Health insurance and consumption: Evidence from China’s New Cooperative Medical

Scheme

Chong-En Bai Binzhen Wu*

Tsinghua University

Abstract

We exploit a quasi-natural experiment arising from the introduction of a health insurance

program in rural China to examine how the insurance coverage affects household

consumption. The results show that on average, the health insurance coverage increases

non-healthcare related consumption by more than 5 percent. This insurance effect exists

even for households with no out-of-pocket medical spending. In addition, the insurance

effect is stronger for poorer households and households with worse self-reported health

status. These results are consistent with the precautionary saving argument. Moreover, the

insurance effect varies by household experience with the program. Particularly, the effect is

significant only in villages where at least some households have actually obtained

reimbursement from the insurance program. Within these villages, the program stimulates

less consumption among the new participants than among households that have participated

in the program for more than one year.

Keywords: Precautionary savings; Health insurance; Consumption; New Cooperative

Medical Scheme; Chinese savings

JEL Classification Nos.: D12; D91; E21; I18

* Corresponding author. Email: wubzh@sem.tsinghua.edu.cn; Tel: 86-10-62772371. For their valuable

suggestions, we want to thank participants in the workshops for Saving and Investment in China at Tsinghua

University. Chong-En Bai acknowledges supports from National Planning Office of Philosophy and Social

Sciences Major Grant 10zd&007. Binzhen Wu acknowledges supports from the National Natural Science

Foundation of China (Project ID: 70903042).

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1 Introduction

Over the past several years, considerable international attention has been directed at the

high level of savings by Chinese households. The household saving rate rose by about 10

percentage points from 1995 to 2008, accounting for 28% of the disposable income in 2008.

This increase is higher than that observed in most other countries including East Asian

nations (Prasad, 2009). A popular explanation for China’s high saving rate is that the

dissolution of the traditional social safety net has prompted higher precautionary savings

(Chamon and Prasad, 2010). Accordingly, the Chinese government has exerted extensive

efforts to improve China’s social safety net. The social insurance programs disbursed RMB

1.2 trillion in 2009, with an annual growth rate of 19.4% since 2000; the health insurance

program disbursed RMB 0.28 trillion in 2009, with an annual growth rate of 47% since

2000 (Chinese Statistical Yearbook, 2010).

Existing empirical literature provides mixed evidence on the importance of

precautionary savings. The results range from minimal to substantially important. Recent

studies have exploited the exogenous variations in insurance coverage caused by policy

changes. These studies include Gruber and Yelowitz (1999), Engen and Gruber (2001), and

Kantor and Fishback (1996) for the US; Atella, Rosati, and Rossi (2005) for Italy; Wagstaff

and Pradhan (2005) for Vietnam; and Chou, Liu, and Hammitt (2003) for Taiwan. A few

Chinese studies, including Ma, Zhang, and Gan (2010), Liu et al. (2010), and Brown,

Brauw, and Du (2010), use the launch of public health insurance programs in China to

investigate the size of precautionary savings in the country. However, they deliver different

conclusions because of the differences in methodology and dataset.

2

This paper exploits one of the most important policy changes implemented in rural

areas: the introduction of the New Cooperative Medical Scheme (NCMS) in July 2003.

This public health insurance program was introduced sequentially in different counties, and

household participation is voluntary. To estimate the insurance effect that excludes the

effects of contemporary policy changes, we focus on the double-difference comparison of

the insured and non-participants in the villages where the program has been launched. The

difference-in-difference (DID) framework helps eliminate all time-invariant selection

biases. Selection bias on observables is further reduced by enabling the temporal change in

consumption to vary with observable characteristics such as income and health status, or by

applying the DID matching method. Selection on unobservables is examined using the

counterfactual test that applies the estimation model to the time periods at which

households have not enrolled in the program.

The data we use combine the longitudinal Rural Fixed-Point Survey (RFPS) from 2003

to 2006 and a household survey that we conducted on the NCMS for a subsample of the

2006 round of the RFPS. The results indicate that household consumption other than health

expenditures has increased by about 5.6 percent or 149 yuan (18.64 US dollars at the

exchange rate in June 2006) because of the health insurance coverage. The magnitude of

the increase is much larger than the average premium of the insurance, which was typically

30 yuan in 2003 and 50 yuan in 2006. The program stimulates consumption more

effectively than do the cash transfers from the government because the average propensity

of rural households to consume is only about 0.44. The estimate is robust to different

specifications and consistent with the macro time trend of rural household saving rate: the

NCMS has been rapidly rolled out since 2005, and rural household saving rate has started

to decline beginning that same year (Prasad, 2009).

3

We also find that the insurance effect remains significant for households that do not

spend on health care in a given year. This result cannot be explained by the ―crowd-in‖

concept, which emphasizes that the insurance coverage reduces out-of-pocket health

payments, thereby leaving insured households with more income for non-healthcare

expenditure. Even the income effect of government subsidy cannot explain the magnitude

of the increase in consumption. Moreover, the positive effect of insurance on consumption

is much stronger for poorer households and households with worse self-reported health

status. Given that these households tend to have a higher risk of incurring large health

expenditure relative to income, these results are consistent with the explanation that points

to precautionary savings. In addition, the positive insurance effect increases when the

NCMS provides more generous compensation for household health expenditure at

county-level hospitals, which is also consistent with the precautionary saving justification.

Finally, the insurance effect varies depending on household experience with the

program. Particularly, the level of trust accorded to the program can be crucial to the

stimulation of consumption. The insurance effect on consumption is significant only in the

villages where at least some households have obtained reimbursement from the program,

through which trust in the program is established. Moreover, in such villages, the increase

in consumption is much larger among the experienced members who have participated in

the program for more than one year than that among the new members. By contrast, in the

villages where no household has received any reimbursement, neither the average insurance

effect nor the difference between the experienced and new participants is significant.

To the best of our knowledge, this paper is one of few studies that exploit this policy

change in systemically assessing the effect of the health insurance program on consumption

in rural China. In addition, we have examined the importance of the precautionary saving

4

explanation and heterogeneity of the insurance effect, which are mostly unaddressed in

related Chinese studies. The findings on the roles of trust and experience warrant more

attention in terms of research and policymaking, particularly for public insurance programs

in developing countries where transparency and trust are often lacking.

The rest of the paper is organized as follows. Section 2 introduces the background of

the NCMS and presents the literature review. Section 3 introduces the data and provides

descriptive statistics. Section 4 discusses our econometric specifications. Section 5 shows

the results for the baseline model, and Section 6 presents the robustness tests. Section 7

concludes.

2 Background

2.1 New Cooperative Medical Scheme

Since the dissolution of the rural Cooperative Medical System in the early 1980s,

illness has emerged as a leading cause of poverty in rural China, and the high cost of health

care has deterred households from obtaining necessary health treatment (You and

Kobayashi, 2009). In response to these problems, the Chinese government initiated a pilot

program of the New Cooperative Medical Scheme (NCMS) in 2003. The pilot program was

launched in 310 rural counties of China’s more than 2800 rural counties in July 2003; it was

expanded to 617 counties in 2005 and 1451 counties in 2006. By June 2007 when we

collected the data for this study, the program had expanded to over 84.9% of all the rural

counties and had covered 82.8% of all the rural residents.

Several key features characterize the NCMS: 1) the program targets rural residents; 2)

participation is voluntary but should be on a household basis;1 3) participating households

1

The requirement of participation on household basis is imposed over 97% of the counties in our sample.

5

are required to pay a flat-rate premium, but the insurance is heavily subsidized by the

governments; 4) the program reimburses participants mainly for in-patient expenses; 5) the

program is operated at the county level rather than at the township or village level.

The voluntary nature of the participation raises concerns on the adverse selection issue

that can threaten the financial sustainability of the NCMS. However, the participation rates

in pilot villages are generally very high, with an average of 86% from 2003 to 2006 in our

sample. An important reason for the high participation rate is the generous government

subsidies. The premium for each member in a subscribing household in 2003 was typically

10 yuan while the government paid 20 yuan a year. Since 2006, the government subsidy has

increased to 40 yuan while household contribution has remained the same.2

Along with the NCMS, the government also implemented some supporting policies,

such as improving the quality and delivery of health care services and strengthening

pharmaceutical governance. At the same time, some studies show that the average

expenditure per visit increased after the introduction of the NCMS program (Yao and

Kobayashi, 2009; Mao, 2005). These changes also affected households that chose not to

participate in the NCMS. Finally, the government set up a parallel program, i.e., the

medical assistance scheme, to help the poverty-stricken population.3

Although the central government has issued broad guidelines for how the NCMS

should be designed and implemented, provincial and county governments have retained

considerable discretion over the details of the program, including the placement of the pilot

Some studies suggest that local governments have made considerable efforts to attain high participation rates,

including mandating households to participate. However, our survey shows that less than 1% of households

report compulsory enrollment in 2007. 2

The poor and some other groups are exempted from contributions. In 2008, the government subsidies

increased to 80 yuan per person a year. Household contribution was raised to 20 yuan per person a year. 3 In counties that have introduced the NCMS, this program helps poor families pay the premium of the

NCMS and reimburses the health expenditure below the deductible or above the ceiling. For counties that

have not launched the NCMS, this serves as a subsidized insurance for the poorest families.

