Impacts of health insurance on saving and consumption expenses by income groups in rural China

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1 Impacts of health insurance on saving and consumption expenses by income groups in rural China Diana Cheung and Ysaline Padieu Sorbonne Economic Centre, University of Paris 1 Panthéon-Sorbonne Preliminary Version Please do not cite without permission of authors January, 2012 Abstract: Using data from the China Health and Nutrition Survey for the 2006 wave, this paper assesses the impact of the New Cooperative Medical Scheme (NCMS) by quartile on saving and consumption behaviour. We implement ordinary least square regressions and then control the endogeneity of NCMS participation with an instrumental strategy. We find evidence that NCMS enhances consumption and lessens savings of lower middle income participants, while it does not affect poorest households that might be trapped in poverty. This effect holds when we use propensity score matching. 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. 1

2 1. Introduction Despite the extension of Chinese health coverage from 1993 and 2003, access to healthcare remains difficult, especially for rural households and poorer households. In 2003, 22% of rural residents 15% could not afford to seek healthcare compared to 15% of urban residents (Liu, Rao, Wu and Gakidou, 2008). Liu et al. (2008) pointed out that low-income households had a lower coverage compared to richest households and tended to avoid consuming healthcare. Rising inequalities in access to health care between urban and rural residents, as well as between income groups led Chinese policy makers to set this issue on the agenda. Health is indeed a key issue for development. Promoting health creates a virtuous circle: it improves the health status of the population and enables poorest households to drag themselves out of the poverty trap. From 1997 on, with the collaboration of researchers from diverse worldwide institutions, policy makers started various pilot programs with different designs in order to find the best way to promote access to health care for all marks a turning point in health care reforms with the launch of the New Cooperative Medical Scheme (NCMS) which intends to cover rural residents. The goal of the scheme is to relieve rural households of the burden of health expenditures, but also to reduce precautionary savings in a view of rebalancing Chinese growth towards consumer welfare. In this article, we assess the impact of NCMS on consumption and savings across income groups in rural China. We investigate whether health insurance coverage can decrease household saving rate and encourage their consumption, particularly among poor households. The impact of the implementation of health insurance on household precautionary savings is contrasted in the literature. Chou and al. (2006) proved that insured households in Taiwan reduced their saving rates by by 3% to 7.5% according to the type of insurance, while Starr- Mc Cluer (1996) found a positive impact of private health insurance on wealth assets in the United States. This discrepancy could be due to heterogeneous impact of health insurance on savings across income groups. On one hand a series of studies showed that health insurance has a negative impact on saving of low income households eligible for Medicaid. (Hubbard and al., 1995; Gruber and Yelowitz, 1999). Other studies led on Medicaid households showed that this health insurance only affects middle income participants (Maynard and Qiu, 2007). Few works investigate the impact of health insurance on household savings across income groups to study whether the poorest will benefit from the insurance and consume more. Using 2

3 the China Health and Nutrition Survey, this paper is the first article studying the effect of NCMS on savings as well as consumption expenditures by quartile of income. We run an ordinary least-square regression (OLS) to control for observables. As the NCMS participation is on a voluntary basis, we control for adverse selection with an instrumental variable regression (IV). We find evidence of the impact of NCMS on household saving and consumption according to the three methods for lower middle income group. These results are robust to the propensity score matching method (PSM). The rest of the paper is organised as follows. Section 2 gives a brief presentation on the introduction of NCMS. Section 3 describes the data and section 4 the empirical strategy. Section 5 presents the results. Section 6 tests the robustness of our outcomes using a propensity score matching 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) 1 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). 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 1 Free and universal health coverage organized around the workplace, during the communist period. 3

4 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 2 (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 according to the household level of income. 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 counties where NCMS was introduced. Moreover, we focus on rural 3 households answering to questions on both expenses and health sections. Hence our sample is reduced to 1314 households. In 2006, 71.7% of these households (934) decided to join NCMS (see table 1). 2 See Dong (2009) for further details on premiums or reimbursement ratios. 3 A rural resident refers to citizens holding rural registration and not necessarily someone living in a rural area. 4

