Health Insurance and Consumption:
Evidence from China’s New Cooperative Medical Scheme
Chong-En Bai
Binzhen Wu*
Tsinghua University
Abstract
The precautionary saving motive is a popular explanation for the Chinese Saving
Puzzle, but few empirical studies quantify the importance of the precautionary savings in
China. We exploit the quasi-natural experiment provided by the introduction of a health
insurance program in rural China to examine how the insurance coverage affects household
consumption.
The results show that the health insurance coverage on average stimulates
non-healthcare related consumption by more than 5 percent. The effect exists even for
households that have no health-care expenditure. In addition, the effect is stronger for
poorer households and households with worse self-reported health status, who both have
higher risk of relatively large health expenditure.
We also find that the insurance effect varies with households’ experience with the
program. Particularly, the effect is only significant in villages in which households witness
some reimbursement resulting from the insurance coverage. In addition, the program
stimulates more consumption for experienced participants than for new participants of the
program in these villages.
Keywords: New Cooperative Medical Scheme; Consumption; Health Insurance;
Precautionary Savings; Chinese Saving Puzzle
JEL Classification Nos.: D12, 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, seminar participants at the Central University of Finance and Economics, and participants at the
Stanford-Tsinghua Conference on “Chinese Policy Reform: Topics for Troubled Times.”
1
1 Introduction
Chinese high and rising saving rate has attracted a lot international attention.
Household saving rate has risen by about ten percentage points between 1995 and 2008,
reaching 28 percent of the disposable income in 2008, which is higher than most of other
countries including East Asian countries (Prasad, 2009). The literature has proposed many
explanations for this Chinese Saving Puzzle. A popular one is that the dissolution of the
traditional social safety net has created more precautionary savings (Chamon and Prasad,
2008; Meng, 2003).1 Chinese government has made a lot effort to improve its safety net.
The social insurance programs disbursed 1.2 trillion RMB in 2009, with an annual growth
rate of 19.4% since 2000. However, there have been very few empirical studies that
quantify the size of precautionary savings in China. Given that Chinese saving rate has
important global impacts, it is crucial to learn how much the public insurance programs
affect consumption and savings in China.
The existing empirical literature for the developed countries delivers quite mixed
results on the role of the precautionary savings. The results range from being very small
(Dynan, 1993; Guiso, Jappelli, and Terlizzese, 1992; Hurst, et al., 2010; Starr-Mccluer,
1996) or modest (Engen and Gruber, 2001; Lusardi, 1998) to quite large (Banks et al., 2001;
Carroll and Samwick, 1998; Fuchs-SchÜndeln and SchÜndeln, 2005; Kazarosian, 1997).
Studies in the developing countries are still in their early stages (Lee and Sawada, 2010;
Meng, 2003; Zhang and Wan, 2004). Most of the studies find a substantial amount of
precautionary savings. Recent studies exploit the exogenous variations of the insurance
coverage caused by policy changes, including 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 Vietnan, and Chou, Liu and Hammitt
(2003) for Taiwan. Most of these studies confirm the importance of the precautionary
savings, but it is not clear whether the estimates can be applied to China, not to mention
that Chinese culture remains a popular explanation for the Chinese Saving Puzzle.
1
Other explanations include the life-cycle model with demographic changes (Kraay, 2000; Modigliani and
Cao, 2004), high income growth and habit (Horioka and Wan, 2007), changes in the return rate of investment
(Wen, 2009), the imbalance in the sex ratio (Wei and Zhang, 2009), financial underdevelopment (Caballero,
Gourinchas and Farhi, 2008), income inequality (Jin, Li, and Wu, 2010) and cultural difference.
2
The launch of public health insurance programs in China provides natural experiments
to investigate the size of the precautionary savings in China. This paper exploits one of
most important policy changes in the rural areas: the introduction of the New Cooperative
Medical Scheme (NCMS) since July 2003. This public health insurance program is heavily
subsidized by the government, and has been introduced sequentially in different counties.
Households’ participation is voluntary. We focus on the double-difference comparison
between the insured and the non-participants in the villages that have launched the program.
The reason is that households in the same village are more comparable to each other than to
households in a different village and it can reduce the bias resulted from the contemporary
policy changes that were introduced simultaneously with the NCMS.
The difference-in-difference framework helps remove all the time-invariant selection
bias. Selection bias on the observables is further reduced by allowing the temporal change
in consumption to vary with income and health status or by applying matching
difference-in-difference. The data we use combine the longitudinal Rural Fixed-Point
Survey between 2003 and 2006 and a household survey on the NCMS for a subsample of
the 2006 round of the Rural Fixed-Point Survey.
The results indicate that household consumption other than health expenditure has
increased by about 5.6 percent or 147.7RMB owning to the health insurance coverage. The
magnitude is much larger than the average cost of the insurance that was mostly 30 RMB in
2003 and 50RMB in 2006. This is also consistent with the time trend of the saving rate at
the aggregate level: the saving rate in the rural area has declined sharply since 2005 (Prasad,
2009). The result is quite robust to different specifications that try to control the difference
in the counterfactual trend of consumption between the insured and the non-participants.
We also find that the insurance effect remains significant for households who have no
health care expenditure in the year, which cannot be explained by the “crowd-in” story that
emphasizes that the insurance coverage reduces the out-of-pocket health expenditure, and
thereby insured households have more income left for other consumption. Moreover, the
insurance effect on consumption is much stronger for poorer households and households
with worse self-reported health status, which is consistent with the argument that the higher
the risk of having relatively huge health expenditure is, the more households reduce
precautionary savings after being covered by the insurance.
3
Finally, the insurance effect varies with households’ experience with the program. In
addition, households’ trust in the program can be crucial for the stimulation of consumption.
For the experienced participants, the insurance effect on consumption increases to over 11
percent on average. Besides, the insurance effect on consumption is significant only in the
villages where households witness reimbursement from the program and thus may establish
trust in the program. Moreover, in such villages, the stimulation of consumption is stronger
for the experienced members of the program than for the new members. In contrast, in the
villages that have seen no reimbursement, both the insurance effect on average and the
difference between experienced and new are insignificant.
To our best knowledge, this is the first paper exploiting this policy change to
rigorously assess the extent of precautionary savings in rural China. The finding about the
role of the “trust” warrants more attention in both the literature and the policy making,
particularly for public insurance programs in the developing countries where transparency
and trust are often missing. The rest of the paper unfolds as follows. The second section
introduces the background of the NCMS and the literature. Section 3 introduces the data
and gives descriptive statistics. Section 4 discusses our econometric specifications. Section
5 shows the results for the baseline model. Section 6 shows the robustness tests. Section 7
concludes.
2 Background
The 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 high cost of health
care has deterred households from obtaining necessary health care (You and Kobayashi,
2009). 2 In response, the Chinese government started the pilot programs of the New
Cooperative Medical Scheme (NCMS) in 2003. The primary goal of the NCMS is to reduce
impoverishment due to catastrophic illness and improve the affordability of health care.
The pilot program began in 310 rural counties of China’s more than 2800 rural counties
in July 2003, expanded to 617 counties in 2005, 1451 counties in 2006, and by June 2007,
2
In 2003, 96% of rural households in China had no health insurance, 38% of the sick did not seek health care.
Large negative health shocks reduced annual income by around 12.4% in rural China, and 22% of poor
households attributed their poverty to illness or injury in 1998 (You and Kobayashi, 2009).
4
the time that we collect our data for the study, the program had expanded to over 84.9% of
all rural counties and 82.8% of all rural residents. 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 program and the insurance package.
There are several main features of the NCMS: 1) the program targets at rural residents;3
2) participation is voluntary but should be in the unit of household;4 3) participating
households need to pay a flat-rate premium, but the insurance is heavily subsidized by the
governments; 4) the program mainly reimburses large expense; 5) the program is operated
at the county level rather than township or village level.
The voluntary nature of the participation raises concerns on the adverse selection that
can threat the financial sustainability of the NCMS. However, the participation rates in pilot
villages were generally very high, with an average of 86% between 2003 and 2006 in our
sample. An important reason for the high participation rate is the generous government
subsidies. For the premium, the standard in 2003 was that the participating household paid
10RMB and the government paid more than 20 RMB a year for each member in the
household. Since 2006 (inclusive), the government subsidy has increased to 40 RMB for
each member while household contribution has remained the same.5
Together with NCMS, the governments also implemented some supporting policies,
such as improving the quality and the delivery of the health care service and strengthening
the pharmaceutical governance. At the same time, there are statistics showing that the
average expenditure per visit has increased after the introduction of the NCMS program
(Yao and Kobayashi, 2009; Mao, 2005). These changes also affect households who choose
not to participate the NCMS. In addition, the government has set up a parallel program, the
medical assistance (MA) scheme, to help poverty-stricken population.
Since counties have had substantial authority over the design of the NCMS, there has
been considerable heterogeneity in the package of the benefit, coverage, and management
3
Urban districts and county-level cities that contain rural residents can also receive the program.
The requirement of participation as a household unit is imposed over 97% of the counties in our sample.
Some studies suggest that local governments have made considerable efforts to attain high participation rate,
including mandating households to participate (Wu et al. 2006). However, our survey shows less than 1%
households report compulsory enrollment in 2007, although it is not clear whether it was so in early years.
5
The poor and certain other groups have their contributions exempted. In 2008, the government subsidies
increase to 80 RMB a year per person. Household’s contribution is raised to 20 RMB a year.
