Happiness and Globalization: A Spatial Econometric Approach

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.

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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.

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Correspondence to Chun-Hung A. Lin.

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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

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Keywords

  • Happiness
  • Globalization
  • Spatial dependence
  • Kuznet-curve