6

program and insurance package. This kind of local authority has led to considerable

heterogeneity in the benefit, coverage, and management packages across counties. Table

A.1 shows the main parameters of the insurance packages for the 54 counties, about which

we have detailed information. We first observe that the insurance typically does not offer

generous coverage. In particular, deductibles are high, ceilings are low, coinsurance rates

are high, and outpatient expenditure is usually not fully covered. At the township clinics,

for example, the deductible was about 125 yuan on average, the ceiling was 14838 yuan,

and the coinsurance rate was 49.1% in 2006. However, the insurance program can still

substantially reduce the out-of-pocket health care payments of the insured. For households

whose health expenditure is more than 14838 yuan, they can save 7489 yuan at most.

Furthermore, the insurance plans have become more generous over time for all levels of

health care centers, particularly for the township clinics. Moreover, the insurance covers

most kinds of disease, including childbirth, as long as the health expenditure is related to

in-patient service.

Table A.1 also shows that although all counties cover in-patient care, the coinsurance

rate varies substantially, ranging from 20% to 75%. Moreover, most counties offer better

packages to the lower level health care centers. This feature has magnified over time. By

contrast, the difference in the coinsurance rate for different amounts of health expenditure

is small and declines over time. Finally, the procedure for claiming reimbursement has

become simpler over time. In 2003, about 86% of counties asked households to pay the

providers upfront for all their costs and go to insurance organizations to claim the

reimbursement. The rate declined to 52% in 2006. However, the number indicates that in

7

most counties, the households bear the risk if the government does not pay.4

2.2 Related literature

After the seminal theoretical papers by Zeldes (1989), Deaton (1991), and Carroll

(1992), many studies have used micro data to examine the strength of the precautionary

saving motive. Simulations or structural estimations mostly find that precautionary savings

can explain a sizeable portion—as much as 50%—of US savings (Gourinchas and Parker,

2002; Hubbard, Skinner, and Zeldes, 1994; etc.). By contrast, other empirical studies have

drawn mixed conclusions: Dynan (1993), Guiso et al. (1992), and Starr-McCluer (1996)

find little or no precautionary saving, whereas Banks et al. (2001) (for the UK), Carroll and

Samwick (1998) (for the US), and Fuchs-SchÜndeln and SchÜndeln (2005) (for Germany)

find economically important precautionary motives.

Early studies typically examine the issue by relating wealth accumulation to some

measures of income risks that households encounter. The mixed results stem partly from

the variation in the measure of income uncertainty (Engen and Gruber, 2001). Various

measures have been tested, including the variability of income (Carroll and Samwick, 1998;

etc.), variability of consumption (Dynan, 1993), expectations of future job loss (Guiso et al.,

1992; Lusardi, 1998), actual job loss (Carroll, Dynan, and Krane, 2003), a proxy based on

job characteristics or education (Skinner, 1988), and household insurance coverage

(Starr-McCluer, 1996; Guariglia and Rossi, 2004). In the Chinese context, literature along

this line generally finds strong evidence for the importance of precautionary savings (Meng,

2003; Jalan and Ravallion, 2001; Kraay, 2000). However, these studies all suffer from the

potential bias caused by the likely correlation between income risks and underlying

4

In addition, around 48% of the counties provided insurance for migrants, although the reimbursement is

usually much less generous for health care expenditure in hospitals outside the county.

8

preferences for savings.

Recent studies have exploited the exogenous variations in insurance coverage caused

by policy changes. These studies include Engen and Gruber (2001), Gruber and Yelowitz

(1999), and Kantor and Fishback (1996) for the US; Atella and Rosati (2005) for Italy; and

Wagstaff and Pradhan (2005) for Vietnam. Although these studies focus on different

programs, including unemployment insurance, worker compensation, and health insurance,

most studies confirm that social insurance programs reduce asset accumulation by reducing

income or expenditure risk.

Related research in developing countries is still in its early stages. Wagstaff and

Pradhan (2005) study a case in Vietnam and find that the introduction of a health insurance

program increased nonmedical household consumption. Chou, Liu, and Hammitt (2003)

find that the universalization of health insurance in Taiwan reduced the household saving

rate by about 2.5 percentage points.

For mainland China, Ma, Zang, and Gan (2010) examine the effect of the NCMS

specifically on food consumption among rural households. However, food consumption can

be much less elastic than other kinds of consumptions. Liu et al. (2010) investigate the

effect of introducing the public health insurance program on the consumption of urban

households. Nevertheless, rural residents respond differently to income uncertainty

compared with urban residents (Zhang and Pei, 2007). Both studies apply the

Difference-in-Difference method and find a significant positive insurance effect on

consumption. The paper that is most similar to our study is Brown, Brauw, and Du (2010).

They apply the matching propensity score method to household survey data on two

provinces, and find that the NCMS reduces food consumption but does not significantly

affect non-healthcare and total consumption. However, their study does not thoroughly

9

address the selection problem.

These studies do not examine whether the increase in consumption results from a

reduction in precautionary savings. In addition, the literature on developed countries

indicates that the strength of precautionary saving can vary among different income groups

(Carroll, Dynan, and Krane, 2003), but the analysis of the heterogonous effects are lacking

in these Chinese studies.

3 Data and descriptive statistics

Our data come from the longitudinal Rural Fixed-Point Survey (RFPS) from 2003 to

2006, and a supplementary household survey that aims to evaluate the NCMS. The sample

in the RFPS is selected on the basis of a multi-stage stratified random sampling strategy. The

2006 round includes 19,488 households in 313 villages drawn from 26 Chinese provinces.

The survey uses the weekly book accounting information maintained by the households as

the primary information source. It provides detailed information on income and

expenditure.5 The supplementary survey was conducted by Tsinghua University in May

2007. It surveyed a subsample of the 2006 round of RFPS and covered 23 provinces, 143

villages, and 5728 households. It collected detailed information about the time at which a

household enrolled in the NCMS, and retrospective information on each member’s health

care utilization and expenditure in each year from 2003 to 2006. The survey oversampled

households with economically meaningful health care expenditure.6

5

The identification code for tracking individuals and households is ridden with mistakes. We use conservative

rules based on individual age, gender, and education to match individuals and households over years. If more

than half of the household members cannot be matched across two years, we exclude the household from our

sample. Altogether, we exclude around 8% of the households in our sample because of the inconsistency in the

identification code. 6

More specifically, the survey first ranks all the households in the 2006 round of the RFPS on the basis of their

average health care expenditure from 2003 to 2006. Then, it randomly draws 80% of the observations in the top

one-third of the sample, and 50% of the observations in the remaining two-thirds of the sample.

10

Table 1 shows the enrollment rate of the villages and households from 2003 to 2006. The

enrollment of our sample villages spread over different years: 16.4% of the villages

enrolled in the program in 2003 and the rate increased to 77.1% in 2006. Similarly, the

enrollment rate of households gradually increased from 9.5% in 2003 to 72.3% in 2006.

These figures are consistent with national data (Chinese Statistical Yearbook 2010). In the

villages that have launched the NCMS (referred to as NCMS-villages hereafter), the

participation rate of households increased from 63.7% in 2003 to 94.6% in 2006. Moreover,

most households participated in the year of program launch; the first-year participation rate

was 63.7% in 2003 and 96.2% in 2006. These numbers also indicate that quite a few

households (14.4% on average) chose not to participate in the program in the first year the

program was introduced. Over the four years from 2003 to 2006, about 12.8% of

households in the NCMS-villages did not participate.

To relate consumption to household enrollment status in the NCMS, we exclude some

outliers, such as households that terminated membership in the NCMS or participated in

some cooperative insurance programs from 1993 to 2002. Also excluded are households

that purchased commercial insurance or did not participate in the NCMS but enrolled in

some government insurance programs in 2007. 7

Finally, given that the NCMS was first

piloted in July 2003, we exclude all the observations in 2003 for villages that launched the

program in 2003 (but keep the observations in other years). In so doing, we avoid the

potential complication that arises from the effect of the NCMS actually beginning in the

middle of that year. 8

As a result, year 2003 is regarded as the year during which no counties

7

Only 107 households ever terminated membership in the program; 4.6% of the households enrolled in some

cooperative insurance programs from 1993 to 2002, and most of them participated after 1997. About 6% of

the households have some commercial insurance, and less than 1% enrolled in some government insurance

programs but not in the NCMS in 2007. 8

We include these observations as a robust test, and as expected, find a slightly smaller insurance effect of the

11

introduced the NCMS. The final sample includes 520 villages and 17,715 households over

the 4-year sample period.

Table 2 shows the descriptive statistics for three groups: the insured households and two

kinds of uninsured households: the non-participants who live in the NCMS-villages but

chose not to participate in the program, and the non-exposed households located in the

non-NCMS villages. Because more villages and households join the program over time, the

household compositions of these three groups vary over time. Therefore, we use the 2003

values of the variables that may change over time as a proxy for the underlying household

characteristics at the time before the implementation of the program.

