“The reason the rich have grounds for being arrogant and the poor have to spread their butter thin is because the rich have few, the poor many children.”
Prussian working class woman Source:Moszeik (1909), p. 2.
It is a universally acknowledged fact that a family with many children must be poor. But the question is if a family with many children is poor because of having more children the large number of children, or is having more children the reason why a family is poor? Even if the causal relationship is not clear a priori, this evident correlation motivated Brentano’s Theory of Prosperity at the beginning of the nineteenth century and subsequently Becker’s Theory of the Family in the 1960s.
Chapter 1 shows that there are numerous explanations for both the first and the second demographic transition. But how much of the fertility decline can they explain? It is important to understand the factors at play during the first demographic transition in order to assess the potential impact of the introduction of the social security system.
As both Brentano and Becker’s seminal works are considered to be the cornerstone of modern economic fertility theory, this chapter reviews these microeconomic fertility theories and provides empirical evidence using a novel data set. First, we introduce and discuss the factors that previous research has identified as most influential in shaping fertility at the end of the nineteenth century. Second, we discuss different measures of fertility. Third, we intro-duce the novel data set which we use for all analyses in this book. Finally, to establish that the data set is adequate for analysing the fertility decline, we derive standard results on the fertility decline with the data that can be directly compared with the results from other studies. In other words, we analyse whether the explanations for the fertility decline we touched upon in the previous chapter are a sufficient explanation for the enormous drop in births.
2.1 The Fertility Decline: Theory and Evidence
2.1.1 Microeconomic Foundations of Fertility Theory
Microeconomic theories of fertility choice either model the individual or a household’s fertility decision on the basis of economic decision variables.1 The adaptation view is more closely linked to these microeconomic princi-ples. The microeconomic approach was initiated by Becker (1960) and fur-ther substantiated by Becker and ofur-thers (Becker 1965, 1988, 1991; Schultz 1969; Barro and Becker 1986, 1888, 1989; Easterlin 1975; Becker and Tomes 1976; Cigno and Ermisch 1989).
The approaches to a (economic) theory of fertility are often referred to as the demand model of fertility, as children are modelled as a consumption good and fertility is modelled as the demand for children. In line with this, the marginal benefit of an additional child has to be equal to the marginal cost of rearing the child in equilibrium.
More recently, microeconomic theories have been linked to economic growth (Barro and Becker 1989; Becker et al. 1990; Becker 1992). This provided the missing link between microeconomic theories and the macroe-conomic view on the fertility decline that was adopted by its early observers.
In addition, the impact of institutions on fertility has also become the focus of economic research (e.g. McNicholl 1980; Becker and Murphy 1988; Smith 1989; Guinnane and Oglivie 2008). However, the impact of institutions has not been extensively discussed in the context of the demographic transition in nineteenth century Europe. Guinnane (2011) addresses specific details with regard to considering children as a means for old age provisioning, and the existence of institutions, and particularly a social security system as a possi-bility to substitute away from this. Chapter 4 provides a detailed assessment of the theoretical underpinnings of the relationship between social security, and speciically pension insurance and fertility.
2.1.2 Testing Fertility Theory
Early empirical research on the demographic transition mostly focuses on the question when it took place rather than why.2 Coale (1965) observed that
1Arroyo and Zhang (1997) provides an overview of dynamic fertility models, both of theo-ries and their empirical implementation. Guinnane (2011) provides a concise summary on more recent empirical research on the historical fertility decline.
2Cleland and Wilson (1987) provides an overview of the debate in classic demographic transition theory and link this to, inter alia, early descriptive studies of historical data. Arroyo and Zhang (1998) provides a comprehensive overview of dynamic microeconomic models and the derivation of reduced-form models for estimation. Therefore, they provide an important connection between theoretical advances and the empirical tests of the theories.
fertility in nineteenth century Europe had remained at a certain plateau after which it declined substantially. Based on this observation, Coale introduced the notion of a 10% decline in fertility to marking a period of a decline in fertility, as fertility would never rise following a 10% decline.
