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2 . 2 THE PROBLEM
m e primary goal of this chapter is to present the results of exploratory
macro analyses in which the estimated intercepts and micro parameters for
the socioeconomic variables in the onset (AFB), early fertility (EF), and
later fertility (LF) structural equations are treated as functions of
macro variables. This focus reflects two basic decisions: to select
certain structural equations for examination; and, within these equations,
to examine macro variability in the intercepts and in the effects of the
socioeconomic variables. m e reason we restrict our attention to a subset
of the entire model is that our empirical research has just begun. The
results reported in this chapter are thus in the nature of a progress
report. We concentrate on the AFB, EF, and LF equations because the three
endogenous variables in these equations operationalize the decomposition
of fertility which is at the heart of the theory presented in Chapter 1.
Within these equations, we focus on the effects of the socioeconomic
variables because the theory is concerned with the socioeconomic deter
minants of fertility. Hypotheses presented in Chapter 1 about variability
in the effects of these variables are novel, and their empirical testing
is a priority research concern. Analysis of the remaining variables,
although essential to a complete evaluation of the model, must await
another occasion. Finally, our focus on the AFB, EF, and LF structural
equations provides insight into the main or additive effects of the macro
variables in the determination of AFB, EF, and LF, as discussed in
further detail below.
2 . 3 MACRO HYPOTHESE S
Chapter 1 examined possible changes in the directions of association
between socioeconomic and fertility variables as a function of location
on the traditional/transitional continuum. In that discussion of the
implications of the theory for commonly used endogenous variables such as
children ever born (CEB) , we treated sign changes from traditional to
transitional settings as indicators of changes in magnitude as well as in
sign. Thus, the coefficient of a particular socioeconomic variable which
is positive in an extreme traditional setting but negative in an extreme
transitional setting may be negative and smaller in absolute value, or
even zero, in a less transitional setting. In developing the macro
hypotheses to be operat~onal~zed and tested, we must first be more
precise about the sources of macro variability in the micro parameters.
Next, we develop a macro specification, and translate our notions of
coefficient variability into hypotheses about the effects of macro
variables on micro parameters. Subsequently, we operationalize the micro
and macro formulations.
The traditional/transitional continuum is based on the notion that
at the traditional extreme there is no explicit idea or practice of
fertility control, while in transitional settings, some explicit idea and
practice exist. In general, to operatzonalize this continuum would seem
to involve measuring knowledge, attitudes, and practices concerning
fertility control at both the micro and macro levels. For a given
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country, a time series of this kind of information would provide a profile
of a society's degree of transitionality over time.4 However, such an
operationalization is currently beyond our grasp.
It may nevertheless be possible to measure a society's degree of
transitionality indirectly. For one thing, indicators of socioeconomic
development should be related to the continuum: the greater the develop
ment, the further along the transitional end of the continuum. This
assertion is based on the idea that socioeconomic development, broadly
construed, increases the relevance of familysize targets and birth
limitation, and thus of fertility control. It does so by changing the
social and familial environment in which the advantages and disadvantages
of children are determined, although not necessarily equally or evenly
throughout a society (Leibenstein, 1974; Caldwell, 1976; Bulatao, 1979;
Notestein, 1953; Coale, 1973; Freedman, 1975~. Although this relationship
need not be monotonic for all phases of socioeconomic development, the
least socioeconomically developed societies should be relatively tradi
tional and the most socioeconomically developed societies relatively
transitional. Because macro data on socioeconomic development are
relatively plentiful, it should be possible to study variability in the
micro parameters by relating them to level of development.
A second macro dimension relevant to the degree of transitzonality
encompasses the presence, scope, and vigor of official family planning
programs. Indeed, information on these programs should bear directly on
knowledge and practice of fertility control in Third World countries.
m e effectiveness of this influence is of course at issue (Tsui and Bog ue,
1978; Demeny, 1979~. For precisely this reason, it is of particular
importance to examine the macrolevel impact of these programs on vari
ability in the effects of the micro socioeconomic determinants of fer
tility, as well as in the intercepts of the micro structural equations.