5 b. Variables As this paper aims at assessing the impact of NCMS on rural household consumption and saving behaviours, we focus on two groups of outcome: savings and total consumption expenses. The key dependent variable is the household level of saving. It was constructed as the residual of household total net income and total consumption expenses in durables and non-durables. The China Health and Nutrition Survey provides a detailed section on household behaviours, which allows us to construct household consumption expenditures. Consumption expenses cover food consumption, high tech 4, electrical appliances, health expenses, bride and dowry expenses as well as another subdivision 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 variable of interest 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, introduced before. Health variables include a dummy variable referring to the presence of at least one ill member in the household, 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 institution, the maximum travel time to reach by bike a heath care institution in the community, the maximum and 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 one of the household members is a farmer, the number of households living in the community and quartiles of income. This latter is obtained adding wages, net revenues from production and 4 High Tech items refer to computers, telephones, cell phones, DVDs and VCDs player as well as satellite dish. 5

6 entrepreneurship, net subsidies, income received from rent, from non household family members as well as cash received as a gift. We also create a dummy referring to whether the household lives in one of the five richest province of our sample (Liaoning, Shandong, Jiangsu, Hunan and Hubei). c. Baseline descriptive statistics of the sample In 2006, 64% of the poorest households of our sample (515 households) were exposed to NCMS and 73% -332 households, decided to join NCMS (see appendix A). As for the second income quartile of our sample, 58% lives in counties where the scheme has been implemented. The participation rate for the second quartile of income is 72%. 63% and 70% of households from the third and fourth quartiles were exposed to the scheme and 73% and 66% enrolled respectively. The table in appendix B reports descriptive statistics for the different outcomes of interest - household s savings and total consumption expenditures across NCMS counties (among participants and non-participants). Broadly speaking, in 2006, households from the first and second quartiles spend more than what they earn and thus dissave on average, while households from the third and fourth quartiles do not. Among poorest households, participants spend on average 25% more than non-participants from the same quartile, yuan and 9190 yuan respectively for the first quartile; and yuan against yuan for the second quartile. Furthermore for poorest households, as participants average net income is only 13% higher than non-enrolled households average net income, they have a greater depletion in savings (-7565 yuan and yuan, respectively). For lower-middle income households, participants earn on average less than non-participants (11395 yuan and yuan), consequently they dissave substantially more than non-participants (-3985 yuan vs -60 yuan, that is to say 67 times more). The same observation holds for upper-middle income households, participant average net income is less important than non-participants one and they have greater consumption expenditures (around 12% more). Participants from this quartile of income thus save less than their non-participants counterparts (around 60% less). 6

7 We observe a n opposite relationship for richest households, as non-participants earn on average more (+7%), but also spend more (+23%) and a fortiori save less (-4.5%) compared to participants from the same income group. 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) where, is the level of the dependant variables (saving and consumption expenditures) of the household h from the quartile i (i takes values from 1 to 4),, a set of control variables at the household and community levels for quartile i,, 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 5., refers to demographic and socioeconomic variables for a specific quartile such as household size (whether it is larger than three), number of households living in the community, household income, gender of the head of household, whether this later has received higher education degree or a professional degree, whether the head of households works but also whether one member of this household is a farmer. We also add in the regression counties dummy and omit the richest province of our sample. 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 5 All our variables are expressed for the year 2006, which is three years after the introduction of NCMS. 7

8 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 6. We assume that the higher the coverage is in the community, the more credible 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). 5. Results for OLS and IV regressions Results for OLS and IV regressions are reported in Tables 2 and 3. Using OLS regressions, we find that lower-middle-income households are more likely to decrease their savings and increase their consumption expenditures compared to non-participants from the same quartile of income. When we instrument NCMS enrolment, participant households from the second and quartile of income tend to reduce their savings and consume more compared to nonenrolled households. According to OLS results, lower middle income tend to decrease significantly their savings and increase their consumption expenses compared to non enrolled households from the same quartile. IV regressions confirm this result with a magnitude of impact coefficient that is only one tenth higher than OLS coefficients and lower than household income of this quartile. When we instrument the household participation in NCMS, we observe that upper-middle income tend to increase their total consumption expenses and reduce their savings compared to non participant households from the same quartiles. The magnitude of IV coefficients is high for the third quartile (four times greater than OLS coefficients). The coefficient is half time greater that the mean of household income from this 6 The underidentification test (Kleibergen-Paap rank Lagrange Multiplier statistic) and the weak instrumentation test (Cragg-Donald statistic) check the validity of the instrument. 8