4
5
across counties. Table 1 shows the main parameters of the insurance packages for the 54
counties that we have detailed information on the insurance. We first notice that the
insurance is typically not generous, maybe due to the scarce financial resources of the
government. Particularly, many services, including outpatient care, are not fully covered,
deductibles are high, ceilings are low, and coinsurance rates are high. However, the
insurance program can still reduce the out-of-pocket health care substantially for the
insured. At the township clinics, the deductible was on average about 125 RMB, and the
ceiling was 14838 RMB, and the reimbursement rate was 50.9% in 2006. This implies that
households could save 7489RMB at most. Moreover, the insurance plans have become
more generous over time for all levels of health-care centers, particularly for the township
clinics. Second, all counties cover the inpatient care, but the reimbursement rate varies
substantially across counties, ranging from 20% to 75%. Third, most counties apply
different insurance schemes for different levels of facilities. The insurance is more
favorable to the low-level health-care centers as the coinsurance rate and the deductible are
lower when households utilize health-care service at the township clinics than at county and
(or) above levels of hospitals. And this feature has magnified over time. In contrast, the
difference in the reimbursement rate for different amount of expenditure is small and
declines over time.6
Literature (I think for JpubE, we can skip this part)
After the seminal theoretical papers of Zeldes (1989), Deaton (1991), and Carroll
(1992) that illustrate the potential importance of precautionary savings, many studies have
examined the strength of the precautionary saving motives using micro data. Simulations or
Structural estimations mostly find precautionary savings can explain a sizeable portion, as
much as fifty percent, of U.S. savings (Cagetti, 2003; Gourinchas and Parker, 2002;
Hubbard, Skinner, and Zeldes, 1994; Palumbo, 1999). In contrast, empirical studies that
econometrically assess the role of precautionary savings have reached quiet mixed
conclusions: Dynan (1993), Guiso et al. (1992), Hurst, et al. (2010) and Starr-Mccluer
6
Other statistics include: the procedure to get reimbursement has become simpler over time. In 2006, about
37.5% counties disburse the reimbursement immediately when households pay the health expenditure. In
addition, around 48% counties provide insurance for migrants, although the reimbursement is usually much
less generous for the health-care expenditure at the hospitals outside the county.
6
(1996) find little or no precautionary savings, whereas Banks et al. (2001) (for the UK),
Carroll and Samwick (1998) and Kazarosian, (1997) (for the US), and Fuchs-SchÜndeln
and SchÜndeln (2005) (for the Germany) find economically important precautionary
motive.
Early studies mostly examine the issue by relating wealth accumulation to some
measures for the amount of risk that households face. The mixed results can be partially
attributed to the difficulty in identifying an exogenous measure for the income uncertainty.
Various measures have been tried, including the variability of income (Carroll and
Samwick, 1998; etc.), the 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 households’
insurance coverage (Starr-Mccluer, 1996; Guariglia and Rossi, 2004). In the Chinese
context, the literature along this line generally find strong evidence for the importance of
precautionary savings (Meng, 2003; Zhang and Wan, 2004; Long and Zhou, 2000).
However, these studies all suffer from the problem that individual’s income risks or
subjective assessments of the risks are likely to be correlated with underlying tastes for
savings. Recent studies exploit the exogenous variations of the insurance coverage caused
by policy changes or variations, including 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. An implication of the precautionary-saving
story is that social insurance programs, by reducing income or expenditure risk, would
reduce asset accumulation. Most of these studies all confirm this prediction, although they
focus on different programs, including unemployment insurance, worker’s compensation,
and health insurance.
The research for developing countries is still at the early stage. Lee and Sawada (2010)
investigate precautionary saving under liquidity constraints in Pakistan using household
panel Data and find substantial evidence of the presence of precautionary saving. Wagstaff
and Pradhan (2005) study the case in Vietnam and find the introduction of the health
insurance program increases nonmedical household consumption, mostly non-food
consumption, and the magnitude is much higher than the reduction in the out-of-pocket
health expenditure. Chou, Liu and Hammitt (2003) find that the universalization of health
7
insurance in Taiwan reduced the household savings rate by about 2.5 percentage points. Ma,
Zhang, and Gan (2010) examine the effect of the NCMS on rural household’s food
consumption, and conclude the participants have higher calorie, carbohydrate, fat, and
protein intake than the non-participants. Liu et al. (2010) investigate the effect of the public
health insurance program on consumption for urban households in China, and their
preliminary results show that the insurance has stimulated household consumption by 10.2
percent. There are also several papers trying to evaluate the effect of the program on the
utilization of health care (Lei and Lin, 2009; Wagstaff et al., 2009).
However, the impact of the NCMS on consumption in rural areas has not been fully
explored. Some studies have shown that rural residents respond differently from the urban
households to the income uncertainty (Zhang and Pei, 2007). In addition, the literature
indicates the strength of the precautionary saving can be different between different income
groups (Carroll, Dynan, and Krane, 2003). Notice that the Chinese government has planned
to spend 40 billion RMB to boost consumption in the rural area in 2009, it is important to
learn how the NCMS can help stimulate consumption.
3 Data and Descriptive Statistics
Our data come from the longitudinal Rural Fixed-Point Survey (RFPS) between 2003
and 2006 and a supplementary household survey that aims at evaluating the NCMS. RFPS
has surveyed the same households each year since 1980s. The sample is selected based on 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 information about household and individual characteristics, and details about
income and expenditure.7 The supplementary survey was conducted by Tsinghua University
in May 2007. It surveyed a subsample of the 2006 round of RFP and covered 23 provinces,
143 villages, 5728 households. It collected detailed information about the time that a
household enrolled in the NCMS, and the retrospective information on each member’s health
7
Unfortunately, there are a lot mistakes in the identification code for tracking individuals and households. We
use conservative rules based on individual’s age, sex, 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 delete around 8% households in our sample due to the
inconsistency of the identification code.
8
care utilization and expenditure in each year between 2003 and 2006. The survey
oversampled households with economically meaningful health care expenditure.8
Table 2 shows the participation rate of the villages and households between 2003 and
2006.9 The enrollment of our sample villages spreads over different years with relatively
higher rates in 2006, increased from about 15% in 2003 to about 77% in 2006. Similarly,
the enrollment of households also spread over years, gradually increased from 10% in 2003
to 71% in 2006. These numbers are also consistent with the national data. In the villages that
have launched the NCMS (called as NCMS-villages), the majority of households
participated the program, and the participation rate increased from 64% in 2003 to 94% in
2006. Moreover, most households started to participate in the first year that the village
launched the program, and the participation rate was 64% in 2003 and 96% in 2006 in the
villages that launched the program for the first year. Nevertheless, these numbers also
indicate that quite a few households chose not to participate the program or delayed in
participation: over the 4 years between 2003 and 2006, about 13.9% households in the
NCMS-villages did not participate, and among the participant about 14.4% households did
not participate right after the village enlisted the NCMS.
To relate consumption with households’ enrollment status of the NCMS, we exclude
some outliers, such as households who have ever exited the NCMS or have participated
some cooperative insurance program between 1993 and 2002. 10 Also excluded are
households who have purchased commercial insurance in 2007 or have not participated in
the NCMS but enrolled in some government insurance programs in 2007. 11 Finally,
noticing that the program was first piloted in July 2003, we have excluded the observations in
the year of 2003 for villages that launched the program in 2003, which can avoid the
complication that the effect of the NCMS actually started in the middle of that year. As a
result, the year of 2003 can be seen as the year where no counties have introduced the
8
More specifically, the survey first ranks all the households in the 2006 round of the RFPS based on their
average health care expenditure between 2003 and 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.
9
The table shows the results for the balanced panel. For the unbalanced panel, we have more observations
but the pattern of the results is very similar.
10
Only 107 households exit the program. About 4.6% households have enrolled in some cooperative
insurance program between 1993 and 2003, and most of them participated after 1997.
11
For these two types of households, we exclude their observations in all years as we only have the
information on the commercial insurance and government insurance program in 2007. About 6% of
households report having some commercial insurance in 2007.
9
NCMS.12 Eventually, the sample includes 517 villages and 17,715 households in 4 years.
Table 3 shows the descriptive statistics for three groups between 2004 and 2006: the
insured households and two kinds of uninsured households: the non-participants who lived
in the NCMS-villages but chose not to participate the program, and the non-exposed
households who lived in the non-NCMS villages. Since more villages and households join
in the program over time, the insured group includes more observations in 2006, while the
non-exposed group includes more observations in 2003. To make the values of the variables
that may change over time more comparable between these three groups, we focus on their
values in 2003.
The table illustrates that compared with the non-participants, the households choose to
enroll in
generally have higher consumption and higher income. In addition, they have
more members reporting fair or worse health status, and spend more on in-patient health
care than the non-participants. However, the participants have less members with very bad
health-status, which help explain why they spend less on health-care.13 In addition, their
heads are slightly older, more educated, less likely to single, or being a non-agricultural
worker. They are also more likely to have communist members, while less likely to be a
minority or household in poverty ("Wubao"). Table A.1 in the appendix gives the regression
of households' participation decisions on observable household and village characteristics
given that the village has launched the program. It confirms that most of these differences
are significant. Moreover, the evidence for adverse selection is mixed: the probability of
participation increases with the share of members with good or fair health status, where the
reference group is the share of members with excellent health status; however, the
participation rate actually declines when the share of the members with bad health status
rises. Moreover, column 3 shows that even the positive evidence of the adverse selection
disappears when we focus on the within-village comparison by controlling for the village
fixed effect.14
12
We include these observations as a robust test, and find a smaller insurance effect of the NCMS on
consumption as expected.
13
The pattern is the similar if we use the number of members with different health status. The subscribing
households also on average have less members older than 65 or younger than 10, more migrants, and less
likely to have a female head. However, the regression in Table A.1 shows that these differences are not
significant or the signs are different.
