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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 resultsnot 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 multilevel 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
withincountry differences in the relationships between the micro socio
economic variables and each component of the fertility process. The
second directs attention to betweencountry 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.
WithinCountry 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 (twotailed 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
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81
TABLE 2.9 The Number of Instances in Which Each Socioecor.omic
Explanatory Variable is Statistically Significant in the AFB,
EF, and LF Strutural Equations, Summed over 15 WFS Countries
Response Variable at the Micro Level
Socioeconomic

Explanatory
Variables AFT EF LF
RESC
.
WED
WBM
HED
+

+

+
2 2 0
O 0 1
10 0 0
0 2 2
O O
1 0
o
_ 3
WSM
+ O
_ 2
RES
HOCC
+


o
6
o
Note: The counts are based on a p = .05 level of statistical
significance {twotailed test}. The coefficients used to
derive these counts are the contrasts, described in the text,
which provide the response variables in the macro analysis. A
blank indicates that the explanatory variable was not included
in the particular micro specification.
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82
residence (RESC) and wife's education (WED) on AFB are always positive.
Thus, for example, over the 15 countries the effect of RESC is positive
and significant in 2 and insignificant in the other 13. m e effect of
WED on AFB is positive and significant in 10 of the countries, and
insignificant in 5. Indeed, inspection of the individual contrasts in
the AFE equation shows that even the insignificant contrasts are
virtually always positive. Thus, the direction of the socioeconomic
effects on AFB is a& hypothesized, given the evidence from the 15 WFS
data sets.
Considering next the socioeconomic effects on EF, Table 2.9 shows
that the RESC effect is occasionally positive and never significantly
negative, that the effect of WED is occasionally negative but never
significantly positive, and that the effect of the respondent's working
before marriage (WBM) is once negative but never significantly positive.
Thus, the effect of RESC is as hypothesized, while those of WED and WBM
are not. To this it should be added that out of 44 nonindependent
replications (WBM is not available in the Fiji data set), in only 5 is a
socioeconomic effect statistically s~gnif icant at the selected
probability level (.05~. As can be seen from Table 2.9, EF is less often
affected by prior socioeconomic status than either AFB or LF. This much,
then, is consistent with expectations. On the other hand, the contrary
results for WED and WBM that occur in three separate countries will
require study before research can proceed beyond the results described in
this chapter.
This brings us to the socioeconomic determinants of LF, for which
the significant effects are, with a single exception, negative as hypothe
sized. The exception is for husband's occupation (HOCC), and the country
which yields this result has a sample of less than 250 women aged 4044
in 1974. Of these respondents, no more than five had husbands who were
employers. Thus, this exception does not suggest a substantive anomaly.
For no single indicator of socioeconomic position are there omni
present effects on LF over the 15 countries. In fact, no socioeconomic
indicator has a significant negative effect in even as many as half the
countries. m is does not mean that the data disconfirm the hypothesis
that LF should be most clearly determined by socioeconomic position, with
AFB less so. A simple count of effects, ignoring HOCC, gives 14 signif i
cant socioeconomic effects for LF, as compared with 12 for AFB. Of
course, there are more predictors in the LF equation, a fact which cuts
two ways. On the one hand, it might be argued that any assessment such
as this should adjust for the number of predictors. On the other hand,
the larger the number of correlated predictors, the less the chance that
any of them will be significant. All of this suggests what, on balance,
should in any case be clearthat rigorous assessment of which fertility
component is most, or least, determined by socioeconomic position can
only be carried out within each data set for each country. This is a
task that must be left for another occasion. Nevertheless, the simple
tabulation presented in Table 2.g is highly suggestive. Again it should
be recalled that these data refer only to the cohort aged 4044 in 1974.
This cohort would have spent a high proportion of its reproductive life,
in many of these countries, in a period of rather slow socioeconomic
development and in the absence of welldeveloped family planning programs.
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83
Differentials in targets and opportunities for fertility control would
have been limited. An additional hypothesis, not pursued in this illus
trative presentation of one cohort, is that there will be intercohort
differences in the strength of the socioeconom c effects.l5
To summarize, based on the counts in Table 2.9, it appears that
socioeconomic position affects AFB positively, as expected, and LF
inversely, again as expected. Socioeconomic position affects EF only
weakly, and in both directions. Although this latter result is incon
sistent with expectations, the weakness of the impact is as
hypothesized. m us, there is justification for further analysis of the
data at the macro level.
