

Department of Social Systems and Management
Discussion Paper Series

No.1321The Impact of a Universal Health Scheme on
Precautionary Savings:Evidence from Thailand
byKoichi USHIJIMA
Oct 2014

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANThe Impact of a Universal Health Scheme on Precautionary Savings:
Evidence from Thailand
Koichi Ushijimaª, 䖪ª The University of Tsukuba, 1-1-1 Ten-nodai, Tsukuba-shi, Ibaraki 305-8573, Japan.

September 26, 2014

Abstract
This study examines the impact of Thailand’s 30 baht universal health scheme on household savings
behavior using Thai Socio-economic Survey data from 2000, 2001, and 2004. Two
difference-in-differences (DID) approaches are employed: first, a simple approach using theeligibility of all health schemes and second, a more rigorous approach exploiting the phased
introduction of the 30 baht scheme. The results show that savings behavior changed after the scheme
was introduced. Simple DID analysis shows that the propensity to save out of permanent income
decreases from about 14%–17%, which accounts for 24–30% of the propensity to save out of
permanent income in 2004. More rigorous DID analysis shows that the propensity to save out of
permanent income is reduced immediately after the introduction of the scheme. This means that evenpoor households have greatly reduced levels of permanent consumption in order to prepare forunpredictable health risks.

Keywords: Thailand, 30 baht scheme, precautionary savings, developing countries, healthcare reforms
JEL classification: D1; H4; I1

䖪Email address: ushijima@sk.tsukuba.ac.jp1

1

Introduction

In many developing countries, household health care expenditure is funded mainly by out-of-pocket
payments because health insurance is rarely provided. Furthermore, out-of-pocket payments forhealth care can be large relative to total household expenditure because household income is low inthese countries. If households are unprepared for sudden illnesses and cannot borrow to coverunexpected medical costs, they may have to sell assets or reduce living expenses in order to financeout-of-pocket payments. Even if households do not choose treatment for disease, their permanent
income may possibly decrease via reduced human capital arising from disease. As a result,households fall into poverty or lose opportunities to escape poverty. To make matters worse, poor
households have a greater tendency to fall ill than rich households (Case, 2004).
Previous theoretical models show that households increase their savings when future consumption
or future income becomes more uncertain (Kimball, 1990; Leland, 1968). In addition, an
unpredictable disease affecting a family member is a factor of economic uncertainty. Hence,
uninsured households have a precautionary motive to restrain consumption and to save. Conversely,
the introduction of public health insurance for uninsured households can reduce the precautionary
motive by reducing the risk of unexpected medical expenses. Some empirical studies analyze therelationship between health insurance and precautionary savings in developed countries. However,
the empirical evidence is mixed on the relationship between savings behavior and insurance.
The aim of this study is to assess the impact of Thailand’s universal health scheme, known as the
30 baht scheme, on household savings behavior by using the Thai Socio-economic Survey (SES).
The 30 baht scheme, which was introduced by the Thai government in four phases from April 2001
to April 2002, guarantees comprehensive outpatient and inpatient care. Beneficiaries pay just 30 baht
(approximately 0.75 USD in January 2004) per visit to a registered primary care unit 1. Thus, themain purpose of the 30 baht scheme is to ensure that the whole Thai nation can access medical
services cheaply and sufficiently. Somkotra and Lagrada (2008) show that the 30 baht scheme
reduces the risk of catastrophic medical expenses. As a result, it could reduce the risk of unexpected
medical expenses. However, the effect of the scheme on savings behavior is under-researched.
In order to measure the impact of the 30 baht scheme on household savings, this study employs
two difference-in-differences (DID) approaches. In the first approach, define a treatment group and a
Beneficiaries who were eligible for the Medical Welfare Scheme (MWS, see Subsection 2.2) before theintroduction of the 30 baht scheme were exempt from this payment.
12control group are defined based on eligibility for the 30 baht scheme. This approach assumes thatchanging savings behavior over time is equal in both groups. On the other hand, in the second
approach, the timing of the phased introduction of the scheme is exploited. This second approachallows us to estimate without the assumptions of the first DID approach. In employing these methods,
I attempt to avoid the estimation results bias suffered by many similar previous studies.
This study is one of the first in a developing country about the relationship between healthinsurance and precautionary savings. Even though the expected damage that poor households sufferfrom health risk is bigger than rich households, empirical studies in developing countries have been
researched insufficiently. The 30 baht scheme, which is one of the first universal coverage healthschemes in lower middle-income countries, allows us to study this theme. Furthermore, because the
30 bath scheme has features as natural experiments, it is possible to avoid some empirical problems.
The presented empirical results suggest that the 30 baht scheme has changed household savings
behavior. Based on both DID approaches in analyses of treatment subgroups without the richestgroups, the results show that the 30 baht scheme decreases precautionary savings. In detail, acomparison of saving behavior before and after the introduction of the 30 baht scheme using thesimple DID approach shows the propensity to save out of permanent income decreases from about
14 % – 17 %. This magnitude accounts for 24–30% of the propensity to save out of permanent
income. Moreover, the results of the rigorous DID approach show that the propensity to save out of
permanent income decreases statistically significantly in two treatment subgroups, even immediately
after the introduction of the 30 baht scheme. These results imply that informal sector households
prepare for their health risks, and the health risks lower the level of permanent consumption. That is,the 30 baht scheme improves the living standards of households but not richer households, in theinformal sector.
The remainder of the paper is organized as follows. Section 2 presents the institutional setting andreviews previous studies of the relationship between health insurance and savings. Section 3
describes the estimation strategy used in the present study. Section 4 provides an overview of thedata set. Section 5 reports the estimation results and Section 6 discusses their robustness. Section 7
concludes.

2

Background

3

2.1

The 30 baht scheme’s place in Thailand’s health care schemes

One of the goals of the 30 baht scheme is to remove the risk of catastrophic medical payments fromthe whole nation. If health risk can be removed, it is expected that households reduce precautionarysaving for this risk. The purpose of this study is to examine this precautionary saving reduction. For
our evaluation, we focus on the two settings, as natural experiments, of the introduction of the 30
baht scheme.
There are two natural experiment situations for the introduction of the 30 baht scheme. First, thebeneficiaries of the 30 baht scheme were automatically registered on the scheme. Second, because
the introduction of the 30 baht scheme was carried out in stages, the period of beneficiary enrollment
in the 30 baht scheme can be regarded as random. Using these two features, we can avoid theempirical problem of the endogeneity of health insurance participation.
The history of health insurance schemes in Thailand explains the reason why the 30 baht scheme
has features as natural experiments. The government has aimed for universal coverage of publichealth insurance since the birth of the first Thai democratic government in 1973. Four medical
schemes were introduced before the introduction of the 30 baht scheme. The first was the Medical
Welfare Scheme (MWS), which was introduced in 1975 and aimed to provide free medical care to
low-income households. However, this scheme suffered problems related to funding and targeting.
The second scheme, the Civil Servant Medical Benefit Scheme (CSMBS), was introduced in 1978 toprovide health benefits to civil servants and their dependents. Beneficiaries were free to choose
private or public providers. The CSMBS remains as a superior health care scheme after the
introduction of the 30 baht scheme. The third scheme, the Social Security Scheme (SSS), wasintroduced in 1990. The SSS can be used in both private and public hospitals but covers only formal
sector employees (their families are excluded). The fourth scheme, the Voluntary Health Card
Scheme (VHCS), was introduced in 1993 to cover those ineligible for any of the other schemes.
However, there were concerns that the VHCS was not reaching its intended target population andsuffered from a classic adverse selection problem because the purchase of health cards was notcompulsory. In addition, 21% of nationals did not receive any medical schemes in 2001.
The 30 baht scheme was introduced to solve the problem of the medical schemes for the informal
sector, the MWS and the VHCS, and to provide health security for uninsured nationals. In otherwords, all Thais who were ineligible for the CSMBS and the SSS automatically became
beneficiaries of the 30 baht scheme. Following its introduction in April 2002, its coverage spread4