14
What remains under within-village comparison is that non-participants are more likely to have members
with bad health status, have younger or single head, be "Wubao" families, or have head working in the
10
The difference in the health-status is much smaller between the NCMS-villages and
non-NCMS villages. However, the NCMS-villages on average spend more on in-patient
expenditure than non-NCMS villages. Furthermore, the NCMS-villages are much richer,
having less clinic but more kids taking vaccinations than the non-NCMS villages. They are
also have less migrants, more laborers, higher education and are less likely to be in
mountain areas, western or central areas. Column 4 of Table A.1 confirms these differences
are significant.
The table also indicates that although the non-participant are different from the insured,
they seem to be more similar to the insured than the non-exposed in terms of income and
consumption. This is not surprising as households living in the same village are more likely
to be similar to each other than to people living in a different village.
Column 4 confirms the significant difference between the NCMS-villages and the
non-NCMS villages. Particularly, the NCMS-villages have higher income, less clinics,
more kids vaccinated, less migrants, more laborer, and higher education. They are also less
likely to be in the mountain areas, western or central areas.15
4 Baseline Empirical Model
The empirical analysis exploits the quasi-natural experiment provided by the NCMS to
examine the effect of the insurance coverage on household’s consumption. We start with
applying the Difference-in-Difference (DID) framework to the 4-year panel. More
specifically, the effects of the NCMS are identified by the differences in the dynamic
changes in consumption around the launch of the NCMS between the insured and
uninsured households. The advantage of the double-difference method is to remove all the
time-invariant selection bias. This is crucial in our context because the participation is
voluntary, which implies that households who chose not to participate are different from the
participants in both observable and unobservable characteristics and hence in the
counterfactuals of consumption. Similarly, the program placement over villages can be
non-random. The double-difference method between the insured and the uninsured can still
deliver unbiased and consistent estimations, as long as the temporal change in households’
non-agricultural sector.
15
This regression is based on county-level data. When we run on the household-level data, almost all the
variables become significant.
11
consumption would have been parallel if there had been no NCMS.
In our context, there are actually two groups of uninsured households in each period.
One is the non-participants who chose not to enroll in the NCMS-villages that have
launched the program. The other is the non-exposed who lived in the non-NCMS villages
that have not introduced the program. Their reasons to be uninsured are quite different. To
examine the precautionary saving motive, we focus on the double-difference between the
insured and the non-participants in the NCMS-villages for two reasons. The first one
concerns the fact that other changes happened together with the introduction of the NCMS.
Particularly, the governments provided supporting policies to improve the quality and the
delivery of health care service. In addition, a lot anecdotal evidence indicates that the price
of the health-care service has increased after the introduction of the program (Mao, 2005).
For the precautionary-saving story, we need to identify the insurance effect of the NCMS
that works only through the insurance coverage, and hence exclude the effects of these
contemporary policies or changes. The insurance effect can be estimated by the
double-comparison
between
the
insured
and
the
non-participants
within
the
NCMS-counties because both groups were affected by these changes.16 In contrast, the
double-difference between the insured and the non-exposed delivers the gross effect of the
NCMS on the insured, which includes both the insurance effect of the NCMS and the
effects of other associated changes.
The
second
reason
is
related
to
the
identification
assumption
for
the
difference-in-difference model: the dynamics of consumption between the insured and the
control group should be the same around the relevant period. We argue that the assumption
is more problematic for the comparison between the insured and the non-exposed than the
comparison between the insured and the non-participants. First, households in the same
village are more likely to be similar to each other than to households living in a different
village. This argument is partially justified by table 3 and table A.1, where we see
significant difference between the NCMS-villages and the non-NCMS villages.17 Second,
16
The effects of these policies can be different for the insured and the non-participants. Thus, the insurance
effect has incorporated the difference. The NCMS may also activate or strengthen the precautionary-saving
motive as it has educated people about the risks to stimulate more participation.
17
We have also tried the propensity matching procedure to balance on the observable village and
household characteristics. It turns out to matching is much more successful when matching insured with
the non-participants in the NCMS-villages than matching between the insured with the non-exposed in a
12
the consumption can grow more similarly among people living in the same geographic
areas than among people living in different areas. This is related to the issue of non-random
program placement: the rolling-out of the NCMS was strongly correlated with village’s
average income and hence consumption.
To implement the DID framework for the panel data, the baseline model applies the
fixed-effect regression that controls both household and year fixed effects. All the
time-invariant effects of household characteristics are controlled by the household fixed
effects, and the yearly time trend of consumption that is common for all households is
controlled by the year fixed effects.18 Refinements such as matching DID and tests of the
identification assumptions are discussed in Section 6, which confirm that the baseline
model is reliable. More specifically, the baseline model for the double-difference between
the insured and the non-participants is as following:
Yijt    Family _ insured it   t  Tt   i  Di    X ijt   ijt ,
(1)
where Yijt represents the log value of households’ consumption net of health expenditure
for household i who lives in village j in period t. The reason to exclude health expenditure
is that we want to focus on precautionary savings, and health expenditure is affected by
insurance through other channels. 19 To simplify exposition, consumption means all
consumption expenses net of health expenditure throughout the paper, unless otherwise
specified.
Family_insuredit is the binary variable indicating whether household i having
subscribed the insurance program in year t. Tt includes 3 year dummies. Di includes all the
household indicators. Xijt includes all the observable household and village variables that
vary over time and may affect consumption dn the participation decision, including
log(household income), household size, and log(average income per person in the village),
different village.
18
One advantage of the regression method is that we can easily control the effect of time-variant variables
that may affect household’s treatment status and consumption. Moreover, it is quiet easy to estimate the
difference in the impacts across subsamples using the regression method. However this method imposes the
linearity functional assumption that can be problematic when the treated and untreated are quite different.
19
We also tried other outcomes, such as consumption per person, total consumption including health
expenditure, spending (including consumption and investment), and savings (measured by the savings in the
saving account plus money borrowed out). The results for consumption per person are almost the same as
those for consumption. The results for total consumption and spending show similar patterns, but the
magnitudes are smaller, particularly for the spending. The effect of the NCMS on savings is mostly not
significant, although the direction is consistent with our expectation. However, savings are not well measured
in this dataset.
13
number of members over age 65, number of members under age 10, whether having
communist party members, and whether being officially poor (“Wubao” family).
In equation (1), γ measures the effect of the insurance coverage on consumption. The
precautionary-saving story indicates that >0. Since we control for log(income),  also
represents the effect of the NCMS on the average propensity to consume (or consumption
rate), as log(consumption rate) is equal to the difference between log(consumption) and
log(income). For expositional simplicity, we hereafter use consumption and consumption
rate interchangeably.
A main concern in the baseline model is the identification assumption may not hold,
that is, after conditional on the observable characteristics, the consumption of the insured
and that of the non-participants may not be parallel in absence of the NCMS.
20
The most
likely story is related to the adverse selection: households who expect substantial health
expenditure in the next year are more likely to participate and hence their consumption path
around the implementation of the NCMS would have been different from households who
have no such expectation. By excluding health expenditure from consumption, we actually
avoid the complication that the participants tend to have more expenditure on health-care
after being covered by the insurance. In addition, the selection bias tends to underestimate
the precautionary-saving motive as families with the expectation of huge health expenditure
are more likely to be frugal in consumption.
Finally, although the expectation on the health expenditure in the near future is
unobservable, it can be proxy by the self-reported health status. As a result, we can address
the potential selection bias by allowing households with different health status to have
different time trend in consumption. Similarly, we can allow households with different
income to have different time trend in consumption to address the concern resulted from the
phenomenon that the insured are generally richer than the non-participants and different
income groups may have different income growth rate.
20
Wagstaff et al. (2009) focus on the double-comparison between the insured and the non-exposed to
evaluate the effect of the NCMS on health-care expenditure. We think their choice is reasonable when
the focus is a program evaluation so that the gross effect may be more important. Also when the outcome
interested is health-care expenditure, the potential selection bias due to the voluntary participation is a
serious concern for the double-difference comparison between the insured and the non-participant,
because the reason that the non-participants did not join the program can be that they were confident of
not needing substantial health care in the foreseeable future while the participants were just the opposite.
14
5 Results for the Baseline Model
5.1 Average Treatment Effect on the Treated
Table 4 reports the results for the baseline model that focuses on the double-comparing
between the insured and the non-participants. All the specifications control the household
fixed effect, year fixed effect, and time-variant characteristics of household and village,
including log(household income), household size, log(average income per person in the
village), share of members over age 65, share of members under age 10, whether having
communist party members, and whether being a “Wubao” household.21
The first column shows that the insurance coverage has stimulated more consumption
net of health-care expenditure for the insured. Although the NCMS is often criticized for
being ungenerous, consumption increases by 5.5 percent, which is anything but trivially.
Since the average consumption per person for the participants in 2003 is about 2637.5RMB
between 2003 and 2006, a 5.5 percent implies an increase of 145.1RMB, which is much
higher than the average cost of the insurance that was mostly 30 RMB in 2003 and 50RMB
in 2006. The program is definitely more effective in stimulating consumption than the cash
transfers from the government since the average propensity to consume for the rural
residence is only about 0.437.
To address the potential selection bias, column 2 adds the interaction between year and
household income and interaction between year and village average income to allow the
linear trend of consumption to vary with income.22 Column 3 controls additionally the
interaction term between year and household self-reported health status in 2003.23 Both
columns show an insurance effect similar to that in the baseline model, including both the
magnitude and significance level. The result remains robust when we allow the difference
21
Some of the family characteristics do not vary much over time. But the results are very robust to whether
including them or not.
22
We also try income in 2003 instead of income in the current year, and there are little changes in the results.
23
There are 4 categories for the self-reported health status: excellent, good, fair, and bad (including no
working capacity). The result here considers two measures: the share of household members reporting fair or
bad health status and the share of members reporting bad health status. Results are quite similar if we use
other measures of health status, such as controlling instead the mean value of the health status in the
household or controlling additionally the share of members with good health status. Notice that the health
status can be affected by the insurance coverage, we only consider the health status in 2003 when there was
no NCMS. Using the information in 2003 reduces the number of observations. To check the robustness, we
use the health status in the current year and get very similar results.
