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WHY DOES MIGRATION DECREASE FERTILITY? EVIDENCE FROM THE PHILIPPINES1
Eric R. JENSEN2 and Dennis A. AHLBURG3
April 14, 2003
Prepared for presentation at the 2003 Population Association of America meetings, Minneapolis.
1 The authors thank Lixia Xu and Hanley Chiang for valuable research assistance and Berhanu Abegaz, Phil Martin,
Andy Mason, Karen Mason, Stan Panis, Bob Retherford, Fiona Steele and Peter Xenos for useful comments. 2 Department of Economics, College of William and Mary: [email protected] . 3 Industrial Relations Center and Minnesota Population Center, University of Minnesota and Department of Social
Statistics, University of Southampton: [email protected] .
WHY DOES MIGRATION DECREASE FERTILITY? EVIDENCE FROM THE PHILIPPINES
Abstract: In this paper we use 1993 Philippines data and model the impact of past migration on fertility. Overall fertility declines of women who move and subsequently work actually are slightly larger for moves between equally urbanized areas than for moves to more urbanized areas. If not followed by work for pay, the fertility impact of any type of migration is much smaller. We assume that fertility norms fall with increasing urbanization, and therefore interpret this as evidence in support of the notion that, while women may adapt to lower fertility norms, the impact of changed opportunity costs is a more important determinant of post-migration declines in fertility. Introduction
In many developing countries urban fertility rates are lower than rural fertility rates, there
is significant rural-urban migration, and national fertility rates are falling. From these
demographic observations, the inference is often drawn that rural-urban migration plays a role in
explaining declining fertility rates. The Philippines would seem to fit the stylized demographic
facts, and it is tempting to infer a causal link between migration and declining fertility. As of
1995, the urban total fertility rate (TFR) was 3.5 and the rural TFR was 4.8. In the last two
censuses about seven percent of persons over the age of five years were not resident in the city or
municipality they resided in five years ago (Flieger 1995). Perhaps more importantly for a study
examining fertility, large numbers of women are migrants. In the 1980s one of every five
women in the metropolitan Manila region and one in ten in all other urban areas of the country
was a migrant (Perez 1995). This was consistent with earlier data on Philippine migration,
which showed 20 percent of women in their twenties migrating from Mindanao in the 1960’s
(Hiday 1978). The percentage of the population classified as urban rose from 36 percent in the
mid-1970s to 52 percent in the early 1990s (Flieger 1995), although Flieger shows that some of
this increase in urbanization was due to reclassification of rural areas to urban. The national TFR
fell from 5.5 in the mid-1970s to its 1995 level of 3.9 (World Bank 1995).
The conclusion that migration causes fertility to decrease as urban fertility norms are
adopted by migrants often rests on these sorts of stylized facts, but this ignores more subtle
behavioral patterns. Most notably, in the nationally representative Philippines data we employ in
this paper, more than four out of five moves are to areas no more urbanized than the migrant’s
area of origin. This alone would seem to limit the potential utility of rural-urban migration, and
by extension, of behavioral models such as normative adaptation that rely on such migration, in
accounting for declining fertility rates. With this in mind, we present and examine a model
based on opportunity cost as an alternative to the normative adaptation model. We use the large
number of moves we find in the data between areas that presumably are normatively similar to
identify the impact of competing motives for fertility reduction.
Section 1: Migration and Fertility
A negative association between fertility and migration may exist because of selectivity,
disruption, or adaptation. Selectivity implies that migrants are different from nonmigrants in a
number of ways, both observable (for example, education and age) and nonobservable (for
example, motivation), that lead migrants to have lower fertility than nonmigrants. Selectivity
alone is generally not a causal explanation of the contribution of migration to declining national
fertility, because the lower fertility attributable to being the sort of individuals who elect to
migrate presumably would be observed even if they were somehow prevented from migrating.
Disruption associated with migration can cause lower fertility through the physical
separation of spouses (Goldstein and Tirasawat 1977, Kiningham et al. 1996, Harrison et al.
1986). Disruption also can increase fertility by causing an interruption in the supply of
contraceptives or by the weakening of controls on sexual behavior (Moreno 1994, Bloom and
Mahal 1995, Lansdale and Havan 1996; Ahlburg and Jensen 1997).
