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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

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65 advantaged the social setting: of 94 less-developed countries, Peru ranks 73 and Korea 81. Of the 15 WAS countries studied here, Peru ranks 7 and Korea 11. This indicates, among other things, that this sample of 15 countries is skewed toward the upper end of the social setting index, and toward the upper end of socioeconomic development, a fact that will be important in the later analysis of all 15 countries. It is doubtful whether much importance should be attached to the difference between the ranking of Peru and Korea on the social setting index. Indeed, the relative position of these two countries depends to some extent on which indicators are used. Table 2.4 reproduces the figures used by Mauldin et al. (1978) to determine the social setting index scores for the two countries. As the table shows, Korea had the advantage in education, health conditions, and urbanization, while Peru had the advantage in income and relative size of the nonagricultural labor force. Data for Peru and Korea collected in a separate effort (Entwisle, 1979), paint a similar picture. Table 2.5 presents these macro data . Although social setting differences between Peru and Korea are relatively small, there is a major difference between the two counts ie s in family planning effort scores. Peru did not have a program in 1972, and thus is assigned a score of zero on the Mauldin et al. (1978) index. Korea, on the other hand, had a very strong program at that time. Only Singapore and China rank higher on the family planning effort scale. m e Korean government announced the need for a family planning program in TABLE 2.4 Macro Indicators Reported by Mauldin et al. (1978) Korea Peru Indicator 1960 1970 1960 1970 Percent adult literacy 71 88 61 72 Percent of 15-19 year-olds in primary or secondary 65 76 58 75 Life expectancy at birth 54 59 50 55 Infant mortality rate per 1,000 91 60 92 135 Percent of males 15-64 in nonagricultural occupat ions 31 45 37 41 GNP per capita (in 1974 U.S.$) 189 344 521 659 Percent in c ities 100, 000+ 23 38 15

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66 TABLE 2.5 Socioeconomic Characteristics of Korea and Peru, circa 1965 Indicator Korea Peru Source GDP per capita (in 1960 U.S.S) 99 372 Percent in cities 100,000+ 27 19 Hag en and Hawrylyshyn {1969) Davis (1969) Percent labor force in agriculture 59 47 ILO Yearbook of Labour Statistics Life expectancy at birth 56 53 Total school enrollment 74 70 (per 100 population aged 6-17) U.N. (1975) UNESCO Statistical Yearbook 1961, and initiated it in 1962 as part of a newly formulated national population policy. The aim was to reduce the population growth rate from 2.9 percent in 1960 to 2.5 percent in 1966 and 2.0 percent in 1971 (Han, 1970~. Selection and training of staff occurred dur ing the 1962-64 period (Kim et al., 1972~. The first major push to distribute contracep- tives began in 1964 with the IUD program. The program also offered vasectomies and condoms. In 1968, the pill was added. Abortion, though illegal, was widely practiced. Information and education received emphasis almost from the start. In the mid to late 1960s, these efforts included mass media messages, home visits, Mothers' Clubs, and formal instruction. Throughout this time, the program benefited from the interest and support of the government: in the 1965-75 decade, roughly one-fifth of the nation's health budget was expended on the family planning program (ESCAP, 1975~. To complete this sketch of macro differences between Peru and Korea, it is noteworthy that for the cohort studied here, mean children ever born is about 7.4 for Peru and 5.4 for Korea. This might be expected. For two societies of about equal socioeconomic development, the one with the stronger family planning program should have lower fertility. Further, if the theory outlined above is correct, Peru and Korea should be closer in level of early fertility (EF) than in level of later fertility (LF). In fact, the difference of .5 in mean EF is quite a bit less than the difference of 1.4 in mean LF. The macro differences between Peru and Korea suggest that the effects of the socioeconomic determinants of LF should be smaller for Korea than for Peru, mainly because of family planning program strength. mere should not be major differences between the two countries in the socioeconomic determinants of age at first birth (~FB) and EF. Of course, since the comparison here involves only two countries, results inconsistent with these expectations cannot be taken as decisive. Nevertheless, it is helpful to use the main

