Mortality and Macroeconomic Conditions: What Can We Learn From France?

Abstract

This study uses aggregate panel data on French départements to investigate the relationship between macroeconomic conditions and mortality from 1982 to 2014. We find no consistent relationship between macroeconomic conditions and all-cause mortality in France. The results are robust across different specifications, over time, and across different geographic levels. However, we find that heterogeneity across age groups and mortality causes matters. Furthermore, in areas with a low average educational level, a large population, and a high share of migrants, mortality is significantly countercyclical. Similar to the case in the United States, the relationship between the unemployment rate and mortality seems to have moved from slightly procyclical to slightly countercyclical over the period of analysis.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    Brenner (1971) and Brenner and Mooney (1983) conducted similar research, but their empirical methods have subsequently been heavily criticized.

  2. 2.

    An exception to the rule that higher unemployment is associated with fewer deaths due to cardiovascular diseases might be Sweden (Svensson 2010), even though Tapia Granados and Ionides (2011) cast doubt on this conclusion.

  3. 3.

    Personal communication with Buchmueller (February 28, 2017) confirmed that the project was abandoned because this research group discovered that the results were not robust.

  4. 4.

    See Stevens et al. (2015) for a similar analysis in the United States.

  5. 5.

    In contrast to these more recent theories, earlier research tended to explain procyclical mortality chiefly with people’s own employment status. In this light, the direction of the effect of unemployment on health is not evident a priori. On the one hand, unemployment can be a psycho-social stressor that reduces quality of life and subjective well-being, possibly resulting in higher morbidity and mortality. Downward mobility could also be associated with increased morbidity and mortality. On the other hand, economic downturns could reduce stress and overtime hours, which should particularly reduce deaths due to cardiovascular diseases. Rising opportunity cost of time that accompanies better labor market opportunities might lead to higher mortality (Miller et al. 2009) because it makes it more costly for individuals to undertake time-intensive, health-producing activities. Finally, income growth may increase risky activities, such as drinking and driving (Ruhm and Black 2002). Stress-induced increases in alcohol consumption were found to be more than offset by reductions in incomes during economic crises.

  6. 6.

    We treat Corsica as one département because of data constraints.

  7. 7.

    These latter controls are obtained by interpolating census data from 1982, 1990, 1999, 2009, and 2014.

  8. 8.

    Coefficients in this specification continue to be interpreted as semi-elasticities. For a discussion of the advantages of PPML over a log-linear model estimated by ordinary least squares (OLS), see Manning and Mullahy (2001) and Santos Silva and Tenreyro (2006, 2011).

  9. 9.

    When we split our sample in two parts, the value was .03 from 1982 to 1998 and was stable from 1998 to 2014 (i.e., .03). Ruhm (2015) found this value to be .09 for the period 1999–2010 in the United States.

  10. 10.

    https://www.cepidc.inserm.fr/causes-medicales-de-deces/centre-collaborateur-oms.

  11. 11.

    As an additional robustness check, we present results with the age-adjusted mortality rate as dependent variable in the online appendix (section D).

  12. 12.

    Anderson et al. (2001) provided comparability ratios for the United States. For the specific sources of mortality we are considering, most of the estimated comparability ratios are close to 1, suggesting that a similar number of deaths are reported using either ICD system.

  13. 13.

    http://www.insee.fr/fr/insee-statistique-publique/default.asp?page=connaitre/ddar.htm.

  14. 14.

    For details on how unemployment statistics are calculated in France, see Fougère et al. (2009) or http://www.insee.fr/en/methodes/default.asp?page=sources/ope-taux-chomage-localises.htm.

  15. 15.

    In comparison, the average state in the United States had a population of 6,280,000; the average county, 100,000. The size of geographic unit is expected to have a moderate influence on the estimated coefficients. The channels through which the unemployment rate influences mortality might vary with the level of aggregation. Smaller geographical units will have higher spillover effects (Lindo 2015).

  16. 16.

    Section B of the online appendix shows that the inclusion of département-specific time trends similarly changes most of the results of Buchmueller et al. (2007).

  17. 17.

    See also Ruhm (2015) for a discussion of the influence of weights on the unemployment coefficient for the case of the United States.

  18. 18.

    Another reason for weighting that is sometimes given is to achieve more efficient estimation given heteroskedasticity. However, as shown in Table 1, the standard errors between WLS and OLS estimates hardly differ, indicating that weighting by area population does not do a better job of dealing with heteroskedasticity. See Solon et al. (2015) for a discussion of the relative advantages of WLS and OLS.

  19. 19.

    As an additional check, we ran the département-level regression with the full set of controls, weighting, year fixed effects, and département fixed effects, but we replaced the département-specific time trends by region-specific time trends. This yielded a coefficient estimate of 0.0014 (standard error = 0.0011). This should make our analysis more robust to the claim that the département-specific time trends take too much variation out of the analysis. Unlike in panel B of Table 1, the old regions were used to create the region-specific time trends because these were the administrative levels in place during the analysis period. Using the new regions for the time trends in an otherwise similar specification yields an estimate of –0.0001 (standard error = 0.0015).