Adaptation generally is taken to imply that since average fertility in the destination is
lower than average fertility in the origin, migration may reduce fertility. Typically, in the
demographic literature, an important mechanism is the adoption of prevailing lower-fertility
norms. From an economic perspective, however, adaptation may come from a change in demand
for children generated by changes in prices (woman’s wage, cost of childcare, costs of fertility
regulation) and changes in income (Easterlin and Crimmins 1985; Rosenzweig and Schultz
1985). If many migrants from rural to urban areas subsequently display lower fertility, this is
consistent with either a norms-based explanation, in which fertility declines because migrants
conform to prevailing behaviors at the destination, or an economic explanation, in which fertility
declines as prices rise. In the absence of specific information on post-migration employment, or
where most moves are to more urbanized areas, it is difficult to distinguish empirically between
these alternative explanations. By examining the fertility impact of moves between areas with
similar fertility (and so presumably similar fertility norms), and by distinguishing employed
7 See Lillard and Panis (2000) for a full description of the technique and its implementation.
migrants from other migrants, we hope to distinguish between the norms and opportunity cost
models of fertility adaptation.
Although a large number of empirical studies of developing nations have found evidence
of a negative association between fertility and migration, this finding has not been universal (see
Lee 1989). Lee (1989: 1599) discusses several reasons that have been put forth to explain
differences in the findings among studies: different study designs, different operationalization of
key concepts, and failure to control for selectivity. Selectivity criticisms of analyses of migration
impacts center on the failure to control for the differences that helped make some individuals
migrants in the first place. We will examine this contention in some detail in the next section.
Some studies, notably Goldstein and Goldstein (1983), and Lee and Pol (1993), attempt
to identify separately each of the three explanations (selectivity, disruption, and adaptation) and
find support for each. In two recent studies, Moreno (1994) attempted to identify each of these
effects of migration on contraceptive use in Brazil, and White, Moreno, and Guo (1995) these
effects on fertility in Peru. Contraceptive use is one of several channels through which migration
could affect fertility. Moreno found some association between migration and contraceptive use,
but the relationship was not strong. White, Moreno, and Guo found a negative effect of
childbearing on migration (ever moved) and migration on fertility. They also found some
evidence for selectivity and modest support for disruption and adaptation. A parallel literature
exists on the effect of migration on fertility for migration to developed countries. Much of this
research focuses on the US and has found evidence of temporary disruption followed by rising
fertility (Jasso and Rosenzweig 1990; Blau 1992).Overall, disruption dominated assimilation. In
contrast, recent studies in Europe on completed fertility favor the assimilation model of fertility
adjustment (Schoorl 1990; Mayer and Riphahn 1999). It is not clear why the US and European
experience differs.
Philippine studies have shown ambiguous results with respect to the relationship between
migration and fertility. Hendershot (1971) found strong support for a model of social mobility in
which migrants are selected by their high mobility aspirations, and subsequently, participate
more in the urban culture. Participation creates pressure for migrants to adapt to local family
size norms. Hiday (1978) found a negative association between fertility and social distance from
the rural home community, age at marriage and education; and a positive association between
age at marriage and education, and social distance. She interprets these correlations as
supportive of the social mobility model. Bacal (1988) models the impact of migration on
fertility. She concludes that migration has little impact on fertility; however, Bacal employs as
an explanatory variable contraceptive usage, which presumably is an important mechanism
through which migration would generate fertility decline.
Using Peruvian DHS data, White, Moreno, and Guo (1995) were able to exploit the
longitudinal nature of the retrospective calendar to study separately the impact of migration on
fertility and of fertility on migration. They estimated a hazard model of birth interval duration,
which employed residence and recent migration as predetermined variables. Recent migration
had a large, negative, but statistically insignificant coefficient. Recent migration was included to
examine the impact of disruption on fertility. It is not clear why only recent migration would
influence fertility through adaptation, and so these findings may not bear directly on the question
of adaptation.