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67 hypotheses of the theory to organize this illustrative micro specification for Peru and Korea. Finally, before turning to the micro results of the comparison, it should be noted that we are not interested in ad hoc explanations of differences between Peru and Korea, or any other pair of countries. The micro parameters for each country will vary for historical, cultural, geographic and other reasons having little or nothing to do with level of socioeconomic development or strength of family planning program effort. Some of these factors, such as breastfeeding, kinship, and cohabitation patterns, will directly influence fertility outcomes, and we plan to incorporate a number of these in future analyses. Even if such factors were nonexistent, there would still be differences in what we can observe since we are necessarily working with parameter estimates. Furthermore, even if we envisioned enormous sample sizes, we would still be sampling cohorts and countries. For this reason alone, we do not expect to find strong confirmation of the theory on the basis of a comparison between two, or even fifteen countries. Micro Results for Peru and Korea Tables 2.6 to 2.8 present the AFB, EF, and LF regressions, respectively, for Peru and Korea. Our purpose in examining these regressions is to clarify how the various socioeconomic concepts have been operationalized; to illustrate how the quantities to be analyzed in the subsequent analysis of the 15 WFS countries are derived; and finally, to illustrate the implications of the theory in a simple comparison between two countries.9 Age at First Birth (AFB) Turning first to the AFB regressions, Table 2.6 indicates that the effect of childhood residence (RESC) on age at first birth (AFB) is negative but insignificant for both Peru and Korea. Our theory hypothesizes that this effect should be negative in traditional settings and positive in tran- sitional settings. Given the positions of Peru and Korea on the social setting index, a strong and significant negative RESC effect would thus have been incompatible with the theory. No effect for RESC is compatible with the theory, although we would have expected a slight positive effect In the macro analysis, the RESC effect Is taken to be -.88 for Peru and -.48 for Korea; these are net mean differences ~n LAB between child- hood residence in a city and in a rural (Peru) or village (Korea) setting Since the question will undoubtedly arise in the course of this discus- s ion, it is perhaps worth notch here that the RESC effects for Peru and Korea are not set to zero in the following macro regressions. Instead, we incorporate the estimated variances of the coefficients. For example, the city coefficient for Peru has an estimated standard error of .65 and an estimated variance of about .40. We use the estimated variances of the coefficients as rec. iprocal we ights in weighted regressions.

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68 TABLE 2.6 Age at First Birth (AFB) Structural Equation, Currently Married Women Aged 40-44 in 1974, Peru and Korea World Fertility Surveys Dependent Variable is AFB Explanatory Variables and Other Regression Information Peru Korea Childhood Residence (RESC) [.0033* [~003]* Rural/Villagea ~- ~~ Town -.11 .07 City -.88 -.48 Respondent's Education (WED) [.051*** [.051*** 0 years/0 years ~ 1-2/1-S .62 .33 3-4/6-8 1.18 .65 5-6/9-11 1.95 1.37 7+/12+ 3 47 Religionb [b] i.0051* Intercept 20.81 21.03C R2 .06 .06 N 581 700 Note: The asterisks indicate the statistical significance of selected coefficients (two-tailed test): *p > **.01 < p ~ .1 ***p ~ .01. The absence of an asterisk indicates that a significance test was not applied. When the explanatory dimension is a set of categories, summary information for tests of significance pertains to the global test that all coefficients within a classification are simultaneously zero. Bracketed entries are the squared multiple partial correlations associated with the dummy variable classifications. aWhen two category descriptions are provided, the first pertains to Peru, the second to Korea. Dummy variables omitted from the regressions for estimation (normalization) purposes are indicated by a dash. bThe coefficients for the religion categories are not presented here. This variable is unavailable for Peru. CThe effect of religion has been removed from this intercept so that cross-equation comparison of the intercepts is valid.

