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OCR for page 182
6
Infant Mortality and the Fertility Transition:
Macro Evidence from Europe and
New Findings from Prussia
Patrick R. Galloway, Ronald D. Lee, and Eugene A. Hamme!
INTRODUCTION
Most attempts to understand secular fertility decline include some allusion to
the European experience. It is generally thought that little or no relationship
existed between fertility decline and infant mortality decline in Europe, or that
the findings from relevant studies are inconsistent. We believe that these com-
mon perceptions are mistaken. When more attention is given to the varying
methods of analyses, a more consistent picture emerges. We argue that it is
particularly important to keep in mind whether studies are bivariate or multivari-
ate; whether studies estimate cross-sectional relations between levels of fertility
and of infant mortality, or instead focus on the relation of changes in these
variables; and whether studies take into account the possibility that causality
flows in both directions from fertility to mortality as well as from mortality to
fertility.
We estimate both the impact of infant mortality on fertility and the impact of
fertility on infant mortality, using aggregate data from Prussia from 1875 to 1910
and fixed effects models with instrumental variables. This is followed by an
extensive review of previous research on fertility and infant mortality within the
historical European context.1 By comparing our findings for Prussia with earlier
research looking at both level and change effects, we find considerable evidence
1Our review of earlier research is restricted to those studies using aggregate data. There is a body
of literature on fertility and infant mortality that uses micro-level data, see for example Knodel
(1988). However, it is beyond the scope of this chapter to survey such studies.
182
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
183
for a positive association between the fertility level and the infant mortality level,
as well as a positive association between fertility change and infant mortality
change.
The Long Term
It is clear that in the very long run, in closed populations, fertility and mortal-
ity are linked because of the finiteness of the resource base, which implies that
the average rate of natural increase n must not exceed zero more than slightly.
This is an abstract argument. The historical reality has been that rapid natural
increase sustained over long periods (say an average rate of natural increase
greater than 0.02 over a period of more than two centuries) has not been observed
except in frontier regions2 such as North America. Much more typically, large
populations appear to have had rates of natural increase of less than 1 percent per
year until the Industrial Revolution, and usually with a strong positive statistical
association between fertility and mortality in the cross section. Such a long-term
positive association of fertility and mortality, and a limit to average rates of
natural increase, can be explained in at least two ways. First, positive growth
rates mean increasing population size and density, which under preindustrial
conditions typically meant declining living standards. These in turn caused mor-
tality to rise, or fertility to fall, and therefore growth rates to return toward zero.
This is the Malthusian theory of population equilibration through negative feed-
back (Lee, 1987~. Of course, emigration was another possible outcome, and
technological progress or international trade might intervene between population
growth and declining living standards. Second, it is sometimes argued that the
sociocultural institutions governing fertility evolved in the context of some aver-
age mortality regime so as roughly to balance fertility and mortality on average,
leaving rates of natural increase close to zero. In this version there is no feedback
from population size to the vital rates; rather, growth rates themselves tend to
have average levels not far above zero. Again, migration, technological progress,
and international trade might play a role (Smith, 1977; Yule, 1906~.
The constraint on average growth rates, and hence on fertility and mortality,
implied by these theories and the positive association of fertility and mortality
observed in the historical record, may also help to explain the long-run shape of
the demographic transition (Lee and Bulatao, 1983, for example). Demeny
(1968:502) gave a classic description of the transition: "In traditional societies,
fertility and mortality are high. In modern societies, fertility and mortality are
low. In between, there is demographic transition." In fact, in those national
2By frontier regions we mean from the point of view of agricultural populations, that is, not from
the point of view of hunter and gatherer populations who may have occupied the area at relatively
low density before the arrival of agriculturists.
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184 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
populations that have "completed" the transition, fertility has dropped so low that
growth rates may turn very substantially negative. Nonetheless, it is difficult to
escape the conclusion that, in some vague and unspecified way, and despite all
the accompanying structural changes in the economies and societies, the very
long-term decline in fertility is ultimately due to a very long-term decline in
mortality, or the two are interlinked. In fact, some theories link both declines to
the same set of parental decisions concerning investment in children.
These very long-run relations, both theoretical and empirical, are based on
some sort of slow-acting feedback operating through the macro-economic or
macro-societal level. The posited mechanisms might be expected to operate over
the course of a century or more, but not over the course of decades. For this
reason, they are of little relevance for questions about the policy-relevant time
frame of adjustment over the medium range of, say, 5-30 years.
The Short Term
Although the long-run historical relation of fertility and mortality is doubt-
less positive, it is equally true that the empirical relationship over short-run
fluctuations has been consistently negative in historical populations. This has
been established by a large number of studies of time series of births and deaths,
once the long-term trends in the data have been statistically removed. It is easy to
think of reasons to expect either a positive or a negative association of the two
vital rates. For a positive relationship, note that high mortality will break many
marriages, particularly those of older couples, and that the subsequent remar-
riages of widows and widowers might result in higher fertility than if the mar-
riages had been unbroken. Furthermore, higher mortality would free land hold-
ings and create other economic opportunities permitting new marriages that would
have high fertility. In existing unions, high infant and child mortality would
interrupt breastfeeding, eliminating its contraceptive effects, and therefore lead to
earlier conceptions and a temporary increase in fertility. Reconstitution studies
have often demonstrated this lactation interruption effect. On the behavioral side,
if we are not dealing with a natural fertility regime, we might expect couples who
have experienced the loss of a child to attempt to replace it with another birth
sooner than they normally would have, or by having one more birth than origi-
nally intended. However, many historical demographers dispute that this actu-
ally occurs to any appreciable degree, except in special subpopulations (Knodel,
1978~.
