Skip to main content
SpringerLink
Log in

Economic Disparities and Life Satisfaction in European Regions

  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

This paper investigates the role of economic variables in predicting regional disparities in reported life satisfaction of European Union (EU) citizens. European subnational units (regions) are defined according to the first-level EU nomenclature of territorial units. We use multilevel modeling to explicitly account for the hierarchical nature of our data, respondents within regions and countries, and for understanding patterns of variation within and between regions. Main findings are that personal income matters more in poor regions than in rich regions, a pattern that still holds for regions within the same country. Being unemployed is negatively associated with life satisfaction even after controlled for income variation. Living in high unemployment regions does not alleviate the unhappiness of being out of work. After controlling for individual characteristics and modeling interactions, regional differences in life satisfaction still remain, confirming that regional dimension is relevant for life satisfaction.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. A notable exception is the recent analysis of regional well-being in Europe by using European Social Survey data of Aslam and Corrado (2007).

  2. Because of data deficiencies, Sweden and Finland are considered as whole countries and the French Départements D’Outre-Mer are excluded.

  3. These findings are in accordance to the ones obtained by Scoppa and Ponzo (2008) who used data on Italians subjective well-being from the Bank of Italy Survey of Household Income and Wealth.

  4. Without lacking of generality of the multilevel framework, we treat the outcome y as a continuous variable. In case of generalized linear models, it is necessary to adapt model (1) to the logistic scale (Gelman et al. 2008a, b).

  5. Estimates of the models are obtained by the lmer function in R (R Development Core Team 2006) and are based on the restricted maximum likelihood procedure (REML). The REML procedure corrects the downwards bias of the maximum likelihood estimator of variance components related to the lost of degrees of freedom in estimating the fixed effects. The name lmer stands for linear mixed effects in R but the function works also for generalized linear models. However some technical challenges exist in fitting multinomial models in a multilevel framework. Therefore, for the ordered logit model we use the classical no-pooling regression. The term “mixed effects” refers to random effects (coefficients that vary by group) and fixed effects (coefficient that do not vary) (Gelman and Hill 2007).

  6. As pointed out by, e.g., Di Tella et al. (2003) and Frey and Stutzer (2006), estimated effects should be treated with caution since some personal characteristics can be considered endogenous. Moreover if unobserved personal traits influence reported life satisfaction, results suffer from potential bias.

  7. Due to data availability in the European regional data set we use in models with more complex multilevel structure, we report for coherence the results of the fitting for the period 1997–2002. However, we did not find any significant difference when we use the Eurobarometer data expanding the period backward to 1992. Detailed results of the fitted models are available upon request from the authors.

  8. Income is treated as a continuous variable and centered to reduce the correlation between group-level intercepts and slopes. We allow the coefficients of each employment category to vary. In line with our goal, results are focused only on the unemployment status.

  9. We implemented the weighted local polynomial regression (LOWESS, LOcally WEighted Scatterplot Smoother), as proposed by Cleveland (1979). As Cleveland discusses, rather than calculating one regression line for an entire dataset, one calculates regression estimates for overlapping sets of x values. To find smoothed values, the procedure fits n polynomial regressions to the data, one for each observation j, including the points with x-values that are near x j . We implemented this model in R by choosing an appropriate smoothing span that gives the proportion of points in the plot which influence the amount of smoothing at each value. The choice of the LOWESS procedure among other possible approaches of “smoothing” relies on its robustness to the presence of outliers.

  10. We thank the referee for having emphasized this point. Consistently, Gelman et al. (2008a, b) show how patterns of income, religion, and voting in the U.S. are consistent with Inglehart’s post-materialism hypothesis.

  11. Adding per capita GDP in the model for the whole period (1997–2002) is problematic as a potentially non stationary predictor (the GDP) is introduced to explain an outcome that is naturally stationary (the life satisfaction rated on a four-point scale). Therefore, the estimated coefficients may be “unpersuasive” due to the inapplicability of conventional statistical procedures (Di Tella et al. 2003). The (stochastic) trend of the non stationary variable will in fact dominate all other variations. To overcome this problem we prefer to model the time-series structure of our dataset repeating the model year-by-year. The method of repeated modeling, followed by time-series plots of estimates is rarely used as a data analytic tool but it can be very informative and easy to understand.

References

  • Alesina, A., Di Tella, R., & MacCulloch, R. (2004). Inequality and happiness: Are Europeans and Americans different. Journal of Public Economics, 88, 2009–2042.

    Article  Google Scholar 

  • Aslam, A., & Corrado, L. (2007). No man is an island: The inter-personal determinants of regional well-being and life satisfaction in Europe. Cambridge Working Paper in Economics, CWPE 0717.

  • Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications for subjective survey data. American Economic Review, 91, 67–72.

    Google Scholar 

  • Blanchflower, D. G., & Oswald, A. J. (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88, 1359–1386.

    Article  Google Scholar 

  • Clark, A. (2003). Unemployment as a social norm: Psychological evidence from panel data. Journal of Labor Economic, 21(2), 323–351.

    Article  Google Scholar 

  • Clark, A., & Oswald, A. J. (1994). Unhappiness and unemployment. Economic Journal, 104, 648–659.

    Article  Google Scholar 

  • Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatter plots. Journal of American Statistical Association, 74, 829–836.

    Article  Google Scholar 

  • Diener, E. Sandvik, E., Seidlitz, L., & Diener, M. (1993). The relationship between income and subjective well-being: Relative or absolute? Social Indicators Research, 28, 195– 223.

