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Volatility and Inequality: Household Vulnerability as Uncertain Welfare in Rural China

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Abstract

Rural households in China have experienced higher consumption levels combined with uncertainty associated with increasing volatility and growing relative inequality of their levels of consumption. This raises the possibility that the overall effect – from welfare-increasing rises in average consumption and welfare-reducing increases in uncertainty – has been to make households worse rather than better off. This article finds that, overall, despite substantial poverty reduction, rural Chinese households experienced growing relative welfare losses between 1989 and 2006. Eighty per cent was driven by relative inequality. This suggests that China’s growth-oriented policy has not been entirely successful in improving rural households’ welfare.

Abstract

Les foyers en Chine rurale ont fait l’expérience d’un niveau de consommation élevé, allié à l’incertitude provoquée par une instabilité croissante et par l’inégalité relative de leur niveau de consommation en hausse. Il est donc possible que la situation des foyers ait empiré, plutôt que de s’être amélioré, entre l’augmentation de la consommation, qui accroît le bien-être des foyers, et l’augmentation de l’incertitude, qui le réduit. Cet article conclut que malgré une réduction conséquente de la pauvreté, globalement les foyers de la Chine rurale ont subi une perte croissante de leur bien-être relatif entre 1989 et 2006. 80 per cent de la perte de bien-être relatif a été provoquée par l’inégalité relative. Cela suggère que la politique de croissance de la Chine n’a pas été uniquement une réussite quant à l’amélioration du bien-être des foyers ruraux.

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Notes

  1. Authors’ calculations based on data from China Data Centre, University of Michigan.

  2. Data in this and the next sentences come from Poverty Monitoring Report of Rural China 2008.

  3. Following Thorbecke (2004), ‘regular fluctuation’ means that, in each period, the excess income or consumption above the intertemporal mean is exactly compensated by an equivalent shortfall.

  4. This reference-point hypothesis has also been found in the subjective well-being literature for Nepal (Fafchamps and Shilpi, 2008) and Ethiopia (Akay et al, 2012).

  5. The reference household is assumed to be a rural one as more than 90 per cent of poor people in China live in rural areas and are more likely to compare their standard of living with neighbours in the same village than with more distant urban residents (Brown et al, 2011).

  6. We examined the panel unit root in per capita log of household consumption by using the Levin-Lin-Chu test (LLC), the Im-Pesaran-Shin test (IPS), Harris–Tzavalis and Hadri LM stationarity tests allowing for cross-sectional dependence. All support stationarity within the bounds of statistical significance. In addition, the Hausman specification test significantly rejected endogeneity in income.

  7. There is no reliable estimate of risk-aversion coefficient for rural Chinese households in the existing literature. Xu (2008) and Ho et al (2010) arbitrarily assume γ=4 when calibrating their models. Whalley and Yue (2009) employ a set of values from 0.9 to 10. LS argue that the magnitude of vulnerability and its components are sensitive to the shape of utility function and the value of γ, but the share of different components in vulnerability is less sensitive. Given the lack of empirical estimates of γ, we follow LS’s suggestion and provisionally assign it the value 2, but use a set of alternative values to check the robustness of our estimates in the section ‘Robustness checks’.

  8. Applying equation (3) to the full panel, we find that the average vulnerability in rural China during the period 1989–2006 is 9.2 per cent higher if using LS constant z than the average value derived under z t .

  9. This is similar to Jalan and Ravallion’s (1998) definition of chronic poverty, which compares the household’s intertemporal mean consumption with poverty lines. LS also interpret the inequality component as relative poverty.

  10. Regarding possible measurement errors in consumption expenditures, LS show that poverty and explained risk components would not be biased in the calculation of per-period VUW, but estimates of unexplained risk may be biased as measurement errors may change expected utility.

  11. The CHNS team state that it was not possible to identify weights that would make samples representative of the full population in China or particular sample provinces as they could not obtain a suitable sample frame from the National Bureau of Statistics and had to apply a multilevel multistage random sampling technique to the best available data. They suggest (in a statement that can be downloaded after registering with the CHNS) that researchers control for community-level covariates in order to mitigate possible design effects. Accordingly, we include regional and time dummies in all regressions.

