Can National Health Insurance Programs Improve Health Outcomes? Re-Examining the Case of the New Cooperative Medical Scheme in Rural China

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1 Can National Health Insurance Programs Improve Health Outcomes? Re-Examining the Case of the New Cooperative Medical Scheme in Rural China Xueling Chu Foreign Economic Cooperation Center, Ministry of Agriculture, China Qihui Chen College of Economics and Management, China Agricultural University, Beijing Xiangming Fang College of Economics and Management, China Agricultural University, Beijing Selected Paper prepared for presentation at the Agricultural & Applied Economics Association 2013 AAEA & CAES Joint Annual Meeting, Washington, DC, August 4-6, 2013 Copyright 2013 by Xueling Chu, Qihui Chen, and Xiangming Fang. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. 1

2 Can National Health Insurance Programs Improve Health Outcomes? Re-Examining the Case of the New Cooperative Medical Scheme in Rural China Xueling Chu Foreign Economic Cooperation Center, Ministry of Agriculture, China Qihui Chen Xiangming Fang College of Economics and Management, China Agricultural University Abstract In 2003, China launched a new health insurance system - the New Cooperative Medicine Scheme (NCMS) in its rural areas, where more than 87 percent of China rural residents were not covered by any health insurance programs. By the end of 2009, the NCMS had expanded to cover 95 percent of China s rural residents. Previous research has yielded conflicting results regarding the effects of the NCMS in rural China, but the conflicting results may be due to estimation biases. This paper uses a triple-difference method, which takes into account rural residents unobserved heterogeneity, to re-evaluate the impacts of the NCMS on rural residents health outcomes, including hypertension, diabetes, heart disease, apoplexy and born fracture. Using a longitudinal sample drawn from the China Health and Nutrition Survey, our tripledifference method indicates that the NCMS significantly reduces the incidence of diabetes, heart disease, apoplexy for rural residents over age 55. Our results also suggest that the commonlyadopted impact evaluation method, the double-difference method, is likely to underestimate the health impacts of the NCMS. Keywords: Rural China, New Cooperative Medical Scheme, Impact Evaluation, Triple- Difference 2

3 The ultimate goal of any national health insurance (NHI) program is to improve population health, presumably by lowering out-of pocket expenses of the previously uninsured and increasing their healthcare utilization. In 2003, China officially launched its NHI program targeting its rural population, namely, the New Cooperative Medicine Scheme (NCMS). The NCMS coverage expanded rapidly. By September 2009, it had had 833 million enrollees, accounting for 94 percent of China s rural residents. A total of 21.3 billion RMB had been spent in financing the NCMS nationwide (excluding Tibet) by Given the extensiveness in coverage, the great number of enrollees, and the considerable amount of subsidies, the NCMS has attracted much attention from both researchers and policy makers to inquire: does the NCMS work? Although a number of impact evaluation studies have attempted to answer this question, empirical results thus far have not been conclusive. For example, while Yip et al. (2008) found that the NCMS had no impact on rural residents use of outpatient services, Wagstaff et al. (2008) provided evidence that the NCMS did increase it. Some findings are even somewhat puzzling. For example, Lei and Lin (2009) found that the NCMS significantly increased rural individuals utilization of preventive care, but did not lead to better health outcomes. These inconclusive and somewhat confusing findings prevent one from making general policy recommendations. This paper examines two potential explanations for the lack of conclusive results. First, previous studies may have failed to focus on the most relevant groups of individuals. For example, the five diseases examined by Lei and Lin (2009), namely, hypertension, diabetes, heart disease, apoplexy, and blood fracture, are more likely to occur among the elderly than among young individuals. Including many young individuals in the analysis might disguise the true impacts of NCMS on health outcomes of the elderly. In fact, by focusing on only individuals 3

4 over age 55 in the same data set used by Lei and Lin, we find that the NCMS significantly reduced the incidence of three out of five of these diseases (diabetes, heart disease, apoplexy). Second, in previous studies, there may be biases in the estimated impacts of the NCMS due to unobserved heterogeneity in gains from program participation. Since rural residents can choose whether or not to participate in NCMS, based on their unobserved heterogeneous gains, simple comparison of health outcomes between the participants and the non-participants is likely to be misleading. Some commonly used methods in impact evaluation applications, such as the double-difference method and its variants, are thought to be robust to unobserved heterogeneity. But they require strong assumptions. We adopt a triple-difference method proposed by Moffitt (1991), to relax the parallel trend assumption required by the double-difference method. 1 The triple-difference method exploits the panel structure of the Chinese Health and Nutrition Survey data set, using two pre-treatment panels to control for the fundamental difference between the participants and the non-participants prior to the implementation of the NCMS. Using this method, we find that double-difference method tends to underestimate the impacts of the NCMS. We also used data collected in 1997 to check for robustness, and the results remained very similar. The paper unfolds as follows. The next section discusses our empirical strategy, followed by two sections that describe the data and empirical results. The final section concludes. Empirical Framework Suppose that we have data on rural individuals health outcomes y in three time periods, t 0, t 1 and t 2, and that NCMS is implemented sometime between t 1 and t 2. As a starting point, 1 To the best of our knowledge, this paper is the first to apply the triple-difference method to study the impact of the NMCS. In the context of Vietnam, Wagstaff (2010) is the first to apply the triple-difference method to study the impact of Vietnam s NHI program. 4

