Does Health Insurance Decrease Savings and Boost Consumption in Rural China?

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1 Does Health Insurance Decrease Savings and Boost Consumption in Rural China? Diana Cheung 1 and Ysaline Padieu 2 Sorbonne Economic Centre, University of Paris 1 Panthéon-Sorbonne Preliminary Version Please do not cite without permission of authors January, 2012 Abstract: This paper aims at assessing the impact of the New Cooperative Medical Scheme (NCMS) on household saving and consumption. We use data from the China Health and Nutrition Survey for the 2006 wave and follow a three-step methodology. We first implement ordinary least square regressions and then take into account the possible endogeneity of NCMS take up with a two-stage least square regression. At last, we check the robustness of the results with a propensity score matching approach. We find that NCMS decreases household savings. This effect is not immediate but becomes significant in the two years following the implementation of scheme. We also observe that it is persistent but smoothed over time. Keywords: Rural China; New Cooperative Medical Scheme; Health Insurance Chinese Saving and Consumption; Propensity Score Matching. JEL Classification Numbers: C21, D1, I18, O53 Acknowledgements: This research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention; the Carolina Population Center, University of North Carolina at Chapel Hill; the National Institutes of Health (NIH; R01-HD30880, DK056350, and R01-HD38700); and the Fogarty International Center, NIH, for financial support for the CHNS data collection and analysis files since We thank those parties, the China-Japan Friendship Hospital, and the Ministry of Health for support for CHNS 2009 and future surveys

2 1. Introduction China suffers from macro imbalances impeding the country to enter a sustainable growth path. These imbalances are mostly the consequences of high saving rates which in turn are the result of uncertainty about the provision of public services, such as education, pensions and essentially healthcare. Chamon and Prasad (2008) pointed out education, health, housing and macroeconomic uncertainty associated with the transition towards market economy as new saving motives in China. According to them, the increasing private burden of education and health expenditures seems one of the strongest candidates for explaining the increase in saving rates (p.25). They also showed that the savings rate of households facing high health expenditure was 20 percentage points higher than the savings rate of households not facing it. The idea that the implementation of a social safety net in China would help to decrease high savings rates and boost its domestic consumption is largely spread among economists (Blanchard and Giavazzi, 2005; Fitoussi and Saraceno, 2008; Baldacci and al., 2010). Indeed, health insurance may reduce household precautionary savings by reducing risk on income due to large out-of-pocket expenditures. However, the negative effect of health insurance on household precautionary savings remains equivocal in the literature due to two opposite effects of the health insurance on household health expenditures. If refunding lowers enough the cost of healthcare and enables people to seek healthcare services, when they could not afford it beforehand, it might create incentives to engage in a precautionary behaviour and lead to an increase in savings. In contrast, if the demand for healthcare services existed prior to the introduction of health insurance, then refunding allows diminishing risk on health expenditures as well as depletion in savings. Accordingly, contrasted evidence on the impact of health insurance on household saving behaviour is provided in empirical studies. Some found a substantial effect when others do not. Chou and al. (2006) found that the extension of the National Health Insurance in Taiwan decreased household savings rates by 3% to 7.5% according to the type of insurance. Conversely, Starr-Mc Cluer (1996) studied the impact of private health insurance and showed that insured households maintained a much higher level of wealth than comparable households in the United States. This article aims to evaluate the impact of the New Cooperative Medical Scheme (NCMS) on consumption and savings in rural China to determine whether health insurance coverage can decrease household savings rate and encourage their consumption. We estimate the effect of NCMS on household income share of savings and income share of consumption expenditures 2