For a negative relationship, note that many factors that tended to raise mor-
tality would also tend to reduce fertility. For example, low real incomes appar-
ently had this effect, as did unusually hot summer months or unusually cold
winter months (see Lee, 1981; Galloway, 1986, 1988, 1994~. The variation of
such factors in the short term would have led to a negative bivariate association of
fertility and mortality, but if observable, they can be netted out in multivariate
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
185
studies. Perhaps more important were unobservable influences, of which ill
health dominated. Fluctuations in morbidity both raised mortality and reduced
fertility, leading to a strong negative association of short-term fluctuations in
fertility and mortality, even after controlling for observed fluctuations in real
incomes or grain prices and temperature. The estimated, strongly negative asso-
ciation of short-run variations in fertility and mortality is not very informative
about structural or causal influences of mortality on fertility or the reverse. The
many short-run studies of the relation of fertility to mortality in historical popula-
tions will therefore not answer the question before us.
The Medium Term
For policy makers, the most relevant time frame for fertility and infant mor-
tality interactions is probably the medium term, say 5-30 years. Assuming a
couple has some notion of a desired number of surviving offspring, infant and
child mortality should be positively associated with the number of births. A
couple can assume that some unknown number of offspring will die, and then
stop when they think they have enough children (often called hoarding behavior).
Or the couple can wait to see if the last child born survives past a certain age. If
the child dies, the couple can then engage in replacement reproductive behavior.
Both strategies are types of "inventory control" (Preston, 1978:10) leading to
some desired number of surviving offspring. Within either strategy, the number
of births should decline as infant mortality declines.
There are also ways in which declining mortality might alter the desired
number of surviving children. It reduces the costs of achieving a given target
number, and therefore might raise the target by increasing discretionary income.
Alternatively, declining mortality might raise the rate of return on investments in
children, which could lead to a substitution of quality for quantity, and a reduced
target. Here, however, we concentrate on the fixed target scenario.
Once infant mortality begins to decline, it might take some time for couples
to perceive the effect of infant mortality on child survivorship, which would
ultimately lead to changes in fertility. It is also possible, however, that the effect
could be almost immediate, as couples hear about and read about mortality de-
cline.3
lit would be difficult to test for very short lags because censuses (from which we derive most of
our independent variables) are nearly always at least 5 years apart, and because it is very likely that
the level of infant mortality rates at year t will be highly correlated with the level of infant mortality
rates at years t- 2, t- 3, or t - 4. using Prussian data and the model shown in Appendix Table 6A-
2, we added the variable infant mortality lagged 5 years and found that, in the fixed effects model
which estimates changes, the regression estimate on infant mortality with no lag was 0.267 whereas
the regression estimate on infant mortality lagged 5 years was -0.062, suggesting that the lagged
variable was relatively unimportant.
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186 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
Elevated infant mortality tends to shorten the birth interval because the death
of an infant curtails lactation amenorrhea along with its contraceptive effects. An
increase in infant mortality will cause an increase in fertility, ceteris paribus,
although few children may ultimately survive, of course. This short-term phe-
nomenon can persist over time, becoming an important factor over both the
medium and the long term.
Fertility variations, whether deliberate or accidental, can also affect infant
and child mortality as demonstrated by a host of contemporary studies. There is
good reason then to expect that exogenous increases in infant and child mortality
caused increases in marital fertility and that exogenous increases in fertility caused
increases in infant and child mortality. (However, this micro-level reasoning
about motives and relations does not translate exactly to the macro level because
of expected nonlinearities in the relationships.)
Infant and Child Mortality
Matthiessen and McCann (1978) provide a useful overview of the findings of
historical studies of macro-level data, with an emphasis on the early results of the
European Fertility Project. They are particularly critical of the use of infant
mortality as the explanatory mortality index, because they find that in practice
other more appropriate measures, such as mortality of children age 0- 15, began to
decline earlier than did infant mortality, so that the European Fertility Project's
studies of timing, for example, are of little value. When they reexamine the
timing of the fertility transition in relation to i5qo, they find that mortality decline
almost always preceded fertility decline. We believe that it is very difficult to
estimate the onset of secular i5qo decline. In general, we suggest that there is
often no clear point at which one can categorically state that mortality or fertility
has begun to decline, a suggestion with which Matthiessen and McCann (1978:52)
clearly agree. Concerning the onset of infant mortality decline, van de Walle is
appropriately cautious, noting that "in most instances we are left ignorant of past
trends: the data do not allow us to go back in time and the existence of an earlier
decline cannot be ascertained" (1986:213~.
It might be useful to address the issue of infant (under age 1) versus child
(age 1-9) mortality in multivariate analyses of secular fertility decline. When a
husband or wife thinks about procreation in terms of offspring survivorship, he or
she considers both infant and child mortality (Matthiesson and McCaan,1978:52~.
Although infant mortality rates can generally be found in most historical registra-
tion material, the more detailed measures of child mortality are often unavailable.
However, in a high infant mortality regime, the bulk of infant and child deaths
will be infant deaths, and infant mortality should be very highly correlated with
infant and child mortality combined. Using 1890-1891 male mortality data for
the 36 provinces (Regierungsbezirke) of Prussia, we find that the correlation r
between ~qO and 5qo is 0.96, between ~qO and iOqo is 0.96, and between ~qO and
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
87
15q0 is 0.95 (Konigliches Statistisches Bureau, 1904:135-147~. The range of 1q0
in the provinces is 109-273. A similar analysis of the 15 largest cities in Prussia
from the same source reveals respective r's of 0.98,0.97, and 0.91 with a range of
GO of 170 to 326. Plots of each of the six graphs reveal essentially a straight line
with no outliers. It seems likely that the infant mortality rate is an adequate proxy
for infant and child mortality when using aggregate data in high infant mortality
populations.