9 quartile. This difference suggests a decrease in savings fueled by a reduction of household patrimony. Tables 2: OLS, by quartile VARIABLES Savings Total Expenditures NCMSquart1-4,659 4,659 (5,157) (5,157) Constant -1,945 1,945 (11,352) (11,352) Observations R-squared VARIABLES Savings Total Expenditures NCMSquart2-8,398** 8,398** (3,373) (3,373) Constant -17,362* 17,362* (9,989) (9,989) Observations R-squared VARIABLES Savings Total Expenditures NCMSquart3-6,812 6,812 (4,247) (4,247) Constant -18,480 18,480 (14,752) (14,752) Observations R-squared VARIABLES Savings Total Expenditures NCMSquart4 1,817-1,817 (3,472) (3,472) Constant -18,542* 18,542* (9,961) (9,961) Observations R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 9

10 Tables 3: IV, by quartile (1) (2) VARIABLES Savings Total Expenditures NCMSquart1-16,064 16,064 (10,540) (10,540) Constant 7,679-7,679 (14,648) (14,648) Observations R-squared Underid. test statistic Chi-sq(1) P-val 4.44e e-08 Cragg Donald Wald F statistic (1) (2) VARIABLES Savings Total Expenditures NCMSquart2-9,413* 9,413* (5,524) (5,524) Constant -16,960* 16,960* (9,894) (9,894) Observations R-squared Underid. test statistic Chi-sq(1) P-val 0 0 Cragg Donald Wald F statistic (1) (2) VARIABLES Savings Total Expenditures NCMSquart3-27,412*** 27,412*** (10,260) (10,260) Constant -4,649 4,649 (15,973) (15,973) Observations R-squared Underid. test statistic Chi-sq(1) P-val 0 0 Cragg Donald Wald F statistic (1) (2) VARIABLES Savings Total Expenditures NCMSquart (8,655) (8,655) Constant -17,985 17,985 (10,966) (10,966) Observations R-squared Underid. test statistic Chi-sq(1) P-val 0 0 Cragg Donald Wald F statistic Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 10

11 6. Robustness check using another estimation framework: PSM In order to check whether our findings with OLS and IV are robust we control for the endogenous take up of NCMS using a propensity score matching (PSM) method. As we do not need to specify any model for the different outcomes with this method, PSM also allows us to reduce the risk of biases due to inappropriate model specification. a. Method PSM allows empirical ex-post policy evaluation by creating a counterfactual and addressing the household adverse selection problem. Treated individuals covered by NCMS- and nontreated individuals might have personal characteristics that both affect the decision to participate in NCMS and the outcomes of interest: consumption and saving behaviours. PSM balances the observable characteristics of individuals between the two groups and match them according to their probability to enrol. We assume here using this empirical strategy that there is no difference between the two groups in terms of unobservables (Rosenbaum and Rubin, 1983). To compare levels of consumption and saving between participants and non participants, we first predict the probability of participating in the scheme using a probit regression. As the determinants of participation in NCMS might not be the same for poorer and richer households and as our specifications should be balanced 7, we use two different specifications, one similar for the first two quartiles of income and another for the last two quartiles.,,. where, is the household participation in NCMS for each quartile of income,, i takes the values 1, 2, 3 and 4, is a set of controls and is the error term. For the first-half level of income of our sample, the set of controls includes the number of households living in the community, the young and old dependency ratios in the community, the minimum average waiting time at the nearest health institution, the share of ill member in the household during the past four weeks, the enrolment to the old Cooperative Medical Scheme in 2000, the age and gender of the head of household, whether one of the household 7 A balanced propensity score function, p(x), ensures that p(x) represents well the set of controls Z, i.e both groups are identical based on Z. 11

12 member is a farmer and whether the household lives in one of the five richest provinces of our sample. For the second-half income, the causal variables control for household size (whether there are more than three members in the household), the number of households living in the community, the availability of medicines, the travel time by bike and the maximum average waiting time in health care institutions of the community, the presence of at least one ill member in the household during the past four weeks, the enrolment to the old Cooperative Medical Scheme in 2000, whether one of the household member is a farmer, the age of the head of household and whether the household lives in one of the five richest provinces of our sample. Using the propensity score function obtained from the probit, we measure the average treatment effect of the treated (ATT) for the 2006 wave. where and refer to the level 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. b. Results i. Probit regression: participation in NCMS Results of the probit regression for each quartile are reported in Tables 4 (first and second quartile) and 5 (third and fourth quartile) 8. We observe a great disparity in the determinants of NCMS take up across the different income groups. Only one independent variable seems to influence the participation in the scheme for all the households: the longer the household has to wait at the nearest institution, the less likely it will participate in the scheme. 8 All the variables are expressed at the household level or at the community level. All variables (except the number of households in the community and the geographical location of households) were constructed from individual variables provided by the CHNS. 12

