Happiness and Globalization: A Spatial Econometric Approach

Journal of Happiness Studies volume 18, pages1841–1857(2017)Cite this article

Abstract

Based on the data of Gallup World Poll Survey (GWP), this study employs a spatial 2SLS (two stage least square) model to examines the impact of globalization on the level of happiness across 145 nations. We observe endogeneity of the spatial lag term (Wh) on happiness as well as spatial dependencies in the independent variables (WX’s) which represents indirect effects from a change in X’s in the neighboring regions. We observe contrasting spillover effects (own v/s neighboring effect) of the same explanatory variable generating positive or negative effect, respectively, across space. Further, an inverted ‘U’ Kuznet curve reveals a non-linear relationship between average happiness and happiness inequality. At low levels, an increase in well-being appears to hurt the poor; but beyond a certain threshold, it seems to reduce inequality. We observed among countries that happiness inequalities for developed regions are more when compared to developing regions.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Given the fact that the term “happiness” has gained much popularity in the literature on social consequences of economic development, in this paper, we use “subjective well-being”, “happiness”, and “quality of life” interchangeably, although what we are really referring to is subjective well-being. Subjective well-being is regarded as global cognitive judgment of one’s life.

  2. 2.

    Spatial dependence refers to the absence of independence between geographic observations, and is defined as the correlation of a variable across geographic units. Spatial dependence should not be confused with spatial heterogeneity, which occurs when parameters vary across countries or regions depending on their location.

  3. 3.

    Literature on the social-norm effect of unemployment, whereby unemployed individuals may suffer less in areas where more people are unemployed.

  4. 4.

    These studies investigate cross-area distributions of well-being which conclude that there are different distributions of well-being across areas.

  5. 5.

    Social relativity perspective generates reverse or opposite effect than that anticipated by theory.

  6. 6.

    Ahluwalia (1976) launched a furious debate about the applicability of the “inverse-U hypothesis” to developing countries.

  7. 7.

    The so-called inverted-U “Kuznets curve” has been invoked to describe the relationship between growth and inequality in several different frameworks, for instance the environmental Kuznets curve (Stern, 2004), and education Kuznet curve (Ram 1990).

  8. 8.

    Social scientists who have employed GWP dataset in their analysis include Ott (2008); Stevenson and Wolfers (2008).

  9. 9.

    We use KOF index as an indicator of globalization instead of Kearney Index, on the grounds of superiority. The A.T. Kearney index which categorizes globalization into four dimensions; economic integration, personal contact, technological activity and political engagement is available for only 64 countries, allocates equal weights to all variables and hence creates a bias against the larger countries. Studies that use KOF to analyze well-being include Hessami (2011).

  10. 10.

    Data for COC is taken from World Governance Indicator (WGI) which consists of six composite indicators of broad dimensions of governance covering over 200 countries since 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130.

  11. 11.

    Kalmijn and Veenhoven (2005) provide an interesting discussion and analysis on nine different measures of inequality in happiness, and argue that the standard deviation is an appropriate indicator. They argue that the Gini-coefficient is not suited for measuring happiness inequality because it assumes cardinal measurement level and happiness indexes are not cardinal.

  12. 12.

    In the spatial case, the autoregressive term induces simultaneity due to the two-way interaction among “neighbors” (each location is its neighbor’s neighbor).

  13. 13.

    Technical derivations and the selection of optimal instruments are presented in [KELEJIAN and PRUCHA 1998, 1999].

  14. 14.

    This test examines the difference between the OLS and the 2SLS estimators. If no endogeneity, OLS estimator has minimum variance. The test confirms presence of endogeneity in the spatial lag term. Hausman’s test for endogeneity is reported in Table 3 along with the Spatial lag and two stage spatial model.

  15. 15.

    With immigration, there will be more and more immigrants living below the poverty line.

  16. 16.

    R2 which explains ‘goodness of fit’ is higher in a quadratic model than in a linear model.

  17. 17.

    According to utilitarians national policies should prioritize greatest level of happiness, while egalitarians believe in smallest differences and are willing to accept a loss of happiness.