  12. Consumption is not explicitly included in the CHNS. Using Benjamin et al’s (2005) definition that has been recently practised by Imai and You (2013), we compute household consumption as the sum of expenditure on food (including home production and market purchases), durable goods smoothed over time, other living costs, health insurance and medical costs, and the discounted value of housing. All monetary variables are translated into real terms using the spatial price index calculated by the CHNS team. (A detailed construction procedure is available upon request.) The CHNS does not provide a record of consumption expenditure of short-term migrants. This, together with the fact that our consumption measure does not take other items into account (such as education, durable goods that have emerged recently and/or been newly purchased), means measurement errors in consumption may upwardly bias the inequality component. We are unable to rectify these problems with the CHNS. However, we have compared our average household per capita consumption with those of the National Bureau of Statistics Rural Household Surveys (RHS) and found the results are similar – our variable is 2.4–5.1 per cent less than that of the RHS in different waves due to incomplete accounting of expenditure, with the same trend over time. We chose consumption as our main welfare indicator rather than income because consumption can reflect households’ long-term welfare and is widely used in World Bank studies (Ravallion et al, 2009). Moreover, household business income has been underreported in the CHNS (Chen and Zhang, 2009) while in-kind income is imputed with higher values. Moreover, the CHNS did not subtract taxes and depreciations of assets owned by the household when constructing the household net income because arbitrary recorded depreciation expenses and taxes might confound the household’s earning ability (Benjamin et al, 2005). That said, we have re-estimated VUW using household per capita net income to check the sensitivity of our findings to welfare indicators and find that our main conclusions still hold, though with varying magnitude of the estimates: per-period average VUW is higher than that derived from consumption, increasing from 58.2 per cent in 1989 to 85 per cent in 2006 and in each wave; 60.1–67.4 per cent of vulnerability could be attributed to the inequality component, while the risk component accounted for 33–40 per cent as opposed to 20 per cent in the case of consumption VUW. This is predictable as income tends to fluctuate more than consumption given consumption smoothing.

  13. The income inequality measures in 1989 were much higher than those in the subsequent three waves. This particular finding, which has also been found by Chamon et al (2010) using CHNS data, seems inconsistent with the general trend of increasing income inequality in China and might be caused by significant measurement errors in income in the first wave, especially in the problematic wage data (Benjamin et al, 2003).

  14. There are various definitions of chronic poverty; we follow that of Jalan and Ravallion (1998).

  15. We follow Jalan and Ravallion (1998) and define the chronically poor in a sub-period as those whose intertemporal mean per capita consumption in that period is lower than the poverty line at the adjusted US$1.25/day.

  16. Agricultural income includes income from farming, livestock, gardening and fishing. Household income from family-run business is the sum of individual earnings from participation in household business activities. Non-retirement wage income is the annual income earned from employment. For those who have retired, this income category only includes their earnings if they still work, but excludes pension or any in-kind transfer related to their retirement. These latter two form the category of ‘retirement wage income’. Subsidy income includes all kinds of subsidies the household members received from their work units and/or governments and the subsidies for the household as a whole. Other income is not specified by the CHNS; respondents are merely asked to list all non-specified incomes under this category.

  17. This effect seems inconsistent with the existing literature for rural China that has documented the positive impact of remittances on remaining households’ income (Du et al, 2005), consumption on consumer durables and investment in housing (de Brauw and Rozelle, 2008) and income diversification (Taylor et al, 2003). This surprising finding may have resulted from: (i) our measurement of household consumption, which does not include migrants’ consumption; and (ii) greater fluctuations in income and consumption that tend to accompany the higher average levels of income and consumption associated with migration (Imai and You, 2013).

  18. γ cannot be less than unity since we assume all rural households are risk averse.

  19. Choice of functional form for household utility may affect our results. Constant relative risk aversion (CRRA) is the mostly widely used form, while increasing absolute risk aversion (IARA) and decreasing absolute risk aversion (DARA) may be inconsistent with household behaviour in LDCs. Other forms of risk attitudes, such as constant absolute risk aversion (CARA), IARA and DARA, can change the magnitude of VUW without necessarily altering other qualitative findings may still hold since we rely on ordinal utility and focus on the trend and comparison of VUW over time and across different groups.

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Acknowledgements

We are grateful to Albert Park, Katsushi Imai, Bernard Walters, Xiaobing Wang, Nick Weaver, Indranil Dutta and participants in seminars at the University of Manchester and in the Peking-Oxford International Conference: China and the World – Crisis, Adjustment and Global Prosperity held in September 2010 at Peking University for helpful comments on an earlier draft. We also wish to express our thanks to two anonymous referees for their extremely helpful comments, which have much improved the article. This research is supported by the National Natural Science Foundation of China (Grant No.: 71403282), Humanity and Social Science Foundation of Ministry of Education of China (Grant No. 13YJCZH231) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (i.e., the Ministry of Education). Any remaining errors are, of course, the sole responsibility of ours.

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  1. School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China

    Jing You

  2. Economics, School of Social Sciences, University of Manchester, Manchester, UK

    Adam Ozanne

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You, J., Ozanne, A. Volatility and Inequality: Household Vulnerability as Uncertain Welfare in Rural China. Eur J Dev Res 27, 686–706 (2015). https://doi.org/10.1057/ejdr.2014.55

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