بیمه سلامت مهم است؟ مدارک و شواهد از بیمه پایه پزشکی شهری ساکن چین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|25905||2014||14 صفحه PDF||سفارش دهید||10619 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Comparative Economics, Available online 28 February 2014
In 2007, China launched a subsidized voluntary public health insurance program, the Urban Resident Basic Medical Insurance (URBMI), for urban residents without formal employment. We estimate the impact of the URBMI on health care utilization and expenditure by a fixed effects approach with instrumental variable correction, using the 2006 and 2009 waves of the China Health and Nutrition Survey. We explore the time variation of program implementation at the city level as the instrument for individual enrollment. We find that this program has significantly increased the utilization of formal medical services, including both outpatient care and inpatient care, but it has not reduced total out-of-pocket health expense. We also find that this program has improved medical care utilization more for children, members of the low-income families, and the residents in the relatively poor western region.
Since the Chinese economic reform in 1978, China has been experiencing rapid economic growth. However, the economic success of China does not necessarily translate into social welfare gains for its citizens. For example, along with the economic growth, in rural areas we witnessed the dissolution of the Rural Medical Cooperative System, which was the cornerstone of the health care system in rural China. In urban areas, millions of workers lost their jobs and thus their employment-related health insurance during the retrenchment of state-owned enterprises starting from the mid-1990s. To improve the poor state of health care in China, the Chinese government has been trying to build up a universal public health insurance system in its recent health care reform. This ambitious public insurance system consists of three key programs: the Urban Employee Basic Medical Insurance (UEBMI) for the urban employed, initiated in 1998; the New Cooperative Medical Scheme (NCMS) for rural residents, established in 2003; and the Urban Resident Basic Medical Insurance (URBMI), covering urban residents without formal employment.1 The last of these, the URBMI, is the focus of this paper. After its launch in 2007, the URBMI was rapidly expanded from 79 cities in 2007 to 229 cities (about 50% of China’s cities) in 2008, and to almost all cities by the end of 2009. This program covered 221 million persons in 2011 (NBS, 2012), amounting to around 16.5% of the Chinese population. The main objective of this paper is to investigate the impact of the URBMI on health care utilization and expenditure. Understanding the effects of the URBMI, and comparing the effectiveness of the three major health care systems (UEBMI, NCMS, and URBMI), is an important endeavor. Each of these systems has its unique institutional setup, covers different populations, and has different levels of premiums and reimbursement. The comparison exercise will provide insights into resource allocation, the effectiveness of different components of the health care policy, the role of subsidies, etc. Study of the effectiveness of each individual program is an important step toward this kind of comparisons.2 Nonetheless, there is little empirical research on the effectiveness of the URBMI, mainly because it started only a few years ago, and the proper data is limited. The only available study which examines the impact of the URBMI is Lin et al. (2009). Their study is based on cross-sectional data collected in December 2007, focusing on who are covered by the URBMI, who gain from it in medical expenditure, and whether the enrollees are satisfied with it. Internationally, different aspects of public health care systems are widely studied in the literature. For example, Currie and Gruber, 1996a, Currie and Gruber, 1996b, Currie and Gruber, 1997 and Currie and Gruber, 2001 investigate the impact of the Medicaid expansion on health and health care in the United States, and find that the expansion has improved the health of newborn children and has increased health care utilization by their mothers. Card et al. (2008) find that the rise of Medicare coverage has decreased health disparity and increased health care utilization by the elderly in the United States. Cheng and Chiang, 1997 and Chen et al., 2007 study the impact of the universal health care system in Taiwan, and find that it has significantly increased utilization of both inpatient and outpatient care services by Taiwanese elderly. Given the different development stages, subsidy levels, and copayment policies, it would be instructive to compare findings from developing countries, like China, with findings from the developed countries. Different from public health insurance systems in most developed economies, the URBMI is a voluntary insurance program with heavy government subsidies. To estimate the impact of the URBMI, we use panel data from the China Health and Nutrition Survey (CHNS), which is a longitudinal survey project and has collected eight waves since 1989. The last two waves were collected in 2006 and 2009. This feature of the data and the timeline of the implementation of the URBMI allow us to better control for unobservables and possible selection bias (e.g., Heckman, 1990), which is especially important in the context of a health insurance plan with voluntary enrollment. In this paper, we are interested in estimating the treatment effect on the treated of the URBMI, which is an important measure of the effectiveness of policy programs and a key policy variable with voluntary participation. Lei and Lin, 2009 and Wagstaff et al., 2009 also estimate the treatment effect on the treated when they evaluate the impact of the NCMS, the voluntary health insurance program in rural China. Our starting empirical strategy is the fixed effects approach at the individual level. Admittedly, individuals may select into the URBMI nonrandomly. While fixed effects can be used to control for time-invariant unobservables, it is still vulnerable to bias caused by time-variant unobservables. In order to control for this potential bias, we explore the time variation of the URBMI implementation at the city level as the instrumental variable to correct for possible endogeneity of individual URBMI enrollment status.3 So our main empirical strategy is a fixed effects model with instrumental variable. The remainder of the paper is organized as follows: In Section 2, we briefly introduce the current Chinese health insurance system, and pay special attention to the institutional setup of the URBMI. In Section 3, we describe the China Health and Nutrition Survey, define the main dependent variables and independent variables, and present descriptive statistics. In Section 4, we discuss our empirical strategies. Section 5 gives our main results for the whole sample as well as results for different age groups, income groups, genders, and regions. In that section, we also conduct several empirical tests to validate our instrumental variable. We conclude the paper with Section 6.