rapidly to 74.7% in 2003, from when the number of uninsured has gradually decreased (Table 1) 2,3.
In addition, the budget per person of the 30 baht scheme is larger than any previous scheme 4. By
using this feature, Gruber et al., (2013) show that the 30 baht scheme increased health careutilization and decreased infant mortality. Their results signify that informal sector households could
not receive sufficient medical services even if they receive the MWS.
The 30 baht scheme has influenced not only the coverage but also the medical expenses ofhouseholds. Somkotra and Lagrada (2008) examine the financial implications of such payments
using SES data, especially when payments exceed certain threshold values, such as the fraction of
total household consumption or non-food consumption. Somkotra and Lagrada (2008) conclude that
unexpected out-of-pocket payments for medical care decreased following the introduction of the 30
baht scheme. This means that its introduction could automatically decrease the precautionary savings
motive for the health risk of beneficiaries.
The phased introduction of the 30 baht scheme can be regarded as an exogenous shock tohousehold saving behavior. The 30 baht scheme was introduced nationwide from April 2002, having
been introduced in four phases from April 2001. It was introduced in 6 provinces in the first phase ofApril 2001; 17 provinces in the second phase of July 2001; all remaining provinces, but not someareas in Bangkok in the third phase of October 2001; and in the remaining areas of Bangkok in thelast phase of April 2002. Surprisingly, the socioeconomic background of households enrolled in the
30 baht scheme in each stage is hardly statistically different. Moreover, the health care resources ofeach province are not significantly different 5. That is, the introduction of the 30 baht scheme isconsidered as a natural experimental situation for household saving behavior.

2.2

Precautionary savings and health insurance

Some theoretical studies focus on the relationship between household savings and insurance (e.g.,
Hubbard et al., 1995). The precautionary motive diminishes if households join insurance schemes bylowering economic uncertainty. In addition, health insurance shrinks household precautionarysavings in theory. Decreasing unexpected out-of-pocket medical expenses by joining the health2
It is important to note that it is rare for beneficiaries of the CSMBS and SSS change to the 30 baht scheme because
the contents of health care security in the CSMBS and the SSS are richer than those in the 30 baht scheme. That is,the beneficiallies of the 30 baht scheme could not join the CSMBS or the SSS before the introduction.
3
Although the 30 baht scheme is universal, 5% of nationals do not take it up. One of the reasons for this is that
beneficiaries can use the program only in an area in which they are registered as residents.
4
This scheme is financed almost solely from tax revenue and public hospitals are the main providers, covering morethan 95% of beneficiaries (Wibulpolprasert and Thaiprayoon, 2008).

5

See Table 3.
5

insurance reduce the precautionary savings motive (Kotlikoff, 1989).
Some empirical studies confirm the theoretical conclusion. Levin (1995) finds that precautionarysavings are made by many elderly households that do not have private health care insurance. Gruber
and Yelowitz (1999) use panel data to assess the relationship between eligibility for the US social
insurance program Medicaid and household savings behavior. They find that Medicaid eligibility has
a significant positive effect on consumption expenditure. In other words, Medicaid eligibility has anegative effect on household savings.
Few studies do not support the theory of precautionary savings. Starr-McCluer (1996) finds a
positive correlation between health insurance and wealth holdings. Guariglia and Rossi (2004)
investigate whether uninsured individuals tend to save more than insured individuals, but find that
this hypothesis does not hold, even after controlling for the endogeneity of insurance purchases.
Empirical evidence is mixed on theoretical predictions.
These previous studies suffer from three empirical problems. First is the endogeneity of savings
behavior and insurance purchases. For example, in the case of private insurance, a household’s
preference to join a private insurance may be associated with savings behavior. A problem with thisapproach is that insurance status is an outcome of the same choice process that determines savings
decisions (Gruber and Yelowitz, 1999). The second problem is peculiar to means-tested social
insurance, such as Medicaid in the US. Hubbard et al. (1995) argue that asset-tested social insurance
programs affect savings in two ways: one is that social insurance reduces the precautionary savings
motive by decreasing unexpected out-of-pocket payments and the other is that social insurance
reduces household savings in order to meet the eligibility criteria for asset-tested social insurance.
Consequently, it is difficult to identify the effect on precautionary savings.
Chou et al. (2003) avoid the first and the second empirical problems by focusing on the national
health insurance (NHI) scheme that was introduced in 1995 in Taiwan, which is available to all
nationals. This full availability means that we can identify the effect of the introduction of the NHI
scheme on household savings without self-selection bias. They use the DID estimation strategy anddefine the control and treatment groups based on the eligibility of government insurance programs
before the introduction of the NHI. They find that the introduction of the NHI reduces savings fortreatment groups. However, this method could have caused the third problem, which is sample
selection bias because the participants of health insurance do not assign randomly.
Under the DID method, it is assumed that the treatment and control groups have the same time6

trend. To satisfy this assumption, random assignment to each group is ideal. However, it is unlikelythat the definition of each group in Chou et al. (2003) satisfies the DID assumption because theeligibility of health insurance before the introduction of the NHI is defined by the socioeconomic
backgrounds of the population. Thus, the socioeconomic background of the new beneficiaries differsfrom that of the continuing beneficiaries. Because there is the possibility of sample selection, theestimation results may have been biased.
In this study, the 30 baht scheme can avoid these issues because it has rare features. However, thenew beneficiaries are not selected randomly, like in Chou et al. (2003). Accordingly, the first DID
approach in this study could face potential bias caused by this third problem. On the other hand, we
can assume that the phased introduction of the 30 baht scheme is exogenous to household saving. As
mentioned in Subsection 2.1, the 30 baht scheme was introduced in four stages. Surprisingly, there is
no statistical difference in medical resources and socioeconomic status of living households between
provinces of each phase. Because this situation allows us to regard the selection of the treatment as
random, this study can avoid these three empirical problems.

3

Estimation Strategy

This study investigates the relationship between savings and health insurance using the theory of
optimal choice under uncertainty, as in Chou et al. (2003). However, it should be noted thathouseholds balance out spending and savings to smooth consumption, which is shown in Paxson(1992). If savings for consumption smoothing are not separated from precautionary savings, it would
be impossible to identify the effects of the 30 baht scheme on household savings. In this study, amodel is used that measures precautionary savings from permanent income and consumption
smoothing from transitory income based on the permanent income hypothesis in order to separate
these savings.
I assume that the savings of household i (Si) are a linear function of permanent income (YPi),
transitory income (YTi), the income variability of the household (VARi), and the variables (Wi) thatmeasure the household life-cycle stage:Si

D  Yi PD1  YiT D 2  WiD 3  VAR iD 4  H i .

(1)

If households behave completely according to the permanent income hypothesis, the coefficient of
permanent income (D1) should be 0 and the coefficient of transitory income (D2) should be 1. If
7households save as a precaution, it is expected that the propensity to save out of permanent income(D1) is significantly positive.
Now, we focus on the propensity to save out of permanent income (D1) in order to investigate theimpact of the 30 baht scheme on household saving. The propensity to save out of permanent income(D1) decreases if precautionary savings decrease. Thus, the reason for using this method is becausesavings and the uncertainty of medical expenditure are endogenous. If out-of-pocket payments are
exogenous, as implicitly assumed by Chou et al. (2003), we can analyze them using the variables
related to medical expenses, as VARi. However, in Thailand, health care spending would not be
exogenous to total household expenditure. Therefore, I suppose that precautionary savings for healthrisk are included in the savings from permanent income.