3 using a three-step methodology. We first run an ordinary least-square regression (OLS) to control for observables. Then, we use an instrumental variable regression (IV) to deal with the endogeneity of NCMS take up. Finally, we check the robustness of previous results using a propensity score matching method (PSM). To assess these impacts over time, we also control for the year of implementation of the scheme. We assume that the longer NCMS has been implemented, the more significant changes in participants saving and consumption behaviour are, as households adjust their expenses according to reimbursements over time. This paper is the first paper using the China Health and Nutrition Survey to assess the impact of NCMS on household savings in mainland China 3. Most studies only focus on the impact of NCMS on one disaggregated expenditure such as expenses on durables, education or health. This paper contributes to the literature by computing household total consumption expenditures, allowing us to study household saving behaviour. The rest of the paper is organised as follows. Section 2 provides a brief background on the introduction of NCMS. Section 3 describes the data and section 4 the empirical strategy. Section 5 presents the results. Section 6 controls for the year of implementation of the scheme and section 7 discusses the results and concludes. 2. Introduction of the New Cooperative Medical Scheme As China moved towards social market economy, the Cooperative Medical Scheme (CMS) 4 vanished. The dismantling of People s Commune resulted in the collapse of the traditional healthcare system and coverage (Liu, 2004). To address the lack of medical institutions and professionals, the central government encouraged the autonomy of public hospitals and allowed private practitioners as well as private clinics to grow. As a result, medical expenses of rural citizens skyrocketed leaving the poorest behind. From 1980 to 1988, the share of health expenses paid by Chinese households increased from 16% to 38% up to 61% in 2001 (Zhang et Kanbur, 2003). The low health coverage entailed deterioration in health status and also let the population more vulnerable to health risks. Thus Chinese households tended to save more to cover themselves from catastrophic health expenditures (Chamon and Prasad, 2008). 3 The impact of the implementation of the national health insurance in Taiwan on precautionary savings has been assessed by Chou et al. (2006). 4 Free and universal health coverage organized around the workplace, during the communist period. 3

4 The government decided to launch a new health insurance program to improve access to healthcare in rural areas. The NCMS was inaugurated in 2003 and aims at covering the whole country by It was initiated in 162 counties out of more than 2400 counties in the first year and expanded to 333 counties by NCMS is a voluntary scheme and is organised at the county level, which raises the issues of adverse selection and heterogeneity. It aims at covering firstly catastrophic expenditures. The central government, local governments and rural residents are involved in the scheme with contributions that vary across regions and years. For instance, in 2003, the central and local governments both contributed 10 yuan per person annually, while households paid 10 yuan to participate. In 2008, these amounts increased to 40 yuan and 20 yuan respectively 5 (Dong, 2009). These contributions were used to fund an individual account as well as a common pool meant to partially cover the cost of hospitalization and outpatient expenses due to severe diseases. Each county is free to implement the scheme at its discretion. Local governments decide reimbursement ratios, deductible ceiling and provider payment method. As a result, the organisation of health insurance and a fortiori its impact on health expenditures but also vulnerability to health shocks vary across counties. Despite its heterogeneity, we study how the NCMS insurance affects household consumption and saving behaviour. 3. Data and descriptive statistics a. Sources We study panel data from the China Health and Nutrition Survey (CHNS). The CHNS is jointly conducted by the Carolina Population Centre at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Centre for Disease Control and Prevention. This survey was designed using a multistage random cluster process and covers nine provinces from 1989 to The CHNS provides information on socioeconomic and demographic characteristics at the community, household and individual levels. Focusing on health and nutrition outcomes as well as individual and household expenses, the overall survey collects information on about 4,400 rural and urban households (or some 19,900 individuals) for 7 waves. In this paper, we use one round of the CHNS: the 2006 wave, i.e. three years after the implementation of the New Cooperative Medical Insurance. The sample includes only 5 See Dong (2009) for further details on premiums or reimbursement ratios. 4

5 counties where NCMS was introduced. Moreover, we focus on rural 6 households answering to questions on both expenses and health sections. Hence our sample is reduced to 1308 households. In 2006, 71.1% of these households (930) decided to join NCMS (see table 1). b. Variables As this paper aims at assessing the impact of NCMS on rural household consumption and saving behaviours, we focus on two different outcomes: the share of household savings and the share of household total consumption expenses. We compute the household share of consumption expenses from the household level of consumption expenses on its total net income. Household savings are constructed as the residual of household total net income and total consumption expenses in durables and non-durables. We calculate the household savings rate using this variable and household total net income. Thus, we will only present coefficient estimates of NCMS impacts on household savings, as the effect on consumption expenditures is opposite by construction. The China Health and Nutrition Survey provides a detailed section on household behaviours, which allows us to construct household consumption expenditures. Consumption expenses cover expenditure on food, high tech items 7, electrical appliances, healthcare, dowry and marriage 8 as well as expenses called other expenses according to the question asked in the survey. This last variable refers to presents as a gift or in cash offered to non-household family members. Our outcome variables then consist in aggregated variables (savings and consumption expenses). The key explanatory variable is the household enrolment to the New Cooperative Medical Scheme (NCMS). In order to construct this variable from the CHNS, we first created a dummy variable of introduction of the NCMS at the county level. As NCMS is a voluntary scheme, we couple the latter variable with the household participation and obtain this variable of interest. As regards household characteristics, we use several key variables gathering information on health, demographic and socioeconomic factors. Health variables include a dummy variable referring to the share of ill member in the household, the availability of medicines at the nearest health care institution, the travel time to reach by bike the nearest health care 6 A rural resident refers to citizens holding rural registration and not necessarily someone living in a rural area. 7 High Tech items refer to computers, telephones, cell phones, DVDs and VCDs player as well as satellite dish. 8 Marriage expenditures cover spending on gifts and organization of a wedding. 5