There are a number of mechanisms by which family planning programs can
have an impact on fertility. For a variety of reasons, these mechanisms
are not readily distinguished from each other empirically. Perhaps the
chief difficulty is that such an analysis would require a large sample of
micro data from countries located across virtually the entire range of
socioeconomic development. That is, the effects of a program may well
depend on the socioeconomic environment within which it is implemented.
Unfortunately, the WFS data available reflect a relatively narrow range
of development. Therefore, we will restrict our attention to a single
mechanism underlying potential family planning program effects on the
micro parameters of our model, as descr ibed below.
A fundamental hypothesis about family planning programs is that over
time they reduce socioeconomic differentials in fertility. m is hypothe
sis implies that in an idealtypic traditional society, family planning
programs will primarily benefit those of higher socioeconomic status,
who, because of health and other advantages, are likely to have the
highest fertility. (Programs in such settings are, of course, extremely
unlikely.) In a transitional society, on the other hand, those of lower
socioeconomic status will have higher fertility and so be prime program
beneficiaries.
Of the three fertility components outlined in Chapter 1, family
planning programs are most likely to relate to the lastlater fertility
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(LF). There is no reason to suppose that these programs are presently
focused directly on timing and spacing, factors relevant to age at first
birth (AFB) and early fertility (EF). muS, the effect of family planning
programs in suppressing socioeconomic differentials in fertility should
be visible for the LF structural equation, but not for the other two
equations. This point is supported by Table 2.1. This table extends
Table 1.1 to show the sign hypotheses for socioeconomic effects in the
AFB, EF, and LF structural equations ~ conditional on the extremes of
socioeconomic development and family planning program strength. The
lefthand column of the table, corresponding to settings with no family
planning programs, displays the same pattern of signs found in Table 1.1.
The righthand column contains the extension of Table 1.1. In this
column, the damping of socioeconomic differentials in conjunction with
strong family planning programs occurs only for LF.
Although other macro forces may be hypothesized to drive the micro
socioeconomic parameters, we consider the two discussed abovesocio
economic development and family planning program strengthto be on a
Priori grounds worthy of particular consideration. The empirical
research reported in this chapter will therefore depend on measurement of
these two dimensions.
If these dimensions affect the socioeconomic micro parameters of the
AFB, EF, and LF structural equations, they do so differentially depending
on the particular structural equation being considered. That is the force
of the argument thus far advanced and summarized in Table 2.1. To trans
late these hypotheses into specific expectations capable of empirical
TABLE 2.1 Expected Signs of Micro Relationships Between Socioeconomic
Variables and Fertility Components in Particular Macro Settings
Fertility
Component
Lowest Extreme of Socioeconomic Development
No Program Very Strong Program
AFE 
EF ~
LF + Toward Zero
Considerable Socioeconomic Development
AFB
EF
LF
+
+
Toward Zero
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testing would require the specification of functions linking the micro
parameters to the macro dimensions. The simplest functions compatible
with our substantive micro and macro reasoning are as follows. Let
Djkm be the effect in the jth (j = 1,...,J) country of the kth {k =
1,...,K) micro predictor on the mth (m = 1,...,M) endogenous variable.
Since our analysis is focused only on the micro intercepts and the
effects of the socioeconomic determinants on fertility, assume that k
subscripts only the intercepts and socioeconomic variables, and not othe r
variables potentially in a structural equation, such as adjustment
variables. Furthermore, let m = 1 denote AFB, m = 2 denote EF, and m = 3
denote LF. Labeling socioeconomic development by S and family planning
program strength by FP, the specification in its most general form becomes
(2.1) ~jkm = nOkm + ~lkmSj + n2km(FP
Id ~ n3km~s.Fp~i + Ajkm
for k = 1~...,K; m = 1,2,3, where the ecoefficients are macro regres
sion coefficients, and the \term denotes error in the determination of
the micro coefficients.5 Thus, when ~ km = Djl:, the dependent variable
is the intercept of the AFB micro equation, and observations are over
countries. When Djkm = Djk3 for k > 1' the dependent variable is the
coefficient of a socioeconomic variable in the LF micro equation, and
observations are again over countries. Expression {2.1) specifies 3K
macro equations. Our hypotheses about the effects of S and FP (i.e. the
n ' s ~ in these equations can now be stated.