References

  1. Ahluwalia, Montek S. (1976). Inequality, poverty and development. Journal of Development Economics, 3, 307–342.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Anselin, L. (1988). Spatial Econometrics: Methods and Models. The Netherlands: Kluwert Academic.

    Book  Google Scholar 

  4. Bentham, J. (1789). Introduction to the Principles of Morals and Legislation. London: T. payne.

    Book  Google Scholar 

  5. Cantril, H. (1965). The pattern of human concerns. New Brunswick, NJ: Rutgers University Press.

    Google Scholar 

  6. Chadi, A. (2014). Regional Unemployment and Norm-Induced Effects on Life Satisfaction. Empirical Economics, 46(3), 1111–1141.

    Article  Google Scholar 

  7. Clark, A., Frijters, P., & Shields, M. (2008). Relative Income, Explanation for the Easterlin Paradox and Other Puzzles. Journal of Economic Literature, 46(1), 95–144.

    Article  Google Scholar 

  8. Cliff, A. D., & Ord, J. K. (1981). Spatial processes: models and applications. London: Pion.

    Google Scholar 

  9. Di Tella, R., & MacCulloch, R. J. (2006). Some Uses of Happiness Data in Economics. Journal of Economic Perspectives, 20(1), 25–46.

    Article  Google Scholar 

  10. Dreher, A. (2006). Does Globalization Affect Growth? Evidence from a new Index of Globalization. Applied Economics, 38(10), 1091–1110.

    Article  Google Scholar 

  11. Dreher, A., Gaston, N., & Martens, P. (2008). Measuring Globalization—Gauging its Consequence. New York: Springer.

    Google Scholar 

  12. Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9–28.

    Article  Google Scholar 

  13. Frey, B., & Stutzer, A. (2002). What can Economists Learn from Happiness Research? Journal of Economic Literature, 40(2), 520–578.

    Article  Google Scholar 

  14. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1271.

    Article  Google Scholar 

  15. Helliwell, J., & Huang, H. (2008). How’s your government? International evidence linking good government and well-being. British Journal of Political Science, 38, 595–619.

    Article  Google Scholar 

  16. Henderson, H. (2002). Globalization and quality of life. Talking points for the Calvert group press briefing. Washington, DC: National Press Club.

    Google Scholar 

  17. Hessami, Z. (2011). Globalization’s winners and losers—Evidence from life satisfaction data, 1975–2001. Economics Letters, 112, 250–253.

    Article  Google Scholar 

  18. Jorda, V., & Sarabia, J. M. (2014). Well-being distribution in the globalization era: 30 years of convergence. Applied Research in Quality of Life. doi:10.1007/s11482-0149304-8.

    Google Scholar 

  19. Kalmijn, W., & Veenhoven, R. (2005). Measuring Inequality of Happiness inNations. In Search for Proper Statistics, Journal of Happiness Studies, 6, 357–396.

    Article  Google Scholar 

  20. Kelejian, H., & Prucha, I. (1998). A generalized spatial two stages least square procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Estate Finance Econ, 17(1), 99–121.

    Article  Google Scholar 

  21. Kelejian, H., & Prucha, I. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Econ Rev, 40(2), 509–533.

    Article  Google Scholar 

  22. Kuznets, S. (1955). Economic Growth and Income Inequality. American Economic Review, 45(l), 1–28.

    Google Scholar 

  23. Lee, L. F. (2003). Best spatial two-stage least squares estimators for a spatial autoregressive model with autoregressive disturbances. Econometric Reviews, 22(307), 335.

    Google Scholar 

  24. LeSage, J. P., & Pace, K. R. (2009). Introduction to Spatial Econometrics. Florida: CRC Press.

    Book  Google Scholar 

  25. Lin, C. A., Lahiri, S., & Hsu, C. P. (2014). Happiness and Regional Segmentation: Does Space Matter? Journal of Happiness Studies, 15, 157–183.

    Article  Google Scholar 

  26. Okulicz-Kozaryn, A. (2011). Geography of European life satisfaction. Social Indicators Research, 101(1), 435–445.

    Article  Google Scholar 

  27. Oswald, A. J., & Wu, S. (2011). Well-being across America. The Review of Economics and Statistics, 93(4), 1118–1134.

    Article  Google Scholar 

  28. Ott, J. (2005). Level and inequality of happiness in nations: does greater happiness of a greater number imply greater inequality in happiness? Journal of Happiness Studies, 6, 397–420.