Section 2: Model and Methods
Conditional estimation of the impact of migration on fertility
The literature supports, to some extent, the notion that migration causes fertility to fall
through some form of adaptation. We have found no examination of the importance of
competing models of adaptation, notably the sociological normative adjustment model versus the
new home economic model. To examine the question, we develop a simple model that allows us
to assess the importance of the two frameworks. We will compare instances in which norms
may be reasonably presumed constant to others in which the same can be said about opportunity
costs, and examine the relative importance of the two effects. Before proceeding to the model,
we will discuss the notion that migrant selectivity requires that fertility and migration must be
estimated simultaneously.
The first concern we have with a simultaneous model of migration and fertility is
pragmatic. The empirical difficulties in identifying such a model are formidable, requiring either
exclusionary or distributional assumptions to resolve them. Exclusionary restrictions, familiar in
a simultaneous linear equations context, involve specifying structural equations for fertility and
migration that each exclude a sufficient number of predetermined variables to allow for a unique
solution for the estimators. Among the variables typically affecting migration available in a
Demographic and Health Survey (or similar data set), only moving cost can reasonably be
excluded from a structural equation for fertility. Unfortunately, because it is contingent on
distance from origin to destination, this cost is only available for those who actually migrated.
Credible exclusions of fertility determinants from structural migration equations are equally
difficult to find. For example, contraceptive costs (in money and time) theoretically affect
fertility, but probably not migration, directly. However, they often are reflective of social
infrastructure (in fact are often the only such indicator in common surveys such as Demographic
and Health Surveys), and so in practice also are likely determinants of migration. Less clearly
theoretically relevant instruments may exist, but the cautionary tone of the literature on weak
instruments (e.g., Bound, Jaeger and Baker 1995) is instructive here.
Lillard (1993), Lillard, Waite and Brien (1995), Upchurch, Lillard and Panis (2002),
Steele and Curtis (2003) and others have pursued an alternative identification path based on
distributional assumptions7. In Steele and Curtis, for example, time to contraceptive
discontinuation is a function of contraceptive method chosen, and method choice is in turn
modeled as a multinomial probit. In the current context, fertility could be modeled as a waiting
time to conception, with a set of discrete past (or possibly concurrent) migration choices made
that influence waiting time. The duration model is very appealing for fertility, contraceptive
usage and other demographic processes, in that women are assumed to make a choice, and then,
as time passes, to incur the consequences of that choice. The residuals of the various (hazard and
probit) regressions represent unmeasured individual heterogeneity. They are assumed to be
draws from a multivariate normal distribution, and allowed to be correlated. In this way
simultaneity of the underlying processes is captured, without the need for exclusionary
restrictions.
For the problems to which this method has been applied, the payoff is the ability to make
unconditional statements about randomly chosen members of the population. Steele and Curtis
examine the probability that a woman will choose and then use a contraceptive, and Lillard and
coauthors examine the interrelationship of marriage, cohabitation and fertility. This strategy
certainly would be applicable to the migration-fertility nexus, with fertility hazards dependent
upon past, potentially endogenous migration choices. However, the Lillard approach assumes
that unmeasured heterogeneity in hazards is normally distributed. A well known cost of
employing such a parametric strategy is the strong dependence of results on the specific
parameterization of frailty (Heckman and Singer 1984).8 An alternative is to employ
nonparametric frailty specifications. Unfortunately, such methods are extremely computationally
intensive, even for a univariate frailty (Heckman and Singer, 1984; Ahlburg, McCall and Na,
2002).
The issue that either simultaneous equation method is designed to address is the
estimation of structural parameters that allow for the joint impact of multiple processes on
outcomes. This is as familiar problem in many contexts. Consider, for example, the basic
Heckman (1979) model of wages and labor force participation. An exogenous increase in the
demand for labor causes wages of current workers to increase, and also causes wages of new
entrants to increase from zero. An unconditional estimate of the impact on labor supply of the
demand-for-labor shift must consider both of these effects, a fact that forms the basis of the
selectivity bias literature. Estimation of the impact on wages of an increase in demand,
conditional on continuous participation in the labor market, would simply be the regression of
wages on the relevant covariates, and is straightforward to obtain. However, this conditional
estimate often is not of central interest in the labor market example.
