Quantile Treatmenet Effects of National Health Insurance on Household Precautionary Saving: Evidence from a Regime Switch in Taiwan

Quantile Treatmenet Effects of National Health Insurance on Household Precautionary Saving: Evidence from a Regime Switch in Taiwan CHUNG-MING KUAN a Institute of Economics Academia Sinica CHIEN-LIANG CHEN b Department of Economics National Chi Nan University This version: June 09, 2008 a. Institute of Economics, Academia Sinica, Taipei 115, Taiwan. E-mail: ckuan@econ.sinica.edu.tw; URL: www.sinica.edu.tw/as/ssrc/ckuan. b. Department of Economics, National Chi Nan University, Nantou, Puli 545, Taiwan. E-mail: clchen@ncnu.edu.tw

Abstract Purpose of this study is to estimate the extend to which the regime switch of welfare program crowd-outs precautionary saving of the household. Focusing on the implementation of National Health Insurance (NHI) in March 1995 in Taiwan, this study pioneer to incorporate the concept of quantile treatment effects in the difference-in-differences estimator to stress the notion of heterogenous treatment effects of NHI on saving. The empirical results demonstrate that there indeed exists heterogenous crowd-out effects of NHI implementation on precautionary saving across saving distribution. Moreover, the crowd-outs concentrate on households with lower income and retiring heads in particular. Novelty of this study is to depict the distinct patterns of quantile treatment effect plane of an exogenous universal health insurance on precautionary saving that has not yet explored in the literature. Keywords: quantile treatment effects, National Health Insurance, precautionary saving, regime switch 2

1 Introduction Illness and indigence are closely interdependent. Hubbard et al. (1994) denote that medical expenses is one of the major uninsured idiosyncratic risks facing the household over their life-cycle, 1 and induced uncertain health care cost may be highly persistent and disastrous (Feenberg and Skinner, 1994). Because physical stress tends to induce financial distress and result in poverty for a household without health insurance, threats of catastrophic medical care expenditure, therefore, enforce the household to save more and consume less. Introduction of comprehensive health insurance system is meant to alleviate the linkage between physical and financial risks, consequently, may results in household saving reduction for consumption smoothing. However, the threats of medical care expenses risks may not be identical for all households. The extent to which the implementation of universal health insurance crowd-outs saving heterogeneously for different types of households is less discussed but a crucial empirical question related to welfare program assessment as well as economic development. Removal of the risk of uncertain medical care expenditure under social safety net leads to lower precautionary saving in theory (Hubbard et al., 1994a, 1994b, 1995; Kotlikoff, 1979). Nevertheless, evidences of the empirical literature do not reach a consensus with regard to the effects of welfare policy on saving, let alone the signs and magnitudes of the displacement rates. For example, Bernheim (1987)and Feldstein (1974) document that the replacement rate of social security benefits on saving is greater than or equal to one dollar for one dollar. Carrol and Samwick (1995), Kazarosian (1997) and Palumbo (1999) all agree that uncertain medical expenses make the elderly save more under precautionary motive. Gruber and Yelowitz (1999) find medical eligible dollar can only replace small amount of saving. Diamond and Hausman (1984) select matured worker sample in analysis and they find that social security benefit substitute household saving in a much smaller proportion than theoretical prediction of Kotlikoff (1979). Reversely, Starr-McCluer (1996)and Ziliak (1004) find no evidence to support the crowd-outs of medical insurance on household saving. Guarigila and Rossi (2004) even claim that saving and health insurance are complementary to each other. Refer to Hsu (2006) for comprehensive survey and detailed discussion. Some studies try to reconcile the controversy in between theoretical prediction and empirical findings. On the one hand, Dynan et al. (2002) argue that precautionary motive and bequest motive of saving coexist simultaneously that makes the true effect of 1 the other two are the risks of earnings and length of life 3