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69 Considering next the effects of wife's education (WED) on AFB, we observe that for both Peru and Korea, this effect is positive and monotonic with increasing years of schooling. Given that both Peru and Korea are in the high category of the social setting index, this is consistent with the theory. Notice that the implied education slope is much greater for Peru than for Korea. m e highest education category for Peru is seven or more years, whereas for Korea it is twelve or more years. Nevertheless, the coefficient for the top education category for Peru (3.47) is very nearly the equal of that for Korea (3.77), and it is these coefficients that we use in the macro analysis. What does this mean? Polytomizing education, as we do here, suggests a slightly stronger effect for Korea than for Peru. On the other hand, if we were to treat education as a scaled variable and compute slopes for Peru and Korea, the slope for Peru would be greater. Given the similar social setting scores for these two countries, little difference in the slopes of micro education effects would be expected. For reasons noted at the end of the previous section, this apparent discrepancy will not be explored here. It does reaffirm, however, that there is no single all-purpose coding of education, just as there is no single all-purpose macro measure of culture, family planning program effort, or socioeconomic development. Hermalin and Mason (1980} consider at length the problem of, and need for, different education scales. Our research will not proceed blindly in the face of this problem. The WFS collected data on neither ethnic nor religious differen- tiation in Peru, but did so for Korea. m us, the specification for Per u does not include an ethnic or religious classif ication as one of its explanatory dimensions, while that for Korea does. me additive effects for religion in Korea prove to be insignificant for the determination of AFB. In any case, regardless of their statistical significance, we do not include ethnic or religious coefficients in the macro analysis. The role of ethnicity/religion in this analysis is to adjust the other coefficients in the regression. That is, we obtain a single set of parameters to be studied at the macro level, controlling for ethnicity or religion. Since all of the discrete regressors are normalized using dummy coding (i.e., zero-one coding), it should be noted that the intercepts-- which are studied at the macro level--are always adjusted for ethnicity when this dimension is present in the source data. To understand this adjustment, note that the intercept for Peru is completed as Y - b2RESC2 - b3RESC3 - g2WED2 - g3WED3 - g4WED4 - g5WED5, where terms for RESC1 and WED1 do not appear since RESC1 and WED1 are the omitted categories in the use Of dummy coding for RESC and WED. The intercept for Korea as derived in the regression computations includes not only the terms indicated above in the intercept for Peru, but also the terms {-c2REL2 - c3REL3), where REL1 is the omitted category in the religious classification used for Korea. To purge this "rawer intercept of its religion component, we simply remove the additional terms for the religion classification. This yields the intercept for Korea presented in Table 2.6, and makes the intercepts for Peru and Korea comparable while still allowing for the additive effect of religion in Korea.l0 We apply this treatment to intercepts whenever we use an additive

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70 religion or ethnicity classification in the AFB, EF, and LF regressions whose coeff icients provide the quantities studied in the macro analysis. We do this for Fiji, Guyana, Jamaica, Korea, Lesotho, Malaysia, and Sri Lanka. Given that the AFB intercepts for Peru and Korea are comparable, what do they tell us? For the AFE regression, they tell us that estimated AFB is virtually the same for those who grew up in a rural or village environment and received no formal education--about 21 years of age--for both Peru and Korea. Recall that, when we regress the intercepts on the macro variables using all 15 countries, the results will provide the remains effects of the macro variables on AFB. Thus, the intercepts perform double duty, and do not merely provide predicted values for ~ particular concatenation of categories of the explanatory variables. In terms of this second role, we hypothesize that increases in socioeconomic development should lead to increases in the intercept of the AFB equation. As long as both countries are placed in the high category of the social setting index, the essential equality of the AFB intercepts for Peru and Korea is compatible with this hypothesis. Early Fertility (EF) In turning next to the EF regressions, recall that the theory indicates that EF should be least affected by socioeconomic position, and LF most affected. Examining Table 2.7, which presents the EF regressions for Peru and Korea, note that, as expected, none of the socioeconomic vari- ables (RESC, WED, and WBM) contributes significantly to the determination of EF. The only included variable that affects EF is AFB--an adjustment variable--whose effect is negative, as hypothesized. The later the AFB, the less the "exposure" between then and age 30, and hence the lower the EF. Since we have no expectations for variability in the effect of AFB on EF by setting, we do not attempt to interpret the difference between the AFB coefficients for Peru and Korea. For purposes of the macro analysis, the coefficients for the city, highest education, and WBM dummy variables are used. As noted earlier, we also employ the reciprocal of the estimated variance of each coeffici- ent in weighted regression. Of course, since we hypothesize that these parameters do not depend on socioeconomic development or family planning program strength, we hypothesize no relationship between the coefficients and setting variability as characterized here. The only parameter in the EF equation for which there is a macro hypothesis is the intercept, which is expected to increase with level of socioeconomic development. That the EF intercept is larger for Peru than for Korea is inconsistent with our hypothesis of a positive relationship between the social setting index and this intercept. Given that there is not much of a difference in the social setting index for these two countries, there should be little difference in the intercepts.ll