5 consider a cross-section, single difference (SD) between the outcomes y of the participants (P = 1) and those of the non-participants (P = 0) after the NCMS has been implemented, conditioning on individuals observed characteristics (X): SD E[y X, P = 1, t = t 2 ] [y X, P = 0, t = t 2 ] (1) It is easy to imagine that the SD estimator is likely to yield biased estimates of the program impact, as the participants and the non-participants are likely to be different in many aspects other than their treatment status. A refinement of the SD estimator is the double-difference (DD) estimator: DD {E[y X, P = 1, t = t 2 ] E[y X, P = 1, t = t 1 ]} - {E[y X, P = 0, t = t 2 ] E [y X, P = 0, t = t 1 ]} (2) which subtracts the pre-treatment means (t = t 1 ) from each of the above two post-treatment means (t = t 2 ). The fundamental assumption needed for the DD estimator is the parallel trend assumption, i.e. the mean change in y for the participants is the same as that for the nonparticipants in the absence of program. In other words, E[y X, P = 0, t = t 2 ] - E[y X, P = 0, t = t 1 ] is assumed to be a consistent estimate of the counterfactual for E[y X, P = 1, t = t 2 ] - E[y X, P = 1, t = t 1 ]. Since all unobserved fixed factors can all be differenced out in (2), the DD estimator has been the workhorse for impact evaluation applications that are based on nonrandom program placements. 5

6 However, the parallel trend assumption might be too restrictive. After all, individuals choose to participate in the program might be fundamentally different from individuals who choose not to participate, including the time trends of y. The triple-difference (TD) method, with another available pre-treatment data set, uses the two pre-treatment panels to estimate the fundamental difference between the two groups. Mathematically, TD {(E[y X, P = 1, t = t 2 ] E[y X, P = 1, t = t 1 ])} - {(E[y X, P = 1, t = t 1 ] E[y X, P = 1, t = t 0 ])} - {(E[y X, P = 0, t = t 2 ] E[y X, P = 0, t = t 1 ])} - {(E[y X, P = 0, t = t 1 ] E[y X, P = 0, t = t 0 ])} (3) Intuitively, the TD estimator subtracts the pre-treatment change (between t 0 and t 1 ) in y from the change between t 1 and t 2 for both the participants and the non-participants. By doing so, the TD estimator allows the time trends to be group-specific, which relaxes the parallel trend assumption. Data The data we analyzed came from the China Health and Nutrition Survey (CHNS), an ongoing international collaborative project between the University of North Carolina and the Chinese Center for Disease Control and Prevention. It is a longitudinal data set that covers 9 provinces in China, including Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou. The CHNS collects data at the individual, household and community levels. The first wave was collected in 1989 and the most recently released wave was collected in We mainly use data from three consecutive panels, namely, the, 2000, 2004 and

7 waves, to analyze the impact of health insurance on rural residents health outcomes. The data collected in 1997 are also used for robustness checks. Given the focus of our paper on NCMS, we only include rural residents living in rural sites in our analysis. The resulting data include 3,595 individuals and 13,981 person-wave observations. After excluding all individuals who are younger than 55, we have a final sample of 1,175 individuals and 3,466 person-wave observations. For the purpose of our study, the treatment group is defined to be all individuals who acquired NCMS coverage between 2004 and There are two potential comparison groups. The first consists of non-participants who were living in the NCMS communities but had not been enrolled by We call them the non-participants group. The second consists of rural residents living in the non-ncms communities, who never had the opportunity to participate in the NCMS. We call them the non-exposed group. Both comparison groups have their advantages and disadvantages. The most obvious advantage of the non-participants group is that individuals in this group live in the same communities as those in the treatment group. Thus they share the same environmental factors with the treatment group. The disadvantage of this group is that there may be spillovers in the NCMS communities due to improved medical conditions. For example, if the NCMS reduce the incidences of commutable diseases among the treated individuals, non-participants living in the same areas will be less likely to be affected by these diseases. This is likely to underestimate the health impact of the NCMS, because in the case of spillover effects, the health outcomes of the non-participants would be better than in the counterfactual case where the NCMS had not been implemented. The problem of spillover is not a significant concern when using the non-exposed group as the comparison group, since individuals living in the non-ncms communities were not 7

