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Does Health Insurance Lead to Ex ante Moral Hazard? Evidence from China’s New Rural Cooperative Medical Scheme

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Abstract

This paper examines whether participating in the New Rural Cooperative Medical Scheme (NRCMS), a publicly subsidised health insurance programme in rural China, encourages individuals to engage in risky health behaviours. Despite its rapidly increasing coverage rate, relatively little attention has been paid to the impact of NRCMS on the lifestyle choices of its enrollees. On the basis of the 2000–2009 longitudinal data from the China Health and Nutrition Survey (CHNS), we find that NRCMS participation has a statistically significant (although quantitatively small) impact on people’s tendency towards smoking, heavy drinking (among males), spending time in sedentary activities, consuming high-calorie food and being overweight. The increase in these unhealthy lifestyles in turn leads to elevated disease risks, indicating that insurance-induced, “ex ante moral hazard” is present in rural China. The findings are robust to the variation in model specification and sample selection, as well as to the introduction of an instrumental variable that controls the endogeneity of insurance participation. Our results provide implications on reforming the pricing and administration practice of China’s largest health insurance campaign and on evaluating public insurance schemes in other developing countries.

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Notes

  1. Wagstaff et al. (2009a).

  2. The traditional rural Cooperative Medical Scheme (CMS) was an integral part of the agriculture collective system in China. From the 1950s through the 1970s, CMS together with rural health post and barefoot doctors provided grassroots health care to rural residents and contributed significantly to the improvement of rural health in China.

  3. Lei and Lin (2009), Wagstaff et al. (2009b).

  4. Pauly (1968).

  5. Evans (1974).

  6. Lei and Lin (2009), Yip and Hsiao (2009), Wagstaff et al. (2009a, 2009b).

  7. Ehrlich and Becker (1972).

  8. Stanciole (2008).

  9. Dave and Kaestner (2009).

  10. Andersen (2012).

  11. Newhouse (1993).

  12. Courbage and de Coulon (2004).

  13. Yilma et al. (2012).

  14. Trujillo et al. (2010).

  15. Miller et al. (2009).

  16. Kelly and Markowitz (2009).

  17. Bhattacharya et al. (2011).

  18. Bengt (1998).

  19. Lopez et al. (2006).

  20. Plunk et al. (2014).

  21. Lee et al. (2012).

  22. Cohen and Dehejia (2004).

  23. Fortin and Lanoie (2000).

  24. Buchmueller et al. (2005).

  25. Anderson and Mellor (2009).

  26. See Qin et al. (2012) for the recent NRCMS-related evidence.

  27. Fang et al. (2008).

  28. It is suggested that, for binary outcome variables, the IV probit estimator is consistent only when the endogenous regressors are continuous (Dong and Lewbel, 2012). Given the binary nature of our endogenous regressor, we use 2SLS assuming linear relationship in Eq. (5). As a robustness check, we also used the “biprobit” command in Stata with non-linear model specification, and obtained similar results.

  29. Terza et al. (2008) suggest that the two-stage IV estimates might be inconsistent when the first-stage equation is regressed using nonlinear models (such as the probit model). We thus choose to estimate Eq. (4) using OLS.

  30. Lei and Lin (2009).

  31. NRCMS is implemented in a county-by-county fashion in China; thus a county is defined to have NRCMS if any community (village) within it reports NRCMS coverage in the current year (based on community questionnaire). As a consistency check, we also manually verified each county’s enrollment time.

  32. A comparison between sample counties that adopted NRCMS before 2006 and those in or after 2006 indicates that the two groups do not significantly differ on health behaviours (such as prevalence of smoking, heavy drinking and being overweight) in year 2000, suggesting that the timing of NRCMS expansions is independent of unobserved factors that would influence individual health behaviours.

  33. Stock et al. (2002).

  34. The detailed first-stage IV regression results are provided in Table A1 in the Appendix.

    Table A1 First-stage IV regression results
    Full size table

  35. Wooldridge (2002).

  36. Since our model is exactly identified (the number of IV equals the number of endogenous explanatory variables), we cannot use the conventional Sargan–Bassman test to confirm the exclusion restriction (the test can only be used in an over-identified model). Thus, we employ the indirect test based on Wooldridge (2002) under the assumption that the second-stage residual captures the unobserved factors that may directly correlate with the IV.

  37. Currie and Cole (1993), Goldman et al. (2001), Bhattacharya et al. (2003), Lo Sasso and Buchmueller (2004), Lei and Lin (2009).

  38. To account for the within-group correlation of health behaviours, standard errors (SE) are clustered at the county level in all regressions. Results based on clustered SE at the household level are quantitatively similar.

  39. In all, 2,212 observations are excluded due to non-NRCMS coverage, among which 1,030 are covered by private insurance and 1,182 by urban insurance (for rural-to-urban migrant workers). The local average treatment effect (LATE) margin thus comes from those individuals who are previously uninsured and are driven to insurance coverage because NRCMS becomes available in their county.

  40. Shaper et al. (1988), Reynolds et al. (2003), Plunk et al. (2014).

  41. Grønbæk et al. (1995).

  42. Dixon (2010).

  43. (Pan et al., 2013)

  44. Wagstaff et al. (2009b).

  45. We also conduct a subsample IV estimation on ever-smokers and find a larger marginal effect (7.1 per cent with 1 per cent significance level), suggesting that most NRCMS smoking effect comes from failure of smoking cessation.

  46. We did not choose FE-IV as the main model because adding individual fixed effects into the regressions results in a significant reduction in the sample size due to attrition (the sample size would become 7,117).

  47. See Angrist and Pischke (2008) for a detailed discussion on the estimated LATE in IV estimation.

  48. Bai and Wu (2014).

  49. The impact of insurance on health behaviours may also work through other channels such as the improved health awareness through contact with medical professionals, thus the uncovered net effect of insurance may also include other incentives in addition to the ex ante moral hazard.

  50. Stock et al. (2008).

  51. Xinxiang is a prefecture-level city in northern Henan province with a population of 5.5 million, and Jiangyin is a county-level city in Jiangsu province with a population of 1.2 million. Both models feature collaboration between the public and private agencies: the local government collects premium funds, while the commercial insurance companies (China Life Insurance Co. in Xinxiang, China Pacific Insurance Co. in Jiangyin) provide professional services including fund management and risk control under the supervision of local health bureaus.

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Acknowledgements

This study is supported by National Natural Science Foundation of China (Grant No. 71103009), Ministry of Education of China (Grant No. 12JZD036), and Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0039). We thank Xiaobo Peng for her contribution to an earlier draft. The authors are responsible for all remaining errors.

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  1. School of Economics, Peking University, Beijing, 100871, China

    Xuezheng Qin & Tianyi Lu

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  1. Xuezheng Qin
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Appendix

Appendix

Table A1

Table A2

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Qin, X., Lu, T. Does Health Insurance Lead to Ex ante Moral Hazard? Evidence from China’s New Rural Cooperative Medical Scheme. Geneva Pap Risk Insur Issues Pract 39, 625–650 (2014). https://doi.org/10.1057/gpp.2014.26

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