Although it is difficult to say much about the timing or onset of secular infant
and child mortality decline, we can examine their relative speed. It is clear from
Figure 6-1 that GO and GO generally declined at about the same rate over the
decades from the 1870s to around 1925. The quality of German infant and child
mortality data before 1875 is questionable. Such consistency lends further sup-
port to the notion that infant mortality is an adequate proxy for infant and child
mortality, at least in Germany.
ANALYSIS OF PRUSSIAN DATA
From a theoretical perspective it seems likely that higher infant mortality
should ultimately be associated with higher fertility, and vice versa. We attempt
to evaluate both the effect of infant mortality on fertility and the effect of fertility
on infant mortality using fixed-effects models and instrumental variables estima-
tion applied to data from 407 Prussian Kreise (administrative districts) and 54
cities in Prussia from 1875 to 1910.4
The following equation system describes the structural relationships between
fertility and mortality:
Fi G = ale + maxi G + ~3Zi,G + ~4Mi,l + ~5,i + FIG + £i,l'
Mi G = 0lY1 + 02Xi G + 03Zi,G + 04Fi,l + P5,i + FIG + ~i,G.
Here F and M refer to appropriate measures of fertility and infant (and/or child)
mortality in the subpopulation of region i at time t. Y is a matrix of unchanging
characteristics of the regions that influence fertility or mortality (indicated by
superscripts). X is a matrix of changing influences on both fertility and mortality
in the regions. Z refers to changing variables in the regions that influence just
fertility or just mortality, respectively. OC5 i and ,135 i are disturbances or fixed
effects in the two equations that do not change over time, but are specific to the
regions. 6~ and 0~ are disturbances to the two equations that are the same across
all regions, but that vary over time. Finally, Ei ~ and Aid are disturbances to the
4See Galloway et al. (1994, 1995) for details regarding these two data sets.
OCR for page 188
88 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
1~0 and 15qO of males in C;errnany
0~400
0.35a
0~3~
onto
0.200
0.150
0.100
at
o
5t
of
1
o
5
~ i,
-
1871-18~0 1881-18GO 1891-19~ 1901-1910 191~1911 1924-1026
Percent change in 1~0 and 1 5qO of males in Germany
o
:
.~
o1~D
_~
FIGURE 6-1 Level and change of male infant and child mortality rates in Germany,
1871-1926. SOURCE: Statistischen Reichsamt (1930:168~.
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
189
equations that are specific to time period and to region. Because our data repre-
sent non-overlapping 5-year averages, the short-term relations will be largely
masked, so long-term and medium-term relations will dominate the estimates.
We use this pair of equations to attempt to approximate a far more compli-
cated dynamic pair of equations that would explicitly include the long-run adjust-
ment processes that bring fertility and mortality to similar levels. In the given
equations, the fixed-effect terms are used to represent the outcome of these long-
term adjustment processes. The coefficients ~4 and p4, which represent the
influence of mortality on fertility and fertility on mortality, therefore abstract
from these long-run adjustment processes and represent only the medium-term
influences of one on the other. For example, they would not reflect the possible
development of social institutions to motivate high fertility in the face of high
mortality. When we estimate this model, which includes fixed effects, therefore,
the estimated coefficients should reflect only the medium-term adjustment pro-
cesses that we believe to be particularly informative for policy considerations.
We often refer to such fixed-effect estimates as change estimates, because they
are shaped entirely by the relation of changes over time within each Kreis, and
not at all by differences in fertility and mortality between Kreise.
By contrast, estimates of these equations based on a single cross section, as
are common in the literature, mainly reflect the outcome of the long-term adjust-
ment processes and of correlations of right-hand variables with persistent fea-
tures of the different geographic units, such as agrarian system, local culture,
political orientation, or breastfeeding practices, yielding coefficient estimates
that are inconsistent or irrelevant for policy. When suitable instruments are
available, two-stage least squares can be used to avoid the biases arising from
correlations of right-hand variables with persistent influences; however, suitable
instruments can rarely be found in this context. Furthermore, instruments will
not solve the problem that long-term adjustment processes probably dominate
cross-sectional estimates.
We expect that over the long run, the levels of fertility and mortality will be
positively correlated. These long-run correlations would show up in correlations
of the fixed effect disturbances OC 5 i and ~5 i. These correlations would bias any
estimate of the structural coefficients relating fertility and mortality directly.
Furthermore, the system described by these equations is simultaneous, with both
fertility and mortality endogenous. Attempts to estimate the equations by ordi-
nary least squares would yield biased estimates.
Now suppose we difference all the variables, representing the differenced
values by lower case letters. We will actually be using fixed-effects estimators,
but looking at the effects of differenced variables is a simple way to examine the
consequences of using the fixed-effect model. Differenced disturbance terms are
represented by the same Greek characters, but without the tildes. In this case, the
region-specific disturbances that do not change over time disappear, as do all
other variables that do not change over time. We then have
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190 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
fi,' = Loci,' + phi,' + fermi,' + 6' + Pi I,
ml,' = 02xi,' + 03Zi,' + 04fi,' + 0t + ~i,{.
The problem of correlated fixed effects has been removed, but the simultaneity
remains. However, we can use the variables z to identify instrumental or two-
stage least-squares estimates of the coefficients. These should, in principle, be
unbiased except that there is an additional problem: We do not observe all the
relevant x variables. For example, breastfeeding behavior, which affects both
fertility and infant mortality, is unobserved.5 But extended breastfeeding re-
duces both fertility and infant mortality, and failure to control for its influence
will lead to a noncausal positive association of fertility and mortality. Put differ-
ently, unobserved and therefore omitted variables in the x matrix will induce a
correlation of the error terms in the two equations £ and lo. To be concrete, the
omission of breastfeeding variables from the estimated model will lead to a
positive correlation in the disturbances, resulting in an upward bias in the esti-
mated structural coefficients on fertility and mortality; the omission of health and
illness variables may have the opposite effect. This could be a serious problem
for which we have no remedy. Therefore, this potential source of bias must be
kept in mind when interpreting the results.