13 As regard other health variables, a greater share of ill members in the family tend to encourage NCMS enrolment for the first quartile, though it does not influence the second quartile. Similarly, having a sick member in the household has a positive significant impact on the participation decision of the richest households, but not for the upper-middle income group. A longer travel time to the nearest institution has a negative impact for both the third and the fourth quartiles. Moreover households from these quartiles of income who participated in the old CMS are also more likely to join the new scheme. As regards community level-data, a higher number of households in the community will induce a lower probability to participate in the scheme for the richest half households of the sample. If the household lives in one of the five richest provinces of our sample, its probability of enrolling is higher for the first, second and third quartiles. The young and old dependency ratios have an impact only on the poorest: a higher old dependency ratio encourages the participation in NCMS and inversely for the young dependency ratio. Finally, regarding demographic data, the age of the head of household is a determinant of NCMS take up for the first quartile, the older the head of household is, the less he will enrol his family. For the second quartile, a woman is more likely to enrol her family when she is the head of household. ii. Propensity Score Matching: average effect of NCMS on households consumption and savings Table 6 (Appendix) reports estimates of average treatment effect of the treated (ATT) at the household level using one-to-one matching method with narrowing caliper 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 only for the lower middle income group, which confirms our findings with the OLS estimation. Participant households decrease their savings by 7700 yuan on average compared to non-participant households and spend on average 7300 yuan more. 13

14 Table 4 : Determinants of enrolment to NCMS for quartiles 1 and 2 Quartile 1 Quartile 2 VARIABLES Enrollment to NCMS Enrollment to NCMS Age of the head of household (hh) * ( ) ( ) At least one farmer in hh 0.546** 0.551*** (0.224) (0.208) Gender of hh ** (0.251) (0.363) Minimum Average Wait *** *** ( ) ( ) Share of ill people in hh ** (0.420) (0.660) Nb of households in the community Richest province of the sample (8.35e-05) 0.627*** (0.228) -7.46e-05 ( ) 0.493** (0.218) Old dependency ratio 1.407** (0.595) (0.719) Youth dependency ratio *** (0.872) (0.746) CMS insurance in (0.397) Constant 1.745*** (0.606) (0.604) Observations Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 14

15 Table 5 : Determinants of enrolment to NCMS for quartiles 3 and 4 Quartile 3 Quartile 4 VARIABLES Enrollment to NCMS Enrollment to NCMS Age of hh ( ) ( ) At least one member of household is a farmer (0.182) 0.575*** (0.178) Size of household (0.171) (0.160) At least one member of household is ill (0.186) 0.433** (0.194) Maximum Average Wait ** *** ( ) ( ) Richest province of the sample Number of household in the community 0.318* (0.174) *** (7.89e-05) (0.194) ** (6.64e-05) Availability of medecine (1.029) (1.015) Travel Time at the nearest institution ** ( ) *** ( ) CMS Insurance in * 0.548** (0.321) (0.230) Constant (1.127) (1.128) Observations Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 15

16 7. Discussion This paper uses data from China Health and Nutrition Survey for the 2006 wave to estimate the impact of New Cooperative Medical Scheme by quartile of income on household saving and consumption behaviour of rural Chinese households. We control for observables implementing Ordinary Least Square regressions and use an Instrumental Variable strategy to deal with endogeneity of NCMS participation. We check the robustness of our result with a propensity score matching using enrolled households as the treatment group and non-enrolled households as the control group. The PSM outcomes confirm the results of OLS and IV regressions. Nevertheless we observe a significant negative impact of NCMS on savings for upper middle income households and a positive impact on total consumption expenses that are not confirmed by PSM. We discuss the impacts of NCMS by quartiles using OLS, IV and PSM corroborating results. Lower middle income households are the only ones that are significantly affected by the scheme. After enrolling NCMS, households from quartile 2 are more likely to save less. This result is confirmed by OLS and IV regressions as well as propensity score matching with coefficients varying from 7000 yuan to 9800 yuan a year. This outcome is positive as it shows that NCMS enables lower middle income participants to increase their consumption expenses by lowering their income risk. However, poorer households do not significantly change their saving and consumption behaviour, suggesting that NCMS does not reduce their income risk enough to make them consume more and save less. This result is consistent 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. Poorer enrolled households will not consume health care if they cannot afford it and if they are uncertain about reimbursements. This could let them in a poverty trap impeding poorest rural households to consume or invest more. Richer enrolled households (from quartile 3 and 4) are not affected by the scheme. This result could be explained by the fact that these households can afford health care even without participating in NCMS. Some of these richer households subscribe another health insurance: 20% of these households have an urban insurance and 10% enrolled a commercial insurance, indicating that they are already covered. These insurances allow households to consume health care even if they do not participate in NCMS, reducing the impact on NCMS on 16

