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Income Comparison and Subjective Well-Being: Evidence from Self-Perceived Relative Income Data from China

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Abstract

This paper studies the impact of self-perceived relative income on subjective well-being (SWB) using data from China. The results show that perceiving a lower relative income in comparison with different reference groups leads to lower life satisfaction and happiness. The effect of the self-perceived relative income on SWB is monotonic—the lower the position of an individual in income comparisons, the larger the negative effect. In addition, favorable and unfavorable relative income positions have asymmetric impacts on life satisfaction, but not on happiness. The results hold when controlling for individual fixed effects by utilizing the panel structure of the data.

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Notes

  1. Pfaff (2013) shows that even a simple change in the measure of the income variable may change the coefficients of the self-constructed relative income on both significance and sign.

  2. More examples are Asadullah et al. (2018), Dumludag (2015), Goerke and Pannenberg (2015), Knight and Song (2009), Otis (2017) and Wolbring et al. (2016).

  3. A number of studies address various other aspects of issues related to SWB in China (e.g., Chen, 2015; Cheng et al. 2014; Knight and Gunatilaka, 2010; Smyth et al. 2010; Smyth and Qian, 2008).

  4. Personality traits have been known as important determinants of individual’s subjective well-being (e.g., Emmons and Diener 1985; Diener et al. 2003).

  5. In a different setting, a recent study by Nobles et al. (2013) also implements the fixed-effect model to eliminate potential endogeneity that could be caused by unobservable personality traits when investigating the impact of individuals’ self-perceived socioeconomic status on their health conditions.

  6. For example, Di Tella et al. (2003) show that income level is not statically significantly correlated with individuals’ happy emotion in the USA or Europe. Kahneman and Deaton (2010) suggest that income is more related with life satisfaction but not daily emotional well-beings.

  7. Totally, 441 observations are dropped when I restrict the sample to respondents who are at least 45 years old. Including these observations in the analyses do not affect the empirical results.

  8. Among all those who report N/A when comparing with schoolmates, 87.5% of them never finish primary school, while all of them do not have a middle school degree. Those respondents who claim to be not applicable to be compared with colleagues are either farmers or unemployed.

  9. Individuals who choose “6. I don’t know” and “7. Not applicable” are dropped in this process.

  10. By doing so, I assume that when the respondents rate their “overall” standard of living, they compare with the five reference groups provided in wave 2013 and these 5 reference groups are equally weighted in the process of comparison.

  11. Only about 1 percent of the respondents consider themselves as having “much better” income than the reference groups. I thus merge the groups of people who perceive “much better” or “a little better” income into a single category. I treat this category has the group of people who perceive better income than the reference groups. This category is the omitted category when estimating Eq. (2).

  12. They are Knight and Song (2009), Senik (2009), Mayraz et al. (2009) and Goerke and Pannenberg (2015).

  13. An ordered probit (logit) model can also be tested because the raw values of the dependent variables of SWB are cardinal. For each value of life satisfaction and happiness, marginal effects can be calculated for the self-perceived relative income dummies. The regression results obtained from ordered probit (logit) models are consistent with those in OLS estimations. The results are provided in the “Appendix.”

  14. Previous studies find that linear fixed effects and fixed-effects logit models give similar results in studying life satisfaction (e.g., Dickerson et al. 2014). I do not conduct fixed-effect (ordered) logit estimation because a logit model would cause a significant drop in the number of observations in our sample due to the fact that for some of the respondents, the outcome variable may display no variation in the two waves of the survey.

  15. Ferrer-i-Carbonell and Frijters (2004) propose an estimator from fixed-effects ordered logit model which may not suffer the problem of losing observations. However, Baetschmann et al. (2015) suggest that the estimator proposed by Ferrer-i-Carbonell and Frijters (2004) is generally inconsistent.

  16. I am aware that the “overall” self-perceived relative income I constructed for the 2013 wave may not perfectly match that in the 2011 wave. The reason is that the income comparisons with the 5 reference groups may be not enough to represent the “overall” income comparison. Moreover, the answer choices for the relative income questions in the 2011 and 2013 waves are slightly different. But based on the information available, this way of constructing the overall relative income in the 2013 wave is reasonable.

  17. F test of the differences between the coefficients of different categories is reported in the “Appendix,” panel A of Table 16. Results suggest that there are significant differences between the coefficients of various categories.

  18. Coefficients for age may contain some measurement errors because some of the respondents report their birth year in Lunar calendar while some of them report in Solar calendar.

  19. F test of the differences between the coefficients of different categories are reported in the “Appendix,” Panel B of Table 16.

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Authors and Affiliations

  1. Private Enterprise Research Center, Texas A&M University, College Station, USA

    Han Yu

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Correspondence to Han Yu.

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I am grateful to Naci Mocan for numerous suggestions and continuous encouragement. I would like to thank the editors of the Eastern Economic Journal (EEJ), Cynthia Bansak and Allan Zebedee, and anonymous referees at the EEJ for useful comments.

Appendix

Appendix

See Tables 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 and 21.

Table 11 Frequencies of self-perceived relative income for males and females
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Table 12 Summary statistics of the panel data sample
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Table 13 Life satisfaction and self-perceived relative income, full results (OLS regressions)
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Table 14 Happiness and self-perceived relative income, full results (OLS regressions)
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Table 15 OLS regression using alternative cutoffs to build Life Satisfaction and Happiness
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Table 16 F tests of the differences between coefficients of self-perceived relative income categories
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Table 17 Life satisfaction, happiness, and self-perceived relative income: ordered probit regressions the dependent variables = 1
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Table 18 Life satisfaction, happiness, and self-perceived relative income: ordered probit regressions the dependent variables = 2
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Table 19 Life satisfaction, happiness, and self-perceived relative income: ordered probit regressions the dependent variables = 3
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Table 20 Life satisfaction, happiness, and self-perceived relative income: ordered probit regressions the dependent variables = 4
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Table 21 Life satisfaction, happiness, and self-perceived relative income: ordered probit regressions life satisfaction = 5
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Yu, H. Income Comparison and Subjective Well-Being: Evidence from Self-Perceived Relative Income Data from China. Eastern Econ J 46, 636–672 (2020). https://doi.org/10.1057/s41302-020-00168-2

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