BetweenCountry Variability
What is the appropriate strategy for exploratory macro analysis of the
micro socioeconomic coefficients and intercepts of the AFB, EF, and LF
structural equations? Although we touched on this question earlier in
this chapter, the results summarized in Table 2.9 raise it again here.
In particular, it could be asked whether we should be dichotomizing micro
coefficients into statistically significant versus statistically insig
nificant groups, with the latter group then set equal to zero. We do not
take this approach here because it dispenses with potentially useful
information. There is, of course, no logical necessity for selecting p =
0.05, as we did to construct Table 2.9. If we incorporate knowledge,
even imperfect knowledge, about the sampling errors of the coefficients,
we can weight a coefficient more heavily the larger its ratio to its
standard error. That is, the more "significant the coefficient, the
more it should count in the analysis.
Moreover, each of the contrasts focused on has been the subject of a
concerted effort in hypothesis development. The generality of our
hypotheses, permitting them to predict different signs in different
settings, allows for variability in the magnitudes of the coefficients:
a coefficient may be small and insignificant without thereby disproving
our theory. Setting insignificant results to zero could thus mask impor
tant and interpretable patterns in the magnitudes of small and
insignif icant coefficients in the micro equations.
Simultaneous estimation of both the micro and macro effects by the
method of maximum likelihood is almost possible. When the computer
programs for this methodology become available, they will permit full use
of all available information at both levels of analysis. In the present
analysis, we carry out least squares regressions at the micro level, and
separate weighted least squares regressions at the macro level. The
implications of this strategy, which is reasonable as an exploratory
procedure, are amply discussed by Mason (19807. In addition to these
regressions, we also rely heavily on scatter diagrams that plot the micro
intercepts and socioeconomic effects against the macro variables. As
will be seen, this combination of data analysis tools proves sufficient
for the task at hand.
To describe the relationships of the intercepts and socioeconomic
effects to the macro variables, Figures 2.1 to 2.23 present bivariate
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84
scatter plots with associated regression lines.16 mese lines are
derived from bivariate weighted regressions, using the estimated vari
ances of the micro intercepts and coeffici~.~ts as reciprocal weights.
Except for the parameters involved in the LF Hero equations, bivariate
analysis is all that is required to test our hypotheses. For the LF
intercept and socioeconomic effects, our hypotheses are multivariate. To
test them, we will also report the results of multiple weighted regres
sions at the macro level.
For those scatter diagrams shown here in which a measure of socio
economic development is the macro predictor, the indicator is social
setting in the Mauldin et al. (1978) index. Use of this variable enables
the reader to dichotomize on the social setting dimension, since the
leftmost six points are always those for countries in the low category
of the index. Similarly, for the diagrams that use family planning
program strength as the macro predictor, the leftmost five are always
those countries in the low category of the Mauldin et al. (1978) family
planning program strength index. Only two of these countries are in the
low category on both dimensions. Certain of the results differ depending
on whether social setting rank or gross domestic product per capita is
the macro socioeconomic development variable. Although we discuss this
at the appropriate point, we do not present additional scatter plots for
GDP per capita. For the 15 countries at hand, the correlation between
the two variables is .86, so that the scatter diagrams would be quite
similar. In addition, where GDP per capita seems to make the greatest
difference, our hypotheses are multivariate, and the bivariate plots
would not be informative.
In testing the macro hypotheses, we first discuss the results for
the socioeconomic position coefficients, and then turn to consideration
of the results for the micro equation intercepts.
AFB and EF Slopes
Figures 2.1 and 2.2 display the bivariate scatter and regression lines
for the coefficients obtained in the AFB micro regressions. In each
instance, the yaxis refers to realized values of regression coefficients,
and the xaxis to social setting rank. We hypothesized that these
relationships would be positive. Instead, they are both negative, with a
poor fit. The dispersion of these scatters suggests that no nonlinear
function would improve the fits meaningfully (although it would take
pictorial representation of the regression weights to confirm this).
These findings are based on the use of the social setting ranks. Use of
GDP per capita yields a positive effect on the SES coefficients, though
again the weighted bivariate regression fits the data poorly. This
discrepancy in results suggests that the data do not support the hypoth
esis (although neither do they disconfirm it).