social welfare on saving ambiguous because reduced saving caused by lower precautionary motive may be in exchange for the increased saving under bequest motive. On the other hand, Hsu (2006) and Starr-McCluer (1996) both focus on institutional factors to explain the controversy. The former stresses that the asset-based means-testing welfare program may encourage the wealthier to save more because they have lower likelihood to be covered by social welfare system when facing bad luck. The latter argues that the design of welfare program may actually motivate the poor to save less in order to keep the benefits of social welfare. Although these studies clearly propose possible explanations to solve the inconsistency and their contribution cannot be overstated, they do provide little clue toward the definitive estimates of the change of precautionary motive through the welfare regime switch. Due to the dual roles of savings and institutional factors that contaminate the effects of health insurance on saving, regression results may capture the net effects of savings rather than precautionary saving per se in response to welfare coverage (Hsu, 2006). Accordingly, there is a line of inquiry focusing on regime switch of institution to specifically estimate the pure effects of welfare policy on household saving and consumption. A standard model to capture the changing behavior in response to social experiment is a difference-in-differences estimator (Card, 1990; Card and Sullivan, 1988; Hamermesh and Trejo, 2000). No matter what kinds of model specification are used to deal with the crowd-out effects, most of the existing studies, however, inevitably suffer from the interferences of econometric restrictions such as selectivity of program participation (e.g., Diamond and Hausman, 1984; Hubbard et al., 1995; Starr-McCluer, 1996) or endogeneity of welfare benefits (e.g., Hurst and Ziliak, 2004; Attanaiso and Brugiavini, 2003). It is not an easy task to identify the change of precautionary saving with respect to welfare regime switch unless there is an economy implementing an universal welfare program with neither asset-testing nor means-testing. Taiwan is one of the few that meets the requirement for the estimation of precautionary motive. After decades of miraculous economic growth, Taiwan government has substantially expanded the social welfare system during the late 80s and early 90s. The unique features of Taiwan s institutional regime switch have attracted considerable studies to analyze the impacts on labor market and households behavior. Beginning from the late 80s, Taiwan expanded the labor market benefits through the extension of Labor Standard Law. Gruber (1994), Kam and Lin (2006), Lai and Masters (2005), Leverson (1996) and Zveglich and Rodgers (2003), among others, use difference-in-differences estimators to measure the impact of on wage, job turnover and working hour in Taiwan. Most of them 4

find significant effects of social welfare reform at every aspects except Leverson (1996). 2 Meanwhile, Taiwan introduced an universal health insurance system in the mid-90s which affected almost half of Taiwan s population at the time. Chou et al. (2003) and Chou et al. (2004) apply difference-in-differences estimator to gauge the impact of NHI on household saving and the results of both studies strongly suggest that NHI significantly crowd-outs household saving. Whereas the evidences of the existing studies concerning the relationship between health insurance and saving/consumption are consistent with the precautionary saving hypotheses, two specification limits are usually seen. First, and the most important, is the problem of data selection. Because negative value is unable to define in logarithm, it is a common practice to exclude sample households with negative saving (either income net of consumption or net wealth holding) from the analysis, such as Chou et al. (2003), Chou et al. (2004), Diamond and Hausman (1984), Guiso et al. (1992), among others. Although some of these studies add inverse Mill s ratio to correct sample selection or use Tobit model to deal with censoring problem, exclusion of samples might still suffer from information loss when they are not unobservable. Furthermore, some studies restrict sample on the older head households only, such as Feenberg and Skinner (1994), Kazarosian (1997) and Palumbo (1999), to demonstrate the behavior of the elderly. Knowing the responsiveness of the elderly to welfare program is partial to have a comprehensive appraisal of policy effects. From the life cycle aspect, understanding the crowd-out impacts on saving of the young household is as important as that of the elder counterpart. Second, the consideration on the heterogeneities of household responsiveness to NHI has long been overlooked. Several studies try to depict the various saving behavior over cohorts or income distribution, e.g., Attanasio and Brugiavini (2004), Hubbard et al. (1994, 1995) and Diamond and Hausman (1984). In particular, Hubbard et al. (1995) run quantile regression (Koneker and Bassett, 1978) to plot age-wealth profile and Diamond and Hausman (1984) control income spline in the estimation of saving function. Their findings suggest that the marginal effects of health insurance on saving may not be homogenous in between younger and older cohorts, neither in between lower and higher income households. These studies stress the heterogeneity of marginal effects underlying different levels of the covariates (right hand side independent variables) but shed no lights on that across the conditional distribution of saving (left hand side dependent variables). 2 Model setup of Leverson (1996) is not a precise difference-in-differences model. 5