Macro Determinants of Micro Slopes
The reasoning in Chapter 1 about the signs of the effects of the socio
economic variables at the micro level leads to identical hypotheses about
the signs of the effects of each of those variables in a given structural
equation. For example, there should be identical sign variation across
the traditional/trans~tional continuum for wife's and husband's education
in the determination of LF. For this reason, in stating our expectations
about the signs, but not the magnitudes, of the effects of S and FP in
expression (2.1), it is unnecessary to distinguish among the socioeconomic
variables (K > 1~. Where we expect the effects of S and FP to be
nonzero, there are no hypotheses about magnitude. Hence, if we restrict
our attention to the directions of the S and FP effects, it suffices to
reduce expression (2.1) to just two equations for each micro structural
equationone for modeling the micro intercept, and another for modeling
micro socioeconomic parameters.
Table 2.2 shows our sign hypotheses about the effects of S and FP.
This table contains three kinds of entries. Where we have a directional
hypothesis, it lists the appropriate sign. Where we hypothesize a null
effect, it lists either a zero or a ~*.. me n*n entries are for coeffi
cients which, if not zero, can be either positive or negative. For these
entries there are no directional hypotheses, and no a priori interpreta
tion of a nonzero coefficient. These entries always pertain to FP and
S.FP, variables which are relevant only for modeling the LF micro
parameters. The empirical macro analysis reported in Section 2.6 below
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TABLE 2.2 Expected Relationships Between Macro Variables and the
Intercepts and Slope Coefficients of the Micro Equations
Macro Coef f icient
S Main Effect
AFB Equation
FF Main Effect S FP
Intercept ~
Micro SES Effects + * *
EF Equation
Intercept +
Micro SES Effects O * *
LF Equation
Intercept
Micro SES Effects

Note: The asterisks indicate that there is no reason to expect a
relationship.
does not test the null hypothesis that the ncoeff icient associated
with a particular n*. entry in Table 2.2 i ~ 7.r~ baa i nod The
~ ~ ~ ~ ~ _ _ ~ _ ~ . . ~ _ .

+
em "~ernaClve. again' a nonzero result would require post
hoc speculation, and inclusion of FP and S.FP in the macro equation
amounts to specification error resulting from inclusion of irrelevant
variables. Finally, where Table 2.2 lists a zero, we do inspect the data
for a relationship. The reason for this, which will become clearer
below, is that one of the sign hypotheses depends intimately on the
validity of the hypothesized null effect.
The sign hypotheses displayed in Table 2.2 are listed separately for
the intercepts and socioeconomic ef feats of the AFB , EF , and LF struc
tural equations. As for the hypothesized signs of the macro variables on
the micro socioeconomic effects, these can be deduced immediately from
Table 2.1. Consider the AFB equation. The hypothesis that the socioeco
nomic determinants of AFB will be negative in traditional settings and
positive in transitional settings regardless of family planning program
strength translates into ~ positive effect of S on 8.kl for k > 1. The
hypothesis of no impact of fa'.~:Ly planning program strength on the
directionality and magnitude of the effects of socioeconomic status on
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AFB translates into a null effect of both FP and S.FP. For the reasons
discussed above, we do not test hypotheses about these null effects, and
Table 2.2 reflects this with arm entries. The hypothesis that socioeco
nomic differentials in EF are independent of any variability along the
traditional/transitional continuum leads to the hypothesis that the
effects of S. FP, and S.FP are all zero. In this instance, the FP and
S.EP entries contain Ares for the reasons discussed above, but the S
entry contains a zero. The explanation is that the macro hypothesis
about the dependence of the EF intercept on S rests in part on the
assumption that the effect of S on the socioeconomic coefficients in the
EF equation is nil. This assumption is examined below in Section 2.6.
It is only in the macro equations determining the levels of the
socioeconomic differentials in LF that we expect to see effects for S.