Next, we hypothesized that there would be no relationship between
the magnitudes of the SES effects in the EF equations and level of
socioeconomic development (see Table 2.2~. Figures 2.3 to 2.~ display
regression lines with consistently negative slopes. The fits associated
with these lines, however, are in all cases very poor. Furthermore, the
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85
1. t200 ~
4.
.69667
.27333
·. 15000
4,
 .57333 ~
4.
 . 99667
 1.4200 4.
 1.8433
2 .2667
.F
2.6900 ~
.
.
Am_
I_
.
1 . 0000
2.5556
4.1111
5 .6667
7.2222
~ ~ ~ 4 ~ 4 _ _ ~ _ _ ~ _ _ _ ~ _ _ _ ~
10.333 13 144 SSR
8. 777 a t 1.889 15 Coo
FIGURE 2.1 Plot of Childhood Residence (RESC) Coefficients from Al?B
Micro Equations Against Social Setting Ranks (SSR), with Bivariate
(weighted) Regression Line: Y = .11  .019 (SSR)
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86
8.0700
7.1644
$;.2589
 ·1
S.3533
.4478
3.5422
2 .6367
1.7311
+
.82556
 . 8 OOC)0  1
.
.
.
.
.
·
2.5556
5.6667
8.7778
FIGURE 2 .2 Plot of Wife ' s Education (WED) Coeff icients from AFB Micro
Equations Against Social Setting Ranks (SSR) with Bivariate (weighted)
Regression Line: Y = 3.77  .0085 (SSR)
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87
. ssooo ~
4.
.251t1 4~.
 .87778  1 +
4~
 .42667
.76556
 1 . 1044 ~
 1.4433 4.
 1.7822
4.
2. 121 t
 2 . 4600


5.6667
7. 222~

~ 4 ~ _ _ ~ _ _ _ _ ~ _ _ ~
10.333
8.7778
.
1 t .889
.
, _ ~ _ ~ ~
13.444 SSQ
15.000
FIGURE 2.3 Plot of Childhood Residence (RESC) Coefficients from EF Micro
Equations Against Social Setting Ranks (SSR), with Bivariate (weighted)
Regression Line: Y = .23  .034(SSR)
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.61000 4.
.43889 4
.26778 4.
.96667  1+
 . 74444  1 +
4.
 .24556
.41667
4P
 .58778 ~
4.
 .75889
 .93000
1 . 0000
.
2.5556
88
.
4 . 1 1 1 1
5 .6667
7. 2222
,
8.7778
.





~ _ _ _ ~ _ _ _ ~ _ ~ _ _ ~
10.333 13 444 S5Q
1 1 . 8 8 ° 1 5 . 00
FIGURE 2.4 Plot of Wife's Education tWED) Coefficients from EF Micro
Equations Against Social Setting Ranks (SSR), with Bivariate (weighted)
Regression Line: Y ~ .046  .026 (SSR)
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89
. 70000 ~
4.
.S311 1
.36222
. 19333
4.
. _
_

. 24444
_ ~ 4444
 .31333
 .48222
.6511 1
 .82000
.
.
.
4. _ _ _ _ 4. _ _ _ _ ~ _ _ _ _ 4. _ _ _ _ ~ _ _ _ _ + _ _ _ _ ~ _ _ _ _ ~ _ _ ~ 4. 4. ~ ~ ~ 4 · ~ + 4
1.0000 4.1111 7.2222 10.333 13 444 SSQ
2.5556 5.6667 8.t778 1 1 889 15.000
FIGURE 2.5 Plot of Work Before Marriage (WBM) Coefficients from EF Micro
Equations Against Social Setting Ranks (SSR), with Bivariate (weighted)
Regression Line: Y ~ .16  .0098(SSR)
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so
parallel results based on GDP per capita are consistent with those seen
for social setting rank. Nevertheless, the results here are ambiguous.
If we focus on fit, we can conclude that the data are consistent with our
hypothesis. On the other hand, if we feel that the negative slopes cannot
be ignored, then we have a puzzle. Our hypothesis of no relationship
indicates that we have no a priori basis for predicting a positive or a
negative relationship, not that there can be no systematic relationship.
Should we treat the three replications of a negative association between
the EF socioeconomic coefficients and level of socioeconomic development
as meaningful, or should we treat these replications as artifacts of a
highly unusual accidental sample of countries? m e answer to this
question must await later analysis.
