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CHAPTER 1 1. 1 INTRODUCTI ON When the first round of World Fertility Surveys (WFS) is complete, some forty Third World nations will have collected comparable individual-level data on fertility and contraceptive use patterns. These surveys provide a unique opportunity for comparative analysis of countries that vary demographically, socially, cuturally, and economically. Such an analysis Yes an organizing framework. '1'0 ants end, the present chapter des- cribes a model of the fertility process that can be applied to behavior in diverse settings.] To some extent, this model is tailored to the information available on the WFS standard recode tapes; however, crucial assumptions and requirements of the model fit other fertility surveys as well. The model we develop here integrates diverse hypotheses on the determinants of fertility behavior, including many of the insights contained in frameworks proposed by Davis and Blake (1956), Freedman (1975), and Easterlin (1978~. The purpose of this chapter is to outline the model's rationale and predict the directions and rough orders of magnitude of the relationships among the variables included. The propo- sitions embodied in the model are in principle empirically testable. Chapter 2 presents results of a tentative empirical application of the model, and future research efforts will extend this application. Tn essence, the model is multi-level. For the sake of simplicity, the present discussion will be largely restricted to just two levels-- within countries (micro) and between countries (macro). The former can be formalized as a set of structural equations, the same set applying to each country. Because the coefficients of these equations are expected to vary across countries, we use a related macro-level model to explain this variability. In the development of the multi-level model, the greatest complexity and subtlety were encountered in the specification of the micro-level coefficients. Therefore, the discussion concentrates on exposition of the micro-level structural equations. Although the essential macro-level considerations are introduced, a specific set of macro-level structural vacations i ~ not In The present chapter is organized as follows. The remainder of this section describes essential characteristics of the model--its use of an ideal-typ~c distinction between traditional and transitional settings, or 1

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2 the context of fertility behavior; the conceptual and analytic leverage gained by representing reproductive behavior as a process; and the causal assumptions employed. Section 1.2 provides an overview of the model, while Section 1.3 explains each micro-level structural equation. These sections also introduce hypotheses concerning between-country variability in the micro-model coefficients. Section 1.4 discusses the reduced and semireduced form implications of the micro model. Finally, Section 1.5 reviews the key points raised by the chapter and discusses issues arising from the exposition of the model. The Context of Fertility Behavior The most important use of the micro model is to describe the effects of socioeconomic variables on fertility behavior. me theoretical reasoning behind the model makes it equally meaningful for estimating the micro equations for relatively undifferentiated and economically underdeveloped societies, for highly fragmented and inegalitarian societies, and for somewhat modernized bureaucratic societies. The magnitudes, and sometimes the directions, of the effects of specific socioeconomic factors can be expected to vary across societies in meaningful ways. Indeed, the point of the full multi-level model is not only to describe key micro-level relationships, but also to explain why they differ across societies. To organize such between-country differences, both in reproductive behavior and in the relationships between that behavior and socioeconomic determinants, we use an ideal-typic distinction between traditional and transitional societies. At certain points in the discussion, it will also be helpful to apply this distinction to intrasocietal ethnic or regional variations. Therefore, the discussion below refers to tradi- tional and transitional settings. In traditional settings, fertility is not controlled deliberately or explicitly. In transitional settings, family size and contraceptive use are viewed as subject to decision making and control. m is distinction is exemplified at the country level by Nepal and Panama, two WFS countries. Although possibilities for variation in reproductive behavior are considerably more complex than this distinction, it is a convenient summary device. As will be seen, even when such a dichotomy is used to characterize setting differences, there is great complexity in the model and some indeterminancy in its reduced and semireduced form implications. Broadly speaking, there is a continuum between these two settings, depending on the distribution of views and practices concerning fertility decision making and control. As defined here, this continuum need not be extended to "modern" settings, a label avoided here because its connota- tions are too broad. The concepts of fertility decision making and control can be established in settings where levels of income and educa- tion are low, settings which might be judged "nonmodern" according to criteria of socioeconomic development. Moreover, the present discussion is not concerned with developing a fertility model for modern, industrial- ized settings, both for policy and practical reasons. Although the micro equations of the model could be estimated for such settings, they were not created for that purpose and would not provide especially enlighten- ing information.