The table illustrates that compared with the non-participants, the households that chose

to participate generally had higher incomes, total consumption, and non-healthcare

consumption in 2003. The evidence for adverse selection is mixed. There are five

categories of self-reported health status: excellent, good, fair, bad, and no working capacity,

the last two of which are collectively called ―poor.‖ The participants had more members

reporting fair or worse health status and spent more on in-patient health care than did the

non-participants in 2003. However, the participants had fewer members with poor health

status (including ―bad‖ and ―no working capacity‖) and had less total health-care

expenditure in 2003. Column 3 of Table A.2 shows that even the positive evidence for the

adverse selection disappears when we focus on within-village comparison by controlling

for the village fixed effect. For the demographics, the heads of the participating household

are slightly older, more educated, and less likely to be single or be a non-agricultural

worker. These households are also more likely to have communist members, and less likely

to be a minority or a household in poverty (―Wubao‖). These differences are confirmed by

NCMS on consumption.

12

the regression results in Table A.2 in the Appendix.9

Additionally, the table indicates that although the non-participants differ from the

insured in observable characteristics, they are more similar to the insured than to the

non-exposed households in terms of income and consumption. This observation is not

surprising because households located in the same village are more likely to be similar to

one another than to those located in a different village. The comparison of village

characteristics indicates that the placement of the pilot programs may not be random: the

NCMS-villages are richer and have fewer clinics but more children receiving vaccinations

than the non-NCMS villages. They have fewer migrants and more laborers, and the

residents have higher educational levels. They are also less likely to be in mountainous,

western, or central areas. Column 4 of Table A.2 confirms that these differences are

significant.

4 Baseline empirical model

Our empirical analyses exploit the quasi-natural experiment arising from the NCMS to

examine the effect of the insurance coverage on household non-healthcare consumption.

We exclude health expenditure because we want to focus on precautionary savings, and

health expenditure is affected by the insurance through other channels. To simplify

exposition, consumption refers to all consumption expenses net of health expenditures

throughout the paper, unless otherwise specified.

We begin by applying the Difference-in-Difference (DID) framework to the four-year

panel. More specifically, the effects of the NCMS are identified by differences in dynamic

changes in consumption between the insured and uninsured households in the time periods

9

Table 2 also indicates that the subscribing households have, on average, fewer members older than 65 or

younger than 10, more migrants, and are less likely to have a female head. However, the regression in Table

A.2 shows that these differences are not significant or that the differences take the opposite direction.

13

before and after the launch of the NCMS in the villages. The framework can eliminate all

time-invariant selection biases. This is crucial to our context because participation in the

program is voluntary, implying that the households that chose not to participate can differ

from the participants in both observable and unobservable characteristics. Moreover,

program placement over villages can be non-random. As a result, insured households and

uninsured households may have consumed differently in the absence of the NCMS. The

double-difference method can still deliver unbiased and consistent estimates as long as the

temporal changes in household consumption would have been parallel were there no

NCMS.

As mentioned previously, there are two types of uninsured households in each period.

One is composed of the non-participants in the NCMS-villages and the other comprises

the non-exposed households in the non-NCMS villages. To examine the precautionary

saving motive, we focus on the double difference between the insured and non-participants

in the NCMS-villages. This focus is driven by two reasons. The first is that other changes

occurred along with the introduction of the NCMS. In particular, the governments

implemented supporting policies to improve the quality and delivery of health care services.

In addition, anecdotal evidence indicates that the price of health care services increased

after the introduction of the NCMS (Mao, 2005). For the precautionary saving explanation,

we need to identify the insurance effect of the program that occurs only through the

insurance coverage and excludes the effects of the aforementioned contemporary policies

or changes. This insurance effect can be estimated by the double difference between the

insured and non-participants within the NCMS-counties because both groups were affected

14

by these changes.10

By contrast, the double difference between the insured and

non-exposed determines the gross effect of the NCMS on the insured, which includes both

the insurance effect of the NCMS and effects of other associated changes.11

The second reason is related to the identification assumption for the DID model: the

consumption dynamics of the insured and that of the control group should be parallel even

in the absence of the NCMS. We argue that the assumption is more problematic for the

comparison between the insured and non-exposed than that between the insured and

non-participants. First, households in the same village are more likely to be similar to one

another than to households located in a different village. This argument is partly justified by

Tables 2 and A.2, where we see significant differences between the NCMS-villages and

non-NCMS villages. The argument is further confirmed in the matching procedure, in

which balancing the observable village and household characteristics between the insured

and non-exposed groups is much more difficult than balancing the characteristics between

the insured and non-participant groups.12

Second, consumption can grow more similarly

among people living in the same geographic areas than among those living in different

areas, particularly when the different areas have various incomes, and hence, consumption.

To implement the DID framework for the panel data, the baseline model applies the

fixed-effect regression that controls for both household and year fixed effects. All the

10

The effects of these policies can differ for the insured and non-participants. Thus, the estimate of the

insurance effect has incorporated this difference. 11 Wagstaff et al. (2009) focus on the double difference between the insured and non-exposed to evaluate

the effect of the NCMS on health care expenditure. Their choice is reasonable when the focus is a

program evaluation, so that the gross effect may be more important. Furthermore, when the outcome is

health care expenditure, avoiding the selection bias caused by the voluntary participation is crucial

because the insured and non-participants have different expectations on future health expenditure.

However, they also point out that the NCMS-villages and non-NCMS villages are different, and they

actually focus on the comparison between the insured and non-participants in the early versions of their

paper. 12 In terms of reducing bias, matching the insured with non-participants in the NCMS-villages is

considerably more successful than matching the insured with the non-exposed.

15

time-invariant effects of household characteristics are controlled by the household fixed

effects, and the yearly time trend of consumption that is common to all households is

controlled by the year fixed effects. Refinements such as matching DID and tests of the

identification assumptions are discussed in Section 6. More specifically, the regression

model for the double-difference comparison between the insured and non-participants is as

follows:

)1(,_ ijtijtiittitijt XDTinsuredFamilyY

where Yijt represents the log value of household non-healthcare consumption for household

i located in village j in period t. Family_insuredit is the binary variable that indicates

whether household i subscribes to the NCMS in year t. Tt includes three year dummies. Di

includes all the household indicators. Xijt includes the observable household and village

variables that vary over time and may affect consumption and participation decision. Such

variables include log(household income), household size, share of members over age 65,

share of members under age 10, whether there are communist party members, whether

households are officially categorized as poor (―Wubao‖ households), and log(average

income per person in the village). 13

In Eq. (1), γ measures the effect of the insurance coverage on consumption. The

precautionary saving explanation indicates that >0. Given that we control for log(income),

also represents the effect of the NCMS on the average propensity to consume

non-healthcare expenditure because log(average propensity to consume) is equal to the

difference between log(consumption) and log(income).

A primary concern in this model is that the identification assumption may not hold even

13

Some of the family characteristics do not vary much over time. However, the results are highly robust to

whether these variables are included.

16

after conditioning on the observable characteristics. The most likely situation is related to

the adverse selection problem: households that expect substantial health expenditure in the

next year are more likely to participate; hence, their consumption dynamics would have

differed from that of households that have no such expectation were there no NCMS. By

excluding health expenditures from consumption, we partially avoid the complication

arising from the possibility that participants would spend more on health care than the

non-participants would in the absence of the program. This selection bias tends to

underestimate the precautionary saving motive because families who expect to incur huge

health expenditure are more likely to be frugal in other consumption.

To address the potential selection bias, we first use the self-reported health status to

proxy the unobservable expectation on future health expenditure. Then, by adding the

interaction term between year and measures of household health status, we allow

households with different health statuses to have varied time trends in consumption. Health

status can be affected by the insurance coverage. For the estimations in our sample,

therefore, we use the self-reported health status in 2003 at which time none of the villages

introduced the program. Similarly, we add the interaction between year and income to

allow the linear time trends in consumption to vary with the income. This approach address

the concern over the phenomenon that the insured are generally richer than the

non-participants, and that different income groups may have varied income growth rates.

5 Results for the baseline model

5.1 Average treatment effect on the treated groups

Table 3 reports the results for the baseline model that focuses on the double-difference

comparison between the insured and non-participants. The first column assumes that all

17

households have the same counterfactuals of the time trends in consumption. It shows that

the insurance coverage has stimulated non-health care consumption by 5.5 percent for the

insured, an effect that is not negligible. Given that the average non-healthcare consumption

per person for the participating households was about 2660.7 yuan in 2003, an increase of

5.5 percent implies an increase of 146.3 yuan, which is much higher than the total premium

of the insurance that was typically 30 yuan in 2003 and 50 yuan in 2006. Moreover, the

program more effectively stimulates consumption than do the cash transfers from the

government because the average propensity of rural households to consume is only about

0.437.

