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Economic-Demographic Interactions in the European Long Run Growth

Handbook of Cliometrics
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Abstract

Cliometrics confirms that Malthus’s model of the preindustrial economy is a good description for much of demographic-economic history; increases in productivity raise population, but higher population drives down wages. A contributor to the Malthusian equilibrium was the Western European marriage pattern, the late age of female first marriage, which promised to retard the fall of living standards by restricting fertility. The demographic transition and the transition from Malthusian economies to modern economic growth attracted many cliometric models surveyed here. A popular model component is that lower levels of mortality over many centuries increased the returns to, or preference for, human capital investment so that technical progress eventually accelerated. This initially boosted birth rates and population growth accelerated. Fertility decline was earliest and most striking in late-eighteenth-century France. By the 1830s, the fall in French marital fertility is consistent with a response to the rising opportunity cost of children. The rest of Europe did not begin to follow until near the end of the nineteenth century. Interactions between the economy and migration, mainly focused on the long nineteenth century, have been modeled with cliometric structures closely related to those of natural increase and the economy. Wages were driven up by emigration from Europe and reduced in the economies receiving immigrants.

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Notes

  1. 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. 2.

    The Cambridge team also published a much more detailed analysis based upon 26 English parishes (Wrigley et al. 1997).

  3. 3.

    Lee and Anderson (2002) contend that the resulting population estimates are inaccurate for taking into account international migration, but a fair representation of population excluding migration.

  4. 4.

    For an interpretation on film of the impact, see Ingmar Bergman’s The Seventh Seal.

  5. 5.

    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.

  6. 6.

    Spain appears to be an exception in Western Europe (Alvarez-Nogal and Prados de la Escosura 2013)

  7. 7.

    Defining W as log w and p as log P (the labor force), the marginal productivity condition is W = a − 0.5(pq) where q is the log of output 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.

  8. 8.

    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.

  9. 9.

    When birth and/or death rates respond to wages, as, for example, in Lee (1973), then Eq. 2 explains the change in population and should be modified by the addition of – P t − 1 to the right-hand side. In the interests of simplicity, this modification is not implemented here.

  10. 10.

    And with Arthur Lewis’ (1954) model of economic development with unlimited supplies of labor, although here, the perfectly elastic supply of labor comes from migration, rather than natural increase.

  11. 11.

    Enter the parameters of the population difference equation in say cells A1 and A2 (respectively, 0.64 and 1.8 in this case). Fill a column (say B) with a series starting at zero and increasing by one 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 1.097 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.

  12. 12.

    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).

  13. 13.

    Williamson (2005) also discusses the corollary that the position of blacks deteriorated after 1970 because of competition from immigrants.

  14. 14.

    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. (2019). Economic-Demographic Interactions in the 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_17-2

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  1. Latest

    Economic-Demographic Interactions in European Long-Run Growth
    Published:
    04 August 2023

    DOI: https://doi.org/10.1007/978-3-642-40458-0_17-3

  2. Economic-Demographic Interactions in the European Long Run Growth
    Published:
    07 February 2019

    DOI: https://doi.org/10.1007/978-3-642-40458-0_17-2

  3. Original

    Economic-Demographic Interactions in Long-Run Growth
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    28 July 2014

    DOI: https://doi.org/10.1007/978-3-642-40458-0_17-1