Abstract
The paper presents evidence of a positive but very small long-run relationship between income growth and happiness. Such finding is usually presented as a refutation of the Easterlin Paradox. The paper, however, argues that what the evidence actually reveals is that income growth has very little impact in terms of increasing happiness over the long term. The paper, in turn, argues that a rejection of the Easterlin Paradox requires the evidence to indicate economic significance. That is, the magnitude of the estimated long-run relationship between income growth and happiness is the more appropriate yardstick for an evaluation of the Easterlin Paradox.
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Notes
I agree with McCloskey (1985) and Ziliak and McCloskey (2008) on their objection regarding the strict application of statistical testing. Indeed, more powerful regression procedures and large-scale datasets can easily detect a statistically significant but minute relationship between two variables. An estimate of, say, 0.0001 that is statistically significant at p < 0.05 merely proves that the figure is not due to chance; but, in closer inspection, 0.0001 is practically equal to zero, and so it has no economic significance.
Suppose β 0 > 0 and set θ 0 = φ 0 = 0. The initial impact of y t on H t is β 0. All things the same, the initial impact β 0 translates as β 0 λ 0 on H t−1, β 0 λ 0 λ 1 on H t−2, β 0 λ 0 λ 1 λ 2 on H t−3, etc. Suppose, too, y t−1 has an impact of β 1 > 0. All things the same, the subsequent impacts of β 1 are β 1 λ 0 on H t−1, β 1 λ 0 λ 1 on H t−2, β 1 λ 0 λ 1 λ 2 on H t−3, etc. Graham (2011), for example, argues the case of an “unhappy growth” or β 0 < 0. If so, the impacts of −β 0 are −β 0 λ 0 on H t−1, −β 0 λ 0 λ 1 on H t−2, −β 0 λ 0 λ 1 λ 2 on H t−3, etc. In either β 0 or −β 0 notice a “steady” adjustment process as H t moves toward its new equilibrium.
The World Happiness Database has data for the sample countries in this paper as well as for Japan and the United States, but there is an issue concerning the comparability of the data because the survey procedure and measure for happiness are different in the cases of Japan and the United States. The Gallup World Poll is an alternative source but the data are very costly to acquire.
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Appendices
Appendix
Recall Eq. (5) in the main text:
where \( F\left[ {\Delta Y^{*} } \right] = F\left[ {\Delta Y_{t} ,\Delta Y_{t}^{e} ,\Delta Y_{t}^{r} } \right] \) represents the income stimuli. Below, Eqs. (9) and (10) as well as Eqs. (11) and (12) make up the “basic models” from which dynamic specifications are possible for regression analyses.
Zero rate of adaptation
In the case of zero happiness adaptation to income, set α to zero. No social comparison in income implies ΔY e t = ΔY r t = 0. If so, Eq. (8) becomes
where h t = ΔH t . The above expression is the recent workhorse of the Easterlin group, especially Easterlin and Angelescu (2009), Easterlin and Sawangfa (2010), Easterlin et al. (2010), and Easterlin (2013).
Setting ΔY e t ≠ 0 and ΔY r t ≠ 0 and keeping α = 0 obtains the expanded formulation of
Instantaneous rate of adaptation
In the case of instantaneous happiness adaptation to income, set α to one. Once again, no social comparison in income implies ΔY e t = ΔY r t = 0. If so, Eq. (8) reduces into
In fact, the above expression is the original workhorse of the Easterlin group; that is, Easterlin (1974, 1995, 2001).
Setting ΔY e t ≠ 0 and ΔY r t ≠ 0 and keeping α = 1 obtains expanded formulation of
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Beja, E.L. Income growth and happiness: reassessment of the Easterlin Paradox. Int Rev Econ 61, 329–346 (2014). https://doi.org/10.1007/s12232-014-0211-y
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DOI: https://doi.org/10.1007/s12232-014-0211-y