3.1

Estimation framework

Although information on permanent income and transitory income is necessary in order to estimate
the saving function (1), it is generally unavailable. Hence, this study employs the estimation
framework of Paxson (1992). Paxson (1992) uses a sample of farmers in Thailand to investigate
whether households are able to balance out spending and savings in order to smooth consumption.
She assumes that the change in annual rainfall is orthogonal to permanent income and then divides
household income into permanent income and transitory income by assuming that the variables
measuring the change in rainfall are instrumental variables of transitory income.
Following this approach, we first assume that the total income of household i at time t is the sumof permanent income and transitory income. In other words,

Yit

YitP  YitT .

(2)

Next, we assume that permanent income is expressed as:

YitP

EtP  E c  XitP E P  H itP .

(3)

XPit is a vector of household characteristics that are determinants of permanent income. These
include 15 dummy variables indicating household socioeconomic classification and the compositionof household members in 17 age/sex/education categories. Ec represents a province fixed effect ofhouseholds living in province c. EPt represents a year fixed effect of permanent income. HPit is a
residual of permanent income.
In a similar way, transitory income is defined as a linear function of XTrt, a vector of the8region-specific variables that are determinants of transitory income. Transitory income is expressed
as

YitT

EtT  XTrt E T  H itT ,(4)

where XTrt includes deviations from the amount of average rainfall each month and its squared.
Ideally, it is necessary to contain the variable, such as the health conditions of household members,
which influences a household’s transitory income in XT, but such information as sickness is notfreely available. However, this is not a serious problem because this study focuses on the change inthe propensity to save out of permanent income. Because the factors of unexpected income
fluctuation are orthogonal to permanent income, the accuracy of the estimation of transitory income
does not affect the accuracy of the estimation of permanent income. ETt represents a year effect oftransitory income. The residual HTit includes all the transitory income of household i that is not
explained by XTrt.
As previously stated, Equations (3) and (4) cannot be estimated, because permanent income YPit
and transitory income YTit cannot be observed. Then, the income equation is derived by substituting
Equations (3) and (4) for Equation (2), and permanent income and transitory income are obtained
based on the estimated result. Then, the income equation is expressed asYit

Et  E c  X itP E P  X rtT E T Q it ,(5)

where Et = EPt + ETt. The income of household i, Yit, uses average monthly income, which is annual
income divided by 12.
The estimated result of income equation (5) is shown as the sum of three components, namely
permanent income, transitory income, and unexplained income. The unexplained income isconstructed from unestimated permanent income and unestimated transitory income, and also
includes year fixed effect, Et, because the year fixed effect is constructed from components ofpermanent and transitory income in this study. In addition, unexplained income includes household
fixed effects as an element of permanent income, and the health conditions of household members
and short-term unemployment as elements of transitory income.
The saving equation is derived from the estimation result of the income function (5). In thisestimation, permanent income, transitory income, and unexplained income are added to the saving
equation as an explanatory variable. As a result, the base model is shown by the following
expressions9

Sit

D t  ª¬ Eˆc  X itP Eˆ P º¼ D1  ª¬ X rtT Eˆ T º¼ D 2

 ª¬ Eˆt Qˆit º¼ D 3  YitOD 4  WitD 5  VAR rD 6  H it ,(6)

where Dt = D + EPtD1 + ETtD2. In this study, Sit is calculated as the difference between total income
and total expenditure. Total income is the sum of average monthly income Yit and other money
receipts YOit. YOit is average monthly money receipts, which comprises inheritance, proceeds frominsurance, and lottery winnings. Thus, YOit is not included in income equation (5) and is added to thesaving equation, because YOit does not correlate with average income and does not have a reverse
causality relationship with monthly expenditure before the investigation month. Total expenditure is
the sum of monthly expenditure, which is deflated using the monthly consumer price index by regionin order to adjust average monthly income and average non-consumption expenditure, such as taxes.
The standard deviation of rainfall, VARr, is used as a proxy of income fluctuation. Observed weather
information uses the regional variable, VARr, to become the same value by household in region r.

3.2

DID approachThis study estimates the change in the propensity to save out of permanent income using two DID
approaches developed for the saving equation (6). The first DID approach is to compare changes in
the propensity to save out of

permanent income between treatment and control groups. The

beneficiaries of the 30 baht scheme are all nationals not covered by the CSMBS or the SSS. That is,
the treatment group and control group are defined based on the eligibility of medical schemes.
Because the CSMBS covers all family members of beneficiaries, the control group is composed ofhouseholds in which at least one member receives the CSMBS 6. The treatment group is composed ofhouseholds in which no members receive the CSMBS or the SSS.
A decrease in the precautionary savings of the treatment group is shown as a change in thecoefficient of estimated permanent income, (D1), in Equation (6). Then, in order to identify thechange in the coefficient of permanent income, permanent income and the DID term interact as per
the following equation

Sit D t  ª¬ Eˆc  X itP Eˆ P º¼ u DID  ª¬ X rtT Eˆ T º¼ D 2
 ª¬ Eˆt Qˆit º¼ D 3  YitOD 4  WitD 5  VAR rD 6  Treat itD 7  H it .
6

(7)

On the other hand, the SSS covers only beneficiaries and does not cover family. Then, a household
may benefit from the 30 baht scheme as long as not all its members have joined SSS. Thus, thetreatment and control groups do not include beneficiaries of SSS.
10

In the first DID model,

DID D1  D 2004it J 1  Treat it J 2  D 2004it u Treat it J 3 .
D2004 is an indicator variable for the period after the introduction of the 30 baht scheme. Treatit is anindicator variable for the treatment group. The other variables in Equation (7) are similar to those in
Equation (6). The effects of the introduction of the 30 baht scheme on household savings, '30baht, canbe expressed as follows'30bahtª¬ J 1  J 2  J 3  J 2 º¼  >J 1  0@ J 3 .

This equation means that J3 is the difference between a change in the propensity to save out of
permanent income in the treatment and control groups. If precautionary savings for a health risk ofthe treatment group decreased following the introduction of the 30 baht scheme, J3 would besignificantly negative.
To estimate the saving equation (7), it is necessary to develop the income equation (5) and thesaving equation (6). There are two steps to develop the income equation (5). First, it is developed
into a model that expresses changing income structure from 2000 to 2004. For instance, there is apossibility that changing income structure results in permanent income increases as a result of the
improvement in health following the introduction of the health insurance scheme. Nevertheless, theDID estimator J3 might include the change in household savings, according to changing income
structures peculiar to the treatment group, if the explicit measurement of permanent income wereconstant throughout the sample period. Then, interaction terms of the socio-economic classification
dummy variables are made with the instrumental variables of permanent income that are included inXPit and the year dummy variables. Second, we consider the possibility that the influence of rainfall
is different in the control group and in the treatment group. Ersado et al. (2003) estimate transitory
income using the interaction terms urban area dummy and rainfall deviation from the long-range
average in order to control for the possibility that a rainfall shock has a different influence on urban
areas to rural areas. Many treatment group households reside in rural areas, whereas half the control
group households reside in urban areas. Then, the interaction term of XTrt is added, as well as acontrol group dummy.
In addition, the saving equation is developed. Consumption (savings) behavior in the life-cycle
stage could be different for the treatment group and for the control group. For example, expenditurein the formal sector is higher than it is in the informal sector because formal sector workers are11required to demonstrate suitable levels of consumption in order to maintain their social positions. To
control for different consumption behavior in each group, interaction terms of the variables ofhousehold life-cycle stage, Wit, are made with each group dummy variable.
Furthermore, it is necessary to consider measurement error as an econometric issue. The
household survey data on income and consumption could contain measurement errors. If so, ameasurement error is included in the error term, Hit, and unexplained income, vit, of the savings
function. As a result, D3 will be biased because the measurement error correlates with unexplained
income. If the original model of Paxson (1992) is used, the coefficients of permanent income andtransitory income are not affected measurement errors because unexplained income is orthogonal topermanent income and transitory income. However, the interaction term of permanent income andthe DID term are not orthogonal to unexplained income. Thus, this study confirms the strength of thecorrelation of the interaction term and unexplained income by comparing the coefficients of
unexplained income in the estimation result in Equations (6) and (7).