15
in the trend to vary year by year by controlling the interaction terms between the year
dummies and household income, village average income, and household health status
(column 4). In column 5, we future allow different provinces to have different trend in
consumption by adding into the covariates the interaction between provincial dummies and
year. The magnitude declines slightly, but still significant and substantial.24
There can be other differences between the insured and the non-participants. Similar to
the idea of matching, we can summarize the difference by a one-dimension variable, the
household’s “propensity score” of participating the program. The estimation of the
propensity score is discussed in details in Section 6. Here, we control the interaction
between year and the propensity score to allow the trend in consumption to vary with the
propensity score. The estimate is in column 6, which shows a similar result again.
25
Controlling the interaction between the year dummies and the propensity score does not
change the result either.26
Since more and more counties and households enroll in the program, we have an
unbalanced panel in table 4. Appendix A.2 reports the estimates for the balanced panel.
Although there are much fewer observations, the insurance effect is still substantial,
actually is stronger, and the effects are robust to allowing different trends between the
insured and the nonparticipants. More specifically, the effect is on average 9.6% after we
allow the trend to vary linearly with income and health (column 3). The difference in the
magnitude between the balance and unbalanced panel can be explain by the fact that the
unbalanced panel consists more new members of the NCMS, and the experienced members
of the program show more increase in consumption than new members, which will be
shown later.
In summary, the estimates of the insurance effects are quite robust to the specifications
that allow the observable difference between the treated and the untreated to bring on
24
If the insurance coverage stimulates the growth of consumption, which is the case as we show later, this
method would underestimate the positive insurance effect. The reason is that for provinces that unveiled the
program in 2003 or 2004 and most residents participated, the time trends of consumption have incorporated
the insurance effect to some extent.
25
The consistency of the estimate in this specification requires an additional assumption: the conditional
expectation of the outcome given the propensity score is linear in the propensity score. Moreover, the standard
errors are not consistent as we have not adjusted for the first-stage estimation for the propensity score.
26
Another specification we have tried is to consider the first-difference version of the baseline model.
Particularly we study how the change in the insurance coverage affects the change in consumption. The
insurance effect is 4.5% and significant.
16
different trends of consumption. The results also survive in many robust tests, such as
excluding family variables that are quite stable over time, using more homogenous sample,
applying to two periods before participation, matching DID, and regression with matching.
These increase our confidence that the baseline model gives a reliable estimate for the
insurance effect.
5.2 Precautionary Savings
This section examines whether the increase in consumption net of health expenditure
really represents the reduction in precautionary savings. The estimates in this section all
control household fixed effect, year fixed effect, time-variant household and village
characteristics, and the interaction between year and household income, average income in
the village, and household health status in 2003, similar to the specification in column 3,
table 4.
Precautionary Savings and Crowd-in Effects
An alternative explanation for the positive effect of the NCMS on consumption net of
health expenditure is that the insurance reduces households’ out-of-pocket health
expenditure; therefore, the insured households can have more income left for other
consumption. This is a simple ex-post “crowd-in” effect. Since the “crowd-in” story only
works for the households who have health expenditure in the current year, we can exclude
this channel by estimating the effect only on households who have no health expenditure in
the current year. Column 1 of Table 5 shows that the insurance effect remains significant
for households have no health expenditure in the current year. Actually, the magnitude is
even stronger than that when we pool all households together.
27
The “crowd-in” story highlights that the insurance coverage affects health expenditure
and consumption net of health expenditure simultaneously and the effects may not be
independent. In the second column of Table 5, we consider the effect of the NCMS on total
consumption that includes health expenditure. The result shows that the insurance coverage
stimulates total consumption by 6 percent, which is not much different from the effect on
27
When we consider only households having health expenditure in the current year, the estimate of the effect
of the NCMS is insignificant in many specifications. However, there are only a few non-participants in this
estimation, and therefore the estimate may not be reliable.
17
consumption net of health expenditure.28
The Insurance Package
If the increase in consumption comes from the reduction in precautionary savings, we
expect that the insurance effect increases with the generosity of the insurance program.
Column 3 and 4 of Table 5 test this conjecture by exploiting the information of the NCMS
collected for 54 villages. The result shows that households do respond to the generosity of
the insurance for the health expenditure at county facilities, and the direction is consistent
with the expectation: the lower the deductible or the coinsurance rate is, the more
consumption the insurance program can stimulate. However, the generosity of the insurance
for the health expenditure at the village clinics does not have such significant effects. This
is somewhat reasonable as most households go to the county hospitals when they face
serious health problems that demand the insurance most.
Insurance Effect and Risks
Table 6 studies how the insurance effect varies with the uncertainty about future health
expenditure. We first look at the difference between income groups. Since poor households
are more likely unable to afford large health care expenditure, we expect the effect of the
insurance on consumption is stronger for the poor. The first three columns of Table 6
confirm this conjecture. The first column confirms that the positive effect of the NCMS on
consumption decreases with income. The second and third columns run the regression
separately for the two halves of the income distribution, where the bottom half is labeled as
the poor and the top half labeled as the rich. The results show that the positive insurance
effect is only significant for the poor. When further dividing the population into 3 income
groups, we find the group with the least income shows the largest increase in consumption
after acquiring the insurance coverage.
The second part of Table 6 concerns the risk in regard to household’s health status. On
28
We have estimated the effect of the NCMS on the out-of-pocket health expenditure. The result shows no
significant effect, which contradicts with the “crowd-in” story. This finding is consistent with the results in the
studies that evaluate the NCMS (Wagsaff et al. 2009; Lei and Lin, 2009; and Mao 2005). However, no
significant reduction in the out-of-pocket health expenditure does not mean that the insurance coverage does
help reduce the risk. Households may have visited health-care facilities more often after being covered by the
insurance; or the price of the health care may have increased after the launch of the NCMS. If the latter is true,
for a given visit, households’ health-care expenditure would have increased if they had no insurance.
18
the basis of the self-reported health status for each member of the household, we construct
two measures of the risk. The first measure first assigns a 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, and then calculate the average value of the self-reported health status for
the household in 2003. As shown in column 4 to 6, the positive insurance effect decreases
as the average health status gets better, and the effect is only significant for the bottom half
of the health distribution, labeled as “poor health”. If we future divide the population into 3
groups base on the average value of the health status, the group with the worst health shows
strongest response to the health insurance coverage. Columns 7 to 8 consider the second
measure of the health risk, which is whether having household members self-reporting fair
or bad health in 2003, including no working capacity. In our sample, about 89% of
individuals report good or excellent health. Therefore, reporting fair or bad health seems to
indicate serious health problems that may need substantial health expenditure in the future.
The results confirm that after being covered by the insurance, households having members
with fair or bad health status consume much more than households having no such
members.
29
The results of Table 6 are consistent with the argument that the insurance effect reflects
the reduction in precautionary savings and the effect is stronger for households having
higher risk of incurring “expensive” health care spendings in the future.30
5.3 Dynamics of the Insurance Effect and Trust
This section analyzes how the insurance effect varies with household’s experience with
the insurance program. After the dissolution of the old Cooperative Medical System in
1980s, most households in the rural areas have not been covered by any health insurance
for a long time. Moreover, the NCMS differs from the old CMS in many aspects. As a
result, it takes time for the households to understand and establish trust in the new program.
Anecdotal evidences show that many households were not clear about the details of the
29
Use the health status in the current year delivers quite similar estimates.
One concern for the regressions in this section is that there are only a small number of non-participants
when we divide the sample into smaller groups. Here are the numbers of non-participants in each estimate:
324 among the rich, 435 among the poor; 410 among the group with good health, 349 among the group with
bad health, 242 among the households with bad or fair health members, and 517 among households with no
such members.
30
19
package, including the exact deductible, the coinsurance rate, and the ceiling, particularly
before any households in the village went through the procedure and diffused the
information. Therefore, it is possible that households participated the program without
realizing the essence of the insurance or having enough trust in the program.
Column 1 of Table 7 adds a dummy for the experienced participants to the baseline
model to allow the experienced have a different insurance effect from the new participants.
Here, the new members are those who have participated the NCMS for less than one year
while the experienced have participated for more than one year. It shows that although the
insurance effect is significant among the new, about 4.5 percent; it is much lower than the
effect on the experienced participants by 6.7 percent. So compared with the
non-participants, the experienced members increases consumption by 11.2 percent due to
the health insurance coverage. The results are similar when we estimate the coefficients in
the subsamples: respectively comparing the new members with the experienced, comparing
the new with the non-participants, and comparing the experienced and the non-participants.
When we further allow the insurance effect to differ in the second, third, and fourth year of
a household’s subscription in the program, we find that the increase in consumption mainly
complete in the second year of subscription.31
The dynamics of the insurance effect can result from the fact that the experienced
learned more about the benefit of the insurance. An alternative explanation for the
dynamics is that the NCMS becomes more generous over time, and people reduce more
precautionary savings in response to the increasing generosity. We add an interaction term
between household’s enrollment status and year to study how the insurance effect change
over time in column 2. It shows no significant upward trend in the insurance effect. The
third column further confirms that the difference between the new members and the
experienced members remains significant after we control the time trend of the insurance
effect. Therefore, the change in the generosity of the insurance cannot fully explain the
31
We have also distinguished two types of the new members: one is the real new participants for whom the
program was also new; the other is the delayed new participants who participated the program for the first
year but the program has been implemented for more than one year. The delayed new might have learned
about the program from their neighbors who have enrolled in the NCMS earlier. The results indicate that in
terms of the insurance effect, the delayed new members are not significantly different from the experienced
participants; however, they are also not significantly different from the real new members either. Therefore,
the knowledge from the neighbors can help learn, but learning from self-participation is better than learning
from the neighbors.
