) of respondents lived in rural areas.
Differentials in education level of women
Table 4.3 Level of education of ever-married women, Vietnam 2002 (%)
Percent distribution of ever-married women by the highest level of education completed, according to background characteristics, Vietnam 2002
Background No Some Completed lower higher of
Characteristics education primary primary secondary secondary+ Total women Age
Family planning message
to broadcast media than older women (see Table 4.4) All ever-married women by age prefer to hearing messages on TV than radio Urban women are slightly more likely than rural women to have been exposed to family planning messages, especially those on television The majority of ever-married women have been exposed to messages on both radio and television
Table 4.4 Exposure to family planning messages on radio and television (%)
Percent distribution of ever-married women by whether they had heard a radio or television message about family planning according to background characteristics, Vietnam 2002
Heard family ~Ianning message on radio or TV
Background Radio and Radio Television radio nor of
Characteristic TV only only TV Total women
Children ever born
On average, women in their early thirties have given birth less than two children while women in their early forties have given birth to more than three children At older ages, women have more children
Table 4.5 Children ever born by ever-married women aged 15-49, classified by place of residence and education level
Mean of children ever born Number of
Source: Computed from VDHS2002 sub-data set
Mean number of children ever born in rural is greater than mean number of children ever born in urban areas Women in rural areas trend to have more children than women in urban areas As shown in Table 4.5 the higher education level women have, the less children ever born they have Women who are illiterate have the number of children more than three, while that of women with higher education level is less than two
This chapter describes the general characteristics of the 5,665 ever married women aged 15-49 and their children Regarding to the relationship between socio - characteristics the analysis shows that the older the women the higher the number of children born In the urban the number of children born by a woman is less than that for a woman living in the rural Although fertility rate is reducing by many factors, women education seems to be one of the most important determinants in Vietnam
The higher educated women are, the fewer children ever born they have Further analysis will be continued in the next chapter in order to identify empirically determinants of fertility in Vietnam
CHAPTER FIVE FACTORS AFFECT WOMEN'S FERTILITY IN VIETNAM This chapter analyzes and presents the empirical results and findings on effect of women's education on fertility in Vietnam through data of VDHS2002
The chapter starts with the presentation of estimation results Then estimated parameters will be used to estimate the change in number of children ever born using the value of independent variables.
Empirical model
Fert = {1 0 + {J 1 edprimar+ {J 2 edsecond + {J 3 eduhigher+ {J 4 know+ {J 5 plan + {J 6 age_mar + {J 7 place + {J 8 hus_edprimar + {J 9 hus_edsecond + {J 10 hus_eduhigher + {J 11 age2024 + {J 12 age2529 + {J 13 age3034 + {J 14 age3539 + {J 15 age4044 + {J 16 age4549 + J1
Estimation results
As shown in Table 5.1, variables edprimar, edsecond, eduhigher, know, age_birth, place, hus_eduhigher, age2024, age2529, age3034, age3539, age4044 age4549 are statistically significant at the one percent level The variables plan is 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
- - - - - 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
"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
• edsecond -0.264* 0.026 eduhigher -0.356* 0.038 know -0.043* 0.013 plan -0.040** 0.018 age_birth -0.058* 0.002 place -0.201 * 0.013 hus _ edprimar
-0.122* 0.039 age2024 0.395* 0.033 age2529 0.809* 0.031 age3034 1.109* 0.030 age3539 1.354* 0.031 age4044 1.565* 0.031 age4549 1.721 * 0.032 cons 1.324* 0.048
Notes: nS81; * denoted significant at 1% level
0.768 0.700 0.958 0.961 0.944 0.818 0.994 0.963 0.885 1.484 2.247 3.033 3.872 4.780 5.594 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
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 reduced- form 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
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 decision- making, encourages the diffusion of an innovation such as fertility limitation, offers 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 decision- making 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
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
• 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
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
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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 2: log pseudolikelihood log pseudolikelihood log pseudolikelihood
Number of obs 5381 Wald chi2(16) 8222.69 Prob > chi2 0.0000
I Robust fert I Coef 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 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 (NS81) : Factor Change in Expected Count Observed SD: 1.5210555
-+ - edprimar -0.17783 -7.151 edsecond -0.26364 -10.172 eduhigher -0.35654 -9.453 know -0.04319 -3.284 plan -0.04011 -2.237 age_birth -0.05799 -36.952 place -0.20130 -15.095 hus _edprimar -0.00562 -0.185 hus edsecond -0.03811 -1.248 hus_eduhig-r -0.12205 -3 115 age2024 0.39458 12.059 age2529 0.80945 26.317 age3034 1.10957 36.261 age3539 1 35383 43.573 age4044 1 56448 49.979 age4549 1 72173 54.032 b raw coefficient z P>lzl z-score for test of b=O p-value for z-test
0.000 0 8371 0.9237 0.4463 0.000 0.7682 0.8802 0.4838 0.000 0.7001 0.9343 0.1905 0.001 0.9577 0.9814 0.4346 0.025 0.9607 0 9871 0.3240 0.000 0.9437 0 8116 3.5997 0.000 0.8177 0.9189 0.4203 0.853 0.9944 0.9977 0.4105 0.212 0.9626 0.9825 0.4635 0.002 0.8851 0.9729 0.2255 0.000 1.4838 1.1144 0.2746 0.000 2.2467 1.3579 0.3780 0.000 3.0330 1 5476 0.3936 0.000 3 8722 1.7295 0.4047 0.000 4.7802 1.8549 0 3949 0.000 5.5942 1 8475 0.3565 e b e bStdX SDofX 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