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3 A shift from a traditional to a transitional pattern can lead to a decline in fertility. It is a central goal of demographic transition theory to understand this shift, as well as its role in both the histor- ical fertility decline in Western societies and changing fertility levels in developing countries (Cochrane, 1979; Beaver, 1975; Leibenstein, 1974~. Subject to the general methodological limitations inherent in applying inferences from cross-sections to time-series berg., Kuh, 1959), applying the model developed here to data such as those collected by the WFS should contribute to this understanding. How are differences across settings in the coefficients of the micro- model to be explained? Three classes of macro or contextual factors seem important: level of socioeconomic development, the extent and vigor of organized family planning programs' and culture and ethnicity. A fundamental hypothesis of social demographic theory is that, among high-fertility nations of the Third World, socioeconomic development will lead to fertility decline (Bollen and Entwisle, 1981~. If this hypothesis is correct, development is an important macro factor explaining between- setting variability in the micro-model coefficients. Since macro data on socioeconomic development are available, its effects on micro coefficient variability can be analyzed empirically. The traditional/transitional and development continua are related but not identical. The two may reflect different parts of the same whole, but movement away from tradi- tionality will not necessarily occur at the same pace as socioeconomic development. m us, although it is difficult to envision a socioeconomi- cally developed society with traditional fertility behavior, a socioeco- nomically ~ ass-developed society with explicit fertility regulation is plaus ible. The latter circumstance could occur as a consequence of organized family planning efforts. Freedman and Berelson (1976) have pointed out that an active family planning program can encourage fertility reduction, even in the absence of marked socioeconomic advances. Since family planning effort scores (Mauldin et al., 1978) and other indicators of government-sponsored family planning programs (Nortman and Hofstatter, 1980) are available, the impact of intersocietal differences in these programs on the coefficients of the micro model can be examined. Finally, analyses of historical fertility declines show how important cultural and ethnic factors are in determining the responsiveness of fertility levels to socioeconomic change (e.g., van de Walle and Knodel, 1980~. There seems to be better communication about fertility matters within than between ethnic groups. Major world religions vary in their emphasis on the value of children and their prescriptions regarding family size and contraception. Likewise, ethnic groups differ for historical and cultural reasons in their receptiveness to Western values concerning work, achievement, and economic advancement, all of which can relate to fertility regulation. Thus, intersocietal differences in fertility behavior are partly linked to ethnicity, religion, and culture more generally. For this reason, our attempt to model and explain between- society variability in the coefficients of the micro model will include cultural and ethnic factors as explanatory variables. In particular, and where possible, we will estimate separate micro models for each ethnic group. This will permit assessment of the fertility effects of cultural differences across ethnic groups.

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4 Fertility as a Process An essential feature of our approach is that fertility is modeled on a cohort-specific processual basis. A complete version of the micro model applies only to individuals well over 30 years old at the time of the survey, limiting the sample to ~ 10- or 15-year age group of WFS respon- dents. Our attention is further restricted to five-year birth cohorts, for several reasons. First, this reduces the need to consider age as a determinant of fertility. Second, it allows assessment of the differen- tial response of cohorts to contextual change. Finally, it provides unambiguous reference points for assessing the influence of contextual or macro fertility determinants. We model fertility as a process rather than a single outcome, as would be the case if children ever born were an endogenous variable in the structural equations of the model. This is accomplished by dividing the fertility process into three components--onset, early, and later-- each defined by reference to the age of the mother: onset refers to age at first birth, early fertility to births occurring before age 30, and later fertility to births thereafter. This use of only three components reflects a balancing of conceptual needs against the analytic tractability of data. A continuous time approach, or a discrete time approach with three or more intervals (e.g., early, middle, late), would better approxi- mate the fertility process, but would also pose severe data analysis problems at the micro level. The threefold distinction used here just suffices to capture the critical aspects of the fertility process. Onset is a crucial component both because it constrains fertility and because it can reflect explicit decision making about subsequent fertility behavior. Early and later fertility are distinguished to permit accurate modeling of the fertility process at the micro level, as well as to clarify differences among social settings at the macro level. Figure 1.1, inspired by a similar but simpler diagram of Coale's (1972:6) , schematically relates the three components of the fertility process to setting. Four different fertility curves are shown, corresponding to the cross-classification of early/late onset and traditional/transitional setting. m ese four curves, best thought of as ideal-typic patterns, will serve the purposes of the present discussion. me micro-level socioeconomic determinants of fertility can be expected to operate differentially at various stages of the reproductive process and within different societal contexts. The directions of these socioeconomic effects should be as indicated in Table 1.1. In an ideal- typic traditional setting, explicit fertility control is absent, and levels of early and later fertility are high. As will be shown, this leads to the hypothesis that socioeconomic status, globally defined, varies positively (though weakly} with both early and later fertility. For reasons of health, wealth, and social desirability more generally, i. may also be expected that the onset relationship in traditional settings will be, if anything, negative. In an ideal-typic transitional setting, the effects of control are seen on both onset and later fertility. The onset and later fertility relationships can therefore be expected to be relatively strong, the former being positive and the latter negative. Because early fertility in these settings probably reflects little

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5 EARLY FERTILITY J UJ LL Early / / Onset / / ': / 15 _ Late Onset 30 LATER FERTII'TY Transitional \ Society \ (Contracepting) \ \ AGE FIGURE 1.1 Fertility Rates by Setting Traditional Society (Non-Contracepting) 50 explicit decision making, it would not be surprising if the early fer- tility relationship were similar to that found in traditional settings. This relationship can therefore be expected to be positive, but not strong. Causality m e decomposition of the fertility process into onset, early fertility, and later fertility is crucial to the determination of causal links within the micro-level model for three reasons.2 First, these three components are themselves causally related, and modeling these relation- ships should prove revealing. Onset circumscribes early fertility by constraining exposure time. Both onset and early fertility have implica- tions for later fertility, the hypothesized nature of which depends on setting. In traditional contexts, delayed onset and low early fertility may reflect fecundity problems, depressing later fertility as a result.