The result is quite robust when we relax the assumption by allowing the linear time

trend in consumption to vary with the observable characteristics. In particular, column 2

controls for the interaction term between year and household income and that between year

and village average income.14

Column 3 additionally controls for the interaction term

between year and initial household health status, which is measured by the share of

members reporting fair or worse health status and share of members reporting poor health

status in 2003. 15

Both columns show an insurance effect similar to that in the first column,

including both the magnitude and significance level. Particularly, column 3 shows that after

being covered by the NCMS, the consumption of the insured households increased by 5.6

percent or 149 yuan.

The results are also similar when we allow the difference in trends to vary year by year

through the control of the interaction terms between the year dummies and household

income, village average income, and household health status in 2003 (column 4). Aside

14

When we use income in 2003 instead of income in the current year, the results are almost unchanged. 15

Results are similar when we additionally control for the share of members with good health statuses, or

instead control the mean value of the health status in the household. Finally, we also consider the health status

in the current year instead of the health status in 2003 to increase the number of observations.

18

from income and health status, other differences are observed between the insured and

non-participants. As in the propensity score matching method, we can summarize the

differences using a one-dimensional variable, a household’s ―propensity score‖ of joining

the program. The estimation of the propensity score is discussed in detail in Section 6. Here,

we control for the interaction between year and propensity score to allow the trend in

consumption to vary with the propensity score. The estimate is shown in column 5, which

shows a slightly stronger insurance effect on consumption. Controlling for the interaction

between the year dummies and propensity score yields a similar estimate (column 6). 16

Given that an increasing number of counties and households enroll in the program, we

have an unbalanced panel in Table 3. Table A.3 reports the estimates of the balanced panel,

in which we have much fewer observations. The insurance effect is stronger. Particularly,

the insurance effect on non-healthcare consumption is, on average, 9.6 percent after we

allow the trend to vary with the income and health status in 2003 (column 3). The

difference in the magnitude of the insurance effect between the balance and unbalanced

panel can be attributed to the fact that the balanced panel has a higher proportion of

experienced NCMS members who have participated in the program for more than one year,

and at the same time, the insurance effect on consumption for the experienced members is

stronger than that for the new members (shown later in the paper).

In summary, the estimates of the positive insurance effects on non-healthcare

consumption are robust to the specifications that enable the linear time trends in

consumption to vary with the observable variables that are the important determinants of

participation decisions. The results also withstand the other robustness tests shown in

16

The consistency of the estimate in the last two columns requires an additional assumption: the conditional

expectation of the outcome given that the propensity score is linear. In addition, the standard errors here are

not adjusted for the first-stage estimation of the propensity score.

19

Section 6. These tests increase our confidence that the baseline model provides a reliable

estimate of the insurance effect. Therefore, we first examine the economic explanations for

the estimates and heterogeneity of the effects, and discuss robustness thereafter.

5.2 Precautionary saving explanation

This section examines whether the increase in consumption net of health expenditures

represents the reduction in precautionary savings. All of the estimates in this section control

for the household and year fixed effects, time-variant household and village characteristics,

as well as the interaction term between year and household income, between year and

average income in the village, and between year and household health status in 2003.

Aside from the precautionary saving perspective, a potential explanation for the

positive effect of the NCMS on non-healthcare consumption is that the insurance reduces

out-of-pocket health payments, thereby leaving the insured households with more income

for other consumption expenditure. This is a simple ex post crowd-in effect, a concept that

is applicable only to the households that incurred health expenditure in the current year.

However, column 1 of Table 4 shows that the insurance effect remains significant for

households with no health expenditure in the current year. The magnitude is even stronger

than that when we pool all households together, although the significance level declines.

Therefore, the ex post crowd-in story cannot explain the positive insurance effect.17

If the ex post crowd-in effect is the only explanation, then the higher non-healthcare

consumption by the participants is caused only by their lower out-of-pocket healthcare

expenditure, which implies that participation in the NCMS may have little effect on total

17

We have also estimated the effect of the NCMS on the out-of-pocket health expenditure. The result shows

no significant effect, which contradicts the crowd-in perspective. However, we find that the insurance

coverage stimulates more visits to health care facilities among the insured households. These findings are

consistent with the results of the studies that evaluate the NCMS (Wagsaff et al., 2009; Lei and Lin, 2009; and

Mao, 2005).

20

consumption. However, the second column of Table 4 shows that the insurance coverage

stimulates total consumption by 6 percent, which is even higher than the effect of the

NCMS on non-health care consumption.

Another potential explanation is related to the income effect of government subsidies.

Participants in the NCMS receive a government subsidy of 20 or 40 yuan for the premium

payment. However, the effect of an income increase of 40 yuan on consumption is only

about 17 yuan, implied by an estimated propensity to consume of 0.437. Even if households

treat the subsidy as being permanent and the propensity to consume is around 1, the

increase in consumption is no more than 40 yuan. The amount is much smaller than 149

yuan, our estimates for the insurance effect of the NCMS. This result strongly suggests that

the income effect of subsidy is not the primary explanation.

The precautionary saving explanation indicates that the insurance effect will strengthen

when the insurance program becomes more generous, reducing the expenditure risk faced

by the consumer. Columns 3 and 4 of Table 4 test this hypothesis by exploiting the detailed

information on the NCMS program for 54 counties. The result shows that households

respond to the generosity of the insurance scheme for the health expenditure at county

facilities: the lower the deductible or the coinsurance rate, the more consumption the

insurance program can stimulate. However, the generosity of the insurance scheme for the

health expenditure at the village clinics does not exhibit such significant effects. This

insignificant effect is somewhat reasonable given that most households seek health care

services in county hospitals when they encounter serious health problems that most

strongly demand the insurance.

Finally, the precautionary saving explanation implies that the insurance effect is

stronger for those who have a higher risk of incurring health care costs that are expensive

21

relative to income. Table 5 examines how the insurance effect varies with uncertainty about

future health expenditure. We first look at the difference between income groups. Poor

households are more likely to be unable to afford large health care expenditures than rich

households; thus, their precautionary saving motive would have been stronger without the

insurance program. As a result, the effect of the insurance on consumption should be

stronger among the poor than among the rich. Column 1 of Table 5 confirms this conjecture:

the positive effect of the NCMS on consumption decreases with income. The second and

third columns run the regression separately for the bottom half (the poor) and top half (the

rich) of the income distribution, and the insurance effect is significant only for the poor.

The second part of Table 5 focuses on the risk related to household health status. On

the basis of the self-reported health status of each member in a household, we construct two

measures of household health status. Columns 4 to 6 consider the first measure of the

health risk: whether at least one household member report fair or worse health status in

2003. In our sample, about 89% of individuals report good or excellent health. Therefore,

reporting fair or worse health indicates serious health problems that may demand

substantial health expenditure in the future. The results confirm that after being covered by

the insurance, households that have members with fair or worse health status consume

much more than do households with no such members. The second measure first assigns an

ordinal value to each category of the self-reported health status: 5 for ―excellent,‖ 4 for

―good,‖ 3 for ―fair,‖ 2 for ―bad,‖ and 1 for ―no working capacity.‖ It then calculates the

average value of the health status of all the members in a household in 2003. Columns 7 to

9 yield similar results: the positive insurance effect on consumption decreases as the

household health status improves, and the effect is significant only for the bottom half of

the household health distribution (designated as ―poor health‖).

22

5.3 Dynamics of the insurance effect and trust

After the dissolution of the old CMS in the 1980s, most households in rural areas were

not covered by any health insurance for a long period. Moreover, the NCMS differs from

the CMS in many aspects. As a result, households need time to understand and establish

trust in the new program.

Column 1 of Table 6 controls for a dummy for the experienced participants who have

participated for more than a year. This enables the insurance effect on the experienced

members to be different from the effect on the new members who have participated in the

NCMS for less than a year. The result shows that the insurance effect on consumption is

significant among the new members (about 4.5 percent). Moreover, the effect among the

experienced participants is much higher, and the difference is about 6.7 percentage points,

which indicates that compared with the consumption of the non-participants, that of the

experienced members increases by 11.2 percent because of the insurance coverage.18

The dynamics of the insurance effect can result from the fact that the experienced

members learned more about the benefits of the insurance program. However, an

alternative explanation is that the NCMS coverage becomes more generous over time and

people reduce precautionary saving in response to the rising generosity. Column 2

examines the issue by controlling for an interaction term between household insurance

status and year. The result yields a negative answer to the alternative explanation because

no significant increase in the insurance effect occurs over time. The third column further

confirms that the difference between the new and experienced members remains significant

after the time trend of the insurance effect on consumption is controlled for.

18

When we further allow the insurance effect to differ in the second, third, and fourth years of household

subscription in the program, we find that the increase in the insurance effect occurs primarily in the second

year of subscription.