Thus far, we have examined the micro results for the socioeconomic
effects in the AFB and EF micro equations. We have found that the macro
equations relating the micro coefficients to social setting rank have
very poor fits, as can be seen from the scatter diagrams; moreover, the
slopes over countries for the AFB socioeconomic determinants are slight,
and either positive or negative, depending on the macro indicator used.
For the socioeconomic effects in the EF micro equations, the between
country slopes are barely negative, the fits poor. These results
certainly do not strongly support our hypotheses. There should be
positive slopes for the coefficients in the AFB equation, and no slopes
for the coefficients of the EF equation. We could decide to emphasize
the lack of fit in these macro bivariate relationships, or we could
conclude that the unexpected negative slopes merit study in future work
with these data. It would be premature to reach a decision at this
point, since the results for the socioeconomic determinants of LF also
need to be taken into account.
LF Slopes
Our hypotheses about macro variability in the micro socioeconomic
determinants of LF are multivariate. In particular, we have theorized
that the level of socioeconomic development should have a negative effect
on the coefficients; that the level of family planning program effort
should interact with socioeconomic development; and that the ~main" effect
of family planning programs should be negative, with the interactive
effect positive. In considering the realities of macrolevel data
analysis based on 15 observations, it is clear that the interaction we
have hypothesized may not be detectable. If so, it is essential that we
develop a hypothesis for a simpler model. Given the need to retain both
the developmental and family planning dimensions, this simpler model
should be multivariate, include socioeconomic development and family
planning program effort as predictors, and be additive. With this
simplification, we can hypothesize that the effect of family planning
program effort should become positive. Our reasoning is that most of the
15 countries fall into the high category of the Mauldin et al. (1978)
social setting index, and that the coefficients in the LF equation that
are statistically significant by the criterion used in the construction
of Table 2.9 are negative. Thus, the data are weighted toward the high
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104
~ . 74~ +
4.1522 ~
4
3.5644 ~
4
2 . 9767 +
4.
2.3889
1 .801 1
1 .2 133
.62556
4.
4~
.37778  1+
+
.55000 4.

_
.
· _ + _ ~ _ _ _ _ ~ _ _ _ _ ~ _ _ _ 4~ _
1.0000 ~ . 1 1 1 1 7.2222 10.333 13. 444 SSP
2.5556 5.6667 8 . 7778 1 1 889 15 . O'J~
_
FIGURE 2.18 Plot of Kusband~s Occupation (HOCC) Derived Effects from LF
Micro Equations Against Social Setting Ranks (SSR), with Bivariate
(weighted) Regression Line: Y a .19  .036(SSR)
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105
. 74= +
4 . 1 522 4.
3.5644 ~
4~
2 .9767 4.
2.3889 4.
1.8011
1.2 133
4
.62556
· 37778 1+
. 55000

2 .6667
.
8 . 0000
.
~ ~ ~ _ 4. _ _ _ _ 4, _ _ _ ~ _ _ _ _ . _ ~
10 657 16. 000
t ~ . 33 3
18.667
_ _ ~ _ _ _ _ ~ _ _ _ _ ~
2 1 . 3 3 3 F P S
21 .00O
FIGURE 2.19 Plot of Husband''; Occupation (HOCC) Derived Effects from LF
Micro Equations Against Family Planning Program Scores {FPS), with
Bivariate "weighted} Regression Line: Y  .21  .019 (PPS)
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106
In sum, only in a highly specific way can the bivariate information
in the scatter diagrams be used to test the multivariate macro hypotheses
about betweencountry variability in the effects of the socioeconomic
determinants of LF.
To test the multivariate macro hypotheses about the micro socio
economic determinants of LF (shown in Table 2.2), we use the alternative
measures of socioeconomic development and family planning program effort
described in the third section of this chapter. These alternatives are
labeled as follows: SSD is the dummy variable for social setting, SSR is
the rank variable for social setting, GDP is gross domestic product per
capita, FPD is the dummy variable for family planning program effort, and
FPS is the family planning effort score. Since our hypotheses are the
same for each micro socioeconomic variable, we begin by pooling all of
the micro coefficients in the LF equation and specifying
(2.8) Yj = 40 + 41Sj + 42(FP~j ~ 43(S.FPJj + Yk + ad
where j (j = 1,...,15) subscripts countries, 40 is the intercept, S
is a measure of socioeconomic development, FP is a measure of family
planning program effort, S FP is the interaction of S with FP, and Yk
(k = 1,...,K) is an additive nuisance parameter that distinguishes the
net mean value over the 15 countries of the kth micro socioeconomic
effect from similar means for other socioeconomic effects. The nuisance
parameters allow for consistently different magnitudes of micro effects.