FP, and S FP in expression (2.11. The implications of the argument for
the joint effect of S and FP can be determined by taking partial deriva
ti~res of expression (2.1~. With no loss of generality, let k = 2. Then
(2 .2a)
and
(2.2b)
a523
aS = "123 + Dl23FP
a623
—aFp = n223 +n323S.
We can determine the signs of nl23 and q323 from expression (2.2a) as
f allows. Taking FP = 0 to indicate total lack of family planning program
effort, the derivative must be negative (as suggested by the lefthand
column of Table 2.1~. Taking FP at a positive value indicating a high
degree of family planning program effort, the derivative must not be
positive, and must be closer or equal to zero. Thus, nl23 0,
and the appropriate signs for these coefficients are accordingly entered
into the bottom panel of Table 2.2.
This leaves the sign of the "additive" component of FP to be deter
mined. In expression (2.2b), we know that n323 > 0 according to our
hypotheses. Taking S = 0 to indicate the lowest extreme of socioeconomic
development, it is clear that the derivative must be negative at that
point, based on The reasoning that at the lowest levels of socioeconomic
development, the introduction of family planning programs will lead to a
damping of positive socioeconomic effects on fertility. At the other
extreme, taking S at a high positive value indicating considerable socio
economic development, expression (2.2b) should be positive. This implies
that n223 must be negative and Table 2.2 thus indicates a negative
~additive" effect of FP in the bottom panel.
Macro Determinants of Micro Intercepts
In considering the directionality of the effects of S and FP on the
intercepts of the AFB, EF, and LF equations' we completely characterize
the hypothesized macro variability of all of the micro parameters in the
AFB structural equation. Since we have no hypothesis about setting
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variability in the effect of AFB on EF, we also characterize all of the
hypothesized macro variability in the micro parameters of the EF struc
tural equation. This is not true, however, for the LF structural
equation. For this equation, there are predetermined variables whose
effects we are not studying, some of which interact with the micro
socioeconomic factors. To complicate matters further, a number of the
micro effects not included in the present analyses are hypothesized to
vary systematically over the traditional/transitional continuum. To the
extent that our hypotheses about these effects are incorrect, our expecta
tions about macro variability in the LF intercept will be subject to
distortion. For purposes of the present analyses, then, our expectations
about the impact of S and FP on the LF intercept are less certain than
those about the AFB and EF intercepts. In our later research, we will be
examining all of the coefficients in the LF equation.
Outside of the analysis of covariance, the intercept is perhaps the
most neglected quantity in analyses of generalized linear models (e.g.,
regression and logistic response models). Nevertheless, an understanding
of macro variability in the micro intercepts is of critical importance
for understanding the main effects of the macro variables on the micro
endogenous variables. This point is disguised as long as we think of
micro equations on one hand, and macro equations on the other. However,
it can be revealed by the following simple example.
With no loss of generality, suppose the micro model is bivariate and
can be written as a single regression equation:
Yii Boj + BljXij + £ij, (i = 1,...,nj; j = 1,...,J),
where i subscripts individuals in countries and j subscripts countries.
Then the macro model consists of two equations. For them, we suppose,
again with no loss of generality, that there is only one macro explana
tory variable, and that it is the same for both equations:
~Oj = nOO ~ nQlGj + loj;
clj = ~10 + DllGj + Fiji
Then substituting for 60j and Dlj in the micro equation yields
(2.3) Yij = DOG + n01Gij + DloXi; + n~lKijGii + (error)ij,
where Gij = G for i = 1,...,nj and "error. denotes a complex error
structure that plays no role in the derivations below. This combined
micro~macro nKxlel shows that the effects of the micro and macro variables
are inseparable. The effect of G on Y is given by
(2.4a) aG = 001 + nllX'
while the effect of X on Y is given by
(2.4b) aX n1O + nllG.
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As can be seen, it is the macro equation for the intercept that contri
butes the main effect of the macro variable e As noted above, this result
generalizes to any number of micro and macro predictors.