23

Another explanation for the dynamics of the insurance effect is related to household

trust in the program. The effect of knowledge on the program is double edged. Particularly,

if households find that the alleged benefits of the program do not take effect, more

knowledge about the insurance cannot reduce precautionary savings. Therefore, household

trust in the insurance program may be the factor that matters most. To identify the trust

effect, in column 3 we control for the interaction between household insurance status and

the indicator of whether some households in the village have received some reimbursement

from the NCMS (―village reimbursement‖=1 if yes; 0 otherwise). The result shows that the

insurance effect on consumption becomes significantly stronger by 17 percentage points

when the benefits of the insurance are experienced by the residents. For the villages that

have not received any reimbursement, the insurance effect is even negative. 19

These results are confirmed by the succeeding estimates on the subsamples. Columns 4

and 5 consider only the villages that have not experienced any reimbursement. Herein, no

significant increase in consumption is observed among the participants (column 4),

regardless of whether the participant is experienced (column 5). By contrast, when we

consider only the villages where some reimbursements have been received, the insurance

program stimulates consumption by 6.3% on average for the insured households (column 6),

which is higher than the average insurance effect of 5.6% reported in the baseline model. In

addition, the experienced participants in these villages exhibit significantly more

consumption than do the new members (column 7), which may be because they acquired

more information about the insurance or because they accord more trust to the program.

These results emphasize that only when households trust the program do they begin to

19

We need to be cautious in explaining the negative insurance effect here because the result is sensitive to

specifications, and there are only a few villages—around 18.6% from 2004 to 2006—that have launched the

program but no household has received reimbursement.

24

reduce precautionary savings and consume more. The responses related to trust are also

consistent with the pattern of participation decision. We find that for those who did not

participate immediately after their villages introduced the program, the participation rate is

67% in the village that have received some reimbursement, but only 36% in the villages

that have not received reimbursement.

6 Robustness, refinements, and gross effects

6.1 Counterfactual tests and other robustness tests

To test whether the identification assumption of the baseline model holds, we apply the

same model to the periods at which households were not covered by the insurance. In

columns 1 to 5 of Table 7, we consider only the households that have not enrolled in the

program in each year. In addition, household status of insurance coverage in period t is

defined as the status in the succeeding period (t+1). That is, the treatment group in this

model represents the households who are not insured in period t but are insured in period

t+1. By construction, the NCMS should not affect household consumption in period t.

The first column shows no significant insurance effect, as it should be. This is also true

when we consider only households who have no health expenditure in the current year

(column 2). These results enhance our confidence that the estimate of the insurance effect

in the baseline model represents the causal effect of the NCMS. The next three columns

confirm that the heterogeneity in the insurance effect also disappears.

The primary concern of the baseline model is that households that enrolled in the

NCMS are not comparable to households that did not. Particularly, we are alerted that

households that have not participated by 2007 may have been covered by other insurance

policies or have very special concerns regarding the participation. Therefore, we exclude

25

such households in the sixth column of Table 7 to test the robustness of our core results.

The result shows that the estimate of the average insurance effect is 5.1 percent, which is

similar to that derived in the baseline model.

Aside from the introduction of the NCMS, another policy change during the same

period is regarded as important: the reduction of agricultural taxes and fees in rural areas.

The tax reduction was piloted in 2004 and ended with the national abolition of the

agricultural tax in 2006. This change increased household disposable income and

consumption. If the amount of tax reduction is correlated with the launch of the insurance

program, then our estimate of the insurance effect is biased. However, the influence of the

tax policy is partly addressed by the fact that we have controlled for the disposable income

with subtracted tax and fee payment. To further test how serious the problem is, we include

the log value of the tax and fee payment as a covariate in column 7. The estimate of the

insurance effect changes only minimally.

Finally, the precautionary motive should not affect basic or subsistence consumption

for human needs. We use food consumption to proxy the subsistence consumption in

column 8. As expected, both the magnitude and significance level of the insurance effect

decline substantially. Nevertheless, the effect remains significant primarily because food

consumption in our dataset still includes non-basic food.

6.2 Gross effects

Our previous analyses all focus on the insurance effect that is estimated based on the

double difference between the insured and non-participants in the NCMS-counties. In this

section, we examine the gross effect of the NCMS that may incorporate the effects of other

changes that occurred simultaneously with the introduction of the NCMS. In principle, this

26

effect can be estimated by the difference in difference between the insured and non-exposed

through the application of the following regression:

)2(._ ijtijtiittit

g

ijt XDTinsuredFamilyY

The equation is the same as that of the baseline model with the exception that here, we

consider only the insured and non-exposed households. The potential selection bias stems

first, from the non-random placement of the NCMS across villages and second, from the

fact that the insured are not randomized into the program. Again, to partly address the issue,

we allow the time trend in consumption to vary with the household income, village average

income, and health status in 2003. We accomplish this by controlling for the interaction

terms between year and corresponding variables.

The first column in Table 8 shows that the gross effect is not significant, implying that

although the NCMS has a positive insurance effect on the consumption of the insured

households, the other contemporary changes reduce the consumption of these households.

This may have resulted from the increase in the market price of health care after the

introduction of the NCMS in the counties. Unfortunately, we do not have sufficient

information on the market price of health care to verify this hypothesis. This issue warrants

more in-depth research.

Although the average gross effect is not significant, the succeeding columns in Table 8

show that the gross effect varies substantially across different groups. First, the gross effect

is significantly positive for the poor households, but declines rapidly with income (column

2). Second, the gross effect increases as the households have more members with fair or

worse health status in 2003 (column 3). Third, the gross effect is significantly positive for

the experienced participants, but not significant for the new members (column 4). The

average gross effect also tends to be more positive among villages where some households

27

have received some reimbursement (column 5). The experienced members in the villages

that have received reimbursements show a much stronger positive gross effect than do their

counterparts in the counties where no households have received any reimbursements

(column 6 and 7). To summarize, the variations across different groups are similar to those

for the insurance effect. This is not surprising because in comparing the gross effect among

insured households, we differentiate the effects of other contemporary policies when the

effects of contemporary policies are similar among insured households.

Although our estimates of the gross effect of the NCMS on consumption are

insignificant, such results do not mean that the insurance program itself is ineffective in

increasing consumption. Rather, they suggest that measures should be taken in conjunction

with the insurance program to help reduce the cost of healthcare services. Potential

measures include monitoring the market price of health care and enhancing the governance

and regulation of health care organizations. In addition, our results also suggest that it may

take time for the gross effect to become significant. As time goes by and more households

become familiar with and gain more trust in the program, they may adjust their

consumption so much so that the gross effect becomes significantly positive.

6.3 Matching difference-in-difference

We couple the DID approach with matching to reduce the selection bias on the

observables. The identification assumptions for the linear regression and matching

approach are the same. However, the matching method does not require strong assumptions

on functional form. Thus, it can address two kinds of potential biases of the simple linear

regression method: the bias caused by the difference in the supports of the observable

covariates between the treated and untreated groups, and the bias due to the difference

28

between the two groups in the distribution of the observables over the common support of

the observables (Smith and Todd, 2005).

The matching method applied here is propensity score matching. We estimate the

propensity score in two steps. First, in each year, we estimate the probability of household

enrollment in the villages that have launched the program. This is estimated on the basis of

households in the NCMS-villages. For households in the non-NCMS villages, we predict

their probability of enrollment on the basis of the estimate. Second, we estimate the

probability of village enrollment in the NCMS in each year.20

For both steps, a probit

model is estimated.21

For the comparison between the insured and non-participants, the

predicted probability from the first step is the propensity score used in the matching. For

the comparison between the insured and non-exposed, we need to consider both the

similarity between households and that between villages; hence, the (composite) propensity

score in each year, i.e., the product of the two probabilities predicted from the

aforementioned two steps, is the one used in the matching estimation.

Figure 1 shows the histogram for the (composite) propensity scores for the three groups:

the insured, non-participants, and non-exposed. As expected, the distribution of the

propensity score is more skewed to the right for the insured than for the non-participants.

Nonetheless, the region of common support is adequate. The propensity score distribution

for the non-exposed is more skewed to the left than that for the non-participants because of

the lower possibility of village enrollment.

20

To weigh the data by the number of households surveyed in each village, the second step is estimated for

the household-level data. 21

Note that the propensity score functions only to reduce the dimensions of the conditioning; thus, it has no

behavioral assumptions attached to it. For the choice of covariates to be included in the estimation of the

propensity score, we begin with all of the plausible variables that affect the participation decision, including

all the covariates considered in Table A.2. Then, the variables for inclusion or exclusion are determined solely

by the balancing requirement (tested by the ―pscore‖ logarithm in Stata). As a result, the ultimate covariates

vary across years. The estimates are available from the authors upon request.

29

Given that we consider more than two periods, the traditional DID matching method

requires modification. We first estimate the insurance effect in each year from 2004 to 2006.