Two-Stage Least-Squares Estimation
Our estimation model reflects the general theoretical perspective outlined
above in the use of fixed effects and controls for the endogeneity of infant
mortality. However, we have taken a rather eclectic approach to inclusion of
socioeconomic influences on fertility and have not imposed any mathematical
structure on the relations to be estimated beyond the usual assumption that our
linear model approximates some true but unknown nonlinear specification.
One of the findings from our review of earlier research on fertility decline in
Europe is that many studies that purport to say something about fertility decline
(change) only examine fertility level. Preston (1978:1) states "a central problem
in modern population studies . . . is . . . the degree to which changes in mortality
can be expected to induce changes in fertility." This clearly poses the question in
terms of changes and not of levels. We believe that this is indeed the appropriate
question, and that it is not an issue that can be resolved by studying the relation-
ship between levels of fertility and mortality.
sin Prussia, longitudinal data on breastfeeding are available only for Berlin. sreastfeeding in
Berlin decreased significantly from 1885 to 1910 (Kintner, 1985:169-170) while both marital fertil-
ity and infant mortality were declining substantially. It is not known whether other areas in Prussia
experienced similar trends.
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
191
In our analyses of pooled cross-sectional time series we first examine the
"between" estimators, regressions on the means over time of each Kreise or city,
that give US level effects. We also generate "within" or "fixed-effects" estima-
tors, regressions that allow each Kreis or city to have its own intercept. This
effectively measures how changes in our independent variables affect changes in
our dependent variable and is the more appropriate approach for explaining fertil-
ity change.6
We have dealt at length with the theoretical expectations and empirical find-
ings of fertility decline in 407 Prussian Kreise (Galloway et al., 1994) and 54
cities (Galloway et al., 1995) using quinquennial data from 1875 to 19107 and
pooled cross-sectional time series ordinary least-squares methods.8 A detailed
analysis of infant mortality decline in the Kreise and cities of Prussia is in progress
(Galloway et al., 1996~.9 We expect fertility to influence infant mortality and
simultaneously we expect infant mortality to influence fertility. Instrumental
variables estimation, two-stage least squares in this case, appears to be an appro-
priate method for estimating these effects. All the variables in all the models are
defined in Appendix Table 6-A1. Our fertility model is shown in Appendix
Table 6-A2 followed by a summary of regression results in Appendix Tables 6
6Brass and Barrett (1978:212) also favor pooled cross-sectional time series studies done of areas
within a country.
7Prussia became a state within Germany in 1871, but continued to maintain its own statistical
bureau that published detailed demographic and economic data until the early 1930s. In 1910 the
population of Prussia was just over 40 million, about 70 percent of Germany. If Prussia had been a
country, it would have been the largest country in Europe excluding Russia. It covered most of
modern-day Germany north of the Main River and most of the western half and northern quarter of
modern-day Poland. Registration data are available annually and census data quinquennially from
1875 to 1910 for Kreise and major cities. Kreise are administrative units similar to U.S. census tracts
though much larger (average population 60,000) and, like census tracts, tended to split over time.
There were about 400 Kreise in Prussia in 1875 and about 600 by 1910, with the total area of Prussia
being virtually constant. To maintain spatial consistency over time, we combined many of the split
Kreise. This resulted in 407 Kreise, each with a constant area, from 1875 to 1910. Only seven of the
407 Kreise were 100 percent urban. To examine fertility decline within a strictly urban setting, we
created another data set consisting of 54 cities, whose area we allowed to change over time, realizing
that any area incorporated into a city was itself likely to be highly urban.
8We use general marital fertility rate (GMFR) as our measure of marital fertility in this and in all
our previous analyses of Prussian Kreise. GMFR is defined as the number of legitimate births per
1,000 married women aged 15-49. A 5-year average centered on the census year is used. More
, O
detailed marital age structure data are not available. Coale and Treadway (1986:153) note that there
would have been little difference in their findings if GMFR had been used instead of Ig. In fact we
find that in 54 Prussian cities from 1875 to 1910 where data are available to calculate both Ig and the
GMFR that Ig and GMFR are highly correlated (r = 0.97) and that the two measures are virtually
interchangeable.
9The average infant mortality rate in Prussia in 1900 was 179, somewhat above that found in most
less developed countries today. In 1992 Mozambique had an estimated infant mortality rate of 162,
the highest of any county in the world (World Bank, 1994:214).
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192 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
AS and 6-A4. The infant mortality model can be found in Appendix Table 6-A5
with regression results summarized in Appendix Tables 6-A6 and 6-A7. We
focus on the relationship between fertility and infant mortality. The findings for
the other right-hand side variables have been discussed elsewhere (Galloway et
al., 1994, 1995, 1996).
Estimation of the Fertility Equation
High fertility and the shorter birth intervals it involves might well cause
higher infant and child mortality. To avoid the possible bias associated with
ordinary least-squares estimation, we need instruments for infant mortality that
are correlated with infant mortality but not correlated with the component of its
variance that might be influenced by fertility. We believe that male mortality at
older ages provides a nearly ideal instrument in this case. Fertility should affect
only the mortality of children. We do not know the upper limit of the range of
ages that might be affected by high fertility, so we avoided using the mortality
even of teenagers. The mortality of women depends in part on maternal mortal-
ity, which would depend on fertility, so we avoided using female mortality. For
these reasons, we decided to use the mortality of adult males. Because of the cost
of data entry, we limited ourselves to male mortality in the 30-34 age group. This
choice was based in part on an examination of a correlation matrix for mortality
at different ages for both Prussian Regierungsbezirke and historical Swedish
data, which showed that death rates at age 30-34 were relatively highly correlated
with infant mortality. Other age groups had correlations that were nearly as high,
so the exact choice makes little difference, and ideally we would have used
mortality over a broader range.