They are included additively since to include them interactively would
amount to separate specifications for each micro socioeconomic effect.
However, because we do not have separate hypotheses, our analysis begins
by pooling all of the coefficients in the fashion described. We estimate
equation {2.8) in three different ways, using (1) SSD, FPD, and SSD.FPD;
(2) SSR, FPE, and SSR.FPE; and (3) GDP, FPE, and GDP.FPE. Table 2.10
presents the results.
The signs of the regression coefficients presented in Table 2.10 are
strikingly consistent with our hypotheses about the effects of the macro
variables on the micro coefficients. In particular, when socioeconomic
development and family planning program effort are dichotomized, the two
~main. effects are negative, and the "interactive" effect is positive.
This pattern is also present in the estimates based on GDP and FPS. It
is not apparent, however, in the results based on SSR and FPS. For that
operationalization, only the effect of socioeconomic development is as
hypothesized. Even so, the results presented in Table 2.10 are remark
able, given the complexities of the theory we are testing, and the nature
of the sample we are working with.
To determine whether the findings based on pooling over coefficients
are based on particular micro socioeconomic variables (e.g., those of
RESC), we have also examined the data in disaggregated form. Table 2.11
presents the results of this inquiry in nonnumeric form. For each type
of micro coefficient, we have fit the three alternative representations
of the macro variables, trying both the interactive and the additive
specifications. Table 2.11 indicates whether the signs of the macro
effects (but not magnitude or statistical significancewhich are not
readily ascertained by the methodology we have used) are as hypothesized.
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107
TABLE 2.10 Weighted Regression of Socioeconomic Coefficients from
the LF Micro Equation on Alternative Macro Indicators of S and FP
Alternative Measures of S and FP
Explanatory Variables and SSD SSR GDP
Other Regression Information FPD FPS FPS
Coefficient typea
~ .2761 r .276 ~ ~ .2763
S . 22 . 0009 . 002
FP .16 . 0009 . 02
S FP
.003 .0015 .00004
Intercept .19 .01 .4S
R2 .353 .354 .400
MPR2b .107 .107 .171
N 101 101 101
aNuisance parameters not presented; [.276] is multipl e R2 due
to inclus ion of dummy var. tables for coef f icient type .
bMPR2 is the multiple partial correlation squared, due to
adding in S. FP, and S FP into a specif ication already including
the dummy var tables for coef f ic lent type .
In each instance, the analysis beg ins with the interactive specif ication.
If the signs are not all as hypothesized, we try the additive specif i
cation. me entries in Table 2~11 indicate whether the interactive
specification yields the hypothesized Signer and if note whether the
additive specification does. Lack of an entry means that neither
specification xesu:~~ Are all coefficients smith signs as hypothesized.
In every case. ant least ane no the m;an ~ff~1~ w:~= ; n the TV; ~^A
direction ~
~ ~ _ _ ~ ·  __ As,! 41~_ sac ~ ~ _
m e pattern in Table 2.11 indicated that for every single micro
socioeconomic determinant of LF, there is at least one alternative among
the measurement choices made for which the interactive specification
yields all three macro coefficients with signs as hypothesized.
In general, the data provide a certain amount of support for our
hypotheses about the macro variability of the socioeconomic effects in
the LF equation across settings. Given the complexity of the hypotheses,
the paucity of macro data, and the discrepancies found between data and
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108
TABLE 2.11 Model Corresponding to Predicted Sign Pattern of Macro
Effects, Separately for Each Socioeconomic Coefficient of the LF
Micro Equation
Alternative Measures of S and FP
Model Based on Micro SSD SSR GDP
Coefficient For: FPD FPS FPS
RESC
WBM
HED
WSM
RES
WED
HOCC
Additive Additive Interactive
Interactive Additive
Interactive 
Additive  Interactive
Interactive
Interactive  Interactive
Interactive Interactive Interactive
Note: See text for description of criteria underlying choice of
model.
hypotheses in other parts of this analysis, we do not wish to state too
strongly this congruence between results and hypotheses. If we could
establish statistical significance for the macro coefficients, it Is
quite possible that none of the effects would be significant at
conventional levels. On the other hand, the consistency across
coefficient type is not to be dismissed, not even on the grounds that
coefficients from each micro data set are bound to be correlated.