Then, we calculate a weighted average of the insurance effect over these three years by

weighting the effect in each year on the basis of the ratio of the number of the treated in

that year over the total number of the treated in three years. The standard errors are

bootstrapped with 100 replications. For each year, we use the five nearest-neighbors

matching with replacement and caliper 0.01, and impose a common support condition.22

The distance between the propensity scores is measured by the Mahalanobis metric. We

confirm that the results are not sensitive to the number of neighbors (including 3 and 10)

and to the other choices of calipers (including 0.005 and 0.0025).23

Another complication here is whether to consider the experienced participants as the

treated. The temporal difference in the consumption of experienced members represents the

increase in consumption in addition to the first-year increase caused by the insurance

coverage. To simplify the analyses, we exclude all the experienced participants and focus

on the first-year effect of the NCMS on the consumption.24

Table 9 reports the results, which confirm the corresponding estimate in the baseline

model: the insurance effect estimated by comparing the insured new members with the

non-participants is about 5.2%; the gross effect estimated by comparing the insured new

members with the non-exposed is not significant. We also show the reduction in bias on the

22

Among the three choices, the first and second options increase bias but reduce variance, whereas the third

exhibits the opposite effect. Because there are only a handful of untreated units in our sample, allowing

replacement is expected to enable better performance than the case in which no replacement is allowed. Our

sample is not a random sample but oversamples households with substantial health care expenditure that is

affected by the insurance status. Smith and Todd (2005) suggest matching on the odds ratio in this case, which

delivers the same outcome as matching on the propensity scores for the nearest neighbor matching. 23

However, the estimates are somewhat sensitive to the estimation of the propensity score and matching

methods such as the Kernel estimation. Yet, the insurance effect is much less sensitive than the gross effect. 24

We also attempt to consider the experienced as the treated. Thus, the estimate is a weighted average of the

insurance effect in the first-year of participation and the extra insurance effect in later years of participation.

The estimate of the insurance effect on the insured is 4.8% with a bootstrapped standard error of 0.031.

30

observables achieved through matching. The first column indicates that when the

non-participants in the NCMS-villages are used as the controls, the mean absolute

standardized bias after matching is substantially reduced by 41%. However, the reduction

in the pseudo R2 statistics from a probit model is modest, exhibiting a reduction by 19%.

When the non-exposed are used as the comparison group (column 2), matching diminishes

only the mean ―bias‖ by 9% and even raises the pseudo R2. These results imply that

matching is more successful when the non-participants are used as the controls than when

the non-exposed serve as the controls.

6.4 Regression with matching

The simple matching DID estimator continues to be problematic in finite samples when

the matching is inexact; that is, the covariates for the treated groups and those for the

matches are not equal, although they are close after the matching process (Imbens and

Wooldridge, 2009). Moreover, in our context, matching is sensitive to the estimation of the

propensity score. Literature has proposed a combination of weighting (or matching) and

regression to attain ―double robustness‖: as long as the parametric model for either the

propensity score or the regression function is specified correctly, the resultant estimator for

the average treatment effect on the treated groups will be consistent. This is particularly

valuable when one method alone is insufficient for obtaining consistent or efficient

estimates.

There are three ways to combine regression and weighting. First, the regression method

is applied to the common support of the treatment and control groups in the matching

procedure. Second, the regression method is applied to the matched pairs in the matching

procedure. These two methods can easily be incorporated into the baseline fixed-effect

31

model for the panel data. The third method applies the weighted least regression, in which

the weight is 1 for the treated unit, and P/(1-P) for the untreated unit; P is the estimated

propensity score. This method cannot directly be applied in the baseline model because

weighting is not allowed in the fixed-effect model. As a compromise, we consider the DID

regression model for the repeated cross-sectional data that can incorporate weights. Given

that the baseline model treats the experienced members as the treated and estimates the

average insurance effect of all the participants, we also treat the experienced as the treated

when we implement the regression with matching.

Columns 3 to 5 of Table 9 display the results for the insurance effect, from which we

observe a larger insurance effect than that seen in the baseline model. In the fifth column,

for example, the insurance effect is as high as 9.5%, although the estimate is less precise.

The next two columns estimate the gross effect, and both show no significant effect. On the

whole, the robustness tests visibly show that the insurance effect in the baseline model is

relatively robust and reliable.

7 Conclusion

This study exploits the introduction of the NCMS in examining the effects of the health

insurance coverage on consumption in rural areas. Our baseline specification shows that

insurance stimulates the non-health care consumption by around 5.6 percent for the insured

households. This effect does not result from the crowd-in concept because the effect is

substantial even for households that do not incur any health care expenditure. In addition,

the stimulation of consumption is stronger for those expecting a higher risk of having

relatively expensive health care costs, including households with lower income or inferior

health status. Furthermore, the insurance effect increases with the generosity of health

32

coverage at the county hospitals. These results are all consistent with the precautionary

saving motive.

Additionally, these findings are robust to different specifications, including allowing

the time trend of consumption to vary with income and health status, using control groups

that are more similar to the treatment, implementing matching DID, and conducting DID

regression with matching. Counterfactual tests confirm that no insurance effect is present in

the periods at which the households have not been covered by the insurance.

We also find that the insurance effect varies depending on household experience with

the program. Particularly, in the villages where no household has received any

reimbursement from the program, the insurance effect is insignificant for both the

experienced and new members. In the villages where some households have been

reimbursed for health care expenditure, and hence, have established trust in the program,

the insurance effect is significant for the new participants. Furthermore, the increase in

consumption is much stronger for the experienced members than for the new members

within the villages. These results indicate that people’s trust in the public insurance

program can be crucial to the stimulation of non-healthcare consumption by the program.

The findings have strong policy implications. Although the NCMS is often criticized

for lack of generosity, it nevertheless stimulates consumption that is substantially higher

than the amount of the premium. Moreover, it is more effective than cash transfers because

the marginal propensity to consume in rural areas is much smaller than 1. On the basis of

these findings, we expect a higher increase in household consumption once the insurance

program implements a more generous coverage. To realize the maximum stimulation of

consumption, however, building trust in the public safety net and educating people about

insurance programs are important.

33

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consumption in China. Applied Economics, 36, 2221–2229.

Zhang, Xuheng, Pei, Chunxia, 2007. The consumption study of Chinese urban and rural

households consumption behavior in transitional period. The Journal of Quantitative &

Technical Economics (in Chinese), 10.

Zeldes, Stephen P., 1989. Optimal consumption with stochastic income: deviations from

certainty equivalence. Quarterly Journal of Economics, 104, 275–298 (May).

35

Figure 1 Distribution of propensity scores for three groups.

01

23

01

23

01

23

0 .5 1

The Participants

The Non-participants

The Non-exposed

Density

The propensity score for household enrollment in the NCMS programGraphs by ncmscom

36

Table 1

The enrollment of villages and households.

Year 2003 2004 2005 2006 2007 or

later

Village’s enrollment

Number of villages newly enrolled 23 22 19 44 32

Cumulative enrollment rate 16.4% 32.1% 45.7% 77.1% 100%

Household’s participation

Number of households newly enrolled 458 689 780 1,561 1,336

Cumulative enrollment rate 9.5% 23.8% 40.0% 72.3% 100%

Number of participants in the NCMS-counties 458 1222 2089 3724

Number of non-participants in the

NCMS-counties 261 310 313 214

Participation rate in the NCMS-counties 63.7% 79.8% 87.0% 94.6%

Number of non-exposed households 4,232 3,428 2,622 1,130

Exposure rate of households 16.8% 33.2% 48.8% 78.0%

Cumulative participation rate in villages that

launched the NCMS in 2003 63.7% 82.1% 94.4% 97.4% 100%

Cumulative participation rate in villages that

launched the NCMS in 2004 75.8% 93.9% 95.0% 100%

Cumulative participation rate in villages that

launched the NCMS in 2005 71.2% 87.0% 100%

Cumulative participation rate in villages that

launched the NCMS in 2006 96.2% 100%

Average participation rate in the

NCMS-villages from 2003 to 2006 87.2%

Average participation rate in the first-year of

village enrollment from 2003 to 2006 85.6%

37

Table 2

Descriptive statistics for three groups of households.

NCMS-villages Non-NCMS

villages

Insured

households

Non-participant

households

All

households

Non-exposed

households

Variables:

Household income in 2003 26442 21460 25880 20995

Total consumption in 2003 10873 10637 10847 9166

Non-healthcare consumption in 2003 10462 10131 10424 8719

Health expenditure in 2003 610.3 738.3 625.5 661.5

In-patient health expenditure in 2003 183.3 63.0 168.6 222.3

Share of members with fair or worse health in

2003a 13.9% 13.6% 13.9% 12.8%

Share of members with poor health in 2003 4.2% 7.2% 4.5% 5.1%

Household size 4.01 4.13 4.02 4.15

Head’s age 51.82 50.50 51.68 50.96

Head’s years of education 6.72 6.44 6.69 6.50

Female head 5.2% 6.7% 5.4% 7.6%

Single head 7.8% 12.2% 8.3% 9.0%

Head is a non-agricultural worker 39% 41% 39% 33%

Share of members older than 65 8.8% 9.7% 8.9% 8.1%

Share of members younger than 10 7.1% 7.5% 7.1% 7.5%

Share of migrants in 2003 15.6% 13.1% 15.3% 16.7%

Having communist members 17% 10% 16% 16%

Minority household 9.7% 17.1% 10.5% 14.8%

Officially poor household 0.26% 0.79% 0.32% 0.22%

Village average income per capita in 2003 3396 3241 3379 2727

Capital of the town 15% 12% 15% 14%

Number of clinics in 2003 1.28 1.26 1.28 1.32

Share of children vaccinated in 2003 97.40 98.54 97.53 94.29

Share of migrants in 2003 23% 23% 23% 25%

Share of laborers in the villages 57% 55% 56% 54%

Share of laborers with high school degrees or

higher in the villages 34% 32% 34% 30%

Mountainous area 47% 53% 48% 56%

Highlands 25% 21% 24% 23%

Western area 20% 20% 20% 27%

Central area 42% 41% 42% 48%

Observations (based on consumption) 7,189 1,038 8227 10,364

Note: The category of households is defined on the basis of households’ participation status in year 2004 to 2006.