  20. 20.

    In unreported regressions, we also used the nontransformed mortality rate as a dependent variable and estimated by OLS. However, because mortality rates can be viewed as count data, we prefer to use a count model and thus keep the preferred functional form and the interpretation of coefficients as semi-elasticities.

  21. 21.

    In unreported results, we found that the coefficients steadily decline with age. Younger people who are inexperienced drivers are involved in more accidents. It also makes sense that the effect would be weakest among people above the retirement age given that this demographic group is least likely to participate in auto travel, partly because they no longer commute to work.

  22. 22.

    As a robustness check, we created the same figures excluding the weighting by population. Results were qualitatively the same but coefficients were closer to 0, as can be expected from Table 1.

  23. 23.

    The data were provided by IFOP based on available data up to 2009 (Institut Français d’Opinion Publique: Eléments d’analyse géographique de l’implantation des religions en France, unpublished).

  24. 24.

    Interestingly, in his reanalysis of his 2000 paper, Ruhm (2015) actually added such a state-specific time trend.

  25. 25.

    We can compare unemployment with GDP by region only from 1990 to 2014.

  26. 26.

    Importantly, we did not have access to the full time series for all variables, but this is the most complete data set we can find for France.

References

  1. Adda, J. (2016). Economic activity and the spread of viral diseases: Evidence from high frequency data. Quarterly Journal of Economics, 131, 891–941.

    Article  Google Scholar 

  2. Anderson, R. N., Mininõ, A. M., Hoyert, D. L., & Rosenberg, H. M. (2001). Comparability of cause of death between ICD-9 and ICD-10: Preliminary estimates (National Vital Statistics Reports, No. 49/2). Hyattsville, MD: National Center for Health Statistics.

  3. Ariizumi, H., & Schirle, T. (2012). Are recessions really good for your health? Evidence from Canada. Social Science & Medicine, 74, 1224–1231.

    Article  Google Scholar 

  4. Baccäıni, B. (2001). Les migrations internes en France de 1990 à 1999: L’appel de l’Ouest [Internal migration in France from 1990 to 1999: The call of the West]. Economie et Statistique, 344, 39–79.

  5. Brenner, M. H. (1971). Economic changes and heart disease mortality. American Journal of Public Health, 61, 606–611.

    Article  Google Scholar 

  6. Brenner, M. H., & Mooney, A. (1983). Unemployment and health in the context of economic change. Social Science & Medicine, 17, 1125–1138.

    Article  Google Scholar 

  7. Buchmueller, T., Grignon, M., & Jusot, F. (2007). Unemployment and mortality in France, 1982–2002 (Working Paper Series, 2007-04). Hamilton, Canada: Centre for Health Economics and Policy Analysis, McMaster University.

  8. Burgard, S. A., Brand, J. E., & House, J. S. (2009). Perceived job insecurity and worker health in the United States. Social Science & Medicine, 69, 777–785.

    Article  Google Scholar 

  9. Case, A., & Deaton, A. (2015). Suicide, age, and wellbeing: An empirical investigation (NBER Working Paper No. 21279). Cambridge, MA: National Bureau of Economic Research.

  10. Currie, J., & Gruber, J. (1996). Saving babies: The efficacy and cost of recent changes in the Medicaid eligibility of pregnant women. Journal of Political Economy, 104, 1263–1296.

    Article  Google Scholar 

  11. Currie, J., Schwandt, H., & Thuilliez, J. (2018). Pauvreté, égalité, mortalité: Mortality (in)equality in France and the United States (NBER Working Paper No. 24623). Cambridge, MA: National Bureau of Economic Research.

  12. Deryugina, T., Heutel, G., Miller, N., Molitor, D., & Reif, J. (2016). The mortality and medical costs of air pollution: Evidence from changes in wind direction (NBER Working Paper No. 22796). Cambridge, MA: National Bureau of Economic Research.

  13. Durkheim, E. (1897). Le suicide: Étude de sociologie [Suicide: Sociology study]. Paris, France: F. Alcan.

  14. Fougère, D., Kramarz, F., & Pouget, J. (2009). Youth unemployment and crime in France. Journal of the European Economic Association, 7, 909–938.

    Article  Google Scholar 

  15. Gerdtham, U.-G., & Ruhm, C. J. (2006). Deaths rise in good economic times: Evidence from the OECD. Economics & Human Biology, 4, 298–316.

    Article  Google Scholar 

  16. Hamermesh, D. S., & Soss, N. M. (1974). An economic theory of suicide. Journal of Political Economy, 82, 83–98.

    Article  Google Scholar 

  17. Heutel, G., & Ruhm, C. J. (2016). Air pollution and procyclical mortality. Journal of the Association of Environmental and Resource Economists, 3, 667–706.

    Article  Google Scholar 

  18. Ionides, E. L., Wang, Z., & Tapia Granados, J. A. (2013). Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends. Annals of Applied Statistics, 7, 1362–1385.

