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The impact of early-life economic conditionson cause-specific mortality during adulthood


The aim of this study is to assess the effects of economic conditions in early life on cause-specific mortality during adulthood. The analyses are performed on a unique historical sample of 14,520 Dutch individuals born in 1880–1918, who are followed throughout life. The economic conditions in early life are characterized using cyclical variations in annual real per capital gross domestic product during pregnancy and the first year of life. Exposure to recessions in early life appears to significantly increase cancer mortality risks of older males and females. It also significantly increases other mortality risks especially for older females. The residual life expectancies are up to about 8 and 6 % lower for male and female cancer mortality, respectively, and up to about 5 % lower for female cardiovascular mortality. Our analyses show that cardiovascular and cancer mortality risks are related and that not taking this association into account leads to biased inference.

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  1. The category “other causes of death” includes the remaining natural causes of death that are not already included in the analyses.

  2. The HSN data and the CBS data were merged using the number of certificate of death, date and place of death and date and place of birth of the HSN individuals.

  3. This group has on average the same individual characteristics at birth as the other sample members.

  4. CBS was able to retrieve a cause of death for 96.3 % of the HSN individuals who have a registered date of death after January 1, 1937. These individuals were born between 1863 and 1922.

  5. To assess the sensitivity of the results with respect to the window of birth cohort years, we performed analyses with somewhat different windows (results available upon request). It is found that including cohorts up to 1921 leads to some small changes in the coefficients (notably for men), but does not alter the main conclusions in a major way. Excluding the 1916–1918 years does have a sizable effect on the standard errors. This is in part due to a reduction in the number of observations and deaths via the respective causes. For instance, for males, the number of observations drops from 7,300 to 6,769 and deaths due to cancer drop from 1763 to 1541 and to CVD from 2845 to 2692. The most important reason is that these years also relate to a severe recession, and deleting a recession with such a “bite” is expected to have an impact on the results. We also performed analyses for windows starting with 1882 or 1883 instead of 1880. This does not affect the results in any major way.

  6. The lifetime of individuals with no date of death is right-censored at the last date of observation, for instance at a last recorded date of marriage or birth of a child.

  7. The individuals (2,829) born between 1863 and 1880 and who died after January 1, 1937 were excluded from the empirical analyses.

  8. Of the HSN individuals, 7.5 % have either no recorded day of death and/or no recorded month of death. If the day of death only was missing, the day of death was set on the 15th of the month of death. If both the day and month of death were missing, the date of death was set on July 1 of the year of death. The main results remain similar if we exclude these individuals from the analyses.

  9. The results were very similar with a smoothing parameter equal to 100. There was one exception: the decomposition based on a smoothing parameter equal to 100 showed an economic boom at the beginning of World War I, which is not documented in historical literature (see the next paragraph).

  10. It is worth noting that our main results remain the same if we use a Hodrick–Prescott decomposition of the GDP over 1879–1918.

  11. Note that parental socio-economic status during childhood may be endogenous, as other factors such as parental lifestyle may both affect socio-economic position of the parents and health of the HSN participant.

  12. We also present the results of log rank tests to compare the survivals of those exposed in early life to an expansion with those exposed in early life to a recession.

  13. Extended Cox models are equivalent to Cox models that are extended to allow for time-dependent variables (Kleinbaum and Klein 2005).

  14. The conclusions remain qualitatively the same if we use different individual characteristics.

  15. Province of birth was not included in Table 2.

  16. As we have separate estimates by gender, from separate data, each asymptotically normally distributed, with standard errors, we can do a t test for equality of the estimates. The t tests show that the null hypothesis of equal parameter estimates (associated with the early life conditions) for males and females cannot be rejected.

  17. The p-values of the chi-test equal 0.556, 0.827, and 0,425 for males, respectively and equal 0.834, 0,773, and 0,738 for females respectively.


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We thank the editor, two anonymous referees and participants at the European Social Science History Conference, the International Student Congress of Medical Sciences, and the European Conference on Health Economics for their useful suggestions. This project was part of the first author’s extracurricular research programme of medicine at the VU University Amsterdam. We thank Henk de Vries and the referees of that programme for their useful comments. We are indebted to the International Institute of Social History in Amsterdam (IISH), Statistics Netherlands in The Hague (CBS) and Angus Maddison, for access to their data. We thank the CBS Centre for Policy Related Statistics in The Hague for their assistance and Kees Mandemakers (IISH) for his help. This project received financial support from the VU University Medical Centre’s Department of General Practice in Amsterdam for the acquisition of the CBS data.

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Correspondence to France R. M. Portrait.

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Responsible editor: Erdal Tekin

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Yeung, G.Y.C., van den Berg, G.J., Lindeboom, M. et al. The impact of early-life economic conditionson cause-specific mortality during adulthood. J Popul Econ 27, 895–919 (2014).

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  • Life expectancy
  • Cancer
  • Cardiovascular disease
  • Survival analyses
  • Competing mortality risks
  • Recession

JEL Classifications

  • C41
  • I10
  • E32