Accordingly, the descriptive statistics do not include observations in 2003. The health status is self-reported and there

are five categories: excellent, good, fair, bad, and no working capacity. We label both bad and no working capacity as

poor health.

38

Table 3

Fixed-effect regressions estimating the insurance effect of the NCMS: the insured vs. the non-participants.

Dependent variable: log (consumption net of health expenditures)

(1) (2) (3) (4) (5) (6)

Covariates:

Insured family 0.055*** 0.055*** 0.056*** 0.058*** 0.066*** 0.067***

(0.021) (0.021) (0.021) (0.021) (0.021) (0.021)

Log(income) 0.437*** 0.424*** 0.428*** 0.431*** 0.437*** 0.437***

(0.020) (0.021) (0.021) (0.021) (0.022) (0.022)

Household size 0.099*** 0.098*** 0.096*** 0.096*** 0.093*** 0.092***

(0.012) (0.012) (0.012) (0.012) (0.012) (0.011)

Log (village income per

capita)

-0.065 -0.077* -0.085** -0.094** -0.090** -0.093**

(0.041) (0.041) (0.043) (0.043) (0.043) (0.043)

Year * log(income) -0.005 -0.007

(0.005) (0.006)

Year * log(village income per

capita)

-0.017** -0.015**

(0.007) (0.007)

Year * share of members with

fair or worse health 2003

0.010

(0.016)

Year * share of members with

poor health 2003

-0.045

(0.030)

Propensity score of

participation

-0.049 -0.403*

(0.064) (0.240)

Year * propensity score -0.266*

(0.157)

Yearly trend varying with

income, village income, and

health status in 2003

Y

Yearly trend varying with the

propensity score Y

Observations 9,730 9,730 9,068 9,068 9068 9068

R-squared 0.284 0.286 0.293 0.297 0.294 0.294

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all the households

in the NCMS-villages. All the columns control for the household fixed effect, year fixed effect, log(income),

household size, share of members over age 65, share of members under age 10, whether the households have

communist members, whether the household is categorized as a ―Wubao‖ household, and log(village income per

capita). Column 4 controls the interaction among year dummies and log(income), log(village income per capita), the

share of members with fair or worse health in 2003, and the share of members with poor health status in 2003.

39

Table 4

The insurance effect of the NCMS: precautionary saving perspective.

Precautionary savings vs. crowd-in perspective

Insurance package

Dependent

variables:

Log(consumption net of health

expenditures)

Log(total

consumption)

Log(consumption net of health

expenditures)

Sample: Households having no health

expenditure in the current year All the sample

Scheme for the

expenditure at

county facilities

Scheme for the

expenditure at

village facilities

(1) (2) (3) (4)

Covariates:

Insured family 0.072* 0.060***

(0.039) (0.021)

Deductibles -0.046** 0.007

(0.019) (0.037)

Coinsurance rate -0.625*** -0.101

(0.177) (0.128)

Observations 3,917 9,068 2,282 2,299

R-squared 0.317 0.302 0.295 0.282

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all the households in the

NCMS-villages. All the columns control for the household and year fixed effects, log(income), household size, the share of

members over age 65, the share of members under age 10, whether the households have communist members, whether the

household is categorized as a ―Wubao‖ household, and log(village income per capita).

40

Table 5

The insurance effect of the NCMS and risks.

Dependent variable: log (consumption net of health expenditures)

Insurance effects and income Insurance effects and health 1 Insurance effects and health 2

Sample: Entire

sample Poor Rich

Entire

sample

Having fair

or worse

health

members

Having no

fair or worse

health

members

Entire

sample

Poor

health

Good

health

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Covariates:

Insured family 0.652*** 0.055** 0.021 0.040* 0.087** 0.041 0.293*** 0.072** 0.039

(0.244) (0.025) (0.043) (0.023) (0.036) (0.025) (0.093) (0.031) (0.028)

Insured family *

log(income)

-0.061**

(0.025)

Insured family * share of

fair or worse health

members in 2003

0.034*

(0.018)

Insured family * average

health value in 2003

-0.055***

(0.021)

Observations 9,068 4,598 4,470 9068 2,925 6,143 9,065 4,623 4,441

R-squared 0.295 0.339 0.195 0.294 0.325 0.283 0.294 0.332 0.255

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The entire sample includes all the households in the NCMS-villages. Column 2 (3)

considers only the bottom (top) half of the income distribution of this sample. Column 5 (6) considers only the households having (having no) members with fair

or worse health in 2003. Column 8 (9) considers only the bottom (top) half of the distribution of household health status in 2003. All the columns control for the

household and year fixed effects, log(income), household size, the share of members over age 65, the share of members under age 10, whether the households

have communist members, whether the household is categorized as a ―Wubao‖ household, and log(village income per capita).

41

Table 6

Insurance effects and learning and trust in the NCMS program.

Dependent variables: Log (consumption net of health expenditures)

Dynamic effects Trust

Sample: Entire sample Entire

sample

NCMS-villages without

reimbursements

NCMS-villages with

reimbursements

(1) （2） (3) (4) (5) (6) (7) (8)

Covariates:

Insured family 0.045** 0.058** 0.026 -0.104* -0.078 -0.085 0.063** 0.061**

(0.021) (0.028) (0.029) (0.053) (0.089) (0.092) (0.030) (0.030)

Experienced members 0.067*** 0.070*** 0.068 0.069***

(0.016) (0.017) (0.094) (0.019)

Insured family * year 0.002 -0.016

(0.019) (0.019)

Village reimbursement -0.155***

(0.056)

Insured family * village

reimbursement

0.170***

(0.059)

Observations 8,996 9068 8996 8,702 3,690 3,689 7,616 7,595

R-squared 0.294 0.293 0.294 0.288 0.248 0.249 0.307 0.309

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Village reimbursement is a dummy equal to 1 if some households in

the villages have received reimbursements from the NCMS, and 0 otherwise. The entire sample includes all the households in the NCMS-villages.

All the columns control for the household and year fixed effects, log(income), family size, share of members over age 65, share of members under

age 10, whether the households have communist members, whether the household is a ―Wubao‖ household, and log(village income per capita).

42

Table 7

Counterfactual tests and robustness.

Dependent variable: log(consumption net of health expenditures) Dependent

variable:

log(food

expenditure) Counterfactual tests: uninsured in the current year

More similar

control group

Considering

tax policy

Sample: Uninsured

Uninsured with

no health

expenses in the

current year

Uninsured Uninsured Uninsured Participants by

2007

Entire

sample

Entire

sample

(1) (2) (3) (4) (5) (6) (7) (8)

Covariates

―Insured‖ family 0.024 -0.376 0.210 0.023 0.036 0.051** 0.053** 0.031*

(0.029) (0.777) (0.330) (0.032) (0.038) (0.024) (0.021) (0.017)

―Insured‖ family * log(income) -0.019

(0.034)

―Insured‖ family* share of fair or

worse health members in 2003

0.004

(0.025)

Village reimbursement -0.097*

(0.058)

―Insured‖ family * village

reimbursement

-0.022

(0.070)

Log(tax and fee) -0.007**

(0.003)

Observations 5,064 2249 5064 5064 4889 8,648 9,068 9065

R-squared 0.236 0.285 0.236 0.236 0.237 0.294 0.294 0.305

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The entire sample includes all the households in the NCMS-villages. Columns 1-5

consider only the uninsured, and the dummy for ―insured family‖ represents the indicator of whether the family is insured in the succeeding year. All the columns

control for the household and year fixed effects, log(income), household size, the share of members over age 65, the share of members under age 10, whether the

households have communist members, whether the household is categorized as a ―Wubao‖ household, and log(village income per capita).

43

Table 8

Gross effects of the NCMS program.

Dependent variable: Log (consumption net of health expenditures)

Sample: The insured vs. non-exposed

NCMS-village with

reimbursement vs.

non-exposed

NCMS-village

without

reimbursement vs.

non-exposed

(1) (2) (3) (4) (5) (6) (7)

Covariates

Insured family 0.006 0.311** -0.007 -0.002 -0.007 -0.004 -0.011

(0.011) (0.152) (0.013) (0.011) (0.017) (0.013) (0.021)

Insured family *

log(income)

-0.031**

(0.016)

Insured family* share of

fair or worse health

members in 2003

0.029**

(0.013)

Experienced members 0.059*** 0.060*** 0.029

(0.014) (0.015) (0.044)

―Insured‖ family * Village

reimbursement

0.012

(0.018)

Observations 15961 15961 15958 15889 15837 14834 11237

R-squared 0.257 0.257 0.258 0.257 0.252 0.261 0.230

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Column 1 to 6 includes all the insured and the non-exposed

households. All the columns control for the household and year fixed effects, log(income), household size, the share of members over age 65, the

share of members under age 10, whether the households have communist members, whether the household is categorized as a ―Wubao‖

household, and log(village income per capita).

