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61 in the intercept of the EF structural equation was based on another aspect of the theory developed in Chapter 1: expectations about the hypotheses that socioeconomic effects on EF are weak, relative to both the effect of "exposure" on EF and the effects of the socioeconomic variables on other fertility components. Of the three components of the fertility process, it is posited that LF should be most susceptible to socioeconomic posi- tion, onset less so, and early fertility least. This hypothesis is, of course, specific to the range of countries most likely to be encountered in the WFS, and is not meant to apply to the most industrialized or socioeconomically developed societies. Finally, this hypothesis points to another aspect of the theory developed In Chapter 1 which can be tested by means of replications over countries, as illustrated later in this chapter. 2.4 DATA AND VARIABLES EMPLOYED As noted earlier, the empirical analysis presented here is based on the 15 countries for which WTS data are currently available. These countries include Colombia, Costa Rica, Fiji, Guyana, Indonesia, Jamaica, Jordan, Kenya, Korea, Lesotho, Malaysia, Panama, Peru, Sri Lanka, and Thailand. The measure used for family planning program scope and vigor is that of Mauldin et al. (1978), which refers to the state of the program circa 1972. In the macro analyses that follow, this variable is used in its actual scored form, and also as a dichotomy with countries coded as none to weak versus moderate to strong. Socioeconomic development is measured variously. In our macro analyses, we experiment with gross domestic product per capita circa 1965 (Hagen and Hawrylyshyn, 1969~. In addition, we attempt two transformations of the social setting index of Mauldin et al. (1978), which refers to conditions circa 1970. In the first of these, we rank-order the available 15 WFS countries on this index. In the other, we dichotomize the index into low to upper middle versus high. Of course, for any macro analysis only one measure of family planning program effort is considered at a time, and only one measure of socioeconomic development. The micro model is cohort-specific. The results reported here are based on a single birth cohort of currently married respondents aged 40-44 in 1974. We use this year because the survey dates range over a four-year period (1974-771. m is particular cohort was chosen, first, to permit study of between-country variability in the socioeconomic deter- minants of all three components of the fertility process. Selection of an older cohort allows sufficient time for the implications of socioeco- nomic characteristics to develop. In addition, older cohorts experienced first-hand the effects of the family planning programs introduced in many countries during the 1965-74 decade. Respondents aged 40-44 in 1974 were aged 30-34 in 1964 ; according to the logic underlying the decomposition of the fertility process, these women were entering the potentially discretionary stage of their reproductive life as major policy shifts were taking place at the macro level. m is cohort may thus have played a critical role in determining the success or failure of family planning programs in many Third World countries.
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62 The data used to estimate the micro models are from the WFS standard recode files. Table 2.3 lists the variables appearing in the AFB, EF, and LF structural equations and gives their operational definitions. The definitions of the fertility components, early outcomes, and adjustment variables are invariant across countries. Some socioeconomic variables are defined identically from one country to the next (WBM, WSM, HOCC), while others are not (WED, HED, RESC, RES).7 The number and meaning of the categories for the education and residence variables vary across countries. Our intent in allowing this variability is to ensure that the application of the micro model will be as meaningful as possible. For most of the micro variables, this goal does not conflict with that of comparability. However, the education and residence classifications sacrifice a degree of comparability for greater country-specific meaning. The critical cutting points for years of schooling depend both on the nature of the school system and on social context more generally. Because we selected the educational categories used in each country's First Country Report, the highest category of education ranges from Come college" (Panama, Jordan, Malaysia, Korea) to "more than primary. (Colombia, Guyana, Jamaica) , while the number of categories ranges from three (Jamaica, Guyana) to seven (Jordan). Residential classifications vary similarly. For purposes of this exploratory analysis, we have categorized socio- economic variables that are usually treated in scaled form to permit assessment of relationships for shape and monotonicity. In later analy- ses, we expect to scale all variables where possible. As noted in Chapter 1, ethnicity may have an impact on the structural equations. Tts effects can vary among individuals in a way that amounts to setting differentiation. Ultimately, then, it is necessary to test for differences across ethnic groups in the parameters of the structural equations within each country for which ethnicity is relevant. We defer such a full exploration for later analysis, though we account for eth- nicity partially here. In particular, allowing for ethnic differences in all parameters would be equivalent to allowing ethnicity to interact with all of the variables in a structural equation. We estimate models which are not interactive with respect to ethnicity, but additive. This allows for intercept differences between ethnic groups, but not slope differ- ences. Moreover, this specification potentially modifies the coeffici- ents of all other predictors in a structural equation relative to what they would have been had ethnicity been excluded. m e reason for this, of course, is that ethnicity can be associated with the means of the explanatory variables in the structural equation. We use multiple regression to estimate the AFB, EF, and LF struc- tural equations. To assess the validity of the macro hypotheses about micro coefficient variability, we use the first derivatives of the effects of the explanatory variables, derived from the micro regression results. These derivatives are either actual regression coefficients, or transfor- mations evaluated at particular values of the relevant explanatory variables when we have hypotheses about interactions.