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6 TABLE 1.1 Expected Directions of Socioeconomic Effects on Fertility Components, by Social Setting Type of Setting Fertility Component Traditional Transitional Onset (AFB) Early Fertility (EF} Later Fertility (LF) + + In transitional contexts, delayed onset may lead to low later fertility because of linkages between the decision to delay marriage and the decision to limit family size. At the same time, because women in these settings probably make contraceptive decisions based on their early fertility experience, low early fertility should reduce the motivation to contracept, leading to relatively high later fertility. A second advantage of using the three-component fertility process is that it serves as the basis for a richer and more precise structural equations model than would be possible if we were to use children ever born in place of early and later fertility. For example, our division of child mortality into early (child deaths occurring before mothers reach age 30) and later (child deaths thereafter) has considerable payoff in modeling the child mortality-fertility nexus. In addition, our use of the three components permits a distinction between exogenous and endogen- ous socioeconomic variables. We treat childhood residence and respondent education as exogenous. The erogeneity of childhood residence within the context of the model is clear. Respondent education is predetermined with respect to onset, early fertility, and later fertility since women in less-developed countries generally complete their schooling well before marriage. Husband's education, on the other hand, is an endogenous variable in the model. Women's characteristics affect their opportunities in the marriage market and hence the characteristics of their husbands. Furthermore, WFS surveys collect information about current husbands' achievements and activities. Since marital dissolution exceeds 40 percent in some WFS countries (Durch, 1980:21), current husband characteristics cannot be considered as predetermined with respect to onset or early fertility. Use of the fertility components thus permits a more precise and thorough understanding of the relationships between socioeconomic variables and fertility. A third advantage of the three-component fertility framework is that it provides leverage in sorting out simultaneities. Consider, for example, the relationship between child mortality and children ever born. As discussed more fully in Section 1.2, child mortality can

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7 increase fertility in a number of ways. In populations where breastfeed- ing is common, an infant death shortens birth intervals by reducing the length of postpartum amenorrhea (Chowdhury et al., 1976~. Parents may deliberately replace a lost child (Preston, 1977) or hoard children in anticipation of additional deaths in the future (Taylor et al., 19761. In the other direction, an increase in family size can increase mortality risks, since resources must be spread over more family members (Watson et al., 19797. Our model isolates an effect of early child mortality on later fertility and an effect of early fertility on later child mortality. This reduces the need for concern about simultaneity between child mor- tality and fertility. It also provides leverage on the simultaneity between child mortality and contraceptive use. Thus, the temporal disag- gregation contained in the model resolves simultaneities that arise when process is ignored and fertility is perceived simply as children ever born. Our three-component decomposition does not remove all potential simultaneities from the model. We considered each remaining potential simultaneity, and in each instance concluded that there is insufficient theoretical or conceptual justification for identifying the relevant parameters, given the variables in the WFS. For this reason, the m~cro- level structural equations model is block-recursive. We felt that the encroachments of simultaneity bias were more than offset by the benefits of partitioning children ever born into early and later fertility. Finally, there is a derived advantage resulting from modeling process rather than a count. Cochrane (1979) and Hermalin and Mason (1980), among others, envision a curvilinear relationship between education and children ever born at the micro level. Since children ever born is the sum of early and later fertility, it follows that our micro model contains an implied relationship between education and children ever born. Any endogenous variable or linear combination of endogenous variables in a structural equations model can be expressed as a function of nothing more than the exogenous variables. Because children ever born is a linear combination of early and later fertility, it can be expressed as a func- tion of respondent education and childhood residence. Our structural equations model contains curvilinear relationships and interaction terms. The implied reduced-form expression of children ever born as a function of respondent education and childhood residence also involves a curvilinearity in the education-children ever born relationship. The rationale for this curvilinearity is more extensive than and has a somewhat different basis from that for the direct modeling of children ever born. 1. 2 OVERA7Ih'W OF THE MODEL As a basis for further discussion of the model, Figure 1.2 depicts its causal structure and component variables. Abbreviations used in Figure 1.2 and hereafter are defined in the Glossary (page 1211. The model contains four blocks of variables indicated by Roman numerals across the top of Figure 1.2. Block I contains the exogenous variables, while Blocks II-IV correspond to the three components of the