17 participant saving and consumption behaviors. To conclude, NCMS does have an impact on lower middle income participants reducing their income risk and enabling them to access more consumption goods. Poorest households are not affected by the scheme in terms of savings and consumption expenditures, making us think that they could be trapped in poverty, being too poor to afford health care consumption and change their consumption and saving behaviour. Further research could be done to understand the mechanisms at work in this poverty trap and improve their situation. Poorest households should be covered by a specific insurance targeting their needs. Moreover, we could focus, as we did in a previous preliminary paper, on the credibility of NCMS. We observed that the longer NCMS has been implemented in a community, the more people understand how the scheme works, the more significant changes in participants saving and consumption behaviour are, as households make intertemporal adjustments. Unfortunately such a study is not possible in this paper as the number of observations by quartile in our database is too low to implement a propensity score matching. 17

18 Appendix A Table 1a. Distribution of implementation of NCMS and participation in the scheme by income groups Sample number NCMS counties of observation All Participants Non-participants Quartile Quartile Quartile Quartile Source: CHNS, Authors' calculations Table 1b. Share of implementation of NCMS and participation in the scheme by income groups (in %) NCMS counties All Participants Non-participants Quartile Quartile Quartile Quartile Source: CHNS, Authors' calculations 18

19 Appendix B Mean characteristics of participants and non-participants across NCMS counties in 2006 Participants Non-participants Net income 4847, ,202 SD ( ) (2340,147) N Quartile 1 Total expenditures 12413, ,562 ( ) (13687,45) Savings -7565, ,36 ( (13303,36) Net income 11393, ,85 (2195,261) (2099,493) Quartile 2 Total expenditures 15378, ,44 (23201,84) (14250,53) Savings -3985,567-58,58537 (23262,04) (14263,51) Net income 20562, ,6 (3539,61) (3521,171) Quartile 3 Total expenditures 18449, ,23 (29589,44) (17855,64) Savings 2112, ,368 (29736,75) (18340,8) Net income 51100, ,86 (30191,54) (32645,59) Quartile 4 Total expenditures 21706, ,65 (26527,33) (26257,74) Savings 29393, ,21 (37709) (34377,51)

20 Appendix B Table 6: Results for the propensity score matching One to One K-nearest Kernel 0,5 0,01 0, no 0,01 0,005 ATE p-val ATE p-val ATE p-val ATE p-val ATE p-val ATE p-val ATE p-val ATE p-val ATE p-val Quartile1 Savings 7173,3 0, , , ,4 0, ,23 0, ,4 0, ,53 0, ,83 0, ,099 Total Expenditures -2989,96 0, , ,2 0, ,03 0, ,78 0, ,3 0,802-58,602 0, ,2 0, ,3 0,252 Quartile2 Savings -8733,9 0, ,4 0, ,8 0, , , ,8 0, , , ,045 Total Expenditures 8224,19 0, ,49 0, ,53 0, ,6 0, ,1 0, ,96 0, ,5 0, ,7 0, ,9 0,054 Quartile3 Savings -252,203 0, ,9 0, ,4 0, ,2 0, ,5 0, ,8 0, ,7 0, ,4 0, ,1 0,542 Total Expenditures -447,585 0, ,24 0, ,82 0, ,43 0, ,79 0, ,804 0, ,27 0, ,53 0, ,77 0,711 Quartile4 Savings 7173,3 0, , , ,4 0, ,23 0, ,4 0, ,53 0, ,83 0, ,099 Total Expenditures -2989,96 0, , ,2 0, ,03 0, ,78 0, ,3 0,802-58,602 0, ,2 0, ,3 0,252 20

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