Although we do not have seven independent replications (there are seven
micro socioeconomic effects in the LF equation), there is certainly
greater statistical leverage than could be gained with just a single
coefficient type. In sum, then, we are cautiously optimistic about the
findings for the LF equation.
Intercepts
As the last step in the macro analysis, we test the hypotheses about the
intercepts of the AFB, EF, and OF micro equation. Our hypotheses about
the AFB and EF intercepts are bivariate; for them, the scatter diagrams
and bivariate weighted regressions reported in Figures 2.20 and 2.21 are
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109
appropriate. There should be a positive relationship between socio
economic level and the intercepts for AFB and EF.
For the AFB intercepts, the relationship with social setting rank is
essentially nil. Although the regression coefficient is negative, as is
the parallel regression employing GDP, the coefficient for social setting
dichotomized is positive. Regardless of the measurement used, the fit is
weak, although that for GDP is distinctly the best. For the EF inter
cepts, the relationship with socioeconomic development is positive,
regardless of which among the three measures defined earlier is used.
Moreover, the fits of the alternative bivariate regressions are stronger
than those for the AFB intercepts, though still comparatively very weak.
Thus for the AFB and EF intercepts, the evidence for our hypotheses
is mixed. For the AFB intercepts, there seems to be virtually no systems
atic variability that can be captured with the measures of socioeconomic
development. For the EF intercepts, the sign of the socioeconomic
development effect is positive as hypothesized, but again the variability
across countries is not highly systematic.
Finally, for the LF intercepts, Figures 2.22 and 2.23 present
bivariate scatters and regressions for social setting ranks (SSR) and
family planning program scores (FPS), respectively. In general, all of
the discussion preparatory to the multivariate analysis of the micro
socioeconomic determinants of the LF micro equation is applicable here.
Recall, however, that our hypothesis about the S.FP interaction is that
the coeff icient for the product term should be negative. Moreover, to
allow for the possibility that the data will not support estimation of
the interactive specification, we must consider the signs of the additive
effects of S and FP on the intercepts. Inspection of the additive
specification makes it clear that these signs must both remain negative.
We have computed the same family of specifications for the LF inter
cepts that we used in studying the LF socioeconomic coefficients, looking
at both interactive and additive specifications, and trying the different
measures of socioeconomic development and family planning program effort.
The results of these regressions do not at all support our hypotheses,
regardless of the measures selected. This is unsurprising, given the
tentativeness with which our hypotheses about the LF intercept were
advanced.
Failure to obtain strong confirming results concerning the intercepts
does not necessarily demonstrate a problem with our theory. The
hypotheses about the macro determination of the intercepts are easily the
most tentative, least compelling, and most complex of the entire theory.
and depend on the validity of hypotheses about numerous parameters,
including some that do not appear explicitly in the operationalization of
the model. For example' we have had to make assumptions or develop
hypotheses about the nature of the macro association between the means of
certain micro variables and the macro indicators.
In future research efforts, we will focus on the constituents of the
i ntercepts, trying to pin down the reasons for the lack of suppor t for
our hypotheses about them, and revising these hypotheses if necessary. A
starting point in these efforts might well be to examine the between
country variability in the means of AFB, EF, and LF. For the 15 countries
at hand, we find for example, that AFB varies independently of SSR and
OCR for page 80
110
22 . 520
22.''7
2 1 . 7 1 3
2 1.3 10
+
20.907 +
20 1CO
9 697 ~
9. 2~3 4.
_
+
18.830 4.
+ ~ ~ ~ 4. ~ ~ ~ ~ ~ ~ ~ ~ 4  4  ~ ~ ~  ~
1 .0000 4. t 1 1 1 7.2222 10.333 13. 44.1 SS~
2.5556 5.6667 8 7778 1 1 . S89 15 . 00O
FIGURE 2.20 Plot of Intercepts from AFB Micro Equations Against Social
Setting Ranks (SSR), with Bivariate (weighted) Regression Line: Y =
20.63  .027(SSR)
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111
14.230 4.
1 3. 6 1 2
1 2 . 994
12.377
.
~ 1.759 ~
1t. 131 ~ J
10.523
9.9056
9.2878
4
S.67~0 +.