8 Heckman and Singer (1984) have a striking example for unemployment duration in which they compare Weibull
models with differing heterogeneity assumptions. For their first model, they assumed normally distributed heterogeneity, and estimated a positive impact of local labor market conditions on unemployment duration. A second was based on log-normal frailty, and predicted a negative estimate of the impact of local conditions on duration. A third model, in which they assumed heterogeneity was gamma distributed, yielded a positive but insignificant effect of labor market conditions on duration. Thus, any empirical conclusion on the effect of local
In contrast, the case for the usefulness of conditional estimators in the present context is
strong. Most discussions of the impact of migration on fertility, whether new home economic
adjustment to wages or normative adaptation, contain an implicit timing element. Migration
occurs, and then fertility outcomes obtain. Moreover, as we have alluded to already, much of the
migration decision is masked for nonmigrants. These individuals made the same sort of
calculations that migrants did, but, because they chose not to migrate, we do not observe much
about them. Therefore, an unconditional estimate of the joint effects of migration and fertility is
empirically difficult to obtain (at best).
A key insight was made on this point by Sjaastad (1962). Sjaastad realized that psychic
costs were a major component of migration, and that psychic costs were by their nature
unobservable. However, all migrants have proved themselves willing to bear this cost, and so
confining his attention to migrants allowed Sjaastad to focus on other, more measurable costs. In
so doing, he showed that key variables of interest could be studied from a comparison of migrant
origins and destinations, conditional on observed migration. We follow this general framework
in obtaining conditional estimates of the impact of various types of migration on the fertility of
migrants. It is important to note that, as a result, we are not able to make statements about joint
fertility-migration choices. We are able to make conditional statements about the impact of past
migration choices on fertility. The models of fertility adaptation with which we are concerned
are explicitly conditioned on past migration, so this is not a significant limitation of our
approach. We cannot test directly for the importance of migrant selectivity in a conditional
framework; however, we control for its potential impact by including only migrants (presumably
labor market conditions on unemployment durations is supportable with the proper choice of heterogeneity.
self-selected) into our analysis. Moreover, a simple Hausman test for the importance of
selectivity can be constructed by comparing the results from the conditional model with an
identical model estimated on the full sample of migrants and nonmigrants; differences in
coefficients between samples is evidence of inconsistency in parameter estimates, and would be
interpreted as evidence of the importance of selectivity in accounting for lower migrant fertility.
Theoretical model
Let ‘child services’, that is, the product of the quantity n and quality q of children, be
represented by C, and all other utility be provided by a composite good Z. The underlying
theoretical model is as follows:
),(),()1( ZnqUZCUU ==
),()3(
),()3(
),()2(
),()2(
,
,
,
,
KKKq
KKKn
qKq
nKn
Ywstb
Ywrta
Xtqqb
Xtnna
=
=
=
=
We assume that number and quality of children in a family are increasing functions of time t and
other commodities X devoted to their production. Furthermore, we assume that times spent on
producing n and q decrease as wages in location K increase. Therefore, migration from K to
some other location L will have a fertility effect contingent upon the wage differential. For wL >
wK, migration will cause time contributions to the production of both number and quality of
children to decrease, all else constant. Purchased inputs are likely to increase as a result of
higher incomes, especially quality-enhancing inputs, potentially concentrating the effect of
increasing wages in decreasing numbers of children. We will interpret results showing post-
migration fertility responsiveness to wages as supportive of a new home economic framework.
If post-migration fertility is lower than pre-migration fertility without evidence of wage change,
we will interpret this finding as supportive of the less specific sociological model of normative
adaptation.
The empirical model of conception hazard, conditional on migration, is as follows:
tMtTttTtPMth k
tij δδ
δ
),|(lim)|()4(0
≥+≤≤=
→.
Adding covariates yields
),,,(),,|()5( πγβ ijkijiij MwxtgMwxth = .
where hij represents the hazard of conception to woman i in interval j, conditional on having
made migration M of type k. The function g(.) represents a generic hazard function, which we
will assume is Weibull13 in our empirical work. Some variables (x) are fixed for women over
multiple conceptions, and others (w) vary between conceptions. Migration also may vary from
conception to conception, and Mijk denotes that migration of type k was the last migration
13 The key results are largely robust to a range of typical parameterizations and most reassuringly, to a
semiparametric Cox specification.
occurring prior to observing woman i in conception interval j. Migration types may include the
direction of migration (to a more, less or equally urbanized destination) as well as the purpose of
migration (migration that is followed by employment in the month of, or month after, arrival).