3.3

Analysis exploiting the phased introduction of the 30 baht scheme

In order to check the robustness of the first DID approach, this study additionally estimates thesaving equation, which exploits the introduction of the 30 baht scheme in four phases, using SES
2001. The standard SES in Thailand is constructed from data collection processes that are carried out
every month. SES 2001 was a smaller survey than the standard SES, with data collected in February,
March, April, July, August, and September. SES collects the information about consumption andincome in the month (or year) before the survey. Therefore, investigations from February to April
collected information before the introduction of the 30 baht scheme in all provinces. On the other
hand, investigations from July to September collected information after the introduction of phases 1
and 2 in provinces, and collected information before the introduction in other provinces. Therefore,
this analysis examines whether the precautionary savings of households decreased in provinces
where the 30 baht scheme was introduced in phase 1 or phase 2.
The following DID term is used with Equation (7),SimP ˆP ºD m  ª¬ Eˆc  Xim
E ¼ DIDc ¬ª XTrm Eˆ T ¼º D 2  ¬ª Eˆm  Qˆim ¼º D 3  YimOD 4  WimD 5  VAR rD 6  Treat cD 7  H i ,DIDc D1  D After J 1  Treat cJ 2  D After u Treat c J 3 .

12where Treatc is a dummy variable that takes a value of 1 if the household lived in province c, which
introduced the 30 baht scheme in phase 1 or phase 2, and 0 otherwise. DAfter is a dummy variable forthe period after the introduction of the 30 baht scheme introduced in phase 1 or phase 2, that is, thehousehold was investigated after August. If households undertake precautionary savings in provincesin which phases 1 or 2 is introduced, it is expected that J will be negative. The fixed effects of theinvestigation month, Dm, are added in order to control for the seasonality of expenditure because theanalysis focuses on the change in monthly savings behavior.
The second DID approach focuses on the 30 baht scheme introduced in provinces, comparedwith the first DID approach that focuses on individual eligibility of the scheme. There is a possibility
that in the second approach, many households included in the treatment grouphad not yet registered
for the 30 baht scheme, since immediately after the introduction. In addition, if households do nottrust the sustainability and effectiveness of the 30 baht scheme, household saving behavior would
not change much immediately after the introduction. For these reasons, it is expected that themagnitude of the second DID estimator, J, becomes smaller than that of the results of the first DID
estimator.

4

Data
4.1

SES data

This study uses three types of data sets in order to undertake the estimation. The first data sets are
SES2000, SES2001, and SES2004, which collect detailed socioeconomic information on households,
such as income, expenditure, and members’ characteristics, such as age, sex, occupation, andeducational attainment. SES collects information by two-stage stratified sampling and reports anational representative household’s sampling weight. All the descriptive statistics and estimation
results of this study are the values that are considered as survey design. Household panel data cannot
be constructed because SES is a cross-sectional survey.
The most important point to enable analysis is the availability of detailed individual information
on medical scheme coverage. Generally, it is difficult to know what kinds of insurance one receives.
To make matters worse, in the case of Thailand, health scheme eligibility is determined in principle
by individual occupation but exceptional cases exist. For example, 24.5% of civil servants do notreceive CSMBS; on the other hand, 10.5% of non-civil servants receive CSMBS in 2004. If we
simply assign to the control group and the treatment group individuals according to work status, one

13who does not receive the 30 baht scheme is included in the treatment group and one who receives the
30 baht scheme is included in the control group. If this group misassignment is not solved, the effectof the 30 baht scheme is underestimated.
Fortunately, we can identify one’s medical insurance scheme because this information is reported
in SES 2004. However, SESS2000 and SES2001 do not collect this information. Thus, as a first step,
the probability of individuals receiving the CSMBS or the SSS in each occupational status isestimated using SES 2004 7. Next, based on the estimator in the first step, the probability of
individuals receiving any medical scheme is calculated in SES 2000 and SES 2001. Finally, thecontrol group and the treatment group are defined on the basis of the predicted value. The treatmentgroup comprises people who have a probability of receiving CSMBS or SSS of less than 10% andnone have a civil servant in their family. The control group comprises people with at least one personin the family who is a civil servant or has a probability of receiving CSMBS of more than 90%. By
this sample selection, the proportion of CSMBS beneficiaries who are included in the treatmentgroup becomes 3.5% and the proportion of non-beneficiaries of CSMBS who are included in thecontrol group becomes 3.24% in the individual level dataset of SES2004.

4.2

Inflation bias

Household savings are measured as the difference between total income and total expenditure
because SES does not ask direct questions about savings. However, reported household income isthe average monthly income that is based on income during the year or month before theinvestigation, whereas household expenditure is a value based on expenditure during the month
before the investigation added to the other average monthly expenditure led by annual costs, such as
tax. If there is inflation over the survey year, savings would have a downward bias (Paxson, 1992).
Using the monthly consumer price index as the second data set, this study adjusts for inflation bias ina manner similar to Paxson (1992) 8.

4.3

Constructing the rainfall variable

7

The reason for estimating the receiving probability of health scheme by each classification is toimprove the estimation accuracy. For example, there are many recipients of SSS at a specificoccupational classification, and there is an occupational classification that is often live with civil
servants. As explanatory variables for the prediction, occupation, age, area of residence, gender, andthe number of civil servants within the family are used.

8
Monthly consumer price index by region can be downloaded from the homepage of the Thai Bureau of Trade andEconomic Indices (as of May 25, 2009).

14

In order to construct the rainfall variable, this study uses daily rainfall data derived frommeteorological observatories across the whole country. The National Climate Data Center releases
observed meteorological data every day on more than 20,000 observatories in many parts of theworld 9. In Thailand, there are 114 meteorological observatories where observation data have beencollected from 1973 to 2004 10. However, owing to data limitations for some observatories, in thisstudy, we use weather data observed in 52 weather stations and their geolocations. These weather
stations are distributed uniformly across Thailand, as shown in Figure 1.
Households are matched to these 52 weather stations by choosing the station closest to the gravity
point of the province in which the household resides. SES collects the residential information and the
National Climate Data Center data includes the latitude and longitude of weather stations. ArcGIS
allows us to calculate the gravity point of the province in order to find the closest weather station.
The variables, XTrt and VARr, are constructed based on this rainfall information.
XTrt represents transitory income. If deviation from the average rainfall were serially correlated,
XTrt would be inappropriate as an instrument of transitory income because one can expect the valueof rainfall. The Portmanteau test shows that deviation from the average rainfall in almost all monthsof all weather stations follows a white noise process.

4.4

SampleTable 2 shows the summary statistics of observations used for the first DID estimation 11. Panel A
reports the summary statistics of the sample of the first DID estimation. The average monthly
income of the treatment group is 7,015 baht in 2000 and 8,279 baht in 2004. The average income percapita of the treatment group is about 67 baht per day in 2000 and 80 baht per day in 2004
(approximately 2–3 dollars). Although Thailand is classified as a lower middle-income country by
the World Bank, the living standards of the treatment group are not expected to be high. In contrast,
the average monthly income of the control group is more than four times that of the treatment group.
The treatment group is constructed from four subgroups based on a socioeconomic classificationof the SES, namely farmers, economically inactive households, production/construction worker

9

National Climate Data Center Online: http://lwf.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html (accessed January
14, 2009).
10
Because only seven weather stations show weather information from 1966 to 1972, observation data after 1973 areused.
11
In order to remove outliers, the observations of the top 1% of income and expenditure are dropped. The estimation
results are almost the same when the top 0.5% is dropped, or when the top 2% is dropped.