    Article  Google Scholar 

  29. Ott, J. (2008). Government effectiveness and happiness in 131 nations in 2006. Mimeo: Erasmus University Rotterdam.

    Google Scholar 

  30. Pace, K., & LeSage, J. (2008). Biases of OLS and spatial lags models in the presence of omitted variable and spatially dependent variables. In A. P´aez, J. Le Gallo, R. Buliung, & S. Dall’Erba (Eds.), Progress in spatial analysis: Theory and methods, and thematic applications. Berlin: Springer.

    Google Scholar 

  31. Ram, R. (1990). Educational Expansion and Schooling Inequality: International Evidence and Some Implications. Review of Economics and Statistics, 72(2), 266–274.

    Article  Google Scholar 

  32. Sanz, I., F., Martinez-i-Coma & Steinberg, F. 2008. Does Social Spending Increase Support for free Trade in Advanced Democracies? Fundación de las Cajas de Ahorros, Documento de Trabajo No 432/2008.

  33. Seligman, M. E. (2012). Flourish: A visionary newunderstanding of happiness and well-being. New York: Simon and Schuster.

    Google Scholar 

  34. Shultz, C. J., II, Rahtz, D. R., & Speece, M. (2004). Globalization, Transformation and QOL: Reflections on ICMD-8. Journal of Macromarketing, 24(2), 168–172.

    Article  Google Scholar 

  35. Sirgy, J., Lee, D. J., Miller, C., & Littlefield, J. E. (2004). The impact of globalization on a country’s quality of life: toward an integrated model. Social Indicators Research, 68(3), 251–298.

    Article  Google Scholar 

  36. Stanca, L. (2010). The Geography of Economics and Happiness: Spatial Patterns in the Effects of Economic Conditions on Well-Being. Social Indicators Research, Springer, 99(1), 115–133.

    Article  Google Scholar 

  37. Stern, D. I. (2004). The Rise and Fall of Environmental Kuznets Curve. World Development, 32(8), 1419–1439.

    Article  Google Scholar 

  38. Stevenson, B., & Wolfers, J. (2008). Economic growth and subjective well-being: reassessing the Easterlin paradox. Brookings Papers on Economic Activity, 1, 1–87.

    Article  Google Scholar 

  39. Tsai, M. C., Chang, H. H., & Chen, W. C. (2011). Globally Happy: Individual Globalization. Expanded Capacities, and Subjective Wellbeing Social Indicators Research, 108, 509–524.

    Google Scholar 

  40. Veenhoven, R. (1990). Inequality in happiness, inequality in countries compared between countries. Paper presented at the12th Work Congress of Sociology, Madrid, Spain.

  41. Veenhoven, R. (2005). Return of Inequality in Modern Society? Test by Dispersion of Life-Satisfaction Across Time and Nations. Journal of Happiness Studies, 6(4), 457–487.

    Article  Google Scholar 

  42. Yang, T. C., Noah, A. J., & Shoff, C. (2013). Exploring geographic variation in U.S mortality rates using a spatial durbin approach. Population, Space and Place, 21(1), 18–37.

    Article  Google Scholar 

Download references

Author information

Affiliations

  1. Department of Industrial Economics, Tamkang University, 151 Ying-Chuan Road, Tamsui, New Taipei City, Taiwan

    Chun-Hung A. Lin

  2. Sutherland Global Services Private Ltd., Mumbai, India

    Suchandra Lahiri

  3. Taiwan Institute of Economic Research, Taipei, Taiwan

    Ching-Po Hsu

Authors
  1. Chun-Hung A. Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suchandra Lahiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ching-Po Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chun-Hung A. Lin.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lin, CH.A., Lahiri, S. & Hsu, CP. Happiness and Globalization: A Spatial Econometric Approach. J Happiness Stud 18, 1841–1857 (2017). https://doi.org/10.1007/s10902-016-9793-2

Download citation

Keywords

  • Happiness
  • Globalization
  • Spatial dependence
  • Kuznet-curve