8 exposed to the program. But, as mentioned above, the disadvantage of using this group is that these communities may not be very similar to the NCMS communities. Keeping these advantages and disadvantages in mind, we report results of using both comparison groups below. Results We used the incidences of five diseases, hypertension, diabetes, heart diseases, apoplexy, and born fracture, as the health outcomes of interest, which have been previously studied in Lei and Lin (2009). But we restricted the sample to include only individuals over age 55. In the CHNS surveys individuals were asked if they had ever been diagnosed for these diseases by the time of the interview, so once an individual answered yes in one wave, his or her answer will be yes in subsequent waves. Thus, the TD method essentially compares the incidences of newly diagnosed diseases in the treatment and comparison groups. Estimation results using the SD, DD, and TD estimators are reported in Table 1, which uses the participants as the comparison group, and Table 2, which uses the non-exposed individuals as the comparison group. Four findings are notable. First, using the TD method, the estimated impacts of the NCMS are in general both qualitatively and quantitatively similar in the two tables (although the results using the non-participants group are somewhat more significant). The TD results (Panel C) in both tables suggest that the NCMS significantly reduced the incidences of diabetes, heart disease and apoplexy by 3-8 percentage points. Second, replacing the 2000 CHNS panel, i.e. the t 0 data set needed for the TD application, with the 1997 CHNS panel for robustness check (Panel D of Tables 1 and 2) does not alter the general pattern of the estimates in either table. Third, the TD estimates are in general two to three times as large as the DD estimates, suggesting that the commonly-used DD estimator is likely to underestimate the 8

9 health impacts of the NCMS. These findings clearly indicate that the NCMS has significant beneficial impacts on rural individuals health outcomes. One potential concern is that an individual will be diagnosed for a disease only when he or she actually sees a doctor, and thus focusing on diagnosed diseases might confound the impacts of NCMS on health outcomes with its impacts on health care utilization. However, since the NCMS participants are more likely to utilize health care services, they will be more likely to be diagnosed for diseases if the NCMS does not have any significant impact on their health. Therefore, finding significant health impacts actually imply that the actual impacts NCMS has on individuals health outcomes are larger than what we found in Tables 1 and 2. Conclusion In an attempt to explain the lack of consensus on the impacts of the NCMS in previous studies, we re-examined the health impacts of the NCMS, focusing on a vulnerable group of rural individuals the elderly in the CHNS data. Our triple-difference method, which not only takes into account unobserved heterogeneity but also relaxes the restrictive assumption needed by the double-difference method, shows that the NCMS significantly reduced the incidences of diabetes, heart disease, apoplexy for rural individuals over age 55. But when all rural individuals in the CHNS data (not only the elderly) are included in the analysis, the estimated impacts became much smaller and largely insignificant (results not shown but are available upon request). This suggests that a national health insurance program may have different impacts on different subgroups of the population. Thus, finding no significant impacts of the program on the general population does not necessarily mean that the program is not beneficial for the vulnerable subsamples. We believe that the failure to focus on the most relevant subgroup is one of the 9

10 reasons for the lack of conclusive findings in previous studies. Meanwhile, our results suggest that the commonly-adopted impact evaluation method, the double-difference method, is likely to underestimate the health impacts of the NCMS. This suggests that another reason for the lack of conclusive findings in previous studies: the inadequate control for the possible influences of unobserved heterogeneity in estimation. Therefore, to fully assess the impacts of the NCMS, future research is needed on other vulnerable groups, such as low-income individuals, young children, and individuals with poor health conditions; at the same time, unobserved heterogeneity should be carefully controlled for in future research. 10