15

households, and other households 12. The features of each subgroup are summarized as follows.
Although the average monthly income of farmers is similar to that of economically inactive
households or production/construction workers’ households (See Table 2), farmers are more affectedby the weather, and therefore, face strong seasonality in comparison with the other subgroups(Paxson, 1993). Income seasonality increases the incentive to treat diseases in farming seasons
because farming is labor-intensive work, and thus, health-intensive work. Behrman and Deolalikar(1989) show that farmers’ short-run health conditions during the farming season affect their laborproductivity. The rate of return on health investment in the farming season is high. Therefore,
farmers could have stronger precautionary savings motives against health risks than other groups.
The economically inactive households depend on remittances. If remittances play the role ofinsurance, economically inactive households that depend on remittances might have weak
precautionary motives (Yang and Choi, 2007). Conversely, it is possible that to be dependent on
remittances leads to greater economic uncertainty because the remitter may be poor to begin with.
Returns on health investment as labor income should be lower than other sub-groups if households
depend on remittances but the ratio of medical expenditure to income is relatively higher. The
precautionary saving motive of the economically inactive households might be strong.
On the other hand, it is possible that precautionary savings have not been reduced by the
introduction of the 30 baht scheme for households that cannot be satisfied with the medical services
available under the scheme. The scheme decreases unpredictable medical expenditure only if abeneficiary visits a primary care unit that has registered each person. In addition, the 30 baht scheme
does not apply if a beneficiary wants to receive a medical service in a medical institution other than a
primary care unit. The medical expenditure and income of the entrepreneur, trade, and industry
household is the highest in the treatment subgroup. It is possible that their health investment
preference differs from other treatment subgroup households.
In Table 2, panel B provides descriptive statistics of household medical expenditure. This isconstructed from expenditure for medical supplies, outpatient fees, and inpatient fees. The samplesof each group are conditional on a positive level of medical expenditure for each household and thisaccounts for between 58.8% and 71.3% of the observations of each group in panel A. Table 2 allows
us to compare the ratio of medical expenditure in household income in 2000 with that in 2004. In alltreatment subgroups, this ratio following the introduction of the 30 baht scheme decreases
12

There are other socioeconomic classifications of beneficiary households (e.g., forestry, fishers, and unpaid
workers). However, only about 3% of the total samples belong to these classifications.

16

statistically significantly at the 5% level. Furthermore, the observation ratio of panel B to panel A
not only tends to decrease, the standard deviation of the ratio of medical expenditure in householdincome in all groups also decreases. These statistics suggest that the uncertainty of medical
expenditure decreases in the treatment group. By contrast, the ratio of medical expenditure to income
for the control group does not change.
Table 3 compares some features of provinces in which the 30 baht scheme was introduced inphases 1 and 2 and provinces in which it was introduced in phases 3 and 4. This is because we define
provinces in which it was introduced in phases 1 or 2 as the treatment group, and provinces inwhich it was introduced in phases 3 or 4 as the control group in the second DID estimation. This
table reports household attributes using SES2000 in panels A and B, and health resources ofprovinces using Thai public health (2002) in panel C. Column (1) reports summary statistics of thetreatment group. Column (2) reports summary statistics of the control group. Column (3) reports thedifferences between treatment group provinces and control group provinces.
Because a phased introduction does not mean random assignment in general, the second DID
estimation does not necessarily meet DID assumptions directly. Moreover, in developing countries, it
is very rare for a phased introduction to be regarded as a random assignment. However, surprisingly,
Table 3 shows balance between the treatment and control groups in terms of household status andhealth resources in province䡏 13. Household income is statistically significant at the10% level. The
reason why differences in income are statistically significant is that phase 3 and 4 provinces include
the Bangkok metropolitan area. This significant difference is lost if households that live in Bangkok
are removed from phases 3 and 4. In other statistics, the control group and treatment groups arebalanced, even if Bangkok is included.

5

Empirical results
5.1

First DID estimation: using eligibility of health schemes

Table 4 shows the estimation results for the second stage of the first DID estimation. The first stage
estimation results are shown in Appendix Table A.1. Column (1) shows the estimates of the saving
function (7), which is the base model, column (2) shows the estimates of the saving function (8),which is the DID model, and column (3) shows the estimates of the saving function (7) using only asample of the treatment group. The DID estimator, J3, reported in column (2), is not significantly
13

More surprisingly, phase 1, 2, and 3 provinces are perfectly balanced in each. Phase 4 provinces
are not balanced because the phase includes only richer areas of Bangkok.
17

negative. Thus, the introduction of the 30 baht scheme does not decrease the marginal propensity ofthe treatment group to save from permanent income.
The interaction terms of the DID term and permanent income may be orthogonal to unexplained
income because the difference between the coefficients of unexplained income, D3, in the base model(1) and in the DID model (3) are not statistically significant. As a result, we can ignore thecorrelation between the measurement error included in the error term and the interaction term of the
DID term and permanent income.

Subgroups
The saving equation (7) is estimated for every subgroup in order to consider the possibility thatthe precautionary savings motive is different according to socioeconomic background. Table 5
presents the estimation results of each subgroup. The DID estimators are significantly negative in all
subgroups without entrepreneurs, trade, and industry households. The magnitude of the significant
coefficient is large. This indicates that the savings propensity of permanent income decreases fromabout 14% to 17%. This decrease accounts for between about 24% and 30% of the propensity tosave out of permanent income in 2004. As stated previously in Subsection 2.2, the possibility thatthese estimators are biased cannot be denied because although DID estimation assumes common
trends of the treatment and control groups, this assumption is not met. However, I believe that thisbias is not large because the estimator of each subgroup is different, despite using the same control
group in each estimation 14.
This result suggests that the 30 baht scheme has not only reduced the risk of catastrophic paymentof out-of-pocket payments, but also reduced the motivation for precautionary savings to the healthrisk of households that are not rich among the beneficiaries. In other words, even poor households
lower their living standards significantly in order to save as a precaution for health risk. The resultthat the effects of the 30 baht scheme are greater for poor households is consistent with theconclusion of Gruber et al., (2012), who show that the effect of the 30 baht scheme is greater in areas
with a high subscriber ratio for MWS, which covers mainly poor households.

5.2

Second DID estimation: exploiting the phased introduction of the 30baht scheme

14

The results in Table 5 may appear to be not consistent with the result in Table 4. This is probably
because the estimated income function in the first stage is not well captured the differences inincome structure between sub-groups.
18

As a robustness check for the first DID approach, the second DID approach, which exploits thephased introduction, is analyzed. The second DID approach defines the treatment and control groups
depending on whether the 30 baht scheme was introduced in a household in a province in which thescheme was introduced the month before the investigation. Thus, households that were not registeredin the 30 baht scheme at the time of investigation are also included in the treatment group. In
addition, because immediately after the introduction of the 30 baht scheme, it is also possible thatsavings behavior of households has not yet changed. That is, the DID estimator of the second
approach should be smaller than that of the first approach.
Table 6 reports the results of the second DID approach. According to the results for the wholesample, namely, column (1) of Table 6, the coefficient J indicates that the effect of the introductionof the 30 baht scheme on household savings is statistically insignificant, which is the same as thefirst DID approach. According to the results for each subgroup, shown in columns (2) – (5), DID
estimators, J, are almost consistent with the results in Table 5. In addition, the magnitude of the DID
estimator of most groups is smaller than the first DID estimators in Table 5. The estimators of (3)Entrepreneurs, trade, and industry households and (5) Economically inactive households are
significantly negative. The result that the estimator of (3) is significantly negative differs from theresult of Table 5; however, this is not a great inconsistentcy because the magnitude of the estimators
implies that savings from permanent income reduce by only 1.9%. Furthermore, the DID estimatorof (2) Farmer and (4) Other work status households is negative but insignificant. The reason why theestimator of column (4) is not significant could be that the fit of the estimation model is poor.
This discussion is necessary because the estimator of column (2) seems to differ from the result inTable 5. The magnitude of the coefficient is as expected, but it is not statistically significant. I
believe that this is due to the income seasonality of farmers. As discussed carefully in Paxson (1992,
1993), farmers faces income seasonality in Thailand. As discussed in Subsection 4.4, because ofincome seasonality, farmers have stronger motives to make precautionary savings for health risk thanother groups. Immediately after the introduction of the 30 baht scheme, effects that weakens the
precautionary motive do not appear because the 30 baht scheme has not spread fully across Thailand.
These results are not inconsistent with those in Table 5.

