آیا پوشش بیمه سلامتی منجر به سلامت و آموزش بهتر می شود؟ مدارک و شواهد از مناطق روستایی چین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|25454||2012||14 صفحه PDF||سفارش دهید||12638 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Health Economics, Volume 31, Issue 1, January 2012, Pages 1–14
Using the 2006 China Agricultural Census (CAC), we examine whether the introduction of the New Cooperative Medical System (NCMS) has affected child mortality, maternal mortality, and school enrollment of 6–16 year olds. Our data cover 5.9 million people living in eight low-income rural counties, of which four adopted the NCMS by 2006 and four did not adopt it until 2007. Raw data suggest that enrolling in the NCMS is associated with better school enrollment and lower mortality of young children and pregnant women. However, using a difference-in-difference propensity score method, we find that most of the differences are driven by endogenous introduction and take-up of the NCMS, and our method overcomes classical propensity score matching's failure to address selection bias. While the NCMS does not affect child morality and maternal mortality, it does help improve the school enrollment of six-year-olds.
Many governments advocate nationwide health insurance in order to improve individual welfare. In this paper, we use the recent expansion of health insurance coverage in rural China to quantify the impact of health insurance on child mortality, maternal mortality, and school enrollment. Although the Chinese economy has continued to grow during the past 20 years, many rural residents remain poor and have difficulty obtaining access to health care when they are sick.1 To address the problems associated with the lack of health insurance in rural areas,2 China initiated the National Cooperative Medical System (NCMS) in 2003, targeting rural residents with large subsidies from central and local governments. Unlike mandatory insurance proposed elsewhere, the NCMS is implemented county-by-county, allowing local governments to decide when to introduce the NCMS, how much premium to charge, and how many benefits to offer. If the county offers the NCMS, a rural household can choose to enroll in the NCMS for either every household member or none of them. Diffusion of the NCMS has been fast: in 2004, 14% of counties offered NCMS coverage (MOH, 2005); by June 30, 2008, all counties offered the NCMS, covering 91.54% of the rural population.3 A more detailed description of the NCMS can be found in Wagstaff et al. (2009). In theory, health insurance can affect a household in many ways, ranging from increased health care utilization, better health, and higher productivity, to greater financial resources freed up from medical costs. Our data do not include direct measures of health care utilization, but they allow us to study the effect of the NCMS on mortality of pregnant women and young children at the village level and school enrollment at the individual level. Mortality and educational outcomes have long been studied as potential consequences of health insurance. For example, the expansion of Medicaid coverage in the US has been shown to improve the mother's prenatal care, reduce infant mortality, and reduce the incidence of low birth weight (Currie and Gruber, 1996a and Currie and Gruber, 1996b); the introduction of the State Children's Health Insurance Program (SCHIP) has been linked to better child health and better school performance (Joyce and Racine, 2005 and Levine and Schanzenbach, 2009); and the adoption of nationwide health insurance has reduced the mortality rate of young children in Taiwan (Chou et al., 2011). There is also evidence that health insurance can relieve financial burdens on individual households (Miller et al., 2009), and an increase in financial resources available can boost children's school performance (Morris et al., 2004 and Dahl and Lochner, 2005). Several survey articles have reviewed research on the impact of health insurance on health (Levy and Meltzer, 2008) and the impact of child health on educational outcomes in both developed and developing countries (Currie, 2009 and Glewwe and Miguel, 2008). Turning to the effects of the NCMS in particular, the existing evidence is mixed. On the positive side, some studies show that the NCMS reduced illness-related poverty, increased inpatient/outpatient utilization of health services and reduced the rate of non-hospitalization after two weeks of diagnosis (Chen et al., 2005, Yuan et al., 2006, Wagstaff et al., 2009, Wang, 2007, Zhu et al., 2007, Fang et al., 2006 and Zhang et al., 2007). Because deliveries constitute a significant fraction of hospitalizations, many studies compare the percent of hospitalized deliveries before and after the NCMS. Cheng et al. (2008) report an increase in the hospitalized delivery rate from 85% to 96.9% in 14 counties of Hubei (2002–2006), from 77.5% to 92.5% in 3 counties of Chongqing (2003–2007), and from 32.43% to 83.24% in the rural area of Qinghai (2002–2007).4 Similar increases have been shown in Guangxi (Liao, 2009) and Yunnan (Lu and Li, 2010). These studies find that the NCMS increased the health of women and infants because mortality risk is much lower for hospital delivery than for home delivery.5 Two of them also report a significant decline in maternal and birth deaths after the NCMS (Liao, 2009 and Lu and Li, 2010). On the negative side, some researchers have expressed concerns that the low reimbursement rate in the NCMS will limit its effectiveness (Zhang et al., 2006, Yi et al., 2009), and that the China Health and Nutrition Survey lacks evidence of better health care utilization and improved health condition after the adoption of the NCMS (Lei and Lin, 2009). This paper aims to provide additional evidence regarding the impact of the NCMS, using a large cross-sectional data set from the 2006 China Agriculture Census. Unlike Wagstaff et al. (2009), we do not track individuals before and after the introduction of the NCMS. But our data cover neighboring areas within a poor inland province including four counties that introduced the NCMS at the time of the survey (end of 2006) and four counties that did not introduce the NCMS until 2007. The eight counties are geographically adjacent to each other, belong to the same administrative district, and are similar in demographics, access to health care services, and access to public education. Because the data were collected as a part of the census, our sample includes 5.9 million individuals, 1.4 million households, and 1.4 million school age children