    Article  Google Scholar 

  • Di Tella, R., MacCulloch, R., & Oswald, A. J. (2001). Preferences over inflation and unemployment: Evidence from happiness surveys. American Economic Review, 91(1), 335–42.

    Article  Google Scholar 

  • Di Tella, R., MacCulloch, R., & Oswald, A. J. (2003). The macroeconomics of happiness. Review of Economics and Statistics, 85, 809–827.

    Article  Google Scholar 

  • Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In P. David & M. Reder (Eds.), Nations and households in economic growth. New York: Academic Press.

  • European Commission. (2008). A new partnership for cohesion convergence competitiveness cooperation. Third report on economic and social cohesion.

  • Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness? Economic Journal, 114, 641–659.

    Article  Google Scholar 

  • Frey, B. S., & Stutzer, A. (2000). Happiness, economy and institutions. Economic Journal, 110, 918–938.

    Article  Google Scholar 

  • Frey, B. S., & Stutzer, A. (2002). Happiness and economics: How the economy and institutions affect human well-being. Princeton: Princeton University Press.

    Google Scholar 

  • Frey, B. S., & Stutzer, A. (2006). Does marriage make people happy, or do happy people get married. Journal of Socio-Economics, 35(2), 326–347.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Gelman, A., Park, D. K., Shor, B., Bafumi J., & Cortina G. (2008b). Red state, blue state, rich state, poor state: Why Americans vote the way they do. Princeton: Princeton University Press.

    Google Scholar 

  • Gelman, A., Shor, B., Bafumi, J., & Park, D. (2008a). Rich state, poor state, red state, blue state: What’s the matter with Connecticut? Quarterly Journal of Political Science, 2, 345–367.

    Article  Google Scholar 

  • Graham, C., & Felton, A. (2006). Inequality and happiness: Insights from Latin America. Journal of Economic Inequality, 4(1), 1569–1721.

    Article  Google Scholar 

  • Inglehart, R. (1990). Culture shift in advanced industrial society. Princeton: Princeton University Press.

    Google Scholar 

  • Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20(1), 3–24.

    Article  Google Scholar 

  • Krueger, A. B., & Schkade, D. A. (2007). The reliability of subjective well-being measures. NBER Workin Paper No. 13027.

  • Layard, R. (2005). Happiness: Lessons from a new science. New York: Penguin Press.

    Google Scholar 

  • Longford, N. T. (2007). Missing data and small-area estimation. New York, NY: Springer-Verlag.

    Google Scholar 

  • Oswald, A. J.(1997). Happiness and economic performance. Economic Journal, 107, 1815– 1831.

    Article  Google Scholar 

  • Pittau, M. G., & Zelli, R. (2006). Empirical evidence of income dynamics across EU regions. Journal of Applied Econometrics, 21, 605–628.

    Article  Google Scholar 

  • R Development Core Team. (2006). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

  • Schmitt, H., & Scholz, E. (2005). The Mannheim Eurobarometer Trend File, 1970–2002 [Computer file]. Prepared by Zentralarchiv fur Empirische Sozialforschung. ICPSR04357-v1. Mannheim, Germany: Mannheimer Zentrum fur Europaische Sozialforschung and Zentrum fur Umfragen, Methoden und Analysen [producers]. Cologne, Germany: Zentralarchiv fur Empirische Sozialforschung/Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributors].

  • Scoppa, V., & Ponzo, M. (2008). An empirical study of happiness in Italy. B. E. Journal of Economic Analysis and Policy, 8(1) (Contributions), article 15.

  • Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis. London: Sage.

    Google Scholar 

  • Tanzi, V., & Davoodi, H. R. (2000). Corruption, growth and public finances. IMF Working Paper No. 182.

  • Veenhoven, R. (2007). World database of happiness, distributional findings in nations. Rotterdam: Erasmus University.

    Google Scholar 

  • Warr, P. B., & Jackson, P. (1987). Adopting to the unemployed role; a longitudal investigation. Social Science Medicine, 25, 1219–1224.

    Article  Google Scholar 

  • Wilkinson, W. (2007). In pursuit of happiness research. Is it reliable? What does it imply for policy? Policy Analysis, 590, 1–41.

    Google Scholar 

  • Winkelmann, L., & Winkelmann, R. (1998). Why are the unemployed so unhappy? Economica, 65(257), 1–15.

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the Columbia University, Applied Statistics Center, and the National Science Foundation and National Institutes of Health for financial support. They also acknowledge Sapienza, University of Rome, for financial assistance under grant number C26F07R754. They would like to thank an anonymous reviewer and participants of the XXX IARIW conference, Portoroz, Slovenia, August 2008, for their precious comments and suggestions.

Author information

Authors and Affiliations

  1. DSPSA, Sapienza Università di Roma, Rome, Italy

    M. Grazia Pittau & Roberto Zelli

  2. Department of Statistics, Columbia University, New York, USA

    M. Grazia Pittau & Andrew Gelman

Authors
  1. M. Grazia Pittau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Zelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Gelman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Grazia Pittau.

Appendix

Appendix

See Table 1.

Table 1 Life satisfaction (LS) in European Regions (NUTS1), average 1992–2002
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pittau, M.G., Zelli, R. & Gelman, A. Economic Disparities and Life Satisfaction in European Regions. Soc Indic Res 96, 339–361 (2010). https://doi.org/10.1007/s11205-009-9481-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-009-9481-2

Keywords

Search

Navigation