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The importance of being mature: the effect of demographic maturation on global per capita GDP


Given that savings behaviour and worker productivity have strong life-cycle components and given that demographic profiles vary across countries, population age structure should be linked to differences in levels of economic development. In this paper, we measure the economic importance of age structure variation for the global economy. We find that demographic maturation has been associated with nearly half of the evolution of global per capita GDP since 1960. We also find that age structure differences can account for just over half of the variation in worldwide per capita GDP (i.e. the lack of sigma convergence) observed since 1960.

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Fig. 1


  1. See Krueger (1968) and work by Modigliani (1986) for some of the earliest empirical analyses of this link. More recently, Sarel (1995), Bloom and Williamson (1998), Lindh and Malmberg (1999), Persson (2002), Feyrer (2002) and Gómez and Hernández de Cos (2003) have confirmed the empirical importance of demographic age structure to the growth process.

  2. Although it may seem that what happens to the working age structure is always prefigured by the working age size effect, it is not necessarily the case that all prime-age countries are subsets of countries with mature working age populations. In fact, Japan is now (or very soon will be) entering a phase in which its working age population will actually be getting younger, but its working age population will shrink in relative terms. This is because, as a whole, Japan is getting older (i.e. there is a growing share of young workers in the 15–65 population, but the 15–65 share is actually shrinking relative to the whole population because of the growth of the 65+ group). Hence, the prime-age effect is not exclusively an additional effect located only among already mature countries.

  3. This is partly attributable to experiential returns, whereby senior employees (other things equal) are more productive than younger counterparts. See Mincer (1974) for a classic reference in this regard.

  4. There is an addendum to this two-part story. As noted by Bloom and Williamson (1998), persistently low birth rates eventually produce a decline in the size of the working age population and an increase in old-age dependency ratios. Ultimately, however, whether the beneficial effects of a demographic maturation are merely transitional is secondary since the transitional element only relates to rates of economic growth. In this paper, we are interested in the cumulative effects of demographic maturation, so for our purposes, any boost to per capita growth will have a permanent effect on the level of per capita GDP. This, we believe, may help account for differences in those GDP levels far into the future, especially across countries that at present appear very similar.

  5. One potential problem with our composite data set is that it treats countries as diverse in population size as India and Sao Tome as independent data points with equal weights. However, this is not as bad an approximation as it sounds since, as noted by Sala-i-Martin (2002), when the analysis centres on the effect of certain country characteristics on per capita GDP, as in our case, it is sensible to treat each country as a single data point. Naturally, if our goal was to estimate global welfare more generally, a population-weighted GDP per capita measure is preferable. See Theil (1979, 1996) and references contained therein for work on this question.

  6. Including countries for which age structure or per capita GDP figures were not missing in at least one observed time period

  7. In this regard, both Weil (1994) and Feyrer (2002) find evidence that savings rates peak for workers in their 50s. This is consistent with life-cycle models of saving as first proposed by Modigliani (1986).

  8. A further two assumptions can also be invoked. First, that these working age categories are imperfect substitutes in production at the micro-level; for example, young workers may perform well on the shop floor but perform rather poorly in the boardroom. We follow Kremer and Thomson (1998), who make this the cornerstone of their own paper on the impact of demographic age structure in forestalling economic convergence across countries. Second, that external migration is not large enough to mute the impact of historical fertility declines. As noted by Bloom and Williamson (1998), “In the late twentieth century, international migrations are simply not great enough to matter...They mattered a great deal, however, in the age of relatively unrestricted mass migration prior to World War 1.”

  9. This is suitable not only for descriptive purposes but also on the grounds that this will allow us to better estimate the effect of population maturation across different country-club demographic groupings. This parallels recent work by Durlauf and Quah (1999), among others, who find evidence of convergence club groupings and twin-peak distributions across development indicators.

  10. See Tables 5 and 6 of Appendix 1 for a detailed descriptive analysis of the characteristics of demographically mature and prime-age countries against younger and non-prime-age country counterparts in three periods—1960s, 1980s and 2000. Bottom line is that mature and prime-age countries typically show higher savings rates, lower income inequality and greater mean GDP levels, with a widening in the gap between mature (or prime-age) and young (or non-prime-age) observed from 1980 onward.

  11. Different cut-off ratio points were tested for both definitions of maturity. The ratios finally selected were those generating the largest real GDP gaps between the two groups. In the case of the prime-age variable, the selected cut-off ratio is also equal to the ratio at which growth rates peak, as found by Gómez and Hernández de Cos (2003). A list of mature and prime-aged countries which fall into these dichotomous categories is available in Table 7 of Appendix 1.

  12. The adjusted gaps are taken from the fixed effect regression of Eq.  (8) with the time period dummies added. The estimated fixed effect coefficients on our mature and prime-age variables are then added to each common time period effect. All time dummy variables showed positive and highly significant coefficients.

  13. The increase in the GDP gap between country groups is interesting. One explanation lies in the divergence in the percentage of prime-age workers observed in both country groups during this time period: the non-mature country group became even younger while the mature country group became even more mature.

  14. Although Sala-i-Martin (1996) finds general support for both absolute and conditional convergence in a variety of data sets, the only data set that displays absolute divergence is the global data set of 100 countries. Others have found divergence in the form of ‘twin peak’ distributions, or a clustering of rich and poor countries (Quah 1996). See Jones (1997) for a useful summary.


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We are particularly grateful to our anonymous referee(s) and the editorial guidance of Junsen Zhang. We are also grateful to Isabel Argimón, Susanto Basu, Cristina Barceló, Olympia Bover, Ignacio Hernando, Morley Gunderson, Eloísa Ortega, Gabriel Pérez Quirós and Javier Vallés for their suggestions and comments and all participants of the seminar at the Research Department of the Banco de España. The helpful research assistance of Wanja Eiche, Kenny Sandorffy and Konstantinos Tzioumis is greatly appreciated.

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Correspondence to Pablo Hernández de Cos.

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Responsible Editor: Junsen Zhang



Table 4 Variable definitions and sources
Table 5 Characteristics of countries by structure of working age population (prime age vs non-prime age) in 1960, 1980 and 2000a
Table 6 Characteristics of countries by size of working age population (mature vs young) in 1960, 1980 and 2000a
Table 7 List of prime-aged and mature countriesa

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Gómez, R., Hernández de Cos, P. The importance of being mature: the effect of demographic maturation on global per capita GDP. J Popul Econ 21, 589–608 (2008).

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  • Age structure
  • Life-cycle model
  • Cross-country growth

JEL Classifications

  • J13
  • J24
  • O40