6 institution, the minimum average waiting time in health care institutions of the community as well as the enrolment in Cooperative Medical insurance in Demographic and socioeconomic variables consist in household size, age of the head of household, gender and education of the head of household, whether there is one child in the household, whether there is an elderly member needing care in the family, whether the head of household works, whether the head of household has a university or a professional degree, whether one of the household members is a farmer, the number of households living in the community, the household total net income and quartiles of income. Household total net income is obtained adding wages, net revenues from production and entrepreneurship, net subsidies, income received from rent, from non household family members as well as cash received as a gift. We also create geographical dummies referring to the province where the household lives. c. Baseline descriptive statistics of the sample In 2006, 64% of the households or our sample (1308 households) was exposed to NCMS and among them 71% (930 households), decided to join the NCMS (See table 1 in appendix A). Tables 2a and 2b in appendix A report descriptive statistics for participants and nonparticipant household net income, total consumption expenditures and savings in 2006, in yuan and as a share of income respectively. Broadly speaking, in 2006, non participant households earn more than participant households, and yuan respectively. They also spend more and save more in level. However, if we express levels of spending and savings as a share of income, then households participating in the scheme have a higher propensity to consume than households who do not participate. Participants spend 70.2% of their income, while non participants only spend 65.4%. Their savings rate is also lower (29.8% vs. 34.6%). Is the difference in consumption expenditures and savings related to the participation in the NCMS? We answer this question using OLS, IV and Propensity Score Matching estimations. 4. Empirical strategy and issues a. Empirical model Impacts of NCMS on household expenses and savings are estimated using the following standard linear regression:.. (1) 6

7 where is the level of the dependant variables (share of saving and consumption expenditures) of the household h, a set of control variables at the household and community levels, α, the constant and, the error term which controls for unobservables affecting the outcome of interest. denotes the participation of the observed household in the scheme 9. refers to demographic and socioeconomic variables such as household size (whether it is larger than three), household total net income, gender and age of the head of household, whether this later has received higher education degree or a professional degree, whether the head of household works but also whether one member of this household is a farmer. We also add in the regression provincial dummies. b. Estimation issues One major concern is the endogenous participation in NCMS. As enrollment to NCMS is on a voluntary basis, participant households might have specific characteristics that might bias the estimation. As showed by Wagstaff et al. in 2007, enrollment is higher among households with chronically sick members. These households may have specific saving and consumption behaviours. If we do not control for this adverse selection problem, it could bias our estimation and then distort the impact of NCMS on savings and consumption expenditures. In order to control for the endogeneity of participation in the scheme we use an instrumental variable strategy using the same set of control variables as for eq. (1). To instrument the enrollment to NCMS, we use the percentage of enrolled household in the community excluding the observed household 10. We assume that the higher the coverage is in the community, the more attractive NCMS is, making the insurance scheme more attractive to households. This community-level variable is correlated with household participation to NCMS but does not affect household consumption and saving. We first predict NCMS take up,, according to the instrument, then include it in eq. (1). c. Robustness check using another estimation framework: PSM We use a non-linear estimation method in order to check whether our findings with OLS and IV are robust. We chose a propensity score matching method (PSM) for three reasons. First, 9 All our variables are expressed for the year 2006, which is three years after the introduction of NCMS. 10 The underidentification test (Kleibergen-Paap rank Lagrange Multiplier statistic) and the weak instrumentation test (Cragg-Donald statistic) check the validity of the instrument. 7