Estimation strategy of the related literature are mostly within the framework of ordinary least square (OLS) regression. It is well known that least square specification derives only the mean tendency of the marginal effects from the conditional mean functions. If the responses of household saving to the implementation of NHI is not homogenous across saving, mean tendency is surely not enough to describe a complete picture of the treatment effects over the distribution. Albeit Chou et al. (2003) explicitly address the heterogenous treatment effects of NHI on saving by using quantile regression, they focus on the result of OLS only and pay no attention to the distribution of independent variables. Theoretically, the transfers of welfare benefits are not designed to be identically flat toward different groups, such as various levels of income or different stage over life span. This calls for means-testing and asset-testing regulations, and consequently brings endogeneity in estimation. NHI in Taiwan basically lacks means-test and asset-test, thus it is relatively easier to focus on the possible heterogeneities of household reaction on saving from every aspects. Taken together, if heterogeneities exist at both the distributions of covariates and the conditional distribution of saving, treatment effect is indeed a plane instead of a point estimate. 3 Whether the heterogeneities exist at both dependent and independent variables of saving function is an empirical question. It deserves further investigation because patterns of these treatment effects are crucial for the effectiveness and consequences of a welfare policy implementation. Taiwan government launched a fairly generously universal health insurance in March 1995. Because NHI is an extension based on pre-existed social insurance programs, distinguishing households who were covered (controlled group) and uncovered (treatment groups) prior to 1995 enables us to identify the impact of health insurance on household saving solely of precautionary motive. The purpose of this study is to estimate the extend to which the implementation of universal health insurance reduces precautionary saving of the household with the consideration of heterogeneities from both the dimensions of dependent and independent variables. It is needed to differentiate control and treatment groups in the application of difference-in-differences estimator. In this study, those households who were covered by social health insurance prior to 1995 are qualified as control group and for those who were not, treatment group. Deviating from the exiting literature 3 Haynard and Qiu (2005) use quantile regression to parsimoniously estimate the heterogenous displacement effects of welfare policy on saving. To our best knowledge, this is the only one which simultaneously considers the heterogeneities driven by the distribution of a covariate and the conditional distribution of saving. Different from difference-in-differences estimator, it is within IV-quantile approach. 6

that focusing on the average tendency of the treatment effect under mean regressions, this study uses quantile regression to demonstrate the heterogenous treatment effects of NHI on precautionary saving across the distribution so called quantile treatment effects. In addition to the heterogeneity of marginal propensity to save across the conditional distribution of depend variable (saving) is fully exploited, we also examine in detail the heterogeneity of saving propensity out of independent variables (income level and head age of the household). Contribution of this study is to pioneer the application of quantile regression within difference-in-differences framework to analyze the entire treatment effect plane with regard to the crowd-out effects of NHI on precautionary saving. Empirical findings of this study support the premise that household do save less under the coverage of NHI and heterogeneities of precautionary saving motive indeed exist at almost all aspects. First, the impact of NHI on saving is around 6.3% in average. The crowd-out effects (in absolute term) over the conditional distribution are, however, positively associated with the conditional distribution of saving, i.e., the higher savers reduce more percentage points of saving after NHI that ranges from 4.5% to 8.0%. Presumably the lower savers have less reserve to dissave than the higher savers after the uniform removal of medical expenses uncertainty. If this is the case, average tendency is neither empirically nor theoretically appealing to describe the NHI treatment effects on household saving. Second, treatment effects of NHI on saving across different grouping of households vary consistently in accordance with the insurance coverage switch after NHI. The results stress the notion that medical expenses risk and precautionary saving are systematically correlated that provide robustness check. Third, decrease of precautionary saving propensity in response to NHI is larger for households of the button quintile of income. The result embodies the hypothesis of Dynan et al. (2004) that poor households suffered the most from the threat of health care expenditure uncertainty on the one hand, but cast doubt on that of Kimball (1990) that wealthier (higher income) households become less prudent on the other hand. Furthermore, households with elder (about retired) heads respond more to NHI than the younger counterparts given significant crowd-out effects existing at all ages of the heads. It implies that the elderly endure higher threat from uncertain health care cost, NHI releases the threat predominately to reduce their demand for precautionary saving. In terms of consumption smoothing, it is suggested that the lower-income and elder head households are benefited the most from NHI than their counterparts. The remainder of this paper is as follows. Section 2 provides a brief explanation 7