20
increase in the insurance effect for the experienced compared with the new members.
Another explanation for the dynamics of the insurance effect is related to households’
trust in the program. The effect of knowledge on the program is two-edged. Particularly, if
households find out the alleged benefits of the program are just void, more knowledge
about the insurance cannot help reduce precautionary savings. Therefore, what matters
most may be households’ trust in the insurance program. To identify the “trust” effect, we
control the interaction between households’ insurance coverage and the indicator on
whether some households in the village have gotten some reimbursement from the NCMS
in column 3 (village reimbursement=1 if yes, 0 if no). The result shows that the insurance
effect becomes significantly stronger, by 17 percentage points, when the benefit of the
insurance is witnessed in the village. For the villages that have not had any reimbursement,
the insurance effect is even negative, although the estimate may become insignificant in
different specifications. The results are confirmed by the estimates on the subsamples.
Column 4 and 5 consider only villages that have not experienced any reimbursement. Now
there is no significant insurance effect for the participants (column 4), no matter whether
the participant is experienced or not (column 5).32 In contrast, when we only consider
villages where there have been some reimbursements (column 6 and 7), households’
consumption increases by 6.3% due to the insurance coverage (column 6), higher than the
average insurance effect 4.9% reported in the baseline model. In addition, the experienced
participants in these villages consume significantly more than the new members (column 7),
which might because they learn more about the insurance or because they have more trust
in the program.
These results highlight that only when households trust the insurance, they start to
reduce precautionary savings and consume more. Noticing that only 39% of the new
participants observe any reimbursement in the village, while for experienced, the
percentage is 86%. Thus, the stronger insurance effect for the experienced in the first
column of Table 7 can be explained by that the experienced are more likely to establish
trust in the program.33
32
We need to be cautious on explaining the negative insurance effect here as there are only a few villages,
around 18.6% between 2004 and 2006, that have launched the program but experienced no reimbursement.
33
The responses related to the “trust” are also consistent with the pattern on the participation decision. We
find that for those who do not participate immediately after the village introduces the program, the
participation rate in the village with some reimbursement is 67%, while the rate in the village with no
21
6 Robustness and Refinements of the Baseline Model
The main concern of the baseline model is that households who enrolled in the NCMS
are not comparable to households who did not. Particularly, we worry that households who
have not participated by 2007 may have been covered by other insurance or have very
special characteristics. Therefore, we exclude this kind of households to test the robustness
of our main results. The first column in Table 8 shows the estimate for the average
insurance effect is 5.1%, which is quite similar to that in the baseline model.
To test whether the identification assumption holds, we apply the same model to periods
that households have not been covered by the insurance. Particularly, we exclude the
observations in 2006 and for other years, we only consider households that have not
enrolled in the program in that year; and households’ treatment status in a year t is defined
as their status in the next year t+1. By construction, the treatment status does not represent
the real status of the insurance coverage. Column 2 of Table 8 shows no significant
insurance effect as it should be. This increases our confidence that the estimate for the
insurance effect in the baseline model does represent the causal effect of the NCMS.
Besides the introduction of the NCMS, there is another important policy change during
the same period: the reduction of the agricultural taxes and fees in the rural areas. The tax
reduction started with pilot programs in two provinces in 2004 and ended with the abolition
of the agricultural taxes nationally in 2006. It increases households’ 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. To test how serious the
problem is, we include the log value of the tax and fee payment as a covariate in Column 3
of Table 8. The estimate for the insurance effect does not change much. We have also tried
to directly control the disposable income that has subtracted the expenditure in taxes and
fees. The result is also quite similar.
We argue that the positive effect of the NCMS on consumption net of health
expenditure results from the reduction in precautionary savings, but this story should not
affect the basic or subsistence consumption for human needs. We use food consumption to
represent the subsistence consumption in column 4. As we expected, both the magnitude
and the significance level of the insurance effect become much smaller. Nevertheless the
reimbursement is only 36%.
22
effect is still significant. The main reason is that this measure still includes non-basic food
consumption.
Besides food consumption, we examine educational expenditure in column 5 and find a
strong positive insurance effect for families who have members under 25. This is consistent
with the finding in Meng (2003), which indicates that urban households cannot smooth
their education expenditure, and they reduce their education expenditure substantially when
income uncertainty increases. However, the result for education expenditure is somewhat
sensitive to the model specification.
7 Gross Effects and Matching DID
Gross Effects
Our previous analyses all focus on the insurance effect that is estimated based on the
double difference between the insured and the non-participants in the NCMS-counties. In
this section, we are going to examine the gross effect of the NCMS that may incorporate
the effects of other changes that accompanied with the NCMS. This effect in principle can
be estimated by the double-difference between the insured and the non-exposed and
applying the following regression:
Yijt   g  Family _ insured it   t  Tt   i  Di    X ijt   ijt
(2)
The equation is the same as the baseline model with the exception that here we consider
only the insured and the non-exposed while in the baseline model we consider only the
insured and the non-participants in the NCMS-counties. The potential selection bias comes
from the non-random placement of the NCMS across villages. Again, to address the issue,
we allow the counterfactual time trend in consumption to vary with income and health
status. And we do so by controlling for the interaction between year and household income,
average income in the village, and household health status in 2003.
The first column of Table 9 reports the estimation. It shows an insignificant gross effect,
which implies that other changes that were contemporary with the introduction of the
NCMS have depressed consumption for the insured. This could result from the increase in
the price of health care after the launch of the NCMS. This explanation is consistent with
the findings from the program-evaluation studies that conclude the overall out-of-pocket
23
payments have not been reduced by the NCMS. However, we do not have enough
information on the price of the health care to confirm this conjecture and this is definitely a
question warrants more research.
Although the average gross effect is insignificant, the next four columns of Table 9
show that the gross effect varies significantly across different groups. First, the gross effect
is significant positive for the poor households, but declines quickly with income (column
2).34 Second, the effect is significant positive for bottom half of the health distribution, but
declines with health status (column 3). Third, the gross effect is significantly positive for
the experienced families and the difference between the experienced and the new members
are significant (column 4). Finally, the gross effect tends to be more positive among
villages that have seen some reimbursement, although it is not significant (column 5).
However, the experienced members in the counties that have seen reimbursement show
much stronger and significant positive gross effect than the counterparts in the counties that
have seen no reimbursement (column 6 and 7). These variations across different groups are
quite similar to those for the insurance effect. This is consistent with the explanation that
the effect of other contemporary policies on consumption is similar among all groups, so
the difference in the gross effect is similar to the difference in the insurance effect.
Previous analyses all rely on the subsample of the population. The next column in table
9 use the whole sample but distinguish the insurance effect and the gross effect as
illustrated as following:
Yijt    Family _ insured it   o  County _ enroll jt   t  Tt   i  Di    X ijt   ijt
(3)
Here, Family_insuredit is the binary variable indicating whether household i having
subscribed the insurance program in year t, while County_enrollit is the binary variable for
whether the village j having launched the NCMS in year t. As a result,  represents the
program’s insurance effect that works only on the insured, while o represents the other
effect of the contemporary changes on all the households in the NCMS-counties. And  + o
represent the gross effect of the NCMS. The identification assumption of equation (3) is
that conditional on the observable characteristics (Tt, Di, Xijt), the whole population,
including the insured, the non-participants, and the non-exposed, would have parallel
It is significant for the bottom one-third of the income distribution, which we do not
report due to space limit.
34
24
dynamics of consumption if there had been no NCMS. If the assumption holds, then the
equations (1) to (3) should give the same estimate for , g and o. Nevertheless, the
estimations based on the sub-samples (equation 1 and 2) require weaker identification
assumptions as they only require parallel trends in consumption within the sub-sample
rather than the whole sample.
Column 9 of Table 9 reports the estimate for the equation (3). Again, we see a
significant positive insurance effect on consumption net of health expenditure. The
magnitude is about 4.8%, similar to that in the baseline model. Also consistent with
previous results on the insurance effect and gross effect, we see a significant negative effect
of other contemporary changes.35 The negative other effect is further confirmed when we
double compare the uninsured with the non-exposed. The last column of Table 9 compares
the insured with all the uninsured. The coefficient for the Family_insured should be a linear
combination of the insurance effect and the gross effect, and the weight depends on the
composition of the uninsured group. The result indicates no significant effect. These results
highlight the importance to consider the sample composition for the estimation and the
corresponding economic meaning.
Matching Difference-in-Difference
Now, we couple the difference-in-difference approach with matching to reduce the
selection bias on the observables. The identification assumption for the linear regression
and that for the matching approach are the same. However, the matching method does not
require strong functional form assumptions, so it can address two kinds of potential bias of
the simple linear regression method: the bias due to the difference in the supports of the
observable covariates between the treated and untreated groups and the bias due to the
difference between the two groups in the distribution of the observables over its common
support (Smith and Todd, 2005).36
Since we have more than two periods, the traditional DID matching method needs to be
modified. We first estimate respectively the insurance effect in each year between 2004 and
35
Although not reported, we have re-estimated the results from table 4 to table 8 based on the specification in
equation (3) and confirm that all the conclusions remain.
36
The matching method also exploits a weighting system that is different from the regression method. In the
cases where the effect of the treatment varies across households, the matching estimate gives us the treatment
effect on those who are most likely to be treated. This is the parameter of interest here.
25
2006. Then we calculate a weighted average of the insurance effect over these three years
by weighting the effect in each year using the ratio of the number of the treated household
in that year over the total number of the treated. Another complication here is how to deal
with the experienced participants. For the experienced participants in year t, although they
enroll in the program, there is no change in the treatment status between year t and year t-1.