across 3977 villages.6 The advantage of such a large sample is that it helps capture severe health risks that are small probability events and could have a catastrophic impact on a rural household without health insurance. Furthermore, the high poverty in this area makes it attractive for identifying the impact of the NCMS on a financially vulnerable population. It is difficult to establish a causal relationship between health insurance and measurable outcomes in observational data because they both may be influenced by unobservable factors. There are two sets of endogenous unobservables that may contaminate the estimation. One is heterogeneous county-level characteristics. For instance, if the NCMS counties are richer and in better fiscal condition, then the population in the NCMS counties could have better health and educational outcomes compared with the population in the non-NCMS counties even without the NCMS. The other set is heterogeneous household-level characteristics. For example, comparing two households residing in the same NCMS county, richer and more health-conscious households may be more likely to take up the insurance. The classical cross-sectional propensity score matching method focuses on estimating the effect of a treatment program (i.e. the NCMS counties in our context) by comparing the treated individual with an untreated one. The validity of the method relies on the assumption that treated and untreated individuals are similar in unobservables if they are matched on observables (Rosenbaum and Rubin, 1983). To correct the two kinds of selection bias mentioned above, instead of directly estimating the treatment effect of enrolling in the NCMS, we estimate the treatment effect of the NCMS being offered in a county. This is an intent-to-treat analysis. Specifically, we propose a difference-in-difference (DID) propensity score method using both NCMS and non-NCMS county data, with which we can explore within-county heterogeneity and cross-county difference. The key assumption in classical cross section matching, that individuals matched in observables are similar in the unobservables, can be relaxed when we construct the propensity score to use households in the non-NCMS counties as a control for similar households in the NCMS counties, regardless of the participation status of the households. The heterogeneity within a county allows us to control for the unobservable county specific attributes and thus account for the endogenous introduction of the NCMS county by county. Our method is similar to the DID matching strategy proposed by Heckman et al. (1997) and Heckman et al. (1998). The main difference is that they use longitudinal (or repeated cross-section) data to difference out the time-invariant factors before and after a treatment program,7 whereas we use all households within each county with unequal propensity to enroll in the NCMS to difference out the county-specific unobservables and use households in non-NCMS counties as a control group. Wagstaff et al. (2009) also use DID together with the propensity score method to evaluate the impact of the NCMS (on health care utilization), but there is an important difference between their methodology and ours: they compare the NCMS-insured individuals with observationally similar individuals in non-NCMS counties, while we compare both the NCMS insured and uninsured individuals in the NCMS counties with individuals in the non-NCMS counties. As detailed below, the two types of comparisons lead to different results and we argue that our method can better address the endogenous take-up of NCMS due to non-observables. Results from our DID propensity score method suggest that most of the seemingly beneficial effects of NCMS are driven by selection. By applying the DID propensity score method to populations of different socioeconomic status, we find that the NCMS may have moderate effects in improving the school enrollment of six-year-olds. The rest of the paper is organized as follows: Section 2 describes the data and the background of NCMS in the studied area. Section 3 presents a data summary and classical analysis (OLS and propensity score matching) of key outcomes. Section 4 specifies our DID propensity score methodology. Section 5 reports the main results. Section 6 offers a brief discussion and conclusion.
نتیجه گیری انگلیسی
Overall, the raw data of a large cross-section from the 2006 China Agricultural Census suggests that NCMS-insured households on average have better outcomes in child school enrollment, young child mortality, and maternal mortality than do non-insured households. However, most of these differences are driven by the endogenous introduction and take-up of the NCMS. Once we control for the selection bias using a difference-in-difference propensity score method, the NCMS has close to zero effect on the average population, although there is moderate evidence that it has reduced the delay of elementary education for some six-year-olds. This finding of zero average treatment effect is consistent with the existing literature (Yi et al., 2009 and Lei and Lin, 2009), who attribute the lack of effect (on health care utilization and health improvement) to the low reimbursement rate and to selection. Wang et al. (2009) do find some positive effects of health insurance coverage on self-reported health status, but the insurance program they studied is different from the NCMS and arguably more comprehensive in outpatient care and could offer more help to deal with non-catastrophic health risk. Since our studied area is much poorer than most areas of China, we suspect that the NCMS does not improve the three studied outcomes in other areas either. In addition to the low reimbursement rate, as noted above, the lack of average effect may be explained by the fact that mortality is an extreme event and the effect of the NCMS on school enrollment may take more than three years to show up in the data. Another possibility is that the NCMS may encourage more health care utilization but the ease of financial burden has not appeared yet, as households need to pay even more money out of pocket when they seek more treatment. Wagstaff et al. (2009) confirm this suspicion: they find that the NCMS is related to more health care utilization but that it does not decrease out-of-pocket spending on health care except for infant deliveries. Thanks to increased government subsidies in 2009, the reimbursement rate has increased over time. Whether this improvement portends better health and educational outcomes is a topic worth studying in the future.