8 PSM allows empirical ex-post policy evaluation by creating a counterfactual and addressing the household adverse selection problem. It recreates a randomized experiment by balancing the distribution of independent variables in the treated and control groups. Treated individuals covered by NCMS- and non-treated individuals may have personal characteristics that both affect the decision to participate in NCMS and the outcome of interest: consumption and saving behaviour. PSM balances the observable characteristics of individuals between both groups and match them according to their probability to enrol. We assume that there is no difference between both groups in terms of unobservables (Rosenbaum and Rubin, 1983). Second, as we do not need to specify any model for the different outcomes with this method, PSM reduces the risk of biases due to inappropriate model specification. Third, PSM consolidates the treatment effect estimates by setting the constraint of a common support region between treated and control groups. To compare consumption and savings rates between participants and non participants, we first predict the probability of participating in the scheme using a probit regression, we use the following specification 11 :. (3) where is the household participation in NCMS, is a set of controls, is the constant and is the error term. The set of controls includes: health controls: the minimum average waiting time at the nearest health institution, the availability of medicines at the nearest health institution, the travel time by bike to the nearest health institution, the share of ill members in the household during the past four weeks and the enrolment to the old Cooperative Medical Scheme in socioeconomic characteristics of households: the household size, whether there is a child and/or an elderly person needing care in the household, whether one of the household member is a farmer, the age the head of household, whether the head of household completed a higher or professional degree and whether the household belongs to the lowest quartile of income. community level controls: the number of households living in the community. Using the propensity score function obtained from the probit, we measure the average treatment effect of the treated (ATT) for the 2006 wave. 11 We checked the balanced property of this specification which ensures that the two groups are identical based on Z. 8

9 where and refer to the shares of savings and total consumption expenditures in 2006 for participants and non-participants, respectively. The term «X» refers to observable variables controlled by the probit. The Stata command psmatch2 developed by Leuven and Sianesi (2010) is used to pair off households according to the set of causal variables, «X». We use three methods with narrowing calipers: one-to-one, k-nearest neighbour and kernel methods with bootstrap replications for the estimations. 5. Results a. OLS and IV regressions Results for OLS and IV regressions are reported in Tables 2 and 3. We find that NCMS participants tend to save less than non-participants with OLS. IV regressions confirm this result with a magnitude that is one half higher than OLS coefficients. When we instrument household participation in NCMS, we observe that participants tend to increase their total consumption expenses and reduce their savings compared to non participants. Table 2: OLS VARIABLES Savings rate (%) NCMS ** (21.28) Observations 1,152 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 3: IV VARIABLES Savings rate (%) NCMS *** (41.50) Observations 1,152 R-squared Underid. test statistic Cragg Donald Wald F statistic 0 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 9

10 b. PSM estimations i. Probit regression: participation in NCMS Results of the probit regression 12 are reported in Table 4. As regards health variables, characteristics of the supply of healthcare influence the participation in the scheme. Not surprisingly, the longer the household has to wait at the nearest institution or the longer the travel time by bike to the nearest health institution is, the less likely it will participate in the scheme. Moreover, households who participated in the old CMS are also more likely to join the new scheme. Regarding socioeconomic data, the younger the head of household is, the more likely he will enrol his family. In addition, households whose head holds a higher or professional degree tend to participate much less in the scheme. This might be due to the fact that 70% of these households take part in other insurances (urban or commercial insurances). Having a farmer in the household is also a determinant of NCMS take up. Finally, as regards community level-data, a higher number of households in the community will induce a lower probability to participate in the scheme. The size of household and the presence of a child or an elderly person needing care do not affect the decision to participate as the participation unit for NCMS is the whole household itself. The availability of medicine does not have any significant impact on participation. At last, households from the lowest quartile of income do not tend to enroll more than other households ii. Propensity Score Matching: average effect of NCMS on households consumption and savings Table 6 (Appendix B) reports estimates of average treatment effect of the treated (ATT) at the household level using one-to-one matching method with narrowing callipers equal to 0.5, 0.01 and 0.005, k-nearest neighbour matching method with a decreasing number of neighbours (7, 5 and 2) and kernel matching methods with narrowing bandwidths (none, 0.01 and 0.005). The use of different methods and narrowing callipers is also a mean to check the robustness of our results. We find a statistically significant impact of NCMS take up on household consumption and savings, which confirms our findings with the OLS and IV estimations. Participant households decrease their savings rate by 42.5% on average compared to non-participant households and symmetrically their consumption rate raised by 42.5% on average. 12 All variables are expressed at the household level or at the community level. 10

11 Table 4 : Determinants of enrolment to NCMS VARIABLES Observations 731 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Enrollment to NCMS Age of the head of household (hh) * (1.89) Size of household (0.70) CMS insurance in *** (3.95) At least one farmer in household 0.385*** (3.34) The head of hh completed higher/professional education ** (2.40) At least one child in the household (0.34) At least one elderly person needing care in the family (1.30) Share of ill people in household (1.47) Minimum average waiting time at the nearest institution *** (5.47) Availability of medicines at the nearest institution (1.19) Travel time to the nearest institution * (1.66) Lowest quartile of income (0.03) Number of household in the community *** (3.26) Constant (1.15) 11