In the linear model, used to estimate the AFB, EF, and LF structural
equations, the intercept is a linear combination of the mean of the
response variable and the effects of the predictors multiplied by the
means of the predictors (the DX terms). Therefore, hypotheses about
the dependence of the intercepts of the micro structural equations on S
and FP must depend on the following: hypotheses about how the means of
the response variables vary as a function of S and FP; hypotheses about
the dependence of the micro coefficients on S and FP; hypotheses about
the dependence of the X terms on S and EP; and hypotheses about the
dominance of variability in the means of the response variables over
variability in the OX terms, or vice versa.
AS summarized in Table 2.2 for the AFB intercept, we hypothesize
that S will have a positive effect, FP will have no effect, and there
will be no interaction between S and FP. For the Third World countries
sampled by the WFS, there should be a positive relationship between AFB
and socioeconomic development. Since we have already hypothesized a
positive effect of S on the socioeconomic determinants of AFB, we expect
that the greater the AF8 mean, the greater the quantity resulting from
micro socioeconomic effects that will be removed (the means of the micro
socioeconomic variables should increase as socioeconomic development
increases). If this reasoning is correct, the relationship between the
AFB intercepts and S should be attenuated relative to the relationship
between the AFB means and S. However, we do not expect it to become
negative, since if X is socioeconomic status and G is socioeconomic
development in expression (2.4a), this partial derivative should be
nonnegative at all levels of micro socioeconomic status. The hypothesis
about the relationship between the AFB intercept and socioeconomic
development is strengthened by recalling that this relationship also
describes the "mains effect of socioeconomic development in the combined
micr~macro model (2.3 ~ .
There is no reason to expect that FP will have an ef feet on the AFB
intercept, both because none is hypothesized for macro variability in the
socioeconomic determinants of AFB and because there is no reason to
suppose that FP should affect the AFB means. Family planning programs
have not, in general, been aimed at this component of fertility in Third
World countries, particularly for the years of concern to us, as will
become clear when we define the cohorts to be studied.
For the EF intercept, we expect S to have a positive impact at the
macro level. Since we hypothesize no effects of S. FP, and S.FP on the
micro socioeconomic effects in the EF structural equation, it follows
that hypotheses about the nature of macro variability in the EF intercept
must depend on the following: expectations about betweencountry vari
ability in the EF means {(EF)j); expectations about the dependence of
mean AFB {AFB) on S and of mean micro socioeconomic status (SES) on S.;
and expectations about the dominance of the AFB effect relative to the
SES effects on EF at the micro level. In general, we_have no compelling
hypothesis about the dependence of EF on S. SES and AFB should be
positively correlated with S. even if the micro effects of SES and AFB do
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not depend on s.6 However, the micro effects of SES and AFB are
offsettingone hypothesized to be positive, the other negative. Further
more, the effect of exposure (AFB) should dominate the SES effect,
regardless of level of socioeconomic development. Putting all of these
expectations together, it follows that, since the effect of AFB on EF
should be negative and dominate that of SES, and since AFB should be
positively correlated with S. the effect of S on the EF intercept should
be positive.
There is no strong reason to expect a relationship between the EF
means and FP because family planning programs in the Third World have not
been targeted at, or markets successful in slowing, early fertility.
Although this is less likely to be the case for recent cohorts, it seems
reasonable for the older cohortswomen aged 40 to 44 in 1974on which
we focus. m us, we expect a systematic' positive effect of S on the EF
intercept, and no relationship for FP or S.FP. The middle panel of Table
2.2 summarizes this conclusion.
Turning now to hypotheses about the effects of socioeconomic
development and family planning strength on the LF intercepts, as noted
earlier, these hypotheses are the most uncertain because the present
discussion focuses only on the socioeconomic determinants of LF and not
the full LF equation. To derive hypotheses about the macro determinants
of the LF intercept, it suffices to define the micro and macro models as
(2.5a) (LF) id = DOj + ~lj (SES) ij + ~ ij ;
fib) Do; Too ~ nOlSj + D02(EP); + no3(s.Fp~i + Aoj; and
(2.5c) ~lj = D10 + Ells; + nl2(EP); + nl3(sFp~i + ll
Then, substituting for the as gives
(2.5d) (LF)i; nOO + nOlSi; + ~02 (FP)i; + no3 (S.FP)i
+ n 10 (SES) i j + n 11 (S.SES) i j + n 12 (FP.SES) i
+ nl3(FP.S.SES)~i ~ (error) ij'
where Sij = Sj, i = 1,...,nj; that is, the 5 values are identical
within countries, as they are for the other terms involving macro
variables.