The idea is that male mortality at age 30-34 is a useful index of the general
level of mortality in the population, reflecting all local factors that influence
mortality, such as standard of living, nutrition, general sanitary conditions, eco-
logical and epidemiological conditions, and the quality of health care. At the
same time, it does not reflect the particular influence of either breastfeeding
conditions or of fertility and therefore should not correlate with marital fertility.
Unfortunately, age-specific death rates are available only for Regierungs-
bezirke (very large areas, similar to provinces) of which there were 36 in Prussia.
Given 407 Kreise in Prussia, there were on average about 11 Kreise per
Regierungsbezirk. We applied Regierungsbezirk male mortality at age 30-34 for
each of the eight quinquennial periods from 1875 to 1910 to the Kreise or city
within the Regierungsbezirk. This instrument captures only broad regional varia-
tions in mortality, but not local differences, reducing its usefulness.
Appendix Table 6-A3 presents the ordinary least-squares and two-stage least-
squares findings for Kreise, for both levels and changes. Looking at levels, we
find that when we use ordinary least squares the estimated coefficient on infant
mortality is negative and marginally significantly different from zero, but, using
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216 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
TABLE 6-A1 Definitions of Variables Used in the Analysis
Variable Definition
Catholic
Slav
Church
Education
Health
FLFPR
GMFR General marital fertility rate (legitimate births per 1,000 married females
15-49).
Catholics per 100 total population.
Slavic speakers per 100 total population.
Employees in religious occupations per 100 population over age 20.
Teaching employees per 100 population aged 6-13.
Health employees per 100 total population.
Female labor force participation rate (employed females per 100 female
population aged 20-69) (excludes agriculture and service).
Income Average real income of male elementary school teachers in Deutsche
marks as of 1900.
Mining Mining employees per 100 employed persons.
Manufacturing Manufacturing employees per 100 employed persons. Used only in the
city model
Urban Urban population per 100 total population. Used only in the Kreis
model.
Banking employees per 100 population over age 20.
Insurance employees per 100 population over age 20.
Post, telegraph, and railway employees per 100 population over age 20.
Population, in thousands. Used only in the city model.
Legitimate infant mortality rate (legitimate deaths under age one per
1,000 legitimate births).
Married males/married females.
Population born in Kreis per 100 total population. Used only in the
Kreis model.
Population born in city per 100 total population. Used only in the city
model.
Cumulative municipal sanitation bond debt per capita in Deutsche
marks. Available only for cities.
Age-specific death rate for males aged 30-34. Data are available only
for Regierungsbezirke.
Bank
Insurance
Communications
Population
Infant mortality
Married sex ratio
Kreis born
City born
Sanitation
ASDR
NOTES: For details and sources see Galloway et al. (1994, 1995, 1996). Data are available quin-
quennially from 1875 to 1910. Vital registration variables are based on 5-year average centered
around each quinquennial year. Stillbirths are excluded throughout. There are 36 Regierungsbezirke
and 407 Kreise in Prussia. We also examined 54 cities. In general, the German occupational
censuses do not lend themselves to calculation of economic sector variables because of a peculiar
redefinition of female agricultural laborers that leads to an improbable 2 million increase in the
category between 1895 and 1907 (Tipton, 1976:153-158). However, city populations were probably
not affected by this problem because there were few agricultural workers in the cities. This is the
reason the variable Manufacturing is available only in the cities. Mining is available for both Kreise
and cities because virtually all miners were men.
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PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
TABLE 6-A2 Models Used in the Fertility Analysis
217
Variable
Kreis Fertility Model
(equation (1))
City Fertility Model
(equation (2))
Expected
Sign
Dependent
In dep en dent
GMFR
Catholic
Slav
Church
Education
Health
FLFPR
Income
Mining
Urban
Bank
Insurance
Communications
Infant mortality
Married sex ratio
GMFR
Catholic
Slav
Church
Education
Health
FLFPR
Income
Mining
Manufacturing +
+
+
+
Bank
Insurance
Communications
Population
Infant mortality
Married sex ratio
+
NOTES: In equation (1) two-stage least-squares age specific death rate (ASDR) for males aged 30-
34 is used as an instrument for Infant Mortality. In equation (2) two-stage least-squares Sanitation
and ASDR are used as instruments for Infant Mortality.
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218 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
TABLE 6-A3 Equation (1~: Summary of Ordinary and Two-Stage
Least-Squares Fertility Regression Results for Kreise in Prussia, 1875-1910
(dependent variable is GMFR)
Level
Variable
Expected
Sign
OLS
Change
TSLS OLS TSLS
Constant
Catholic
Slav
Church
Education
Health
FLFPR
Income
Mining
Urban
Bank
Insurance
Communications
Infant mortality
+
+
+
Married sex ratio +
189.374**
0.693**
0.348**
258.026**
0.716**
0.137
1.01310.586
-5.488t-5.816
-32.456*-42.767*
-0.529t-2.296*
-0.0140.032
1.032**1.251 **
0.034-0.089
-36.0201.850
29.251-48.872
-0.4535.708
-0.052t0.478t
91.828t-107.824
-2.138**
-0.283
23.231 **
-9.075**
-6.596
-1.235**
-0.002
0.107
-1.654**
-1.161**
38.721**
-7.164**
24.430t
-1.153**
0.004
0.757**0.430
-0.385t
-55.325**-26.903t
-133.466**-66.125*
-7.333**-1.799
0.242**1.028**
40.674*-57.928
NOTES: OLS, ordinary least squares. TSLS, two-stage least squares. The unit of analysis is the
Kreise. The level regressions use averages of each variable over eight quinquennial periods from
1875 to 1910. The change regressions are fixed-effects models using data for eight quinquennial
periods from 1875 to 1910. Estimates for the 407 Kreis dummy variables are omitted. In the level
regressions n = 407 and OLS R2 = 0.681. In the change regressions n = 3,256 and OLS R2 = 0.920.