Coale was part of the Princeton European Fertility Project (Coale and Watkins 1986 provide a summary), which had been started to learn more about the timing of the fertility decline in Europe and to draw conclusions for developing economies. The European Fertility Project aimed at analysing the fertility decline in major European countries at a comparable administra-tive level. The Princeton Project’s work concluded that innovations, e.g. in the area of birth control, and the diffusion of the new technologies caused the fertility decline rather than changed economic and social conditions, since es-timates of economic and social conditions were not significant. In addition, the European Fertility Project researchers dated the fertility decline to more or less the same time in all countries, which would support the cultural dif-fusion hypothesis. This is widely known as the ‘Princeton View’. The impli-cation of this view has far reaching consequences. The confirmation of the cultural diffusion hypothesis and the rejection of the hypothesis that changes in external factors directly triggered a fertility response would also reject the microeconomic theories of fertility.
It does not come as a surprise that the results of the Princeton European Fertility Project have been challenged, both since the quality of the data set is questionable (e.g. Galloway et al. 1994) and due to inadequate estima-tion methods (e.g. Richards 1977; Brown and Guinnane 2007; Goldstein and Klüsener 2010). Recently, the heterogeneity of the historical experience has been emphasised, which also contradicts the Princeton View. For example, Hirschman (2001) notes that pre-decline fertility levels were much lower in Europe than in other regions of the world. Does this mean that the cultural diffusion hypothesis does not qualify as an explanation for the fertility de-cline? Are the effects predicted by economic theory confirmed? Only if we understand the fertility decline in terms of the predictions of economic theory can we assess the role that the introduction of pension insurance played.
2.2 Measuring Fertility
Measuring fertility in the historical context is complex. The individual mea-sures that are common in event-history analysis, such as the individual birth history of a woman or a household, cannot be derived from historical data since individual-level data is hardly available. Even detailed fertility mea-sures for an aggregate population are difficult to derive, as meamea-sures such as
the total fertility rate (TFR)3 require cohort-specific fertility rates as well as the size of each cohort. This makes the TFR independent of the age struc-ture of the population and thus provides a measure that is comparable across countries. The most accurate measure is the completed fertility rate (CFR),4 but by definition the CFR can only be computed for all cohorts that have completed their fertile period. Alas, information on the age structure of the population is scarce for the time of the first demographic transition. If it is available, it is only available for census years. However, information on births is mostly available on an annual basis.
As a consequence, the most common measure for fertility that is used with historical data is the crude birth rate (CBR), i.e. the number of births per thousand per annum (Guinnane 2011). To map population dynamics, this number is related to the crude death rate (CDR) to form the crude rate of natural increase (CRNI):CRNI=CBR−CDR.
Fertility depends on the marriage pattern of the population. Thererefore, early research by Coale and his collaborators at the Princeton European Fer-tility Project developed a set of ferFer-tility indices that take into account the marriage pattern and, where possible, the age structure of the population.
2.2.1 Fertility Indices and Natural Fertility
Coale (1965, 1969) and his collaborators developed a set of fertility indices to determine the timing of the fertility decline that were not just widely used (e.g. Wetherell 2001) but also widely criticised (e.g. Guinnane et al. 1994).
These indices first appeared in the studies emerging from the Princeton Fer-tility Project (e.g. Coale and Watkins 1986). Knodel (1974) also uses the fertility indices to measure the evolution of fertility in Imperial Germany. In essence, the Coale fertility indices compare natural fertility to observed (age-specific) fertility.
The term natural fertility was coined by Henry (1961) and describes fer-tility in the absence of any deliberate birth control. For this purpose, Henry measures fertility among Hutterites, an Anabaptist sect in the Midwest of the US and Canada. Henry claims this to be natural fertility, since the Hutterites did not practise birth control for religious reasons. Table 2.1 reproduces this fertility schedule in the absence of any deliberate for of birth control.
Table 2.2 reproduces the Coale fertility indices, which are based on Hut-terite fertility. Note that our notation is slightly adjusted. The indices are
3The TFR is defined asT FRt=∑age=49age=15 (BIRT HSaget )
W OMENtage·1000. That is to say, the TFR in yeartis equal to the sum of all cohort-specific birth rates in yeart.
4The CFR is defined asCFRy∑t=y+48t=y+14 (BIRT HStage)
W OMENtage·1000.That is to say, the CFR of cohortyis equal to the age-specific birth rate of all women of cohortyin all their fertile years.