    Article  Google Scholar 

  19. Lam, J.-P., & Piérard, E. (2017). The time-varying relationship between mortality and business cycles in the USA. Health Economics, 26, 164–183.

    Article  Google Scholar 

  20. Lin, S.-J. (2009). Economic fluctuations and health outcome: A panel analysis of Asia-Pacific countries. Applied Economics, 41, 519–530.

    Article  Google Scholar 

  21. Lindo, J. M. (2015). Aggregation and the estimated effects of economic conditions on health. Journal of Health Economics, 40, 83–96.

    Article  Google Scholar 

  22. Manning, W. G., & Mullahy, J. (2001). Estimating log models: To transform or not to transform? Journal of Health Economics, 20, 461–494.

    Article  Google Scholar 

  23. Miller, D. L., Page, M. E., Stevens, A. H., & Filipski, M. (2009). Why are recessions good for your health? American Economic Review: Papers & Proceedings, 99, 122–127.

    Article  Google Scholar 

  24. Neumayer, E. (2004). Recessions lower (some) mortality rates: Evidence from Germany. Social Science & Medicine, 58, 1037–1047.

    Article  Google Scholar 

  25. Ogburn, W. F., & Thomas, D. S. (1922). The influence of the business cycle on certain social conditions. Journal of the American Statistical Association, 18, 324–340.

    Article  Google Scholar 

  26. Regidor, E., Vallejo, F., Tapia Granados, J. A., Viciana-Fernández, F. J., de la Fuente, L., & Barrio, G. (2016). Mortality decrease according to socioeconomic groups during the economic crisis in Spain: A cohort study of 36 million people. Lancet, 388, 2642–2652.

    Article  Google Scholar 

  27. Ruhm, C. J. (1995). Economic conditions and alcohol problems. Journal of Health Economics, 14, 583–603.

    Article  Google Scholar 

  28. Ruhm, C. J. (2000). Are recessions good for your health? Quarterly Journal of Economics, 115, 617–650.

    Article  Google Scholar 

  29. Ruhm, C. J. (2003). Good times make you sick. Journal of Health Economics, 22, 637–658.

    Article  Google Scholar 

  30. Ruhm, C. J. (2005). Healthy living in hard times. Journal of Health Economics, 24, 341–363.

    Article  Google Scholar 

  31. Ruhm, C. J. (2007). A healthy economy can break your heart. Demography, 44, 829–848.

    Article  Google Scholar 

  32. Ruhm, C. J. (2015). Recessions, healthy no more? Journal of Health Economics, 42, 17–28.

    Article  Google Scholar 

  33. Ruhm, C. J., & Black, W. E. (2002). Does drinking really decrease in bad times? Journal of Health Economics, 21, 659–678.

    Article  Google Scholar 

  34. Santos Silva, J. M. C., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88, 641–658.

    Article  Google Scholar 

  35. Santos Silva, J. M. C., & Tenreyro, S. (2011). Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator. Economics Letters, 112, 220–222.

    Article  Google Scholar 

  36. Solon, G., Haider, S. J., & Wooldridge, J. M. (2015). What are we weighting for? Journal of Human Resources, 50, 301–316.

    Article  Google Scholar 

  37. Stevens, A. H., Miller, D. L., Page, M. E., & Filipski, M. (2015). The best of times, the worst of times: Understanding pro-cyclical mortality. American Economic Journal: Economic Policy, 7(4), 279–311.

    Google Scholar 

  38. Sullivan, D., & von Wachter, T. (2009). Job displacement and mortality: An analysis using administrative data. Quarterly Journal of Economics, 124, 1265–1306.

    Article  Google Scholar 

  39. Svensson, M. (2010). Economic upturns are good for your heart but watch out for accidents: A study on Swedish regional data 1976–2005. Applied Economics, 42, 615–625.

    Article  Google Scholar 

  40. Tapia Granados, J. A. (2008). Macroeconomic fluctuations and mortality in postwar Japan. Demography, 45, 323–343.

    Article  Google Scholar 

  41. Tapia Granados, J. A., & Ionides, E. L. (2011). Mortality and macroeconomic fluctuations in contemporary Sweden. European Journal of Population / Revue Européenne de Démographie, 27, 157–184.

    Article  Google Scholar 

  42. Tapia Granados, J. A., & Rodriguez, J. M. (2015). Health, economic crisis, and austerity: A comparison of Greece, Finland and Iceland. Health Policy, 119, 941–953.

    Article  Google Scholar 

Download references

Acknowledgments

We thank the CépiDc for the mortality data used in this analysis. We thank Thomas Buchmueller for kindly sharing part of his data set with us for the replication of Buchmueller et al. (2007) provided in section B of the online appendix. We also thank Janet Currie for helpful guidance.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Josselin Thuilliez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PDF 464 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brüning, M., Thuilliez, J. Mortality and Macroeconomic Conditions: What Can We Learn From France?. Demography 56, 1747–1764 (2019). https://doi.org/10.1007/s13524-019-00811-4

Download citation

Keywords

  • Health
  • Mortality
  • Recessions
  • Unemployment
  • Macroeconomic conditions