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71 TABLE ~.7 Early Fertility (EF) Structural Equation, Currently Married Women Aged 40-44 in 1974, Peru and Rorea World Fertility Surveys Dependent Variable is EF Explanatory Variables and Other Regression Information Peru Rorea Childhood Residence (RESC) [.007]* [.004]* Rural/Villagea -- ~~ Town -.22 -.01 City -.41 -.19 Respondent's Education (WED) [.02]* [.006]* 0 years/0 years -- -- 1-2/1-5 .31 .11 3-4/6-8 -.14 -.04 5-6/9-11 .003 -.11 7+/12+ -.45 -.22 AFB -.36*** -.28*** WBM .28* .21* Religionb [b] [.004]* Intercept 12.08 9.54c R2 .55 .52 Note: The asterisks indicate the statistical significance of selected coefficients (two-tailed test): *P _ .1 **.01 < p < .1 ***p ~ .01. The absence of an asterisk indicates that a significance test was not applied. When the explanatory dimension is a set of categories, summary information for tests of significance pertains to the alohal tact chop al 1 I;; .,::_ _ classification are simultaneously zero. Bracketed entries are the squared multiple partial correlations associated with the dummy variable classifications. ~ ~ ~ ~ _ ~ I_ .L = ~ ~ ~ O ~ ~ ~ 1 1 1 1 1 ~ aWhen two category descriptions are provided, the first pertains to Peru r the second to Rorea. Dummy variables omitted from the regressions for estimation (normalizations n''rn^~^c are ind icated hv ~ {1A :h i- - ~ z' A__ _ an. ~ The coefficients for the religion categories are not presented here. This variable is unavailable for Peru. CThe effect of religion has been removed from this intercept so that cross-equation comparison of the intercepts is valid.

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72 Later Fertility (LF) This brings us, finally, to the LF regressions for Peru and Korea, presented in Table 2.8. Since the LF specification is complex, we first indicate the coefficients used in the macro analysis, and then discuss the results for Korea and Peru. The macro analysis employs the RESC city dummy, the RES Lima dummy for Peru and city dummy for Korea, the BED 7+ dummy for Peru and 13+ dummy for Korea, the intercepts for each country, and derived coefficients for WED and HOCC. Expressions (2.7a) and (2.7b), given earlier, indicate how the derived coefficients are to be computed. For WED, the highest education category is used (the dummy for 7+ years in Peru, and 12+ years in Korea), with EF = 4 and SOB = 1. For Peru, this gives -.63 - 1.24 + .18 = -1.69; for Korea the corresponding coefficient is -.47 ~ .20 - .25 = -.52. Thus, for women who have borne four children before age 30, of which at least two were boys, those with the highest level of education have 1.69 fewer children in Peru and .52 f ewe r in Korea than those with no education, controlling for the other variables in the regression. Similarly, for husband's occupation (HOCC), we contrast employers with employees who are primarily in the nonagri- cultural sector, again with EF = 4 and SCB = 1. For Peru, this gives .84 - .76 - .49 = -.41, while for Korea, the corresponding value is .64 - .8 - .03 = -.19. Thus, for example, in Peru, women whose husbands are employers and who have had four children before age 30, of which at least two were boys, have .41 fewer children than employees, controlling for the other variables in the regression. We use these derived coefficients in the macro regressions, together with their derived estimated variances. We may next consider how observed directions of relationships between the socioeconomic variables and LF correspond with what the theory hypothesizes for transitional settings, beginning with those effects not involved in the interactions. For both Peru and Korea, the effect of RESC on LF is negative, as expected, though not significant in the case of Korea. The WBM effect has the correct negative sign for Peru, and is insignificant for both countries. Work since marriage (WSM) has a negative effect on LF in Korea. This variable is not available in the Peruvian data. The effect of HOCC is not significant for either country. The signs of AFB and AFB2 are precisely the opposite of expecta- tions. The effect of AFB on LF, with all other variables controlled, is positive for AFB between 15 and 30, and is more pronounced for Peru than for Koreae Since the effect of AEB on LF is not a direct concern of the macro analysis (although it is present in the LF intercept), we will not be able to explore this interesting result further here, although it will surely 12e the subject of study as we proceed with the evaluation of the theory. The effect of ECM is negative instead of positive for Peru, while positive for Korea. There are several lines of speculation that might help to clarify the unexpected result for Peru, assuming it does not result from a Type I statistical error. The effect of self-reported fecundity is positive as expected and significant. This is true also for number of marriages in Peru, but not in Korea . The ef feet of marital duration is positive and signif icant for both countries . Me SCG dummy variable is not signif icant. Turning now to the interactions, we have