**, *, ~ indicate that the coefficient is statistically significant at the 1 percent, 5 percent, and 10
percent levels, respectively, two-tailed test. Age-specific death rate for males aged 30-34 is used as
an instrument for Infant Mortality in the two-stage least-squares regressions for both level and
change. The two-stage least-squares t statistics are based on the structural residuals (Hall et al.,
1992:133-134) and are asymptotically correct. The ordinary least-squares results are discussed at
length in Galloway et al. (1994).
OCR for page 219
PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
TABLE 6-A4 Equation (2~: Summary of Ordinary and Two-Stage
Least-Squares Fertility Regression Results for Cities in Prussia, 1875-1910
(dependent variable is GMFR)
219
Level
Change
Expected
Variable SignOLSTSLSOLSTSLS
Constant 422.365*448.292*
Catholic +0.581**0.628**-0.0442.354t
Slav +0.2750.2182.010*-1.990
Church +25.98918.053-2.11325.009
Education --4.064-4.589-4.585**-4.809t
Health --1.314-1.6187.69396.812**
FLFPR --1.264*-0.976-3.571 * *-0.919
Income -0.004-0.004-0.022* *-0.030* *
Mining +1.828**1.616*0.161-1.221
Manufacturing +0.761*0.4680.357-0.221
Bank --44.682*-53.788*-11.13954.861t
Insurance -17.90123.651-43.001**11.970
Communications --5.276*-6.122t-5.959*-0.045
Population --0.0070.002-0.041 * *0.056
Infant mortality +-0.042-0.1870.337**1.836**
Married sex ratio +-193.165-163.150354.038**194.328
NOTES: OLS, ordinary least squares. TSLS, two-stage least squares. The unit of analysis is the
city. The level regressions use averages of each variable over eight quinquennial periods from 1875
to 1910. The change regressions are fixed-effects models using data for eight quinquennial periods
from 1875 to 1910. Estimates for the 54 city dummy variables are omitted. In level regressions n =
54 and OLS R2 = 0.888. In the change regressions n = 432 and OLS R2 = 0.896. **, *, ~ indicate
that coefficient is statistically significant at the 1 percent, 5 percent, and 10 percent levels, respec-
tively, two-tailed test. Sanitation and age-specific death rate for males aged 30-34 are used as
instruments for Infant Mortality in the two-stage least-squares regressions for both level and change.
The two-stage least-squares t statistics are based on the structural residuals (Hall et al., 1992:133-
134) and are asymptotically correct. The ordinary least-squares results are discussed at length in
Galloway et al. (1994).
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220 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
TABLE 6-A5 Models Used in the Infant Mortality Analysis
Infant Mortality Model
Expected
City (equation (4)) Sign
Variable
Kreis (equation (3))
Dependent
In dep en dent
Infant mortality
Catholic
Slav
Education
Health
FLFPR
Income
Urban
Communications
GMFR
Kreis born
Infant mortality
Catholic
Slav
Education
Health
FLFPR
Income
Communications
Population
GMFR
City born
Sanitation
NOTES: In equation (3) two-stage least squares, Church, Mining, Bank, Insurance, and Married sex
ratio are used as instruments for GMFR. In equation (4) two-stage least squares, Church, Mining,
Manufacturing, Bank, Insurance, and Married sex ratio are used as instruments for GMFR.
OCR for page 221
PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
TABLE 6-A6 Equation (3~: Summary of Ordinary and Two-Stage
Least-Squares Infant Mortality Regression Results for Kreise in Prussia,
1875-1910 (dependent variable is infant mortality rate)
221
Level
Change
Expected
Variable SignOLSTSLSOLSTSLS
Constant 656.315**723.051 **
Catholic +0.325**0.4750.1090.639**
Slav +0.0870.1320.898**0.925**
Education --15.705**-18.071**-0.4831.892
Health --7.386-14.945-43.048**-35.017**
FLFPR +2.567**2.364**0.0500.362t
Income --0.110**-0.111 **-0.009**-0.007*
Urban +-0.256-0.2860.366**0.392**
Communications --12.640**-12.368**-5.862**-3.967**
GMFR +-0.304**-0.5030.297**0.512**
Kreis born --3.418**-3.559**-0.333**-4.10**
NOTES: OLS, ordinary least squares. TSLS, two-stage least squares. The unit of analysis is the
Kreis. The level regressions use averages of each variable over eight quinquennial periods from
1875 to 1910. The change regressions are fixed-effects models using data for eight quinquennial
periods from 1875 to 1910. Estimates for the 407 Kreis dummy variables are omitted. In the level
regressions n = 407 and ordinary least-squares R2 = 0.432. In the change regressions n = 3,256 and
R2 = 0.922. **, *, ~ indicate that the coefficient is statistically significant at the 1 percent, 5 percent,
and 10 percent levels, respectively, two-tailed test. Church, Mining, Bank, Insurance, and Married
sex ratio are used as instruments for GMFR in the two-stage least-squares regressions for both level
and change. The two-stage least-squares t statistics are based on the structural residuals (Hall et al.,
1992:133-134) and are asympototically correct. These results are discussed at length in Galloway et
al. (1996).