Their temporal change in consumption represents the additional increase in consumption
due to the NCMS after the first-year participation. To simplify the analyses, we exclude all
the experienced participants and focus on the effect of the NCMS on consumption for the
new participants.
The matching method applied here is the propensity score matching, where the
propensity score measures the “closeness” between the treated and untreated households.
We estimate the propensity score based on two steps. First, in each year, we estimate the
probability that a household enrolled in the NCMS given the village has launched the
program. This is estimated based on households in the NCMS-villages in that year. For
households in the non-NCMS villages, we predict their probability to enroll based on the
estimate. Second, we estimate the probability that a village enrolled in the NCMS in each
year.37 For both steps, a probit model is estimated.38 For the comparison between the
insured and the non-participants, the predicted probability from the first step is the
propensity score. For the comparison between the insured and the non-exposed or between
the insured and all the uninsured, we need to consider both the similarity between
households and that between villages, hence the (composite) propensity score in each year
is the product of the two probabilities predicted from both steps in that year.
Figure 1 gives the histogram for the (composite) propensity scores for the three groups:
the insured, the non-participants, and the 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 ample. The propensity score distribution for
37
To weight the data by the number of households surveyed in each county, the model is estimated on the
household-level data.
38
Noticing that the propensity score is only to reduce the dimensions of the conditioning; as such, it has no
behavioral assumptions attached to it. For the choice of covariates to include in the estimation of propensity
score, we start with all of the plausible variables that affect the participation decision, including all the
covariates considered in table A.1. Then which variable to be included or excluded is 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 upon requests.
26
the non-exposed is even more left skewed than the non-participants. This justifies the focus
on the comparison between the insured and the non-participants in the NCMS-villages.
We use the five-nearest neighbors matching with replacement and caliper 0.01, and
impose common support condition. 39 The distance between the propensity score is
measured by the Mahalanobis metric. Since there are only a handful of untreated units
comparable to the treated units in our sample, allowing replacement is expected to perform
better than without replacement. Among the three choices: imposing common support
condition, using multiple nearest neighbors, and matching with replacement, the first and
the second ones increase bias but reduce variance, while the third one is on the opposite.
Table 10 reports the results, where the standard errors are bootstrapped with 100
replications. The results in the baseline model are all confirmed: the insurance effect
estimated by comparing the insured with the non-participants is about 5.2%; the gross
effect estimated by comparing the insured with the non-exposed is not significant; and there
is no significant effect of the NCMS on consumption if we simply compare the insured
with all kinds of uninsured. We also show the reduction in the bias on the 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 reduced substantially, by 41%.40 However, the reduction in the pseudo R2
statistics from a probit model is modest, by 19%. When the non-exposed are used as the
comparison group (column 2), matching only reduces the mean “bias” by 9%, and even
raises the pseudo R2. These results imply that matching is more successful when using the
non-participants as the controls than using the non-exposed as the controls.41
39
We confirm the results are not sensitive to other choices of calipers, including 0.005 and 0.0025. Our
sample is not a random sample but over-samples households with substantial health-care expenditure that
itself is affected by the participation choices. Therefore, this is a choice-based sample, but the sampling
weights are unknown. As a result, we need to match on the odds ratio, P/(1-P), where P is the propensity score
(Smith and Todd, 2005). However, for nearest neighbor matching, it does not matter whether he matching is
performed on the odds ratio or on the propensity scores, because the ranking of the propensity score is the
same and the same neighbors will be selected. However, for methods that consider the absolute distance
between observations, such as kernel matching, it does matter.
40 The reduction in biases shown here is based on the “psmatch2” logarithm in Stata.
41
The matching DID estimates are not sensitive to the number of neighbors and caliper, but is somewhat
sensitive to the estimation of the propensity score and the matching method such as the Kernel estimation.
However, the most sensitive estimates are those for the other effect, followed by the gross effect. The
insurance effect is least sensitive.
27
Regression with Matching
The simple matching DID estimator is still problematic in finite samples when the
matching is not exact, that is the covariates for the treated 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 propensity score and
easily breaks down as we do not have a large sample for the non-participants. The 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 resulting estimator for the average treatment
effect on the treated is consistent. This is particularly valuable when one method alone is
not sufficient to obtain consistent or efficient estimates.
We consider three ways to combine regression and weighting. First, apply the
regression method over the common support of the treatment and control groups, where the
common support is the one used in the matching DID; Second, apply the regression method
over the matched pairs in the matching DID; third, apply the weighted least regression,
where the weight is one for the treated unit, and P/(1-P) for the untreated unit, and P is the
estimated propensity score.42
The first two methods can be easily incorporated into to the fixed-effect model for the
panel data. Column 4 and 5 of Table 10 display the estimates for the insurance effect and
we see a even stronger insurance effect than that in the baseline model. The third method,
by contrast, cannot directly be applied to the baseline model because weighting is not
allowed in the fixed-effect model. As a compromise, we consider the regression model for
the repeated cross-sectional data that can easily incorporate weights. More specifically, the
model for the insurance effect is as following:
Yijt    Family _ insured it   t  Tt   i  Gi    X ijt   ijt
(1' )
There are only two differences between this equation and the baseline model. First,
instead of controlling all the household indicators (the household fixed-effect), we only the
time-invariant dummies indicating whether the household participated in 2004, 2005, or
2006, and whether the household has participated immediately after the village launched
42
The second and third methods may be less efficient, as they discard more control observations and weights
some more than others. It has the advantage, however, of only using the most relevant matches.
28
the program. Second, Xijt includes not only time-variant characteristics, but also
time-invariant characteristics of the households and villages, such as health status indicators
in 2003, whether being minority, the head’s gender, marital status, education, and job, and
all village indicators.
The sixth column in table 10 gives the estimate. We see a larger insurance effect that is
still significant, although the estimate becomes less precise. The next two columns estimate
the gross effect and composite effect of the NCMS. Both show no significant effect. In
summary, the robustness checks deliver a clear message that the insurance effect in the
baseline model is quite robust and reliable. The gross effect also keeps insignificant in
different specifications.
8 Conclusion
This study exploits the introduction of the NCMS in the rural area to examine the
effects of the health insurance coverage on consumption in rural areas. We find that the
insurance stimulated the consumption net of health expenditure by around 5.6 percent for
the insured. This effect does not seem to result from the “crowding-in” effect that argues
that health insurance reduces the out-of-pocket expenditure of the insured and hence
increases the disposable income for other consumption. However, we find that even for
households that do not incur any healthcare expenditure, the insurance program has
stimulated more consumption. In addition, the effect is stronger for those expecting higher
risk of having relative expensive health-care expenditure, including households with lower
income or with worse health status. Besides, the insurance effect increases with the
generosity of the package for the expenditure at the county facilities. These are all
consistent with the precautionary-saving story.
We also find that the insurance effect varies with households’ experience with the
program. Particularly, the increase in consumption is stronger for the experienced
participants than for the new participants. However, the dynamic change of the insurance
effect is not significant in the villages that have not witnessed any reimbursement from the
program. Moreover, there is no significant insurance effect at all in such villages. In
contrast, both the insurance effect for the new participants and the increase in the insurance
effect for the experienced are significant in the villages in which households have
29
witnessed some reimbursement and hence established trust on the program. These results
indicate that people’s trust in the public insurance program can be crucial for the
stimulation of consumption for public insurance programs.
The findings are quite robust to different specifications including allowing the time
trend of consumption to vary with income, health status, or propensity, using more similar
control groups, using two periods before joining in the program, matching
difference-in-difference, and regression with matching. Our results also suggest some
negative spill-over or general equilibrium effect, although we need to be cautious on this as
the comparison between households in the NCMS-counties and those in the non-NCMS
counties may face serious selection biases. This is a question warrants future research.
The findings have strong implications. Although the NCMS was often complained for
being ungenerous, still it has stimulated more consumption than the cost in the rural areas.
When the insurance program is expanded to more areas and becomes more generous, we
expect more increase in consumption, which is quite beneficial in rebalancing Chinese
growth. However, to exert the stimulation of consumption to the most, it is important to
make people trust the public safety net and educate them about the insurance.
The results about the gross effect on consumption of the NCMS are less positive than
the insurance effect. Such a result does not mean the insurance program is not useful in
increasing consumption. Rather, it suggests that measures should be taken in company of
the insurance program to reduce the cost of healthcare delivery. Possible such measures
include improving the compensation scheme for healthcare delivery, introducing more
competition in the healthcare delivery market, and improving the governance and
regulation of healthcare delivery organizations. More research is needed to see how one can
bring about significant gross effect of insurance on consumption.