Coefficient vectors β, γ, π and the Weibull shape parameter are to be estimated. Note that while
we have argued that we do not require the specification of frailty to estimate a simultaneous
model, the likely presence of unobserved heterogeneity in durations implies that our omission of
it will bias our results toward negative duration dependence (Heckman and Singer 1984).
Intuitively, this is because those most ‘frail’ (in our case those most prone to conceive for
unobserved biological, behavioral or other reasons) contribute short intervals, on average, to the
analysis.
Section 3: Data
We use data from the 1993 National Demographic Survey of the Philippines (Philippines
National Statistics Office and Macro International 1994) to examine the relationship between
fertility and female migration. This is a nationally representative survey in the Demographic and
Health Surveys series, in which 15,029 women were interviewed. Ever-married women
completed a monthly retrospective calendar of roughly five and one-half years duration on
several events, including migration and conception, pregnancy and childbirth. Out-of-wedlock
birth carries a stigma in the Philippines, a predominantly Catholic country. We therefore
excluded unmarried women from our analysis. We also excluded sterilized and nulliparous
women. Migrations were recorded over the duration of the calendar, as well as the date and type
of last pre-calendar migration (or date of birth, for never-migrants). Migration origins and
destinations were broadly categorized during coding as urban, ‘town’, or rural. We classified
moves as being to a more urbanized area, a less urbanized area, or an equally urbanized area in
comparison to the origin. While very crude, this gives us some indication of the character of the
move. We also identified work status in the month of, or month after, migration, and flagged
women who were working after migrating. Pregnancy histories allowed us to retrieve the
starting date of intervals that were in progress at the start of the calendar. We used all birth
intervals after the first birth for which the subsequent interval extended into the calendar period,
beginning them at the month of previous birth and extending them until the next calculated
conception date, or until censored by the survey.
Our final sample consisted of 7,123 birth intervals, contributed by 6,605 women. Of
these, 2,156 ended during the interval, and the rest were censored. Only 44% of these birth
intervals were contributed by women who had migrated at some time in their lives, so we
constructed a subsample of conception intervals of ever-migrants. This consisted of 3,381 birth
intervals contributed by 3,149 women, with 858 observed births and 2,523 open intervals. In
either sample, we were able to capture the hierarchical nature of the data, and estimate models
that controlled for clustering at the level of the mother. Table 1 presents brief variable
summaries and descriptive statistics for both samples, including the distribution of origin and
destination states. The wealth variable is a factor score based on ownership of the durable goods
stove, refrigerator, television, bicycle, motorcycle, and automobile. Migration dummies were
constructed by examining reported moves within the calendar, together with responses on
precalendar migrations, and reflect the last migration (if any) prior to the end of the conception
interval. 52.5% of women reported no migration. Most of the remaining women reported one
migrating once, though 86 conception intervals ended following a second migration, and 23
intervals ended after a third, fourth, or fifth migration. The ‘work’ migration variables equal one
if the woman moved and reported working the month of the move or the month after. The first
column reports means and standard deviations for all women in the sample, and the second
column reports only on those women who reported some sort of migration.
(Table 1 here)
Section 4: Empirical Results
In Tables 2 and 3, we present the results of survival regressions of durations of intervals
following a birth and either censored or ending in conception. Table 2 shows conditional results
for ever-migrants. The results are reported in the form of time ratios, with probability values for
testing the hypothesis that the time ratio equals 1 against a two-tailed alternative in parentheses.
So for example, from the first column of results in Table 2, an increase of one year in wife’s age
at marriage is associated with a conception interval 93% as long as a woman married at the mean
age at marriage would be predicted to experience. Education of the woman and her spouse are
important lengtheners of conception intervals. A representative woman who entered or
completed secondary school has a conception interval 129% as long as one who did not enter
secondary school. Having entered or completed tertiary schooling, having a husband who has
entered or completed either level of school all have a comparable lengthening impact. Because
the secondary variable is zero when tertiary equals one, and vice-versa, this implies that any
level of education beyond primary carries roughly the same impact. However, the separate
effects for wife and husband are multiplicative. A couple consisting of a woman with post-
primary education married to a man with post-primary education would be predicted to have
conception intervals 1.57 (1.29 times 1.22) times longer than a couple with primary or lower
education. Wealth, a rough proxy for permanent income, shows shorter conception intervals
with increasing wealth, but the standard deviation of the wealth variable is small enough that this
accounts for relatively little variation in fertility. Controlling for the cost of children or taste
concerns, to the extent they are embodied in the education variables, this may reflect a relatively
pure income effect.