Table 2.1: Hutterite fertility
Age group Number of births 15–19 .300
20–24 .550 25–29 .502 30–34 .447 35–39 .406 40–44 .222 45–49 .061
Reproduced as in Henry (1961).
The value for the 15–19 group is an average value as used in Knodel (1974).
nevertheless widely used as they are easy to apply to aggregated historical data. They range between 0 and 1 and measure how close a population’s fertility is to Hutterite fertility. This implies that the Coale fertility indices effectively measure the diffusion of birth control in a population (Galloway et al 1994).5
The overall fertility index relates the total number of births in the popu-lation to Hutterite fertility. However, the marital fertility index only relates marital fertility to Hutterite fertility. The difference between the two can thus indicate the extent of non-marital fertility in a population. The overall fertility index and the legitimate fertility index therefore only differ by the real dif-ference between total births and legitimate births and the difdif-ference between the number of women and the number of married women.
The fertility indices reflect the age structure in a region, and Im,i even reflects the age-specific marriage rates in a region.6 This implies that the Coale indices are also based on information regarding the age structure of the population. Knodel (1974) suggests eliminating the age-structure related component in the indices by using an index that only relates marital to non-marital fertility. This, however, also requires age-specific marriage rates, and thus indirectly reflects the age structure.
Demographers developed some additional measures on the basis of this initial approach to define marital fertility. The Coale and Trussell (1974) measures model fertility within a marriage, and allow for age group-specific
5The exact timing of the more pronounced use of birth control and the exact level of birth control are not central to our study, however. For our study it is important to know that birth control was available, even to the working class. This information is necessary for the claim that even the working classes could limit the size of the family – to a certain extent – if they wanted to. Neumann (1978) and Dribe and Scalone (2009) provide evidence.
6This renders this index incomparable across populations for which the age-specific mar-riage rates differ.
fertility levels and spacing decisions. Age group-specific fertility levels and deviations from these levels were measured for several age groups, just in the same way in which Henry measured natural fertility.
Table 2.2: Fertility indices
Fertility Index Description Overall fertility It,i = ∑nBg,it,iFg,i =
Im,iIc,i+Iu,i(1−Ic,i)
Relates the total annual number of births Bt,ito all women to the demographic com-position and the resulting maximum fertil-ity a province can have: ng,idenotes the number of women n in age group g in provincei, andFg,idenotes the natural fer-tility rate for age groupg.
Marital fertility Im,i=∑mBg,im,iFg,i Relates the annual number of marital births Bm,ito the number of married womenmin age groupgin provincei, andFg,idenotes the natural fertility rate for age groupg.
non-marital fertility Iu,i=∑uBgu,i,iFg,i Relates the annual number of non-marital births Bu,i to the number of unmarried womenuin age groupgin provincei, and Fg,idenotes the natural fertility rate for age groupg.
Contribution of mar-riage to fertility
Ic,i=∑∑mngg,i,iFFg,ii Relates the maximum fertility schedule of married womenmg,ito the maximum fer-tility schedule of unmarried womenng,i. Reproduced as in Knodel (1974).
For our analysis, which requires comparing fertility at more than two or three points in time, it is not feasible to use the Coale fertility indices as the are not available. The age structure of the population is only available for census years. For this reason, we have to resort to using the CBR and the crude marital birth rate (CMBR). The CMBR is defined in the same way as the CBR, but only counts births within marriages.
It is evident from the discussion that birth rates can differ substantially be-tween age groups. The previous chapter also highlights that even measuring age-specific birth rates as in the TFR can be misleading with regard to the total number of births per woman (the CFR). Whereas the TFR overestimated the CFR during the first demographic transition, it is possible that it currently underestimates the CFR. In the end, the CFR remains the most appropriate measure to use. However, information on the total number of children per woman related to the woman’s year of birth is only collected in censuses.
Therefore, it is difficult to calculate the CFR especially for the very early cohorts, i.e. for birth years 1860–1880.
The next section shows that we use regional data for our analyses. Owing to the lack of information on age structure at the regional level, we have to resort to using the CMBR. However, we can compare the Coale indices of fertility, which take into account the age structure for 3 out of 37 years in our sample, to show that the CMBR measures approximately the same regional differences as the Coale fertility indices.