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63 TABLE 2.3 Micro Variables and Their Operational Definitions Fertility Components AFB EF Outcomes EF ECM SCB SCG Adjustment Variables NMAR DUR FEC Socioeconomic Variables . RESC WED WBM WSM HED HOCC RES Age at first birth Number of children born before respondent reaches age 30 Number of children born on or after respondent's 30th birthday See above Number of children dying before respondent reaches age 30 Dummy variable taking the value of 1 if respondent has two or more living sons on her 30th birthday; 0 otherwise Dummy variable taking the value of ~ if respondent has one or more living daughters on her 30th birthday; 0 otherwise Number of marriages contracted by respondent The number of months in union between respondent's 30th birthday and survey date Dummy variable taking the value of 1 if respondent indicates no problems in having children; 0 otherwise Dummy variable ts) indicating place of childhood residence (omitted category is rural childhood residence) Dummy variables indicating level of schooling as coded in First Country Report (omitted category is lowest level) Dummy variable taking the value of 1 if respondent was employed In the modern sector after her first marriage; O otherwise Dummy variable taking the value of 1 if respondent was employed in the modern sector after her first marriage; 0 otherwise Dummy variables indicating level of husband education (omitted category is the lowest level) Dummy variables indicating husband occupation: employee (omitted category); self-employed agricultural; self- employed nonagricultural; employer Dummy variablefs) for current residence (omitted catgegory is rural residence)
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64 The categorical treatment of micro socioeconomic variables imposes constraints on the macr~level analysis. For one thing, it yields more than one coeff icient for micro variables consisting of more than two categories (RESC, WED, HED, HOCC, RES). For exploratory purposes, we focus on a particular contrast of each micro socioeconomic effect: that between the extreme categories. Thus we compare nonagricultural employers with nonagricultural employees;8 the most urban residents with the most rural; the highest educational category with the lowest. We expect betweenrcountry variability in these coefficients because of differences in the number and meaning of the categories used. Differences in the extent to which the contrast between extreme categories captures the maximum empirical contrast between categories of a particular explanatory dimension may also introduce variability. However, neither our theory nor the analyses presented here systematically address such variability. The interaction terms in the LF structural equation require special treatment. There is no single effect of respondent's education (WED) or husband's occupation (HOCC) on LF. The partial derivatives indicate that the effects of WED and HOCC depend on EF and son sufficiency (SCB): (2.7a) a(1~Di, = 82, 16 + 517, 16 (EF) + 619, 16 (SCB); a Cocci 612,16 + 818,16 (EF) + 820,16 (SCB) To obtain single WED and HOCC coefficients for use in macro-level analysis, we not only take extreme categories of each, but also evaluate the partial derivative at EF = 4 and SCB = 1. These values of EF and SCB should magnify the potential effect of WED and HOCC on LF. EF = 4 suggests a relatively large family early in the reproductive cycle, while SCB = 1 indicates at least two sons in that early family. 2 . 5 ILLUSTRATION OF OPE^TION=I ZATION The purpose of this section is to illustrate and clarify the preceding description of the micro and macro variables, and the operationalization of the micro model. To this end, we present a comparison, based on just two countries, that will clarify the hypotheses to be tested using the 15 WAS countries for which micro results are currently available. The countries chosen for this purpose are Peru and Korea. The WFS data sets for these countries are available for unrestricted scholarly use. Moreover, the two different cultural areas involved should provide an interesting contrast. The discussion begins with the macro comparison, and then turns to the micro results. Macro Comparison Peru and Korea are both in the high category of the Mauldin et al., (1978) social setting index, in which the higher the rank, the more