نتیجه گیری انگلیسی
In this paper, we employ the FE-IV strategy to estimate the causal effects of the URBMI enrollment on health care utilization and spending for the non-working population in urban China, based on panel data drawn from the CHNS 2006–2009. We use the time variation of the URBMI implementation at the city level as the instrument for individual enrollment status, and conduct several empirical tests to show the validity of this instrument. Our major results are that the URBMI enrollment has significantly increased the utilization of formal medical services, including both outpatient care and inpatient care, and total health expense. However, we find no evidence that it has reduced out-of-pocket expense in the previous four weeks. As crucial steps towards universal health insurance coverage in China, the URBMI and NCMS are very similar in nature: they are both heavily subsidized voluntary health insurance programs with a primary focus on the coverage of catastrophic expenses and an increasing trend toward outpatient reimbursement in the benefit package. The individual contribution for the URBMI is lower than that for the UEBMI, but is higher than that for the NCMS because of the greater expense of health services in urban areas (Lin et al., 2009). However, the financing of both URBMI and NCMS is limited, which leads to relatively low real reimbursement rates for both programs. Since its inception in 2003, a number of studies have evaluated the impact of the NCMS, and most of them find that it significantly improved the enrollees’ utilization of outpatient services and inpatient services (Wagstaff et al., 2009, Yu et al., 2010 and Babiarz et al., 2012). These studies are consistent with our findings on the impact of the URBMI on health care utilization. Our finding that the URBMI has not reduced out-of-pocket spending in the previous four weeks is not surprising, and is consistent with the existing literature on the impact of the NCMS (Wagstaff et al., 2009, Lei and Lin, 2009, Yip and Hsiao, 2009, Sun et al., 2009, Sun et al., 2010 and Shi et al., 2010). This may result from three channels, including the increase of formal health care utilization, the fact that the URBMI appears to make people more likely to use higher-level providers, and physician-induced demand of the insured patients for high-margin care. These findings are also consistent with previous literature on earlier urban health insurance schemes in China (Wagstaff and Lindelow, 2008). However, since the URBMI only started in 2007, it is still too early to tell its long-term effects, such as the aggregate effect examined in Finkelstein (2007), which is six times larger than the effect estimated from individual studies like ours. We also investigate heterogeneous effects of the program for different age groups, income groups, genders, and regions. The program has improved medical care utilization more for children, members of low-income families, and urban residents in the relatively poor western region. Our findings on the low-income families are consistent with the results of Lin et al. (2009), who find that the poor participants in the URBMI are more likely to feel relieved of a medical financial burden, and also consistent with the results of Cheng et al. (2013), who show that the poor NCMS enrollees have seen a significant increase in health care access. Our findings on the differential effects of the URBMI across regions are also consistent with the literature on the NCMS (Liu and Tsegai, 2011 and Cheng et al., 2013). This study is subject to several data limitations. First, the CHNS only collects inpatient services information for the previous four weeks at the time of the survey. Since inpatient service is a rare event, collecting information only in the previous four weeks instead of a longer time (e.g., 12 months in most surveys) results in few inpatient incidences, which may prevent us from accurately estimating the impact of the URBMI on utilization of inpatient services. This may be the reason why we find that the URBMI enrollment has no significant effect on the probability of using inpatient services, but a significant positive effect on inpatient hospital days. In fact, our FE-IV estimates for the probability of using inpatient care are positive, though they are not significant, due to the small sample size. Second, our data do not have multiple outcomes within a domain. For example, for the domain of hospital utilization, we only have one outcome, viz., inpatient hospital days. This data limitation prevents us from adding further credibility to our main results by summarizing multiple findings across related outcomes within the same domain, i.e., calculating standardized treatment effects as Finkelstein et al. (2012) do in their study on the Oregon health insurance experiment. Third, we only study a limited set of outcome variables, and cannot explore the frequency of formal medical care use, health expenditure per outpatient visit or inpatient spell, the structure of medical expenditure, health outcomes, or other supply-side responses. Research on those issues promises to be fruitful in the future.