~ ~ 4~ 4 4 ~ 4 ~ ~ ~ _ _ _ ~ _ _ _ _ ~ _ _ _ _ ~ _ _ _ _ ~ _ _ _ _ ~ _
. ~ 4 ~ ~ ~ ~ 7. .222 10. 3~3
2 .5556 5.6667 8. 7778 1 t .889
8.7778
1 ~ `] ~ ~ S

FIGURE 2.21 Plot of Intercepts from EF Micro Equations Against Social
Setting Ranks (SSR), with Bivariate "weighted) Regression Line: Y =
10.55 + .024 (SSR)
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112
.69000
 . 80567 4.
.
.
2.3013 ~
4.
 3 . 7970
5 .2927
.h
6.7883 4.
4.
a. 2840 ~
9.7797
 1 1 .275
.
.
.
.
.
.
.
.
.
12 .77 1 +.
4. _ _ _ _ ~ _ _ _ _ + _ _ _ _ ~ _ _ _ _ ~ _ _ _ _ ~ _ _ _ _ + _ _ _ _ ~ _ _ ~ _ _ _ ~ _ _ _ _ 4 _ _ _ _ 4. _ _ _ _ · _ _ _ ~ ~ ~ ~ + ~
1.0000 4.1111 7.2222 10.333 13.444 SSR
2.5556 5.6667 8.7778 1 1 .889 15.000
FIGURE 2.22 Plot of Intercepts from LF Micro Equations Against Social
Setting Ranks (SSR), with Bivariate (weighted) Regression Line : Y =
4 .65 + .085 (SSR) .
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113
.69000
 .80567
.
.
.
2 .3013 4. ~ ~
4.
3.7970
5 .2927 4.
6.7883 4.
4~
8.2840
9.7797
 1 1 .275
 t2 .77 ~
4
4.
O.
.
.

FIGURE 2.23 Plot of Intercepts from LF Micro Equations Against Family
Planning Program Scores (EMS), with Bivariate (weighted) Regression
Line: Y = 5.90 + .12 (FPS)
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114
GDP instead of positively, and that EF, for which we had no strong
hypotheses, is slightly positively associated with SSR and GDP. AS for
LF, it has a very minute inverse relationship with SSR and GDP. Although
the direction of association was expected, the goodness of fit was not.
Indeed, all of these fits involving the means are weak and not
statistically significant at conventional levels. However, this is
unsurprising given the degree of scatter observed repeatedly in the
course of this analysis. Moreover, these results are telling in another
way: mean AFB, EF, and LF are summary measurements based on univariate
d istributions . Rather high correlations at the country level are usual
when measures of this kind are employed. mat we have not found such
correlations in this analysis suggests that the composition of the
15country sample being used may be working against an effective test of
the theory Future research, based on a larger sample of countries, may
overcome this problem.
2 . 7 SUMMARY AND DISCUSSION
The material presented in this chapter divides naturally into three
distinct foci linked by the need to begin assessing the empirical
adequacy of the theory developed in Chapter 1. We began the main work of
this chapter by extending and operationalizing the macrolevel aspects of
the theory. AS part of this effort, we presented a rationale for using
measures of socioeconomic development and family planning program effort
as indicators of what we mean to convey by the traditional/transitional
vocabulary employed in Chapter 1. We then translated Chapter 1 hypotheses
about coefficient sign variation (or the lack thereof) across social
settings into hypotheses about the effects of macro variables on micro
coefficients when the latter are collected over countries to form a
dependent variable at the macro level. This translation process was
carried out with great explicitness, partly because of the complexity of
the ideas, but also to demonstrate that the operationalized macro
hypotheses rest on a serious reasoning effort. In developing these
hypotheses, we attempted to show the power of the multilevel framewor k
(Hermalin and Mason, 1980; Mason, 1980) for developing contextual theory
and, by example, showed how to work back and forth between dual charac
terizations in order to derive hypotheses. This was essentially the key
to deriving hypotheses about the macro determinants of intercept vari
ability. On the one hand, these determinants are driving a particular
kind of coefficient at the micro level; on the other hand, the macro
equation for the intercepts collected across countr ies provides the "main
ef fects ~ of the macro var tables on the micro level response var table. In
sum, our multilevel approach, as well as the methodology we have used to
derive hypotheses, is rarely seen in population research, and we have
therefore spelled it out.
The second major effort in this chapter consisted of an analysis and
comparison of the estimated AFB, EF, and LF structural equations for Peru
and Korea. Although a discussion of the results for one or two countries
seems essential if the macro analysis is to be well understood, presenta
tion of the estimated equations for Peru and Korea has value in its own