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Economic Development and Socioeconomic Inequality of Well-Being: A Cross-Sectional Time-Series Analysis of Urban China, 2003–2011

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Abstract

This study examines how economic development influences the effects of socio-economic status (SES, measured with education and income) on individuals’ well-being (happiness, self-rated health, and depression) in urban China. Building on Tversky and Griffin’s judgment model of well-being, we propose an endowment and a contrast hypothesis for the variation of SES gradients in well-being across economic contexts. Drawing on six waves of the Chinese General Social Survey (CGSS 2003, 2005, 2006, 2008, 2010, 2011) and allowing for non-linear development effects, we obtain four main findings: (1) the widely observed SES gradients in physical health are confirmed not only for self-rated health (SRH) but also for happiness and depression among urban residents in China. However, the SES gradients are contingent on economic development in two systematic ways; (2) consistent with both theoretical hypotheses, SES gradients in happiness are positive at lower levels of economic development but become negligible and insignificant at the highest level; (3) consistent with the contrast hypothesis, SES gradients in SRH and depression are substantially smaller at the highest level of economic development. Inconsistent with the endowment hypothesis, these SES gradients are also smaller at the lowest level, suggesting that an impoverished environment can significantly limit the endowment effects of SES; and (4) given the broad support for the contrast hypothesis, we further consider its generating mechanism. A preliminary test does not support the role of relative status, as emphasized by Tversky and Griffin. We therefore discuss two alternative theoretical mechanisms.

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Notes

  1. In this paper, we use the broader term “well-being” to describe a multi-dimensional concept including both physical health and subjective well-being, as suggested by Diener (2006).

  2. The rationale behind looking at multiple indicators of well-being instead of focusing on one is that the various dimensions of well-being are demonstrated to have different relationships with SES. For example, Kahneman and Deaton (2010) showed that income has a linear positive effect on life satisfaction but that it does not affect emotional well-being when income goes beyond a certain threshold.

  3. For details about the sampling strategy of CGSS, please refer to Bian and Li (2012).

  4. It is worth noting that our data are not panel data at the individual level, so we are unable to control for the individual fixed effects, which would render our estimates of the main effect of SES inaccurate (Ferrer-i-Carbonell and Frijters 2004). We thank one reviewer for pointing out this. However, since our focus is the interaction of SES and regional economic development, the missing individual fixed effects that would induce bias in the estimation of the main effect of SES would not necessarily contaminate the interactions of SES and economic development.

  5. Although our data represent the whole nation, the rural–urban gap is expected to induce complexity when we try to examine the contingency of SES gradients in well-being across levels of economic development; this is because rural areas differ from urban regions not only in economic development but also in other dimensions, such as education (Qian and Smyth 2008) and healthcare (Shi 1993). Unfortunately, we are unable to make these distinctions with the data at hand. We therefore focus on the urban sample only to make the estimations simpler and purer.

  6. “Happiness” has two translations in Chinese; one is more evaluative, xingfu, and the other one is more affective, kuaile. All other waves of CGSS apply the evaluative measure, except for CGSS 2008, which asks about “kuaile”. We test the sensitivity of this measurement inconsistency by replicating the major results with and without CGSS 2008. The qualitative pattern of the results remains the same.

  7. The response categories are different in CGSS 2011: 1 (excellent), 2 (very good), 3 (good), 4 (fair) and 5 (poor). To ensure comparability across waves, we recode the above response to the response category used in CGSS 2008 and 2010. Specifically, “excellent” and “very good” equals to “very healthy”, “good” to “healthy”, “fair” to “average”, and “poor” to “unhealthy”.

  8. 0.05 % (N = 144) of the analyzed sample reported zero family income.

  9. Data for China’s annual inflation rate were acquired from the website of “inflation.eu”: http://www.inflation.eu/inflation-rates/china/historic-inflation/cpi-inflation-china.aspx.