30
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Figure 1 The Distribution of Propensity Scores for Three Groups
34
Table 1 Insurance Schemes for the NCMS Programs
Average reimbursement rate for
in-patient service
Deductibles
Year
Township
clinics
County
hospitals
Upper-level
hospitals
Township
clinics
County
hospitals
Upper-level
hospitals
2003
371
457
743
39.7%
38.4%
32.3%
2004
183
294
570
46.0%
37.5%
29.7%
2005
133
261
550
48.6%
41.4%
32.6%
2006
125
302
641
50.9%
40.4%
31.4%
2007
87
252
574
52.5%
44.2%
33.7%
Mean
138
289
603
49.7%
41.3%
32.2%
Reimbursement rate for in-patient
service under 3000 RMB
Reimbursement rate for in-patient
service between 3000 and 10000 RMB
Township
clinics
County
hospitals
Upper-level
hospital
Township
clinics
County
hospitals
Upper-level
hospital
2003
29.1%
24.0%
16.9%
28.9%
33.4%
26.3%
2004
33.0%
25.8%
17.4%
38.3%
33.4%
25.0%
2005
44.6%
33.6%
23.0%
45.9%
40.2%
29.4%
2006
45.8%
34.6%
23.7%
48.8%
39.9%
29.6%
2007
50.0%
38.2%
24.6%
50.5%
43.3%
30.9%
Mean
44.4%
33.9%
22.7%
46.3%
39.9%
29.2%
Ceilings
Having different schemes for
different facilities
Having different schemes
for different levels of
expenditure
2003
13571
85.7%
85.7%
2004
15200
91.7%
84.6%
2005
13250
95.2%
76.2%
2006
14838
97.2%
69.4%
2007
19321
100.0%
62.1%
Mean
15732
96.2%
71.7%
35
Table 2 The Participation Rate in Different Years
2003
2004
2005
2006
2007 or
later
Number of counties newly enrolled
20
21
18
44
31
Cumulative rate
14.9%
30.6%
44.0%
76.9%
100%
Number of households newly enrolled
424
563
634
1,292
1,201
Cumulative rate
10.3%
24.0%
39.4%
70.8%
100%
Number of participants in the
NCMS-county
424
987
1,621
2,913
Number of non-participants in the
NCMS-county
241
247
273
196
Number of non-exposed households
3,453
2,885
2,225
1,009
Participation rate in the NCMS-county
63.8%
80.0%
85.6%
93.7%
Exposure rate
16.1%
30.0%
46.0%
75.5%
Cumulative participation rate in
counties launched the NCMS in 2003
63.8%
82.4%
94.4%
97.1%
100%
77.2%
94.4%
95.6%
100%
69.1%
85.0%
100%
95.6%
100%
Year
Counties’ participation:
Households’ participation:
Cumulative participation rate in
counties launched the NCMS in 2004
Cumulative carticipation rate in
counties launched the NCMS in 2005
Cumulative participation rate in
counties launched the NCMS in 2006
36
Table 3 Descriptive Statistics for Three Groups
All
Mean
10424
10847
25880
625.5
168.6
Non-NCMS
villages
Non-exposed
Mean
8719
9166
20995
661.5
222.3
NCMS-villages
Variable
Consumption net of health expenditure in 2003
Total consumption in 2003
Household income in 2003
Health expenditure in 2003
In-patient health expenditure in 2003
Share of members with fair or worse health in
2003a
Share of members with bad health in 2003
Share of members older than 65
Share of members younger than 10
Share of migrants in 2003
Household size
Head's age
Head's years of education
Female head
Single head
Head is a non-agriculture worker
Having communist members
Minority household
"Wubao" household
Village average income per capita in 2003
Capital of the town
Number of clinic in 2003
Share of kids vaccinated in 2003
Share of migrants in 2003
Share of laborer in the village
Share of laborer with high school degree or
above in the village
Mountain area
Highland area
Western area
Central area
Observation (based on consumption)
Insured
Mean
10462
10873
26442
610.3
183.3
Non-participants
Mean
10131
10637
21460
738.3
63.0
13.9%
13.6%
13.9%
12.8%
4.2%
8.8%
7.1%
15.6%
4.01
51.82
6.72
5.2%
7.8%
39%
17%
9.7%
0.26%
3396
15%
1.28
97.40
23%
57%
7.2%
9.7%
7.5%
13.1%
4.13
50.50
6.44
6.7%
12.2%
41%
10%
17.1%
0.79%
3241
12%
1.26
98.54
23%
55%
4.5%
8.9%
7.1%
15.3%
4.02
51.68
6.69
5.4%
8.3%
39%
16%
10.5%
0.32%
3379
15%
1.28
97.53
23%
56%
5.1%
8.1%
7.5%
16.7%
4.15
50.96
6.50
7.6%
9.0%
33%
16%
14.8%
0.22%
2727
14%
1.32
94.29
25%
54%
34%
32%
34%
30%
47%
25%
20%
42%
6392
53%
21%
20%
41%
761
48%
24%
20%
42%
7153
56%
23%
27%
48%
10274
Note: 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 bad health.
37
Table 4 Results for the Baseline Model
Dependent variable: Log (consumption net of health expenditure)
(1)
(2)
(3)
(4)
(5)
(6)
Insured family
Log(income)
Household size
Log (village
income)
0.055***
(0.021)
0.437***
(0.020)
0.099***
(0.012)
-0.065
(0.041)
Year*log(income)
Year*log(village
income per capita)
Year * share of
members with fair
and bad health 03
Year * share of
members with bad
health 03
Year * propensity
of participation
Propensity score of
participation
Yearly trend vary
with income,
village income, and
health status
Observations
R-squared
Households
0.055***
(0.021)
0.424***
(0.021)
0.098***
(0.012)
-0.077*
(0.041)
-0.005
(0.005)
-0.017**
(0.007)
0.056***
(0.021)
0.428***
(0.021)
0.096***
(0.012)
-0.085**
(0.043)
-0.007
(0.006)
-0.015**
(0.007)
0.010
0.058***
(0.021)
0.431***
(0.021)
0.096***
(0.012)
-0.094**
(0.043)
0.042*
(0.022)
0.426***
(0.021)
0.097***
(0.012)
-0.120**
(0.050)
-0.002
(0.006)
-0.023***
(0.009)
0.014
(0.016)
(0.017)
-0.045
-0.043
(0.030)
(0.031)
0.066***
(0.021)
0.437***
(0.022)
0.093***
(0.012)
-0.090**
(0.043)
-0.266*
(0.157)
-0.049
(0.064)
Y
9,730
0.284
3,963
9,730
0.286
3,963
9,068
0.293
3,658
9,068
0.297
3,658
9,068
0.303
3,658
9068
0.292
3658
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all the
NCMS-counties and their corresponding observations in 2003, but excludes the observations in 2003 for
counties who enroll in 2003. All the columns control 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 having
communist members, whether being “Wubao” household, and log(village income per capita). Column 4
controls the interaction between year dummies and log(income), log(village income per capita), the share of
members with fair or bad health in 2003, and the share of members with bad health status in 2003.
38
Table 5 A Story on the Precautionary Savings
Precautionary savings vs. “crowd-in”
Dependent
Log(consumption net of
Log(total
variables:
health expenditure)
consumption)
Households having no
health expenditure
All the sample
(1)
(2)
0.072*
(0.039)
0.060***
(0.021)
Insurance package
Log(consumption net of health
expenditure)
Scheme for
Scheme for
expenditure at
expenditure at
village facilities
county facilities
(3)
(4)
Covariates
Insured family
Deductibles
0.007
(0.037)
-0.046**
(0.019)
Reimbursement
rate
0.101
(0.128)
0.625***
(0.177)
2,299
0.282
1,282
2,282
0.295
1,265
Observations
R-squared
Households
3,917
0.317
2,241
9,068
0.302
3,658
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all the
NCMS-counties and their corresponding observations in 2003. All the specifications control 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 having communist members, whether being “Wubao” household, and Log(village
income per capita).
39
Table 6. The Insurance Effect and Risks
Sample:
Covariates
Insured family
Insured family *
log(income)
Dependent Variable: log (consumption other than health expenditure)
Insurance effects and income
Insurance effects and health
Insurance effects and health 2
Households Households
having fair
having no
All the
All the
Bad
Good
All the
The poor
The rich
or bad
fair or bad
sample
sample
health
health
sample
health
health
members
members
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.652***
(0.244)
0.055**
(0.025)
0.021
(0.043)
0.293***
(0.093)
0.072**
(0.031)
0.039
(0.028)
0.087**
(0.036)
0.041
(0.025)
2,925
0.325
1,163
6,143
0.283
2,495
-0.061**
(0.025)
-0.055***
(0.021)
Insured family *
Health
Insured family *
Number of Fair
or Bad Health
Members
Observations
R-squared
Households
0.040*
(0.023)
0.034*
(0.018)
9,068
0.295
3,658
4,598
0.339
2,424
4,470
0.195
2,234
9,065
0.294
3,658
4,623
0.332
1,874
4,441
0.255
1,783
9068
0.294
3658
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Column 2 (3) only considers the bottom (top) half of the income
distribution of this sample. Column 5 (6) only considers the bottom (top) half of the health distribution in 2003. Column 8 considers only households
having members with fair or bad health in 2003, while column 9 considers households having no such members. All the columns control household and
year fixed effect, log(income), household size, the share of members over age 65, the share of members under age 10, whether having communist
members, whether being “Wubao” household, and log(village income per capita).
40
Table 7. Learning and Trust in the NCMS Program
Dependent variables: Log (consumption other than health expenditure)
Dynamic effects
Trust
All the
NCMS-villages without
NCMS-villages with
All the sample
sample
reimbursement
reimbursements
(1)
(3)
(4)
(5)
(6)
(7)
(8)
（2）
Covariates
Insured family
0.045** 0.058**
-0.104*
-0.078
-0.085
0.063**
0.061**
0.026
(0.021)
(0.053)
(0.089)
(0.092)
(0.030)
(0.030)
(0.028)
(0.029)
Experienced
members
0.067***
(0.016)
Insured family *
year
0.068
(0.094)
0.070***
(0.017)
0.002
(0.019)
-0.016
(0.019)
Village
reimbursement
-0.155***
(0.056)
Family_insured
* Village
reimbursement
0.170***
Observations
R-squared
Households
0.069***
(0.019)
(0.059)
8,996
0.294
3,651
9068
0.293
3658
8996
0.294
3651
8,702
0.288
3,648
3,690
0.248
2,788
3,689
0.249
2,788
7,616
0.307
3,567
7,595
0.309
3,563
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Village reimbursement is a dummy that is 1 if some
households in the village have gotten some reimbursement from the NCMS. Column 5 and 6 only include observations in the villages
that have shown no reimbursement, while column 7 and 8 include observations in the villages that have seen some reimbursement. All
the columns control household and year fixed effect, log(income), family size, the share of members over age 65, the share of
members under age 10, whether having communist members, whether being “Wubao” household, and log(village income per capita).
