Abstract
This review uses Malthus’ model of the preindustrial economy to structure the cliometric literature on the interaction of European population with the economy. Increases in wages raise population, and higher population drives down wages. Positive and preventive checks determine long-run population and wages. The Black Death was a positive check, eliminating a large proportion of the population across medieval Europe and temporarily boosting wages. England largely eliminated positive checks (such as famine) by the mid-seventeenth century. The economy was already on the path to sustained economic growth, despite average real wages not rising until after 1800. In eighteenth-century Sweden however, higher food grain prices raised mortality. A major preventive check was the female late age at marriage and high celibacy rate after 1500 or earlier in Western and Southern Europe. This arrangement provided a floor to living standards by restricting fertility and encouraging physical and human capital accumulation and therefore technical progress. Lower mortality rates were achieved in the west than in the east of Europe and were associated with lower fertility across the continent. Even so, population expanded most rapidly in the western, more dynamic European economies, so fertility restriction alone was not obviously the trigger for economic growth. French marital fertility control began at the end of the eighteenth century, yet French living standards were not the highest in Europe. Other European populations increased rapidly for perhaps a century before fertility fell, under pressure from rising child costs including the greater opportunity cost of time spent bringing up children. Interactions between the economy and migration, mainly focused on the long nineteenth century, have been modeled with cliometric structures related to those of natural increase and the economy. European wages were driven up by emigration from Europe and reduced in the economies receiving immigrants.
Notes
- 1.
When interpreting these materials, it is important to appreciate that aggregated data can conceal relations that are apparent in more disaggregated sources of information (Brown and Guinnane 2007). The Princeton Fertility Project in particular (for instance, Coale and Watkins 1986) has been criticized for drawing incorrect inferences from excessively aggregated data.
- 2.
The Cambridge team also published a much more detailed analysis based upon 26 English parishes (Wrigley et al. 1997).
- 3.
In the early nineteenth century, a laborer’s annual income increased by about 14% for each child for which allowances were awarded. Allowance policy varied by local area, beginning with payments for three children under 10.
- 4.
In practice, cultivated land area expanded a little with population in Western Europe, as less productive soils were brought into use. Broadberry et al. (2015) (Table 2.10) estimate that in England, the cultivated land area only exceeded the medieval peak of 1290 by 1836, when population was several times greater than at the earlier date.
- 5.
Spain appears to be an exception in Western Europe (Alvarez-Nogal and Prados de la Escosura 2013).
- 6.
Defining W as log w and p as log P (the labor force), the b parameter is −0.5 in the marginal productivity condition W = −0.5 (p − q) where q is the log of output (which shifts with technical progress) and the elasticity of substitution between factor inputs is unity. An additional assumption is that there should be close-to-perfect competition in labor markets.
- 7.
In reality there may be longer lags in this relationship, which in turn lengthens the periodicity of the cycle discussed below. Autocorrelated shocks or disturbances have the same effect.
- 8.
Among wealthy families, pressures were different.
- 9.
Enter the parameters of the population difference equation in say cells A1 and A2 (respectively, 0.88 and 0.14 in this case). Fill a column (say B) with a series starting at 0 and increasing by 1 with each subsequent cell. Assign the column next to B for P t The first value depends upon the shock to be considered. As a positive shock, use any number greater than 1.16 here. So entering 1 as the first cell in the C column will be a negative population shock. In cell C2, enter “= − $A$1*c1 + $A$2” and fill down column C. The series rises above the equilibrium level in period 1 and falls below it in period 2. The behavior of the equation can be studied by changing the parameters assigned to cells A1 and A2.
- 10.
Both types of shocks may be classified as originating on the supply side and as “real” rather than “monetary,” consistent with real business cycle theory (Kydland and Prescott 1982).
- 11.
Williamson (2005) also discusses the corollary that the position of blacks deteriorated after 1970 because of competition from immigrants.
- 12.
Coefficients are not updated observation by observation as they are with the Kalman filter and the Kalman gain. Instead the recursive system goes all the way through the observations to get one vector of coefficients, minimizing the square of the distance between actual and forecast values of the variables.
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Foreman-Peck, J. (2023). Economic-Demographic Interactions in European Long-Run Growth. In: Diebolt, C., Haupert, M. (eds) Handbook of Cliometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40458-0_17-3
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