12 6. Control for the year of NCMS implementation a. Method We check whether the impact of NCMS is persistent over time. We divide our sample in three sub-samples: one that gathers NCMS counties where NCMS was introduced in 2003 and 2004, another sample gathering NCMS counties where the scheme was implemented in 2005 or 2006 and a last one focusing only on NCMS counties where the health insurance was introduced in We run OLS and IV regressions with the same set of control variables used in the first step of our methodology:,,.,,.,, (3) where, is the level of the dependent variables (share of saving and consumption expenditures) of the household h living in NCMS counties where NCMS was implemented in year year. Year refers to , or 2006., is a set of control variables at the household and community levels,, the constant and,, the error term which controls for unobservables affecting the outcome of interest., denotes the participation of the observed household in the scheme for one of the sub-samples 13. We control for the endogeneity of NCMS and instrument the NCMS take-up rate using the percentage of enrolled household in the community excluding the observed household 14. b. Results Results of OLS and IV regressions are reported in Tables 7 and 8. When we look at OLS impact coefficients, we find that NCMS has a significant negative impact on household savings rate which is smoothed over time. The longer the scheme has been implemented, the weaker the impact is. We observe a strong impact in the two years following the implementation. After two years, the coefficient is lower. 13 All our variables are expressed for the year 2006, which is three years after the introduction of NCMS. 14 The underidentification test (Kleibergen-Paap rank Lagrange Multiplier statistic) and the weak instrumentation test (Cragg-Donald statistic) check the validity of the instrument. 12

13 When we control for NCMS endogeneity using a 2SLS regression, we also find that NCMS has a significant impact on participant saving when NCMS was introduced in the period but also in The impact coefficient is higher when NCMS was introduced in the two years preceding Both methods show that NCMS does not change saving and consumption behaviours of participant households in the year following its introduction. NCMS does have an impact in the two years following its implementation and its impact remains 4 years after but with a lower magnitude Tables 7.1: OLS, Control for the year of NCMS introduction 2003 and 2004 VARIABLES Savings rate (%) NCMS (32.49) Observations 430 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Tables 7.2: OLS, Control for the year of NCMS introduction 2005 and 2006 VARIABLES Savings rate (%) NCMS ** (25.80) Observations 640 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Tables 7.3: OLS, Control for the year of NCMS introduction 2006 VARIABLES Savings rate (%) NCMS (54.92) Observations 200 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 13

14 Tables 8.1: IV, Control for the year of NCMS introduction 2003 and 2004 VARIABLES Savings rate (%) NCMS ** (47.12) Observations 430 R-squared Underid. test statistic Cragg Donald Wald F statistic 0 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Tables 8.2: IV, Control for the year of NCMS introduction 2005 and 2006 VARIABLES Savings rate (%) NCMS *** (43.81) Observations 952 R-squared Underid. test statistic Cragg Donald Wald F statistic 0 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Tables 8.3: IV, Control for the year of NCMS introduction 2006 VARIABLES Savings rate (%) NCMS (315.9) Observations 133 R-squared Underid. test statistic Cragg Donald Wald F statistic Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 14

15 7. Discussion This paper aims at assessing the impact of the New Cooperative Medical Scheme on household saving and consumption behaviour of Chinese rural residents. Using data provided by the China Health and Nutrition Survey for the 2006 wave, we implement Ordinary Least Square regressions and Instrumental Variable regressions to control for observable variables and endogeneity of NCMS participation. We find that NCMS has a positive and significant impact on households propensity to consume and a statistically significant negative impact on their savings rate. We check the robustness of our results with a propensity score matching using enrolled households as the treatment group and non-enrolled households as the control group. This method confirms OLS and IV methods. When we control for the year of implementation of NCMS, we find that the health care scheme does not have any immediate effects, that is to say in the year following its introduction, on consumption and saving behaviour. However, NCMS participants do increase their consumption expenses and reduce their savings in the two years following its introduction. This impact remains significant but lower 3 to 4 years after its implementation. OLS, IV and PSM confirm that NCMS lowers enough participants income risk and allow them to consume more and decrease their precautionary savings. However these adjustments are not instantaneous due to the reimbursement delay. This result is coherent with the conclusion of Wagstaff, Lindelow, Jun et al. (2007). They explained the absence of significant impact of NCMS on health expenses because of the long reimbursement delay of the health care scheme. These changes in savings and consumption expenditures happen in the two years following the introduction of the system and remain 3 and 4 years after. The NCMS impact on savings and consumption expenses is persistent but these changes are smoothed over time. One extension of the paper would be to study how households allocate their decrease of savings and to check whether they do increase their durables expenditures. 15