There are essentially two ways to attempt to derive hypotheses about
n01, no2, and nO3. One is to reason directly about equation {2055~ ; the
other is to recognize that these coefficients are the Main effects" of
the macro variables `n the combined micromacro equation (2.5d), and then
to derive the signs of these coefficients from inspection of the partial
derivatives. We will take the latter course here since it explicitly uses
information discussed earlier about the effects of the macro variables on
the micro socioeconomic effects. Regardless of which alternative is
selected, the inclusion of S.FP in equation (2.5b) must be justified.
Actually, this justif ication is neither more nor less than that required
in support of including S and FP in the micromacro model, equation
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(2.5d). The reason is that, since the micro socioeconomic effects on LF
are held to vary as a function of S. FP, and S.FP, and since this vari
ability is necessarily incorporated into the micro intercepts as well, we
must allow for the dependence of the intercepts on S. FP, and S.FP.
Operating on equation (2.5d), and taking first partial derivates
with respect to S and FP, gives
(2.6a) a(LF) = nO1 + nO3FP + nlltsEs) + nl3(FP.SES)
and
(garb) a {LF) = ~02 ~ DOSS + nl:{SES) + nl3`S.SES,,
where already derived signs have been placed above the appropriate
coefficients. Examining expression (2.6a) first, and setting FP = 0 to
indicate total absence of family planning program effort, we see that the
partial derivative of LF with respect to S depends critically on Sol.
It is at the heart of the theory presented in Chapter 1 that this coeffi
cient be negative. If socioeconomic development leads to a reduction in
fertility, it should certainly be seen in later fertility. A similar
point holds for expression (2.6b). If family planning programs have an
effect on fertility in developing nations, that effect should certainly
be on LF. Thus, setting S = 0 to indicate the lowest level of socioeco
nomic development, it is clear that if the partial derivative of LF with
respect to FP is always to be negative, it must be the case that nO2 < 0.
However, n02 = 0 is too weak as a hypothesis. The effect of FP on LF
should not have to depend on the magnitude of the interaction effect
between micro socioeconomic status and family planning program strength
(n :21. Above and beyond this relationship, there should be a negative
effect of FP on LF. Thus, it is hypothesized that ng2 ~ O
The sign of nO3 can be derived either from (2.6a) or (2.6b), given
that we have the signs for nO' and n02. Focusing on (2.6a) yields the
general hypothesis that the nigher the level of socioeconomic development,
the lower LF should be regardless of the level of family planning program
effort. Fixing SES at any level and setting nO3 > 0 leads to the result
that as FP increases, the effect of S on LF decreases in absolute value
and may even become positive if FP is large enough. On the other hand,
fixing SES at any level and setting n03 < 0 leads to offsetting compo
nents : n O,P + n 1/P.SES, where the first term is negative and the second
positive. Of course, everything depends on the relative magnitudes of nO3
and n13; however, hypothesizing nO3 ~ ~ is the only alternative if (2.6a)
is to have a chance of remaining constant or nonpositive as FP increases.
We have now reviewed all of the directional hypotheses about the
effects of socioeconomic development and family planning program strength
on macro variability in the intercepts and micro socioeconomic parameters
of the AFB, EF, and LF structural equations. Essentially, this has
amounted to an expansion and translation of the reasoning embodied in
Table 1.1 of Chapter 1. In addition, the discussion of macro variability
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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 rankorder 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 cohortspecific. The results reported here are
based on a single birth cohort of currently married respondents aged
4044 in 1974. We use this year because the survey dates range over a
fouryear period (1974771. m is particular cohort was chosen, first, to
permit study of betweencountry 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
firsthand the effects of the family planning programs introduced in many
countries during the 196574 decade. Respondents aged 4044 in 1974 were
aged 3034 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.