Migration to less urbanized areas is the excluded category in Table 2. Neither of the
migration coefficients in the ‘migration only’ column is significant. However, when
employment at the destination is added in (the ‘migration and work column’), we find a much
longer birth interval for women who moved between comparably urbanized places and went to
work for pay when they arrived. The net impact of migration between similarly urbanized areas
followed by employment, calculated as the product of the ‘same move’ and ‘same work’ time
ratios, is to increase birth intervals to 233% of durations experienced by those who moved to less
urbanized places. The Weibull shape parameter is positive, implying positive duration
dependence. This may be an artifact of very low hazards early in the conception interval, since
all intervals start the month after a preceding birth. Alternatively, this may reflect conscious
spacing of fertility, with low conception hazards early in intervals, but increasing hazards as
intervals lengthen. It probably is not due to age-related changes in fecundity, because age enters
as a polynomial elsewhere.
Table 3 shows results for a sample that includes nonmigrants. Based on a sample more
than twice as large as the migrant sample, standard errors are generally smaller than those
underlying the results of Table 2, yielding minor differences in the level of significance of some
control variables. The migration results are qualitatively similar to those in the previous table,
but there are some differences between samples in the size and statistical significance of the
migration variables’ time ratios. In the full sample, merely moving to an urban area increases
conception intervals by a statistically significant 24%, without accounting for subsequent
employment. The ‘migration and employment: less-urban moves excluded’ column shows the
impact of post-migration employment. The combined impact of migration followed by
employment to an urban area is to double conception intervals, although the point estimate of the
impact of employment is statistically insignificant. Women who work following moves between
similarly urbanized areas experience birth intervals that are slightly more that two and one half
times as long as the comparison group, though in this case the impact of moving alone is not
statistically different from zero. Because the comparison group in the full sample includes not
just those who migrated to less urbanized places (as was the case in the migrant sample), but also
nonmigrants, these two groups have been separated in the final column, with no real impact on
the results.
We note that part of the issue here is the relative rarity of the events that we are
examining. Of all migrants, only 9% were employed in the month of, or month after, arrival
from a similarly urbanized area, and less than 2% were employed immediately after moving
from a less to a more urbanized area. That so few women reported being employed soon after
moving to a more urbanized area is of course partly due to the surprising rarity of moves to more
urban areas, as only 17% of migrants and 8% of the full sample made such moves. It is also due
to our strict definition of employment. Our goal is to tie migration tightly to employment, but to
the extent that job searches take longer than the period we have allowed, a clear cost of our
strategy is to understate the true number of women who migrated and were subsequently
employed. Because those migrants who begin work in the second or later post-arrival month are
classified as non working, our results are conservative estimates of the true impact of post-
migration employment on fertility.
Finally, we test for the importance of migrant selectivity in accounting for the lower
fertility of migrants. The estimators in the migrant-only sample are consistent, but possibly
inefficient, while the estimators based on the full sample, because they ignore migrant
selectivity, potentially are inconsistent. Our Hausman test statistic shows no significant
difference in estimates between the two samples in either comparable specification, although we
note that the impact of migrant selectivity appears more important where information about
employment is ignored. We therefore have no evidence that migrant selectivity is an important
determinant of fertility.
Discussion
Migration can affect fertility through selectivity, adaptation (either by adopting
destination fertility norms or by responding to destination opportunity costs), or disruption. By
far the most important in our data is adaptation to post-migration employment, which can result
in birth intervals more than twice as long as those of nonmigrants. We estimate that the adoption
of lower fertility destination norms and migrant selectivity are somewhat less important, together
accounting for a potential post-migration lengthening of birth intervals of up to 24%. We find
little evidence for the importance of migrant selectivity, implying that much of this 24% may be
due to adoption of low-fertility norms. We estimate that disruption may increase birth intervals
by as much as 15%, but our estimates are too noisy on this front to support anything but
speculation.