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73 TABLE 2.8 Late Fertility (LF) Structural Equation, Currently Married Women Aged 40-44 in 1974, Peru and Korea World Fertility Surveys Dependent Variable is LF Explanatory Variables and Other Regression Information Peru Korea Childhood Residence (RESC) [.0091** [.003]* - Rural/Villagea ~~ ~~ Town 004 ~ 04 City -.61 -.20 Respondent's Education (WED) [d] [d] O years/0 years -- -- 1-2/1-5 1.45 .18 3-4/6-8 1.74 -.30 5-6/9-11 1.80 -.26 7+/12+ -.63 -.47 Age at First Birth (AFB) [~079]*** [.009]** Linear Component .596 .226 Quadractic Component -.009 -.004 WBM -.37* .25* ECM -.18** .21*** EF .48d -.lld FEC .64*** .30*** NMAR .42** -.21* OUR .01** .01*** Current Residence (RES) [.0191** (.04)*** Rural/Village ~~ ~~ Town .36 .05 City -.24 -.63 Lima -.50 (e) WSM [e] -.52** Husband's Education (HED) [.0031* [.006]* 0 Years/0 Years ~~ ~~ 1-2/1-5 .~8 .11 3-4/6-8 .34 .17 5-6/9-11 .13 .08 7+/12 .09 .25 --/13+ [e] -.33

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74 TABLE 2.8 (continued) Dependent Variable is LF Explanatory Variables and Other Regression Information Peru Korea Husband's Occupation (COCCI [d] [d] Employee ~~ Self-employed, agriculture .83 -.05 Self-employed, nonagriculture 1.79 -.72 Employer .84 .64 SCB _.13d -.32d SCG -.02* -.002* EF*WED Interaction [.0253*** [.0013* EF*WED 1 ~~ EF*WED 2 .23 .01 EF*WED 3 .28 .04 EF*WED 4 .22 -.04 EF*WED 5 -.31 .05 EF*HOCC Interaction [.006]* [.012]** EF*HOCC 1 ~~ ~~ EF*HOCC 2 -.22 .11 EF*HOCC 3 -.37 .20 EF*HOCC 4 -.19 -.20 SCB*WED Interaction [.002]* [.002]* . SCB*WED 1 -- -- SCB*WED 2 .28 -.35 SCB*WED 3 -.17 -.18 SCB*WED 4 -.31 -.12 SCB*WED 5 .18 -.25 SCB*HOCC Interaction [.018]** [.000]* SCB*HOCC 1 -- - - SCB*HOCC 2 .33 -.02 SCB*HOCC 3 -.S9 -.01 SCB*HOCC 4 - . 49 -. 03 R-1 iaic~nb [b] ~ . 001] * Intercept -8.729 2.303C R2 .32 .32