  10. Although the standard practice is to use quintiles for the classification of levels of economic development, our exploratory analysis suggests that SES effects on well-being are mostly sensitive at the two extremes of the growth level; we thus create the categories of “≤5 %” and “≥95 %” to capture this pattern.

  11. Similarly, we define five tiers of economic development based on the global distribution for SRH models and, separately, for depression models. For SRH models, the top 5 % tier includes only Beijing and Shanghai and the bottom 5 % tier includes Guizhou, Yunnan, and Gansu. For depression models, the top 5 % tier includes only Shanghai, and the bottom 5 % tier includes Guizhou and Gansu.

  12. To capture the trends of these variables across the observed period, we also present summary statistics by survey year in the online Appendix Table A1. Furthermore, we plot the trend of the three well-being indicators in Appendix Figure A1. In general, our data show quite consistent stagnation for happiness from 2003 to 2011; for health from 2008 to 2011, and depression from 2010 to 2011.

  13. The unusually low proportion for the top 5 % group may be due to the sampling process. In urban areas it is less possible to sample the clustered habitation of most migrants.

  14. Following the suggestion of one reviewer, we have tested the potentially quadratic education effects. All the quadratic terms are statistically insignificant except for depression. Even though, the existing literature does not offer any theory that can interpret this finding. Given the supplementary role of depression in this paper, we decided to leave the systematic examination to future studies.

  15. Whilst the “U-shaped” relationship between age and happiness is widely accepted, not all scholars accept this model. Frijters and Beatton (2012) analyzed three panel datasets and concluded that after controlling for selection effects through fixed effects, the U-shape that bottoms at middle age disappeared.

  16. Our search for developmental contingency is based on a model specification that not only directly corresponds to the notion of interaction but also (1) facilitates the most direct testing of the two hypotheses and (2) avoids the loss of statistical power due to the multicollinearity problem common in the conventional specification of interaction. We have also conducted the traditional tests of interaction effects and the results are shown in the online Appendix Table A2.

  17. The detailed results with different sets of fixed effect controls are available upon request from the authors. As our starting point, models without any fixed effects show positive associations between happiness and per-capita GRP. While this estimation would be biased because the per-capita GRP may be influenced by province-specific characteristics—such as economic policy, natural resources and geographic location—we add dummies for each province to account for this potential problem. As a result, the effects of per-capita GRP have increased in various degrees. Besides the confounding sources from time-invariant factors, the dramatic economic transition during the past several decades is an effect shared by all provinces, so we also add year dummies. The originally significant coefficients for economic development all shrink and become insignificant, suggesting that the majority of observed per-capita GRP effects on happiness can be attributed to economic change during the period under investigation.

  18. Because only two or three waves of data are used, the lack of variability in measures of provincial per-capita GRP renders the estimation particularly susceptible to multicollinearity with province dummies.

  19. This difference may also result from the different samples for happiness models and two other indicators. To check this possibility, we re-ran the happiness Models 1 and 2 of Table 3 using the same three waves of data as the SRH models and the same two waves of data as the depression models. The results are qualitatively the same as those based on all the six waves of data. Thus the sample differences are not the reason.

  20. We also present the contextual contingency of the SES gradients across different level of economic development by using 3-D graphs in the online Appendix Figure A2 to A4.

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Acknowledgments

The authors gratefully acknowledge funding support from the Research Grants Council of Hong Kong (GRF-446710). We thank Lei Jin for her collaboration in the GRF grant.

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  1. Department of Sociology, The Chinese University of Hong Kong, Sino Building 4/F, Shatin, Hong Kong SAR

    Hania Fei Wu & Tony Tam

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Wu, H.F., Tam, T. Economic Development and Socioeconomic Inequality of Well-Being: A Cross-Sectional Time-Series Analysis of Urban China, 2003–2011. Soc Indic Res 124, 401–425 (2015). https://doi.org/10.1007/s11205-014-0803-7

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