44

Table 9

Matching difference-in-difference (DID) and regression with matching.

Dependent variable: Log (consumption net of health expenditures)

Matching DID Regression with matching

Sample:

Insurance effect:

insured vs.

non-participant

Gross effect:

insured vs.

non-exposed

Insurance

effect:

common

support

Insurance

effect:

common

support +

matches

Insurance

effect:

weighted

regression

Gross

effect:

common

support +

matches

Gross

effect:

weighted

regression

(1) (2) (3) (4) (5) (6) (7)

Insured family 0.052*** -0.015 0.064*** 0.061* 0.095* 0.004 0.001

(0.024) (0.017) (0.020) (0.032) (0.050) (0.023) (0.013)

Number of treated 2,442 2,442

Number of untreated 712 6,274

Post-matching bias 9.13 9.88

% change in bias through

matching -40.8% -9.1%

Post-matching pseudo R2 0.096 0.098

% change in pseudo R2

through matching -18.8% 19.8%

Post-matching prob value of

Chi-squared 0.000 0.000

Observations 8753 6299 8806 7143 15303

R-squared 0.297 0.170 0.725 0.170 0.714

Note: For columns 1 and 2, bootstrap standard errors with 100 replications in parentheses. For columns 3 to 7, robust standard errors in

parentheses, *** p<0.01, ** p<0.05, * p<0.1. In column 1 and 2, we exclude experienced participants to simplify the analyses.

45

Appendix A

Table A.1

The insurance schemes for the NCMS programs.

Deductibles Ceilings Average coinsurance rate for in-patient

service

Year Township

clinics

County

hospitals

Upper

level

hospitals

Township

clinics

County

hospitals

Upper level

hospitals

2003 371 457 743 13571 60.30% 61.60% 67.70%

2004 183 294 570 15200 54.00% 62.50% 70.30%

2005 133 261 550 13250 51.40% 58.60% 67.40%

2006 125 302 641 14838 49.10% 59.60% 68.60%

2007 87 252 574 19321 47.50% 55.80% 66.30%

Mean 138 289 603 15732 50.30% 58.70% 67.80%

Coinsurance rate for in-patient

service under RMB 3000

Coinsurance rate for in-patient service

between RMB 3000 and 10000

Township

clinics

County

hospitals

Upper

level

hospitals

Township

clinics

County

hospitals

Upper level

hospitals

2003 70.90% 76.00% 83.10% 71.10% 66.60% 73.70%

2004 67.00% 74.20% 82.60% 61.70% 66.60% 75.00%

2005 55.40% 66.40% 77.00% 54.10% 59.80% 70.60%

2006 54.20% 65.40% 76.30% 51.20% 60.10% 70.40%

2007 50.00% 61.80% 75.40% 49.50% 56.70% 69.10%

Mean 55.60% 66.10% 77.30% 53.70% 60.10% 70.80%

Having different

schemes for different

kinds of facilities

Having different

schemes for different

levels of expenditure

Providers pay

up-front and claim

reimbursement

Migrants are

covered

2003 85.7% 85.7% 14.3% 40.0%

2004 91.7% 84.6% 16.7% 44.4%

2005 95.2% 76.2% 40.0% 47.1%

2006 97.2% 69.4% 40.6% 48.3%

2007 100.0% 62.1% 48.0% 52.2%

Mean 96.2% 71.7% 37.5% 48.2%

46

Table A.2

Household participation decision and County enrollment.

Household participation decisiona County enrollment

(1) (2) (3) (4)

Log(Income) 0.149*** 0.139*** 0.049

Log(village income ) 0.443**

(0.040) (0.044) (0.054) (0.186)

Household size -0.002 0.017 0.027 Villages classified as

―Xiaokan‖

-0.352**

(0.017) (0.020) (0.026) (0.176)

Share of members with good

health in 2003 b

0.207*** 0.190** -0.105 Villages classified as

―Pingkun‖

-0.154

(0.070) (0.080) (0.094) (0.289)

Share of members with fair or

worse health in 2003

0.459*** 0.531*** 0.076 Surburb area

0.129

(0.142) (0.159) (0.186) (0.281)

Share of members with poor

health in 2003

-0.868*** -0.813*** -0.643*** Capital of the town

-0.254

(0.169) (0.179) (0.215) (0.326)

Share of members older than 65 -0.015 0.006 -0.109

Agricultural villages -0.308

(0.139) (0.148) (0.171) (0.254)

Share of members younger than

10

0.453** 0.548*** 0.321 Log(population)

0.153

(0.200) (0.207) (0.274) (0.145)

Share of migrants in 2003 0.114 0.101 -0.095

Share of laborer 2.663***

(0.132) (0.141) (0.154) (0.766)

Minority -0.673*** -0.812*** -0.019 Share of high school

or above

0.064

(0.081) (0.106) (0.216) (0.728)

―Wubao‖ household -0.690** -0.826** -1.311*** Share of migrants in

2003

-0.228

(0.304) (0.355) (0.440) (0.482)

Households with communist

members

0.216*** 0.119 0.150 Number of clinic in

2003

-0.072

(0.074) (0.078) (0.095) (0.080)

Female head 0.089 0.257** 0.156 Share of children

vaccinated in 2003

0.013**

(0.112) (0.116) (0.145) (0.006)

Age of the head of household 0.013*** 0.011*** 0.009***

Mountain areas -0.470***

(0.003) (0.003) (0.003) (0.180)

Head is single -0.233*** -0.233*** -0.221**

Highland area 0.535**

(0.084) (0.087) (0.103) (0.219)

Head’s educational years 3–6c

-0.024 0.070 0.223* Western China

-0.602**

(0.101) (0.104) (0.127) (0.256)

Head’s educational years 7–9 0.047 0.166 0.239*

Central China -0.334*

(0.108) (0.114) (0.136) (0.203)

Head’s educational years 10 and

above

0.122 0.230 0.043

(0.143) (0.152) (0.175)

Head is non-farmer

self-employedd

-0.237*** -0.286*** -0.074

(0.074) (0.086) (0.106)

Head is an employee 0.131* 0.017 0.026

(0.074) (0.079) (0.093)

Head works in other

non-farm-related employment

0.020 -0.023 0.184*

(0.071) (0.077) (0.106)

Village dummies no no yes no

Province dummies no yes no no

Observations 6575 6492 3960 Observations 394

Likelihood -1904 -1711 -1154 Likelihood -212.5

Pseudo R2 0.194 0.273 0.406 Pseudo R

2 0.222

Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All columns control year fixed-effects. a). In

columns 1 and 2, we control all the village characteristics considered in column 4. b) The health status is

self-reported. We label both bad and no working capacity as poor health. c) The omitted category is illiteracy or years

of education less than 3. d). The omitted category is farmer. The regression for village enrollment is based on

county-level data. When we run the household-level data, almost all the variables become significant.

47

Table A.3

Results for the baseline model: Balanced panel.

Dependent variable: Log (consumption net of health expenditures)

(1) (2) (3) (4) (5) (6)

Covariates:

Insured family 0.075** 0.101*** 0.096*** 0.111*** 0.094** 0.091**

(0.037) (0.037) (0.037) (0.039) (0.037) (0.038)

log(income) 0.493*** 0.405*** 0.392*** 0.417*** 0.420*** 0.424***

(0.039) (0.041) (0.041) (0.042) (0.043) (0.043)

Household size 0.067*** 0.065*** 0.066*** 0.067*** 0.051** 0.050**

(0.022) (0.021) (0.021) (0.021) (0.021) (0.020)

Log(village income) 0.036 0.008 0.025 0.013 -0.037 -0.040

(0.080) (0.078) (0.079) (0.082) (0.081) (0.081)

Year*log(income) -0.029*** -0.034*** -0.031***

(0.011) (0.011) (0.011)

Year*log(village

income per capita)

-0.061*** -0.057*** -0.072***

(0.021) (0.021) (0.022)

Year * share of

members with fair or

-0.051 -0.055

(0.031) (0.034)

poor health 03

Year * share of

members with poor

-0.058 -0.023

(0.065) (0.076)

health 03

Propensity score of

participation

-0.508* -0.720

(0.299) (0.534)

Year * propensity score 0.010

(0.137)

Yearly trend vary with

income, village income,

health status

Y

Observations 1992 1992 1992 1992 1992 1992

R-squared 0.283 0.303 0.305 0.314 0.311 0.311

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All the columns control for the

household fixed effect, year fixed effect, log(income), household size, the share of members over age 65, the

share of members under age 10, whether the households have communist members, whether the household is

categorized as a ―Wubao‖ household, and log(village income per capita).