16 Appendix A : Sample statistics Table 1. Distribution of implementation of NCMS and participation in the scheme in 2006 No Insurance NCMS All Participants Non Participants number of households share of the sample Source: CHNS, Authors' calculations Table 2a. Mean expenditures and savings of participants and non-participants accross NCMS counties, in yuans, in 2006 Participants Nonparticipants Net income 22254, ,99 SD (23698,82) (27772,92) N Total expenditures 15619, ,89 (20287,63) (20727,38) Savings 6634, ,101 (27705,1) (26872,05) Source: CHNS, Authors' calculations Table 2b. Average shares of expenditures and savings of net income of participants and nonparticipants accross NCMS counties, in %, in 2006 Total expenditures Participants Nonparticipants 70,2 65,4 Savings 29,8 34,6 Source: CHNS, Authors' calculations 16

17 Appendix B Table 6: Results for the propensity score matching Savings rate (%) 728 One to One K-nearest Kernel 0,5 0,01 0, no 0,5 0,01 N ATT p-val ATT p-val ATT p-val ATT p-val ATT p-val ATT p-val ATT p-val ATT p-val ATT p-val -42,02 0,062-31,689 0,321-42,293 0,287-42,56 0,053-41,02 0,047-41,22 0,046-42,04 0,036-42,52 0,034-46,76 0,029 17

18 Bibliography Blanchard, O. and Giavazzi, F. (2006). Rebalancing Growth in China: A Three-Handed Approach, CEPR Discussion Papers 5403, C.E.P.R. Discussion Papers. Baldacci, E., Callegari G., Coady, D., Ding, D., Kumar, M., Tommasino, P. and Woo, J. (2010). Public Expenditures and Household Consumption in China, IMF Working Papers, WP/10/69, March Barnett, S. and Brooks, R. (2010). China: Does Government Health and Education Spending Boost Consumption? IMF Working Papers, WP/10/16, January Chamon M. and Prasad E. (2008). "Why are Saving Rates of Urban Households in China Rising?", NBER Working Papers 14546, National Bureau of Economic Research, Inc. Chou, S.Y., Liu, J. T. and Hammitt, J.K (2006). National Health Insurance and precautionary saving: evidence from Taiwan, Journal of Public Economics, vol. 87, issues 9-10, Sept 2003, Dong K. (2009). Medical Insurance System Evolution in China, China Economic Review, vol.20, issue 4, Dec 2009, Fitoussi, J-P. and Saraceno, F. (2008). The Intergenerational Content of Social Spending: Healthcare and Sustainable Growth in China, Document de travail, OFCE , Septembre Gruber, J. and Yelowitz, A. (1999). "Public Health Insurance and Private Savings," Journal of Political Economy, vol. 107, issue 6, pages , December. Hubbard, G. R., Skinner, J. and Zeldes, S.P. (1995). Precautionary Saving and Social Insurance, The Journal of Political Economy, vol. 103, No. 2, April, 1995, Leuven, E. and Sianesi, B. (2003). PSMATCH2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support and Covariate Imbalance Testing. Liu, Y. (2004). Development of the Rural Health Insurance System in China, Health Policy and Planning, 19, issue 3, Maynard, A. and Qiu, J. (2009). Public Insurance and Private Savings: Who is affected and by How Much?, Journal of Applied Econometrics, vol.24, issue 2, March 2009, Rosenbaum, P. and Rubin, D. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika, 70, Starr-McCluer, M. (1996). Health Insurance and Precautionary Savings, American Economic Review, American Economic Association, vol.86(1), , March Stuart, E. (2010). "Matching Methods for Causal Inference: A review and a Look Forward", Statistical Science, vol.25(1),

19 Wagstaff, A., Lindelow, M., Gao, J., Xu L. and Qian, J. (2007). Extending Health Insurance to the Rural Population: An impact Evaluation of China s New Cooperative Medical Scheme, World Bank Policy Research Working Paper 4150, March Zhang X. and Kanbur R. (2003). Spatial inequality in education and Health Care in China, Working Paper , Department of Applied Economics and Management, Cornell University. 19

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