6

Conclusion

19This study provides the first evidence for the effect of the introduction of health insurance schemes
on household precautionary savings in a developing country. The presented estimation results
suggest that the savings behavior of many beneficiaries of Thailand’s 30 baht scheme does notchange following its introduction. However, in the results of the analysis of four subgroups, it was
confirmed that household precautionary savings is reduced in all subgroups except the richest. Basedon the result that compares the propensity to save in 2000 and 2004 using simple DID analysis, thestudy found that the propensity to save out of permanent income decreased from about 14%–17%.
This decrease accounts for 24%–30% of the propensity to save out of permanent income in 2004. In
addition, based on the result of more rigorous DID analysis, the study found that the propensity tosave out of permanent income is reduced in spite of immediately after introduction of the 30 baht
scheme. This means that even poor households have greatly reduced levels of permanent
consumption, in order to prepare for an unpredictable health risk.
These results imply two things about the effect of the introduction of the 30 baht scheme. First, it
not only improves health utilization and reduces household medical expenditure, but it also improveshousehold living standards in the informal sector. Second, the security of the 30 baht scheme may
not be sufficient because the effect of the 30 baht scheme on the richer treatment subgroup is small.
What is important to note is that the 30 baht scheme has improved greatly the living standard of theinformal sector, even if it does not provide enough security.
The results of this study suggest that poor households not only become ill more easily than richhouseholds but also cut down their permanent living standards to prepare for unpredictable healthrisk. To deal with such a situation, in Thailand, the 30 baht scheme was introduced. It has improved
the living standards of beneficiary households by decreasing precautionary savings, and it may have
improved the health conditions of household members by increasing opportunities to treat diseases.

Appendix A: Constructing the rainfall variable
The weather information observed in Thailand has two types of missing data: a lack of observation
days and a lack of rainfall data on rainy days. Because monthly rainfall data are constructed from asummation of daily rainfall data, monthly rainfall variables have a downward bias if observation
days during a month are missing. Moreover, if rainfall data on rainy days are missing, monthly
rainfall variables can easily become biased for any reason (e.g., instrument failure owing to bad
weather). Because observation information is likely to be missing the older it becomes, the average

20

monthly rainfall data derived from data from 1973 to 2004 could be lower than its true value. In
other words, deviations from the amount of average rainfall, XTrt, could have a large bias, probably
upward, in the weather station when there has been a lot of missing data in the past. To make mattersworse, the tendency for weather information to be lacking is different for each weather station.
First, the problem of this lack of daily information is solved. As mentioned in Section 4, there are
114 weather stations and 76 provinces in Thailand; therefore, the weather stations are distributed
densely. For days when there were no observations at each weather station during 1973 to 2004, I
complement the weather information with the observation from the nearest weather station.
Next, the problem of the lack of rainfall data on rainy days is solved. When rainfall cannot be used,
the information remains available (e.g., observation day, mean temperature, mean dew point, andmean sea level pressure). First, I sort out only the information on rainy days from 1973 to 2004 andestimate a simple linear model with rainfall as an explained variable. Then, I predict unobservable
rainfall and complement it.

Acknowledgements
This study benefitted from discussions and conversations with Atsushi Yoshida. It also substantiallybenefited from comments by Seki Asano, Ryuichiro Ishikawa, Yoko Kijima, Katsuo Kogure,
Tomohiro Machikita, Yasuyuki Sawada, and Yoshito Takasaki, as well as helpful comments from theseminar participants at Osaka University, the University of Tsukuba, Nihon University, Kanazawa
University, and Kobe University. The study was supported by KAKENHI (08J01220) and by the
Nihon University Population Research Institute from the “Academic Frontier” Project for Private
Universities matching fund subsidy 2006–2010 from the Ministry of Education, Culture, Sports,
Science and Technology, Japan.

References
Behrman, J.R., Deolalikar, A.B., 1989. Agricultural wages in India: The role of health, nutrition, andseasonality, in: Sahn, D. (Ed.), Seasonal Variability in Third World Agriculture: Consequences for
Food Security. Johns Hopkins University Press, Baltimore, pp. 107–17.
Case, A., 2004. Does Money Protect Health Status? Evidence from South African Pensions, in: Wise,
D.A. (Ed.), Perspectives on the Economics of Aging, NBER Books, National Bureau of Economic
Research, Cambridge, MA, pp. 287–311.

21

Chou, S.Y., Liu, J.T., Hammitt, J.K., 2003. National health insurance and precautionary saving:
Evidence from Taiwan. J. Public Econ. 87, 1873–1894.
Deaton, A.S., 2003. Health, inequality, and economic development. J. Econ. Lit. 41(1), 113–158.
Ersado, L., Alderman, H., Alwang J., 2003. Changes in consumption and saving behavior before andafter economic shocks: Evidence from Zimbabwe. Econ. Dev. Cult. Change 52(1), 187–215.
Gruber, J., Hendren, N., Townsend. R., 2012. Demand and reimbursement effects of health carereform: Health care utilization and infant mortality in Thailand. NBER working paper series
17739.
Gruber, J., Yelowitz, A., 1999. Public health insurance and private savings. J. Polit. Econ. 107(6),1249–1274.
Guariglia, A. Rossi, M., 2004. Private medical insurance and saving: Evidence from the British
Household Panel Survey. J. Health Econ. 23(4), 761–783.
Hubbard, R.G., Skinner, J., Zeldes, S.P., 1995. Precautionary saving and social insurance. J. Polit.
Econ. 103(2), 360–399.
Kimball, M.S., 1990. Precautionary saving in the small and in the large. Econometrica 58(1), 53–73.
Kotlikoff, L.J., 1989. Health expenditures and precautionary savings, in: Kotlikoff, L.J. (Ed.), What
Determines Savings? MIT Press, Cambridge, MA, pp. 141–62.
Leland, H.E., 1968. Saving and uncertainty: The precautionary demand for saving. Q. J. Econ. 82(3),465–473.
Levin, L., 1995. Demand for health insurance and precautionary motives for savings among theelderly. J. Public Econ. 57(3), 337–367.
Paxson, C.H., 1992. Using weather variability to estimate the response of saving to transitory
income in Thailand. Am. Econ. Rev. 82(1), 15–33.
Paxson, C.H., 1993. Consumption and income seasonality in Thailand. J. Polit. Econ. 101(1), 39–72.
Somkotra, T., Lagrada, L.P., 2008. Payments for health care and its effect on catastrophe andimpoverishment: Experience from the transition to Universal Coverage in Thailand. Soc. Sci. Med.
67, 2027–2035.
Starr-McCluer, M., 1996. Health insurance and precautionary savings. Am. Econ. Rev. 86(1), 285–
295.
Alpha Research, 2002. Thailand Public Health. Bangkok.
Wibulpolprasert, S., Thaiprayoon, S., 2008. Thailand: Good practice in expanding health coverage –22

lessons from the Thai health care reforms, in: Gottret, P., Schieber, G.J., Waters, H.R. (Eds.),
Good Practices in Health Financing Lessons from Reforms in Low- and Middle-income Countries,
World Bank, Washington DC, pp. 355–384.
Yang, D., Choi, H., 2007. Are remittances insurance? Evidence from rainfall shocks in thePhilippines. World Bank Econ. Rev. 21(2), 219–248.