2.2.2 Data
Our analysis is based on a regional data set for Imperial Germany that is de-rived from two primary data sources. Appendix A details the data sources and how we combined the two data sets. The regional entities in our final data set after harmonising the two data sets are shown in figure 2.1. Figure 2.1 provides the names for the regions used in this study in German. We use the names in German throughout the study, as some regional names have an English equivalent while for some regional names there is no English equiva-lent. However, when we refer to a broad region, e.g. the Kingdom of Prussia, we use the names in English. Therefore, as a rule, when we use the names in English we refer to a region, while when we use the names in German, we refer to a unit of observation.
2.2.3 Comparison of the CBR and the Coale Indices
As a prerequisite for the analysis that follows, we first show that using the CBR and one of the Coale fertility indices provides similar information. We can only compute the index of overall fertility,It,i, for the years in which the Imperial Statistical Office provides information on the age structure at the provincial level, but not the marital fertility index, as the Imperial Statisti-cal Office did not publish information on age-specific marriage rates at the regional level. We can however approximate the contribution of age-specific marriage rates to fertility on the basis of the percentage of married women compared to the total population. At the provincial level, we do not have information on the proportion of married women in each age group. This means that we can multiply the number of women in each age group with the average fraction of married women in each year.
Figure 2.2 compares the regional distribution of the approximated marital fertility index for 1885 to the CMBR for these years. The maximum figure is above 1 for 1878 and 1885, which proves that using the average marriage rate among the female population is fairly imprecise. The figures correspond to Knodel’s figures only for 1890. Knodel assigns 0.735 as the marital fertility index for 1880, 0.726 for 1885, and 0.706 for 1890. Even when considering that our figures on the marital fertility index are somewhat imprecise, our
calculation of the marital fertility index indicates a relatively sharp drop in both indices between 1885 and 1890.7 In particular, the regional structure of total fertility differs from figure 2.2, in both 1878 and 1885. This implies that it is important to control for marriage patterns. While we cannot control for age-specific marriage patterns, we can use regional figures on non-marital birthsto compute the crudemaritalbirth rate (CMBR).
Figure 2.1: Regions in Imperial Germany
Ostpreußen
West-Preußen Pommern
Posen Branden-burg Berlin Mecklen-burg
Schlesien Kgr.
Sachsen Sachsen-Anhalt Thüringen
Bayern
Württem-berg Baden Elsaß-Lothringen
Pfalz Hessen
Rhein-land Westfalen
Hessen-Nassau
Braunschweig Hannover
Schleswig-Holstein Hanse-städte Olden-burg
2.3 Mapping the Fertility Decline in Imperial Germany Fertility in Imperial Germany declined much earlier than in neighbouring Eu-ropean countries. This section first assesses the timing of the fertility decline in the provinces of Imperial Germany and relates our results to existing re-search. Then we review the strength of the economic theories of fertility to explain the fertility decline in Imperial Germany.
7This is the same if we compare the total fertility index to the crude birth rate. The maxi-mum fertility index of 0.36 in 1878 and 1885 is in line with Knodel’s (1974) figures for these years. Knodel (1974) reports an average total fertility index of 0.404 for 1880 and 0.395 for 1885. These are, however, average figures. Our average for 1878 is 0.294 and 0.301 for 1885.
Figure 2.2: Approximated marital fertility index and CMBR in 1885 Approximated marital fertility index
1 − 1.5 .9 − 1 .8 − .9 .7 − .8 .6 − .7 .5 − .6 .4 − .5
Crude marital birth rate
45 − 50 40 − 45 35 − 40 30 − 35 25 − 30 20 − 25 15 − 20 10 − 15
There are corresponding similarities between the total fertility index and the crude birth rate.
2.3.1 Timing
There is no clear method for dating the fertility decline. The measures used by the Princeton Fertility Project – a decline of 10% from maximum fertil-ity – clearly lack a sound theoretical underpinning. Based on this measure, Knodel (1974) dates the fertility decline to the period post 1870 for Prussia,
There is no clear method for dating the fertility decline. The measures used by the Princeton Fertility Project – a decline of 10% from maximum fertil-ity – clearly lack a sound theoretical underpinning. Based on this measure, Knodel (1974) dates the fertility decline to the period post 1870 for Prussia,