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75 TABLE 2.8 (continued) Note: The asterisks indicate the statistical significance of selected coefficients (two-tailed test): *P > .1 **.01 ~ p < .1 ***p ~ .01. The absence of an asterisk indicates that a significance test was not applied. When the explanatory dimension is a set of categories, summary information for tests of significance pertains to the global test that all coefficients within a classification are simultaneously zero. Bracketed entries are the squared multiple partial correlations associated with the dummy variable classifications. aWhen two category descriptions are provided, the first pertains to Peru' the second to Korea. Dummy variables omitted from the regressions for estimation (normalization) purposes are indicated by a dash. he coefficients for the religion categories are not presented here. This variable is unavailable for Peru. CThe effect of religion has been removed from this intercept so that cross-equation comparison of the intercepts is valid. dWED, HOCC, EF, and SCB play an interactive as well as additive role in the LF equation. eNot applicable. already derived the interaction contrasts for WED and HOCC that will be used in the macro analysis. Hey are negative as expected, with the numerical values for Peru being twice as large as those for Korea. However, these selected interaction contrasts are only pieces of a larger pattern. Moreover, it is unclear what they tell us, given that for Peru, the EF*HOCC and SCB*WED interactions are globally insignificant, while for Korea, the EF*WED, SCB*WED, and SCB*HOCC interactions are globally insignificant. If obtaining the best-fitting parsimonious model for Peru and Korea were our goal' we could re-estimate the model, dropping insig- nificant variables and otherwise tinkering with the spec if ication unti 1 we were satisf led. For present purposes, this will not be done. However, instead of attempting to read off the patterns of effects directly from Table 2.8, we will use the device of standardizing conditional coeffici- ents to remove insignificant interactions; this will yield reasonable approximations of the effects that would be obtained if the regressions were computed with the insignificant terms or classifications omitted.l3 To examine the EF effects on LF in Peru, we standardize over HOCC (using sample composition for this classification) to obtain EF effects conditional on WED--the only significant interaction involving EF in the Peruvian data. The estimated conditional EF coefficients are as follows:

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76 WED1: WED2: WED3: WED4: WED5 .33 .56 .61 .55 .02 where WED5 signifies the highest educational level. This pattern indicates that in Peru, the effect of EF on LF is positive over all but the highest level of education, where it is essentially zero. This is compatible with our hypothesis, although the effect is not as strong as it could be. A negative EF effect conditional on WED5 would be more compatible with expectations about the presence and implications of family-size targets. Nevertheless, given the relatively low level of educational attainment in Peru, this result is quite acceptable. Considering next the effects of WED on LF, there are both EF*WED and SCB*WED interactions, but only the former are globally significant. We therefore standardize over SCB (assuming a 50-50 split between families with two or more boys and those with fewer), and evaluate the WED effect at EF = 4: WED1 WED2 = WEDS WED4 WED5 = -0.38 -0.72 -1.07 -1.79 where WED5 is the highest educational category and WED1 is the omitted category, so that the coefficients for the other categories are deviations from zero. For EF = 4, the effect of education on LF is perfectly monotonic and negative, as expected. m is pattern does not emerge until EF = 3. For lower values of EF, the education effect is quadratic and concave upward. Thus the WED5-WED1 contrast conditional on EF = 4 and SCB = 1 derived earlier, a value which for Peru is -1.69, appears to be informative: because the effect of education is monotonic for the conditioning categories selected, the extreme contrast between WEDS and WED1 is meaningful. m e effect of HOCC depends only on boy sufficiency (SCB), and not EF, in Peru. m at the SCB*HOCC interaction is consistent with expecta- tions is clear from inspection of the terms in this interaction in Table 2.8. For those with at least two boys, nonagricultural employers and self-employed workers outside of agriculture have lower LF than employees, but most importantly, lower LF than farmers. Even though the EF*HOCC interaction is insignificant, the pattern just described is most clearly seen for those with average or greater EF (for Peru, EF = 4.1 for the cohort under examination). With EF = 4, the HOCC effects on LF are as follows:

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77 employee self-employed, agriculture self-employed, nonagriculture employer SCB = 0 SCB = 1 -.06 - .26 .32 -.27 .09 -.40 These results make it clear that for SCB s 0, there is very little difference between occupational groups in EF, while there is a maximum contrast of .66 between farmers and nonfarm employers among those with at least two boys. This same point can be made by examining the effects of SCB conditional on HOCC and standardizing on WED, which yields the following conditional SCB effects: employee self-employed, agriculture self-employed, nonagriculture employer -.13 .20 -.71 -.61 m us, the effect of SCB is positive among farmers in Peru and negative outside of agriculture. Again, this is consistent with the expectations developed in Chapter 1. Considering next the interactive results for Korea, it is clear from Table 2.8 that the SCB variable does not interact significantly with WED or HOCC, using a global significance test. Therefore, we derive a net SCB coefficient standardized for both WED and HOCC. This net value is -.48, which is in the hypothesized direction, and confirms the results of other analyses that report on the importance of sons in Korea (Johnson- Acsadi and Weinberger, 1982). In sum, couples with a sufficiency of sons had lower LF and, for this cohort at least, this effect did not depend on WED or HOCC. WED also fails to ~ nteract significantly with either EF or SCB, using a global signif icance test. Thus, the effect of WED can be determined net of EF and SCB. Since EF and SCB are logically related (EF < 2 implies SCB = 0), we evaluate the effect of WED for SCB = EF = 0 and for EF = 4' but standardized on SCB (assuming a 50-50 split). The results are as follows: EF = 4; Standardized on SCB SCB = EF = 0 WED1 WED2 WEDS WED4 WEDS e 05 ~ ~25 _ dg ~- 38 .18 ~ ~ 3 0 ~ e26 ~ ~ 4 7 These results indicate that although the effect of WED on LF would be negative if education were scaled by years of schooling completed, it would be fairly weak. At most, there would be a difference in LF of no

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78 more than Half a child, and this contrast is between vast extremes in social position--between those with no formal education and those at the upper extremes of the education distribution. Compared to the effect of WED on fertility in Peru, the effect for Korea is weaker. This result is consistent with expectations, given Korea's strong family planning program. This leaves for consideration the effects of EF and HOCC on LF. According to the result. of the global signif icance tests for the Korean data, EF interacts only with HOCC, and HOCC interacts only with EF; therefore, examination of the EF effects conditional on HOCC will tell the same story as examination of the HOCC effects conditional on EF. Nevertheless, since HOCC is not a continuous variable, it is difficult to infer the nature of its effect from examination of the conditional EF effects. For this reason, we present the interaction both ways. Since the EF*WED interaction is insignificant, we present the EF effects conditional on HOCC and standardized for WED: employee self-employed, agricultural self-employed, nonagricultural employer -.10 .01 .11 -.30 These results indicate a marked inverse EF effect on LF among families in which the husband is an employer in the nonagricultural sector, but fairly modest effects of EF for other occupational categories. This result is moderately consistent with our hypothesis, since employers are presumably in the more transitional sector of societies undergoing change. Moreover, there is also a negative EF effect for the employee category--which is a largely nonagricultural group. This, too, is consistent with our hypothesis. Whether the EF effects for the remaining occupational categories are consistent with expectations is unclear. However, we can gain insight into this pattern by examining its complement--the HOCC effect conditional on EF. To do this, we standardize over SCB (assuming a 50-50 split) where sensible. We present the HOCC coefficients at two values of EF: four children and none. Since EF = 0 implies SCB = 0, the HOCC coefficients given these conditioning values are not standardized for SCB. When EF = 4, however, the HOCC coefficients are standardized for SCB: EF = 4; Standardized on SCB employee self-employed, agriculture .39 self-employed, nonagriculture .09 employer -.18 EF = SCB = 0 ~- ~ e05 ~e72 e 64 If our hypothesis about the interaction between EF and HOCC is correct, the results must certain conform when EF is high. Since EF = 3.5 for this cohort in Korea, the choice of EF = 4 to evaluate the conditional HOCC effect should be adequate to determine whether the results are as