23

Table 1: Health Insurance Coverage (percentage)
1998

2001

2003

2006

30 baht

-

-

74.7

74.3

MWS

45.1

32.4

-

-

CSBMS

10.8

8.5

8.9

8.0

SSS

8.5

7.2

9.6

11.4

VHCS

13.9

20.8

-

-

Private health insurance

2.0

2.1

1.7

2.3

Total insured

80.3

71.0

94.9

96.0

Uninsured

19.7

29.0

5.1

4.0

Sources: Wibulpolprasert and Thaiprayoon (2008)24

Table 2: Summary Statistics (weighted)
Treatment Group(Subroup)
Treatment Group
Variable
Panel A
Attributes of household
Income (baht)

Pre 30 Baht

Post 30 Baht

Entrepreneurs, tradeand industry

Farmer
Pre

Post

Pre

Post

Other work status
Pre

Post

Economically inactive
households
Pre

Post

12950
4976
6049
6901
6508
7015
8279
5599
7791
11638
(4566)(5779)(7833)(8277)(2991)(3514)(6111)(5534)(6030)(6511)
1719
4531
0
1485
619
2397
Savings
489
2810
21
2802
(4473)(4365)(4936)(3930)(4822)(5584)(6499)(2498)(2752)(5058)-0.241
0.239-0.0050.254-0.1440.199-0.172
0.284
Savings/Income-0.1570.242(0.608)(0.585)(0.509)(0.582)(0.432)(1.037)(0.635)(0.729)(0.559)(0.710)194
135
Medical Expenditure
154
137
137
128
220
113
96
159
(271)
(453)
(464)
(455)
(441)
(503)
(437)
(466)
(544)
(631)
0.028
0.019
0.023
0.015
0.026
0.016
0.029
0.023
The ratio of medical expenditure to income
0.027
0.018
(0.073)(0.063)(0.072)(0.054)(0.075)(0.061)(0.067)(0.039)(0.069)(0.046)
Sex of head(proprtion of men)
0.787
0.724
0.89
0.83
0.77
0.72
0.80
0.76
0.52
0.49
49.3
43.4
45.3
44.2
46.1
44.3
52.1
Age of head
44.6
48.3
45.5
Household size
3.5
3.3
3.8
3.7
3.4
3.2
3.5
3.5
2.9
2.7
Number of observations
7907
11290
2423
3252
2694
3723
1580
2434
1210
1881
Panel B : Conditional on a positive level of medical expenditure
139
243
213
Medical Expenditure
217
200
188
179
314
290
156
(543)
(628)
(753)
(308)
(539)
(555)
(558)
(510)
(597)
(502)
0.024
0.044
0.037
The ratio of medical expenditure to income
0.038
0.027
0.038
0.026
0.033
0.023
0.036
(0.083)(0.086)(0.078)(0.046)(0.079)(0.053)(0.086)(0.076)(0.064)(0.071)
Number of Observations
5465
7437
1727
2274
1845
2416
1123
1603
770
1144
0.608
Observations ratio*
0.691
0.659
0.713
0.699
0.685
0.649
0.711
0.659
0.636
Notes: Observations ratio is the ratio of N of observations in Panel B to N of observations in Panel A. Standard deviation corresponding survery design is reported in parentheses.

25

Control Group
Pre

Post

33380
(21056)10041(13406)0.245(0.430)584(1676)
0.018(0.089)
0.75
48.0
3.7
2339

35346
(21088)14464(16641)0.357(0.360)572(1833)
0.017(0.044)
0.74
49.3
3.5
3289

917
(2027)
0.028(0.110)
1491
0.637

973
(2308)
0.028(0.055)
1934
0.588

Table 3: Treatment group's summary statistics by pahsed group in 2000
Pahse 1&2
Mean

Std. Dev.

Phase 3&4
Mean

Std. Dev.

Difference
Coef.

P-value

Panel A: Attributes of household (weighted)
Income (baht)
6478
5398
7059
6077
-581
0.079 *
Savings
264.2
4074
507.0
4388
-242.80.298
Observations
644
7263
7907
Panel B: Medical expenditure of household conditioning on positive medical exp.(weighted)
Medical Expenditure
221.7
497
216.4
511
5.3
0.870
The ratio of medical expenditure to income
0.033
0.053
0.038
0.084
-0.005
0.144
411
5054
5465
Observations
Panel C: Average Health Resources of Provinces
Number of population/physician
6251
4334
6918
3516
-666
0.523
Number of hospitals
16.2
8.8
17.4
18.5
-1.14
0.721
Number of population/patient bed
531
228
607
232
-76.4
0.191
Number of new out-patients
366082188576396982505228-309000.703
Number of in-patients
10074459847
105838147413
-5094
0.833
23
53
76
Observations
Notes : The differences are estimated by regresstion. In panel A and panel B, P-values are calclated corresponding to thesurvey design. * indicates statistically significant at the 10% level, ** indicates statistically significant at the 5% level, ***indicates statistically significant at the 1%level.

26

Table 4: Key Estimates of Saving Equation (weighted)(1) Base Model
VariableCoef.
YP (D 1)

0.452

Bootstrap
Std. Err.

(2) DID(3) Only treatment group

Bootstrap
Std. Err.

Coef.

Coef.

(0.017) ***Bootstrap
Std. Err.

0.463(0.038) ***0.327(0.014) ***Y !2004dummy (J 1)

0.071(0.028) **0.101(0.018) ***Y !Treatment group (J 2)-0.126(0.042) ***Y !(Treatment group!2004dummy) (J 3)

0.024(0.038)PPP

Y (D 2)0.386(0.060) ***0.466(0.059) ***0.397(0.062) ***Y ( D 3)0.587

(0.019) ***0.582(0.019) ***0.680(0.010) ***Y (D 4)0.404(0.108) ***0.417(0.106) ***0.431(0.123) **1706.7-230.0(88.1) ***(864.8)1836.5111.7(116.5) ***(850.7)1845.8(114.8) ***-1963.0(990.2) **0.627-3181.6(1052.1) ***0.631-4473.0(257.1) ***
0.624

T

U
O

2004dummyTreatment Group Dummy
Treatment group!2004dummy
Constant
2

R
N

24825

19197

Notes 䠖Results show regresstion coefficients after controling for standerd deviatoin of rainfall by month, and life-cycle factor constructed fromfive categories by age by treatment and conrol group. Linearlized standard error corresponding survery design and bootstrapped standard errors
are reported in parentheses. * indicates statistically significant at the 10% level, ** indicates statistically significant at the 5% level, *** indicatesstatistically significant at the 1%level.

27

Table 5: Key Estimates of Saving Equation : Subgroup (weighted)
Entrepreneurs, trade andindustry
Bootstrap
Coef.
Std. Err.

FarmerVariable
Coef.

Bootstrap
Std. Err.

Economically inactive
households
Bootstrap
Coef.
Std. Err.

Other work status
Coef.

Bootstrap
Std. Err.