11 References Chen, L., et al "The effects of Taiwan's National Health Insurance on access and health status of the elderly." Health Economics 16(3): Etilé, F., and C. Milcent "Income-related reporting heterogeneity in self-assessed health: evidence from France." Health Economics 15: Johar, M "The impact of the Indonesian health card program: A matching estimator approach." Journal of Health Economics 28(1): Jones, A. M., et al Applied health economics. Routledge: New York. Lei, X., and W. Lin "The New Cooperative Medical Scheme in rural China: does more coverage mean more service and better health?." Health Economics 18(S2): Trujillo, A. J., J. E. Portillo, and J. A. Vernon "The impact of subsidized health insurance for the poor: evaluating the Colombian experience using propensity score matching." International Journal of Health Care Finance and Economics 5(3): Wagstaff, A "Estimating health insurance impacts under unobserved heterogeneity: the case of Vietnam's health care fund for the poor." Health Economics 19(2): Wagstaff, A., et al "Extending health insurance to the rural population: An impact evaluation of China's new cooperative medical scheme." Journal of Health Economics 28(1): Yi, H., et al "Health insurance and catastrophic illness: a report on the New Cooperative Medical System in rural China." Health Economics 18(S2): S119-S127. Yip W, and Hsiao W The Chinese health system at a crossroads. Health Affairs 27(2): Yip W. and Hsiao W China s health care reform: A tentative assessment. China Economic Review 20: You, X., and Y. Kobayashi "The new cooperative medical scheme in China." Health Policy 91(1):

12 Table 1: Estimated Impacts of CNMS on Diagnosed Diseases using Non-Participants as Comparison Group: Sample = Elderly (Age > 55) (1) (2) (3) (4) (5) Dependent Variables Hypertension Diabetes Heart Disease Apoplexy Bone Fracture A: Single-Difference No X (0.033) (0.008) (0.010) (0.014) (0.020) N With X (0.043) (0.014) (0.016) (0.016) (0.026) N B: Double-Difference No X * ** * (0.033) (0.011) (0.012) (0.017) (0.021) N 1,079 1,081 1,081 1,077 1,078 With X * ** (0.035) (0.012) (0.012) (0.016) (0.023) N 1,052 1,054 1,055 1,051 1,052 C:Triple-Difference (t 0 =2000) No X ** ** *** ** (0.069) (0.024) (0.028) (0.026) (0.043) N 1,363 1,366 1,366 1,362 1,363 With X *** ** *** ** (0.069) (0.022) (0.028) (0.026) (0.045) N 1,291 1,294 1,295 1,291 1,292 D: Triple-Difference (t 0 =1997) No X ** ** *** ** (0.062) (0.029) (0.016) (0.023) (0.053) N 1,280 1,280 1,282 1,276 1,278 With X ** * *** * (0.068) (0.026) (0.017) (0.022) (0.057) N 1,239 1,239 1,242 1,236 1,238 Note: 1. Robust standard errors, clustered at the household level, are in parentheses. 2. *** p<0.01, ** p<0.05, * p< Observed characteristics X include gender, dummy for ethnic minority, dummies for marital status (never married, married, divorced, widowed, separated), years of education, a 4 th order polynomial of age, number of children under age 6 in the household, number of elderly in the household, the log of per capital household income, and a set of community dummies. 12

13 Table 2: Estimated Impacts of CNMS on Diagnosed Diseases using Non-Exposed Individuals as Comparison Group: Sample = Elderly (Age > 55) (1) (2) (3) (4) (5) Dependent Variables Hypertension Diabetes Heart Disease Apoplexy Bone Fracture A: Single-Difference No X 0.055** *** (0.022) (0.007) (0.006) (0.008) (0.013) N 1,010 1,009 1,009 1,006 1,005 With X *** (0.049) (0.015) (0.013) (0.024) (0.028) N B: Double-Difference No X * (0.025) (0.010) (0.009) (0.012) (0.016) N 1,781 1,782 1,781 1,777 1,778 With X ** (0.027) (0.011) (0.009) (0.013) (0.017) N 1,737 1,738 1,738 1,734 1,735 C: Triple-Difference (t 0 =2000) No X ** * ** (0.046) (0.022) (0.017) (0.021) (0.031) N 2,233 2,234 2,234 2,230 2,231 With X *** * * (0.051) (0.022) (0.018) (0.023) (0.032) N 2,124 2,125 2,126 2,122 2,123 D: Triple-Difference (t 0 =1997) No X *** * ** (0.047) (0.026) (0.016) (0.022) (0.036) N 2,113 2,113 2,114 2,108 2,110 With X *** * ** (0.053) (0.027) (0.017) (0.022) (0.037) N 2,051 2,052 2,053 2,047 2,049 Note: 1. Robust standard errors, clustered at the household level, are in parentheses. 2. *** p<0.01, ** p<0.05, * p< Observed characteristics X include gender, dummy for ethnic minority, dummies for marital status (never married, married, divorced, widowed, separated), years of education, a 4 th order polynomial of age, number of children under age 6 in the household, number of elderly in the household, the log of per capital household income, and a set of community dummies. 13

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