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222 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
TABLE 6-A7 Equation (4~: Summary of Ordinary and Two-Stage
Least-Squares Fertility Regression Results for Cities in Prussia, 1875-1910
(dependent variable is infant mortality rate)
Level
Change
Expected
Variable SignOLSTSLSOLSTSLS
Constant 459.350**455.027**
Catholic +0.621t0.609-1.345**-1.296*
Slav +-0.737-0.7431.983**1.287
Education --0.0820.1040.2071.954
Health --51.765-51.793-58.008**-52.045**
FLFPR +0.9771.002-0.5470.614
Income --0.073**-0.073**0.009*0.014**
Communications --7.184-7.084-7.551 * *-3.649
Population +0.053*0.053*-0.053**-0 033t
GMFR +-0.198-0.1830.247**0.517**
City born --2.215 * *-2.220* *-0.852*-0.289
Sanitation --0.906-0.910-0.434* *-0.287 *
NOTES: OLS, ordinary least squares. TSLS, two-stage least squares. The unit of analysis is the
city. The level regressions use averages of each variable over eight quinquennial periods from 1875
to 1910. The change regressions are fixed-effects models using data for eight quinquennial periods
from 1875 to 1910. Estimates for the 54 city dummy variables are omitted. In the level regressions
n = 54 and ordinary least-squares R2 = 0.412. In the change regressions n = 432 and R2 = 0.903. **,
*, ~ indicate that the coefficient is statistically significant at the 1 percent, 5 percent, and 10 percent
levels, respectively, two-tailed test. Church, Mining, Manufacturing, Bank, Insurance, and Married
sex ratio are used as instruments for GMFR in the two-level least-squares regressions for both level
and change. The two-stage least-squares t statistics are based on the structural residuals (Hall et al.,
1992:133-134) and are asympototically correct. These results are discussed at length in Galloway et
al. (1996).
OCR for page 223
PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
ACKNOWLEDGMENTS
223
We are grateful for helpful comments from Barney Cohen, Mark Mont-
gomery, Ken Wachter, and two anonymous reviewers on an earlier version of this
chapter. The research on which this chapter was based was funded by grants
HD25841 and HD07275 from the U.S. National Institute of Child Health and
Human Development.
REFERENCES
Benavente, J.
1989 Social change and early fertility decline in Catalonia. European Journal of Population
5:207-234.
Boonstra, O.W.A., and A.M. van der Woude
1984 Demographic transition in the Netherlands, a statistical analysis of regional differences in
the level and development of the birth rate and of fertility 1850-1890. A.A.G. Bijdragen
24: 1-57.
Brass W., and J.C. Barrett
1978 Measurement problems in the analysis of linkages between fertility and child mortality.
Pp. 209-233 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility.
New York: Academic Press.
Castiglioni, M., G. Dalla Zuanna, and S. La Mendola
1991 Differenze di fecondita fra i distretti del Veneto attorno al 1881. Analisi descrittiva e
ipotesi interpretative. Pp. 73-149 in F. Rossi, ea., La Transizione Demografica net Veneto,
Alcuni Spunti di Ricerca. Bassano: Infosfera.
Coale, A.J., and P. Demeny
1983 Regional Model Life Tables and Stable Populations. New York: Academic Press.
Coale, A.J., and R. Treadway
1986 A summary of the changing distribution of overall fertility, marital fertility, and the pro-
portion married in the provinces of Europe. Pp. 31-181 in A.J. Coale and S.C. Watkins,
eds., The Decline of Fertility in Europe. Princeton, N.J.: Princeton University Press.
Coale, A.J., B.A. Anderson, and E. Harm
1979 Human Fertility in Russia since the Nineteenth Century. Princeton, N.J.: Princeton
University Press.
Crafts, N.F.R.
1984a A cross-sectional study of legitimate fertility in England and Wales, 1911. Research in
Economic History 9:89-107.
1984b A time series study of fertility in England and Wales, 1877-1938. Journal of European
Economic History 13(3):571-590.
1989 Duration of marriage, fertility and women's employment opportunities in England and
Wales in 1911. Population Studies 43: 325-355.
Demeny, P.
1968 Early fertility decline in Austria-Hungary: A lesson in demographic transition. Daedalus
97(2):502-522.
Fialova, L., P. PavlIk, and P. Veres
1990 Fertility decline in Czechoslovakia during the last two centuries. Population Studies
44:89-106.
Flinn, M.W.
1981 The European Demographic System, 1500-1820. Baltimore, Md.: Johns Hopkins Uni-
versity Press.
OCR for page 224
224 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
Friedlander, D., J. Schellekens, and E. Ben-Moshe
1991 The transition from high to low marital fertility: Cultural or socioeconomic determinants.
Economic Development and Cultural Change 39(2):331-351.
Galloway, P.R.
1986 Annual variations in deaths by age, deaths by cause, prices, and weather in London 1670
to 1830. Population Studies 39(3) :487-505.
1988 Basic patterns in annual variations in fertility, nuptiality, mortality, and prices in pre
industrial Europe. Population Studies 42(2):275-303.
1994 Secular changes in the short-term preventive, positive, and temperature checks to popula
tion growth in Europe, 1460-1909. Climatic Change 26:3-63.
Galloway, P.R., E.A. Hammel, and R.D. Lee
1994 Fertility decline in Prussia 1875 to 1910: A pooled cross-section time series analysis.
Population Studies 48(1):135-158.
Galloway, P.R., R.D. Lee, and E.A. Hammel
1995 Urban versus Rural: Fertility Decline in the Cities and Rural Districts of Prussia, 1875 to
1910. Unpublished manuscript, Demography Department, University of California, Ber
keley.
1996 Infant Mortality Decline in the Cities and Rural Districts of Prussia 1875 to 1910. Un
published manuscript, Demography Department, University of California, Berkeley.
Haines, M.