YP (D 1)

0.429

(0.044) ***0.452(0.038) ***0.438(0.039) ***0.444(0.042) ***Y !2004dummy (J 1)

0.129(0.047) ***0.090(0.028) ***0.133(0.048) ***0.119(0.048) **YP!Treatment group (J 2)

0.082(0.054)-0.083(0.044)-0.004(0.045)0.003(0.035)

Y !(Treatment group!2004dummy) (J 3)-0.155(0.066) **0.044(0.036)-0.139(0.066) **-0.167(0.058) ***YT (D 2)0.401

(0.065) ***0.521(0.071) ***0.390(0.076) ***0.512(0.073) ***Y ( D 3)0.567

(0.024) ***0.550(0.020) ***0.528(0.025) ***0.545(0.025) ***Y (D 4)0.167(0.101)*

0.585(0.148) ***0.241(0.126)*0.292(0.130) **2241.41847.0-4221.7(659.7) ***(744.8) **(1360.0) ***0.631

1604.6867.8-3550.2(192.1) ***(780.7)(1162.0) ***0.591

1854.1683.1-3697.9(653.2) ***(964.8)(1458.9) **0.618

3627.51044.6-5132.7(905.5) ***(768.8)(1629.1) ***
0.604

P

P

U
O

2004dummyTreatment Group Dummy
Constant
R2
N

11303

12045

*

9642

8719

Notes䠖Results show regresstion coefficients after controling for standerd deviatoin of rainfall by month, and life-cycle factor constructed from five categories by age by treatment
and conrol group. Linearlized standard error corresponding survery design and bootstrapped standard errors are reported in parentheses. * indicates statistically significant at the
10% level, ** indicates statistically significant at the 5% level, *** indicates statistically significant at the 1%level.

28

Table 6: Key Estimates of Saving Equation (8) (weighted)
Sub-group(1) Treatment Group
Bootstrap
Std. Err.

Coef.

(3) Entrepreneurs, tradeand industry(2) Farmer

Variable

Linearized
Std. Err.

Coef.

Linearized
Std. Err.

Coef.

(4) Other work status
Linearized
Std. Err.

Coef.

(5) Economically inactive
households
Linearized
Std. Err.

Coef.

YP (D 1)

0.403

(0.027) ***0.639(0.039) ***0.450(0.050) ***0.257(0.065) ***0.531(0.090) ***Y !Post 30 baht (J 1)-0.002(0.006)-0.020(0.035)0.003(0.005)0.002(0.001)0.007(0.007)

Y !Treatment group (J 2)-0.020(0.015)-0.003(0.048)0.003(0.006)0.002(0.002)0.001(0.009)Y !Treatment group!Post 30 baht (J 3)-0.010(0.018)-0.002(0.051)-0.016(0.008) **-0.003(0.003)-0.020(0.012)

YT (D 2)

0.392

(0.026) ***0.753(0.044) ***0.453(0.050) ***0.258(0.065) ***0.529(0.087) ***Y (D 3)0.691(0.020) ***0.786(0.022) ***0.639(0.032) ***0.503(0.063) ***0.674(0.056) ***Y (D 4)0.216(0.142)-0.352(0.279)0.384(0.196)*0.410(0.177) **0.127(0.247)

P
P

P

U
O

Treatment Group Dummy
Constant
R2
N(186.7)
251.1
(456)
-323
0.614
4088

279.4
(312.2)(857) ***
-2913
0.700
1382

6.4
(494.1)(1605) ***
4345
0.592
1276

24.2
(236.8)(3896) ***-11156
0.257
809

*

435.2
(399.3)1719(1981)
0.658
621

Notes 䠖Results show regresstion coefficients after controling for standerd deviatoin of rainfall by month, life-cycle factor constructed from five categories by age by treatment and conrol
group, and month dummy variables. Bootsrap standard error corresponding survery design and bootstrapped standard errors are reported in collumn (1) in parentheses. Linearlized standard
error corrected for ckustering at primary sanmpling unit and sampling weight are reported in parentheses. Because there are some stratum with single sampling unit, linearized standerd error
corresponding survery design can not calculate. As result, reporeted stander error is smaller than the corrected value. However, I think it does not matter enough to affect the inrerpretaion of
the results. I check in the estimation of Table 5 on this issue, because it underestimate about 7%. * indicates statistically significant at the 10% level, ** indicates statistically significant at the
5% level, *** indicates statistically significant at the 1%level.

29

Figure 1: Weather Stations in Thailand

30

Table A.1.:䚷Key Estimates of Income Equation (weighted)
Treatment Group
Linearized
Variable
Coef.
Std. Err.

Control Group
Linearized
Std. Err.

Coef.

Socio-economic Classification dummy ! year dummy 2000
Farmer mainly owning land-3082.8(685.3) ***䚷䚷2‒4 railess than 2 rai-3949.8(348.1) ***14212.1

䚷䚷5–9 rai

-3715.5(286.2) ***33843.5

䚷䚷10–19 rai-2967.2(288.3) ***24871.1(2035.4) ***䚷䚷20–39 rai-1784.5(356.6) ***30845.9(11012.4) ***䚷䚷40 rai or more

1884.8(817.5) **32683.5(8849.7) ***䚷䚷less than 5 rai-3654.8(773.2) ***

-

-

-

䚷䚷5–19 rai-3570.3(329.5) ***

-

-

-

䚷䚷20 rai or more-1894.9(477.6) ***-4163.05737.4(700.2) ***17064.4(4976.4) ***9771.1(2936.7) ***(1378.9) ***-(3708.0) ***--(2645.9) ***Farmer mainly renting land(2858.7)

Entrepreneurs, trade and industry
䚷䚷With paid workers
䚷䚷Without paid workers

Base case

Other work status
䚷䚷Employed by others-1487.1(738.3) **11585.6

䚷䚷Farm workers-3500.8(267.3) ***1729.9(2276.8)

䚷䚷Clerical, sales & Services workers-2324.1(411.3) ***5795.0(2004.3) ***䚷䚷Production & Construction workers-2204.1(293.2) ***6759.3(3084.9) **-1078.9(334.1) ***22682.4(7334.3) ***(6904.3)

Economically inactive households
䚷䚷Receiving assistance or pensions

Socio-economic Classification dummy ! year dummy 2004
Farmer mainly owning land
-1752.8(651.0) **5559.2

䚷䚷2‒4 railess than 2 rai-3661.7(447.6) ***-䚷䚷5–9 rai-3435.9(289.8) ***9651.4䚷䚷10–19 rai-2788.6(283.5) ***11905.1(1789.3) ***䚷䚷20–39 rai-1136.7(319.9) ***6390.8(5480.5)

䚷䚷40 rai or more

1378.7(497.0) ***13595.4(6459.2) **--(3651.3) ***Farmer mainly renting land䚷䚷less than 5 rai-3891.4(476.1) ***

-

-

-

䚷䚷5–19 rai-3276.6(373.4) ***

-

-

-

䚷䚷20 rai or more

-628.1(532.6)---5913.5(652.8) ***Entrepreneurs, trade and industry
䚷䚷With paid workers
䚷䚷Without paid workers

Base case26084.1(4517.9) ***12038.5(4047.9) ***Other work status
䚷䚷Employed by others-2204.8(479.0) ***18706.7

䚷䚷Farm workers-4186.3(226.2) ***52.9(1491.8) ***䚷䚷Clerical, sales & Services workers-2879.3(428.1) ***11014.4(1925.3) ***䚷䚷Production & Construction workers-2966.0(241.4) ***9598.9(2634.1) ***-1807.0(285.4) ***30708.6(3545.7) ***(3463.1)

Economically inactive households
䚷䚷Receiving assistance or pensions!"#$%&'(%)(*+$,
Age

Sex

Education (?? categolies)䕿

Rainfall (deviation)䕿

Rainfall䕿

Control group dummy

Province fixed effects

䕿Year Dummy (2000 = 0, 2004 = 1)

1053.3(353.7) ***Contstant

11636.4(725.5) ***
0.660

R2
N

24825

Notes䠖Linearlized standard error corresponding survery design is reported in parentheses. * indicates statisticallysignificant at the 10% level, ** indicates statistically significant at the 5% level, *** indicates statistically significant at the
1%level.

31