41
Table 8. Robustness Tests
Dependent
Log(consumption excluding health expenditure)
Variable
Participants by
Periods before
Consider tax policy
2007
treatments
(1)
(2)
(3)
Covariates
0.051**
0.024
0.053**
Insured family
(0.024)
(0.029)
(0.021)
Log(education
expenditure)
(4)
(5)
0.031*
(0.017)
0.041
(0.061)
9065
0.305
3658
4145
0.084
1979
-0.007**
(0.003)
log(tax and fee)
Observations
R-squared
Households
Log(food
expenditure)
8,648
0.294
3,492
5,064
0.236
2,848
9,068
0.294
3,658
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Column 1 includes households that have participated the
program by 2007. Column 2 considers only the uninsured and “Insured family” represents the indicator of whether the family is
insured in the next year. All the columns control household fixed effect and year fixed effect, log(income), household size, the share of
members over age 65, the share of members under age 10, whether having communist members, whether being “Wubao” household,
and log(village income per capita).
42
Table 9. Gross Effects of the NCMS program
(1)
Dependent variable: Log (consumption net of health expenditure)
Gross effect
Composite
NCMS-village NCMS-villag
Insured vs. All
s without
es with
uninsured
All the
The insured vs. non-exposed:
reimbursemen reimburseme
sample
t vs.
nt vs.
non-exposed
non-exposed
(4)
(5)
(7)
(8)
(10)
(2)
(3)
(9)
Covariates
Insured family
0.006
(0.011)
0.311**
(0.152)
0.280***
(0.067)
-0.002
(0.011)
-0.007
(0.017)
-0.011
(0.021)
-0.004
(0.013)
0.029
(0.044)
0.060***
(0.015)
11237
0.230
4083
14834
0.261
4692
Enrolled Village
Insured family *
log(income)
0.013
(0.011)
16720
0.256
4811
16720
0.255
4811
-0.031**
(0.016)
Insured family *
Health in 03
-0.063***
(0.015)
Experienced
members
0.059***
(0.014)
Village
reimbursement
Observations
R-squared
Households
0.048***
(0.017)
-0.042**
(0.017)
0.012
(0.018)
15961
0.257
4775
15961
0.257
4775
15958
0.258
4775
15889
0.257
4767
15837
0.252
4765
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All the columns control 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 having communist members, whether being “Wubao”
household, and log(village income per capita). Columns 5 estimates based on the insured and the non-participants in the NCMS-counties. Columns 6 estimates
on the insured and the non-exposed. Other columns use all the observations.
43
Table 10. Matching Difference-in-Difference and Regression with Matching
Dependent variable: Log (consumption net of health expenditure)
Matching DID
Regression with Matching
Insurance
Gross
Insurance
Gross
Insurance
Insurance
effect:
effect:
effect:
effect:
Insured vs.
effect:
effect:
insured vs. insured vs.
common
common
uninsured
common
weighted
non-partici non-expos
support +
support +
support
regression
pant
ed
matches
matches
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Insured family
0.052***
(0.024)
-0.015
(0.017)
-0.003
(0.016)
Number of Untreated
Number of Treated
Post-matching bias
% change in bias
through matching
Post-matching
pseudo R2
% change in pseudo
R2 through matching
Post-matching Prob
value of Chi-squared
Observations
R-squared
Households
712
2,442
9.13
6,274
2,442
9.88
6,986
2,442
9.67
-40.8%
-9.1%
-7.9%
0.096
0.098
0.090
-18.8%
19.8%
21.8%
0.000
0.000
0.000
Insured vs.
uninsured:
common
support +
matches
(8)
0.064***
(0.020)
0.061*
(0.032)
0.095*
(0.050)
0.004
(0.023)
0.008
(0.022)
8753
0.297
3648
6299
0.170
3397
8806
0.725
7143
0.170
4062
7485
0.175
4235
Note: Bootstrap standard errors with 100 replications in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Each estimate is a weighted average of the
corresponding effect in 2006, 05, and 04. The weight is the ratio of the number of the treated households in each year over the total number of
treated households over three years. In this table, we exclude experienced participants to simplify the analyses.
44
Appendix A
Table A.1 Household’s Participation Decision and County’s Enrollment
Household’s participation decisiona
County’s enrollment
(1)
(2)
(3)
(4)
0.149***
0.139***
0.049
0.443**
Log(Income)
Log(village income )
(0.040)
(0.044)
(0.054)
(0.186)
-0.002
0.017
0.027
Village classified as
-0.352**
Household size
(0.017)
(0.020)
(0.026)
“Xiaokan”
(0.176)
Share of members with
0.207***
0.190**
-0.105
Village classified as
-0.154
good health in 2003 b
(0.070)
(0.080)
(0.094)
“Pingkun”
(0.289)
Share of members with
0.459***
0.531***
0.076
0.129
Surburb area
fair or bad health in 2003
(0.142)
(0.159)
(0.186)
(0.281)
Share of members with
-0.868***
-0.813***
-0.643***
-0.254
Capital of the town
bad health in 2003
(0.169)
(0.179)
(0.215)
(0.326)
Share of members older
-0.015
0.006
-0.109
-0.308
Agricultural village
than 65
(0.139)
(0.148)
(0.171)
(0.254)
Share of members
0.453**
0.548***
0.321
0.153
Log(population)
younger than 10
(0.200)
(0.207)
(0.274)
(0.145)
Share of migrants in
0.114
0.101
-0.095
2.663***
Share of laborer
2003
(0.132)
(0.141)
(0.154)
(0.766)
-0.673***
-0.812***
-0.019
Share of high school
0.064
Minority
(0.081)
(0.106)
(0.216)
or above
(0.728)
-0.690**
-0.826**
-1.311***
Share of migrants in
-0.228
“Wubao” household
(0.304)
(0.355)
(0.440)
2003
(0.482)
Having communist
0.216***
0.119
0.150
Number of clinic in
-0.072
members
(0.074)
(0.078)
(0.095)
2003
(0.080)
0.089
0.257**
0.156
Share of kids
0.013**
Female head
(0.112)
(0.116)
(0.145)
vaccinated in 2003
(0.006)
0.013***
0.011***
0.009***
-0.470***
Age of the head
Mountain areas
(0.003)
(0.003)
(0.003)
(0.180)
-0.233***
-0.233***
-0.221**
0.535**
Head is single
Highland area
(0.084)
(0.087)
(0.103)
(0.219)
Head’s education years
-0.024
0.070
0.223*
-0.602**
Western China
3-6c
(0.101)
(0.104)
(0.127)
(0.256)
Head’s education years
0.047
0.166
0.239*
-0.334*
Central China
7-9
(0.108)
(0.114)
(0.136)
(0.203)
Head’s education years
0.122
0.230
0.043
10 and above
(0.143)
(0.152)
(0.175)
Head is non-farm
-0.237***
-0.286***
-0.074
self-employedd
(0.074)
(0.086)
(0.106)
0.131*
0.017
0.026
Head is an employee
(0.074)
(0.079)
(0.093)
Head works in other
0.020
-0.023
0.184*
non-farm jobs
(0.071)
(0.077)
(0.106)
Village Dummies
no
no
yes
no
Province Dummies
no
yes
no
no
Year Dummies
yes
yes
yes
yes
Observations
6575
6492
3960
Observations
394
likelihood
-1904
-1711
-1154
likelihood
-212.5
pseudo-R2
0.194
0.273
0.406
pseudo-R2
0.222
Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. a). In column 1 and 2, we also control
all the village characteristics considered in column 4. b). The health status is self-reported and there are five
categories: excellent, good, fair, bad, and no working capacity. Most people report “excellent.” We label both
bad and no working capacity as bad health. c). The omitted category is illiterate or the years of education
less than 3. d). The omitted category is farmer.
45
Table A.2 Results for the Baseline Models: Balanced Panel
Dependent variable: Log (consumption net of health expenditure)
(1)
(2)
(3)
(4)
(5)
(6)
Covariates
0.075**
0.101***
0.096***
0.111***
0.090**
0.094**
Insured family
(0.037)
(0.037)
(0.037)
(0.039)
(0.037)
(0.037)
0.493***
0.405***
0.392***
0.417***
0.393***
0.420***
log(income)
(0.039)
(0.041)
(0.041)
(0.042)
(0.041)
(0.043)
0.067***
0.065***
0.066***
0.067***
0.062***
0.051**
Household size
(0.022)
(0.021)
(0.021)
(0.021)
(0.022)
(0.021)
0.036
0.008
0.025
0.013
0.032
-0.037
Ln(Village
Income)
(0.080)
(0.078)
(0.079)
(0.082)
(0.092)
(0.081)
-0.029*** -0.034***
-0.028**
-0.031***
Year*log(income)
(0.011)
(0.011)
(0.012)
(0.011)
-0.061*** -0.057***
-0.025
-0.072***
Year*log(village
income per capita)
(0.021)
(0.021)
(0.033)
(0.022)
Year * share of
-0.051
-0.064**
-0.055
members with fair
(0.031)
(0.032)
(0.034)
and bad health 03
Year * share of
-0.058
-0.042
-0.023
members with bad
(0.065)
(0.067)
(0.076)
health 03
0.010
Year * Propensity
of participation
(0.137)
-0.508*
Propensity of
participation
(0.299)
Yearly Trend Vary
with Income,
Y
Village Income,
Health Status
Observations
R-squared
Households
1992
0.283
498
1992
0.303
498
1992
0.305
498
1992
0.314
498
1992
0.311
498
1992
0.311
498
Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All the columns control
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 having communist members, whether being “Wubao” household,
and log(village income per capita).
46