Regarding the impact of post-migration employment, we find that women who move
between similarly urbanized places and take paying jobs have birth intervals more than twice as
long as women in the reference category. Employment is important, as the effect of moving to a
similarly urbanized area without working seems if anything to slightly increase fertility. A
typical pattern in the Philippines is for newly married women to leave their village or town and
begin childbearing, so the lack of effect for moves between similarly urbanized places may
reflect this sort of behavior. If so, moves followed by employment are differently motivated than
moves that are not employment related. The former may be moves that are directly tied to
planned employment, and perhaps occur because the migrant has information about jobs at the
destination.
Notably, post-migration employment is not a statistically significant determinant of the
fertility of migrants to more urbanized areas, although our point estimate of the effect is large in
either sample. We estimate that on net, women who migrate between similar areas and then
work for pay have conception intervals 2.3 to 2.5 times longer than the comparison group for the
migrant and full samples. This is slightly larger than the net result of employment following
moves to more urban areas, which is to lengthen birth intervals by 1.8 to 2.0 times. Regarding
these results, with the exception of the point estimate of the impact of more-urban migration in
the full sample, the underlying time ratios are statistically insignificant. The noisy estimation is
due in part to the relatively small number of women who migrate to more urban areas and take
jobs. While slightly under 10% of migrants, either to more urbanized areas or between equally
urbanized areas, subsequently are employed under our restrictive definition of employment,
moves to more urbanized areas were uncommon enough that few women both migrated and took
a job. Besides small-sample imprecision, it also may be the case that there is more uncertainty,
in employment and otherwise, attached to moves to more urbanized places, and this generates a
smaller fertility response.
If fertility norms are comparable in comparably urbanized places, the impact of norms
should be seen in the effect of migration to more urbanized areas, absent employment. We see a
statistically insignificant lengthening, with a point estimate for the time ratio of 1.10, in the
migrant sample. The comparison group is migrants to less urbanized places, which, if norms
operate symmetrically, should highlight the impact of normative change on fertility. The effect
is somewhat larger (1.21 to 1.24) in the full sample, whether the comparison group includes both
migrants to less urbanized places and nonmigrants, or just nonmigrants. In either case, the data
include nonmigrants, and so these estimates also include whatever contribution migrant
selectivity may make to fertility reduction18. While a possible upper bound of the impact of
normative change is a 24% lengthening of birth intervals, some of the observed impact may be
18 Although we find no evidence that migrant selectivity matters, we hestitate to eliminate the possibility based on a
negative result.
due to selectivity, and so the true fertility-depressing effect of adopting urban norms may be
smaller than this upper bound.
Regarding disruption, we note that the estimated time ratio of moves to less urbanized
places is followed by a 15% lengthening of birth intervals, noisily estimated.25 It seems unlikely
that normative pressures to reduce fertility accompany this sort of migration26, and so we offer as
a conjecture the claim that this is an upper bound on disruption effects. Of course, if disruption
is operating to this extent, and moves to equally-urbanized and more-urbanized areas are
otherwise comparable in disruptiveness to moves to less-urbanized areas (in terms of spousal
separation, in particular), this means that, net of disruption, moves between equally urbanized
places would slightly increase fertility in the absence of disruption and employment effects, and
moves to more urbanized areas would have no fertility impact; that is, they have no normative
fertility-decreasing component. The estimates are sufficiently imprecise that we remain agnostic
on the matter of disruption effects.
25 Because we use this group as a control group, and so want a reasonably large number of women in it, we do not
examine the separate impacts of employment for these migrants. 26 If normative effects operate symmetrically, one would predict the normative impact to be fertility increasing for
these moves.