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79 expected. For the two critical occupational-categories, they are. It appears that when EF = 4, employers have the lowest LF, and farmers have the highest. This effect is not large, however, considering the extremes in position. It is at most not much more than "half a child (.39 - (-.18) = .57), which is the same order of magnitude as the extreme in LF observed for WED. With EF = 0, there is a much larger difference in LF between the categories of HOCC, but these numbers are probably not inter- pretable, given the very small cell sizes on which they are based.l4 Also, these coefficients are in fact observed regression coefficients: as can be seen in Table 2.8, they are the coefficients of the "main effect" of HOCC. None of them is as much as twice its standard error. Thus, the effect of HOCC on LF emerges in the predicted pattern as EF increases. Symmetrically, there is an inverse EF effect on LF for occupations in the nonagricultural sector. Either way of stating the result is compatible with our hypothesis. The only coefficients not yet discussed in the LF structural equations for Peru and Korea are to intercepts. Although both are negative, the intercept for Peru is markedly larger in absolute value. Unless we stress the importance of gross domestic product (GDP) per capita and the relative size of the nonagricultural labor force over other macro indicators, this result is inconsistent with expectations. Given Korea's position relative to that of Peru on the social setting index, and given Korea's strong family planning program effort score compared to that of Peru, Korea should have the intercept with the larger absolute value, given that both are negative. There are a number of possible explanations for this result which we cannot pursue here. TO summarize the results for the LF structural equation, for both Peru and Korea there is a mixture of outcomes--some consistent and some inconsistent with our hypotheses. The effects of some adjustment variables have unexpected signs, while others are as predicted. m e effects of the socioeconomic factors are perhaps more consistent with our hypotheses, given that departures from expectations are largely in the failure of effects to materialize rather than associations that have the unexpected sign. Even more strongly, recall that one of our macro hypotheses is that the effect of a strong family planning program effort should be to attenuate socioeconomic effects on LF. The socioeconomic effects for Korea are on the whole weaker than those for Peru, given that there are fewer significant interactions involving socioeconomic status for the Korean data and that those effects present are somewhat smaller. Thus, at least with respect to the socioeconomic effects, the results for Peru and Korea appear to be essentially consistent with the theory developed in Chapter I. On the whole, the results for the AFB, EF, and LF regressions are roughly consistent with our theory. With just two countries being studied' any pattern of results could be found without necessarily undermining the theory, whose test must hinge on data for numerous countries. By the same token, findings for these two countries compatible with the theory do not necessarily confirm it since a larger sample of countries could produce quite contrary results. The chief value of this exercise, then, is that it illustrates the operationaliza- tion use-d in this initial round of investigation. Among other things, it

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80 has shown, for example, that the decision to contrast employers with employees is not as felicitous as expected. It would be preferable to contrast employers with farmers, since the difference in LF between these two categories is by far the largest for both Peru and Korea. It has also shown that AFB does not affect LF in the way expected in Peru and Korea. Since these are disparate countries, there may be a need to review our thinking about AFB, and to closely examine the data for an explanation for this apparent anomaly. We have also gained some insight into potential problems with the macro tests. m e tests we will present assume that the hypotheses about the nonsoc ioeconomic variables are valid. We now have evidence from two countries that this is not always the case for the LF equation. Therefore, our tests may not yield the expected results--not because our reasoning about socioeconomic effects is wrong, but because our reasoning about the adjustment and other variables needs to be revised. 2.6 MACRO ANALYSIS Our two fundamental hypotheses suggest a particular ordering of steps within the macro analysis. The first basic hypothesis is that the socioeconomic determinants of fertility affect the components of the reproductive process differently. m e second is that the components of the reproductive process are both additively and interactively affected by macro phenomena. Translating this second hypothesis into the language of structural equations and multi-level models, we hypothesize that the intercepts, and the effects of the socioeconomic determinants, of the components of the reproductive process vary across societies in meaning- ful and predictable ways. m e first hypothesis directs attention to within-country differences in the relationships between the micro socio- economic variables and each component of the fertility process. The second directs attention to between-country variability in these micro relationships. Before undertaking macro analysis of the second hypothesis, it is worth testing the first. If the socioeconomic determinants of the reproductive process do not differentially affect the components of the reproductive process, there is little point in testing the second hypothesis. Within-Country Differences To test the first major hypothesis, we summarize the contrasts, described earlier, to be used in the macro analysis. Using the criterion that the ratio of each coefficient to its estimated standard error must be at least as large as |1.96|, that is, using a p = 0.05 level of statistical significance (two-tailed test), we have tabulated the number of positive and negative significant effects for each socioeconomic variable present in the micro equations over the 15 WAS countries for age at first birth (AFB), early fertility (EF), and later fertility (LF). Table 2.9 presents these counts and shows that the significant effects of childhood