1979 Fertility and Occupation, Population Patterns in Industrialization. New York: Aca-
demic Press.
1989 Social class differentials during fertility decline: England and Wales revisited. Popula-
tion Studies 43:305-323.
Hall, B.H., C. Cummins, and R. Schnake
1992 Time Series Processor Version 4.2 Reference Manual. Palo Alto, Calif.: TSP Interna-
tional.
Haynes, S.E., L. Phillips, and H.L. Votey
1985 An econometric test of structural change in the demographic transition. Scandinavian
Journal of Economics 87(3):554-567.
Kintner, H.J.
1985 Trends and regional differences in breastfeeding in Germany from 1871 to 1937. Journal
of Family History 10(2): 163 - 182.
Knodel, J.
1974 The Decline of Fertility in Germany, 1871-1939. Princeton, N.J.: Princeton University
Press.
1978 European populations in the past: Family-level relations. Pp. 21-45 in S.H. Preston, ea.,
The Effects of Infant and Child Mortality on Fertility. New York: Academic Press.
1988 Demographic Behavior in the Past: A Study of Fourteen German Village Populations in
the Eighteenth and Nineteenth Centuries. Cambridge, England: Cambridge University
Press.
Konigliches Statistisches Bureau
1904 Ruckblick auf die Entwickelung der preussischen Bevolkerung von 1875 his 1900.
Preussische Statistik 188:1-160.
Lee, R.D.
1981 Short-term variation: Vitalrates,prices and weather. Pp.356-401inE.A.Wrigley and
R.S. Schofield, The Population History of England 1541-1871, A Reconstruction. Lon
don: Edward Arnold.
1987 Population dynamics of humans and other animals. Demography 24(4):443-466.
OCR for page 225
PATRICK R. GALLOWAY, RONALD D. LEE, AND EUGENE A. HAMMEL
225
Lee, R.D., and R.A. Bulatao
1983 The demand for children: A critical essay. Pp. 233-287 in R.A. Bulatao and R.D. Lee,
eds., Determinants of Fertility in Developing Countries. New York: Academic Press.
Lesthaeghe, R.
1977 The Decline ofBelgianFertility, 1800-1970. Princeton,N.J.: Princeton University Press.
1992 Motivation et legitimation: Conditions de vie et regimes de recondite en Belgique et en
France du XVIe au XVIIIe siecle. Pp. 275-317 in A. Blum, N. Bonneuil, and D. Blanchet,
eds., Modeles de la Demographie Historique. Paris: Presses Universitaires de France.
Livi Bacci, M.
1971 A Century of Portuguese Fertility. Princeton, N.J.: Princeton University Press.
1977 A History of Italian Fertility during the Last Two Centuries. Princeton, N.J.: Princeton
University Press.
Lutz, W.
1987 Finnish Fertility Since 1722. Helsinki, Finland: The Population Research Institute.
Matthiessen, P.C., and J.C. McCann
1978 The role of mortality in the European fertility transition: Aggregate-level relations. Pp.
47-68 in S.H. Preston, ed. The Effects of Infant and Child Mortality on Fertility. New
York: Academic Press.
Mosk, C.
1983 Patriarchy and Fertility: Japan and Sweden, 1880-1960. New York: Academic Press.
Preston, S.H.
1978 Introduction. Pp. 1-18 in S.H. Preston, ea., The Effects of Infant and Child Mortality on
Fertility. New York: Academic Press.
Reher, D.S., and P.L. Iriso-Napal
1989 Marital fertility and its determinants in rural and in urban Spain, 1887-1930. Population
Studies 43:405-427.
Richards, T.
1977 Fertility decline in Germany: An econometric appraisal. Population Studies 31(3):537-
553.
Smith, D.S.
1977 A homeostatic demographic regime: Patterns in west European family reconstitution
studies. Pp. 19-52 in R.D. Lee, ea., Population Patterns in the Past. New York:
Academic Press.
Statistischen Reichsamt
1930 StatistiLdesDeutschenReichs, Vol. 360. Berlin: Hobbing.
Sweden Central Bureau of Statistics
1969 Historical Statistics of Sweden. Part l. Population. 1720-1967. Stockholm: Beckmans.
Teitelbaum, M.S.
1984 The British Fertility Decline: Demographic Transition in the Crucible of the Industrial
Revolution. Princeton, N.J.: Princeton University Press.
Tipton, F.B.
1976 Regional Variations in the Economic Development of Germany during the Nineteenth
Century. Middletown, Conn.: Wesleyan University Press.
van de Walle, E.
1978 Alone in Europe: The French fertility decline until 1850. Pp. 257-288 in C. Tilly, ea.,
Historical Studies of Changing Fertility. Princeton, N.J.: Princeton University Press.
van de Walle, F.
1986 Infant mortality and the European demographic transition. Pp. 201-233 in A.J. Coale and
S.C. Watkins, eds., The Decline of Fertility in Europe. Princeton, N.J.: Princeton Uni-
versity Press.
OCR for page 226
226 MACRO EVIDENCE FROM EUROPE AND NEW FINDINGS FROM PRUSSIA
Watkins, S.C.
1991 From Provinces into Nations: Demographic Integration in Western Europe, 1870-1960.
Princeton, N.J.: Princeton University Press.
Wonnacott, R.J., and T.H. Wonnacott
1979 Econometrics. New York: John Wiley & Sons.
Woods, R.I.
1987 Approaches to the fertility transition in Victorian England. Population Studies 41:283-
311.
World Bank
1994 World Development Report 1994. Oxford, England: Oxford University Press.
Yule, G.U.
1906 On the changes in the marriage and birth rates in England and Wales during the past half
century; with an inquiry as to their probable causes. Journal of the Royal Statistical
Society 69:88-132.
Representative terms from entire chapter:
child mortality