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Table 1 Descriptive Statistics
Variable
Full Sample Migrants Only
Wife’s age at conception 34.01 (8.04)
35.02 (7.67)
Wife’s age at marriage 20.31 (4.36)
20.36 (4.31)
Wife secondary (=1 if entered secondary level of school)
0.32 (0.46)
0.32 (0.47)
Wife tertiary (=1 if entered tertiary level of school)
0.19 (0.40)
0.22 (0.41)
Husband secondary (=1 if entered secondary level of school)
0.31 (0.46)
0.33 (0.47)
Husband tertiary (=1 if entered tertiary level of school)
0.19 (0.39)
0.21 (0.41)
Wealth (factor score for a set of durable goods; mean value of 0)
0.09 (0.79)
-0.01 (0.83)
More urban move (=1 if last move was from less to more urbanized area)
0.08 (0.27)
0.17 (0.38)
Same move (=1 if last move was between comparably urbanized places)
0.31 (0.46)
0.65 (0.47)
Less urban move (=1 if last move was from more to less urbanized area)
0.08 (0.28)
0.18 (0.38)
More urban work (=1 if last move was from less to more urbanized area, work followed)
0.01 (0.08)
0.01 (0.11)
Same work (=1 if last move was between comparably urbanized places, work followed)
0.02 (0.14)
0.04 (0.20)
Sample size 7123 3381
Table 2 Survival Regression Results, Migrants
Variable
Migration Only Migration and Work
Wife’s age at conception 1.02 (0.57)
1.01 (0.33)
Wife’s age at conception squared 1.00 (0.00)
1.00 (0.00)
Wife’s age at marriage 0.93 (0.00)
0.92 (0.00)
Wife secondary 1.29 (0.00)
1.28 (0.00)
Wife tertiary 1.26 (0.04)
1.22 (0.07)
Husband secondary 1.22 (0.02)
1.23 (0.01)
Husband tertiary 1.40 (0.01)
0.41 (0.01)
Wealth 0.80 (0.00)
0.80 (0.00)
More urban move 1.10 (0.41)
1.07 (0.57)
Same move 0.89 (0.20)
0.85 (0.07)
More urban work 1.68 (0.19)
Same work 2.74 (0.00)
Weibull shape parameter 1.12
(0.02) 1.12
(0.03)
Notes to Table: Table entries represent time ratios associated with unit changes in the associated variables. Values in parentheses under time ratios are p values for a test of no effect (time ratio equals one) versus a two-tailed alternatives. Underlying hazards are Weibull, estimated using Stata in a model accounting for clustering at the level of the mother. The value in parentheses under the shape parameter is its estimated standard error.
Table 3
Survival Regression Results, Full Sample
Migration and Employment Variable
Migration
Only Less urban
moves excluded
Less urban moves
included Wife’s age at conception 1.01
(0.57) 1.01
(0.44) 1.01
(0.35) Wife’s age at conception squared 1.00
(0.00) 1.00
(0.00) 1.00
(0.00) Wife’s age at marriage 0.93
(0.00) 0.92
(0.00) 0.92
(0.00) Wife secondary (=1 if entered secondary level of school)
1.25 (0.00)
1.25 (0.00)
1.24 (0.00)
Wife tertiary (=1 if entered tertiary level of school)
1.21 (0.01)
1.20 (0.01)
1.20 (0.01)
Husband secondary (=1 if entered secondary level of school)
1.21 (0.00)
1.21 (0.00)
1.21 (0.00)
Husband tertiary (=1 if entered tertiary level of school)
1.37 (0.00)
1.38 (0.00)
1.38 (0.00)
Wealth (factor score for a set of durable goods; mean value of 0)
0.81 (0.00)
0.81 (0.00)
0.81 (0.00)
More urban move (=1 if last move was from less to more urbanized area)
1.24 (0.02)
1.21 (0.03)
1.23 (0.02)
Same move (=1 if last move was between comparably urbanized places)
1.00 (0.09)
0.96 (0.41)
0.98 (0.64)
Less urban move (=1 if last move was from more to less urbanized area)
1.15 (0.08)
More urban work (=1 if last move was from less to more urbanized area, work followed)
1.66 (0.18)
1.67 (0.18)
Same work (=1 if last move was between comparably urbanized places, work followed)
2.68 (0.00)
2.69 (0.00)
Weibull shape parameter 1.18
(0.02) 1.18
(0.02) 1.18
(0.02) Hausman test for consistency of estimators vs. migrant sample (p-value)
17.55 (0.07)
16.66 (0.16)
Notes to Table: Table entries represent time ratios associated with unit changes in the associated variables. Values in parentheses under time ratios are p values for a test of no effect (time ratio equals one) versus a two-tailed alternatives. Underlying hazards are Weibull, estimated using Stata in a model accounting for clustering at the level of the mother. The value in parentheses under the shape parameter is its estimated standard error.