of quantile regression and its application in the framework of difference-in-differences estimator to gauge the quantile treatment effects of NHI on saving. Section 3 presents some background of Taiwan s implementation of NHI with an outline of the data we use and the specifications of the empirical models. Section 4 reports the estimation results in detail. Section 5 summarizes the main conclusions with policy implications and proposes some directions for future research. 2 Saving Function under Quantile Regression Empirical researches usually rely on a parametric model to specify the relationship between a set of variables. Parameters derived from the model is to describe the marginal impacts of the covariates on the dependent variable. The most common specification describing the relationship between variables is a linear regression model with two types of solution methods that are usually seen: one is to derive the solution from conditional mean function through minimization sum of residual squared, so called the OLS method; the other is to solve the parameters from minimizing the sum of absolute deviation from median, namely the least absolute deviation (LAD). As such, the relationship between variables under OLS or LAD are actually a location measure anchoring on mean and median, the two crucial locations in a distribution. Focusing on the mean or median tendencies while ignoring other locations, tails in particular, may lead to fragmentary understanding on the relationships between variables. Specifically, if there exist heterogenous variances and the errors are correlated with the dependent variables in certain patterns, the marginal effects of covariates on dependent variable will not be constant across the conditional distribution. In turn, the mean or median tendency of marginal effects described by OLS or LAD will not be able to represent the whole family of the relationships between the variables (Koenker, 2005). Different from OLS and LAD, quantile regression derives solution through the minimum of sum of unequal-weighted absolute deviation conditioning on certain specific quantiles which is a direct generalization of LAD estimation (Koenker and Hallock, 2001). Quantile regression is a location model with the consideration of the distribution of dependent variable, thus the process of optimization involves sorting and weighting of samples. As an example, to derive a solution conditioning on the θ th quantile, θ (0, 1), the ratio of observations below and above the θ ( th) is θ versus (1 θ). To ensure the emphasis on the specific quantile θ, it has to place asymmetric weights, (1 θ) versus θ, on the absolute deviations in optimization process. 8

Consider a linear regression specification of household saving (S) function: S i = x iβ + e i, i = 1, 2,...n, (1) where x is a vector of covariates containing the relevant determinants of household saving decisions, β is a vector of coefficients corresponding to x. e is the error term. Accordingly, the objective function of household saving as Equation (1) under OLS is n (S i x iβ) 2, i=1 vector ˆβ is the solution of the minimum sum of residual squared as the conditional mean of equation (2). Other than OLS method, Equation (1) can be estimated under quantile regression by choosing a real number θ within (0, 1), the regression specification of the θ th conditional quantile of S can be expressed as S i = x iβ θ + e θ i, i = 1, 2,...n, (3) where β θ is the vector of parameters that depends on θ, e θ i is the corresponding error. Solution of equation (3) is obtained by minimizing the asymmetric weighted sum of absolute deviations: min β R K[ θ S i x iβ θ + (1 θ) S i x iβ θ ], (4) i:s i x i β i:s i x i β where x ˆβθ is an approximation to the θ th conditional quantile of S. If θ is equal to 1/2, Equation (4) degenerates to a LAD estimation such that x ˆβθ describes the central (median) tendency of x influencing S. (2) If θ is close to 1 (0), x ˆβθ characterizes the behavior of S at the right (left) tail of the conditional distribution. Solution of equation (4) can be obtained by taking first order condition n [θ 1 2 + 1 2 sign(s i x iβ)]x, (5) i=1 where sign(λ) = I(λ 0) I(λ < 0) with I(A) an indicator function of the event A. Standard numerical optimization algorithms are not applicable in the optimization process because Equation (5) is not differentiable at S i = x iβ. Koenker and Bassett (1978) and Koenker and d Orey (1987) propose the use of linear programming to efficiently estimate β θ in Equation (4). Under certain regularity conditions, the asymptotic distribution of ˆβ θ is n( ˆβθ β θ ) N(0, Λ). 9

Λ is the asymptotic covariance matrix: Λ = θ(1 θ)(e[f ε x,θ (0 x i )x i x i]) 1 E[x i x i](e[f ε x,θ (0 x i )x i x i]) 1, where f ε x,θ is the conditional probability density of the error term; see Koenker (2005) for a comprehensive elaboration of quantile regression. This study uses STATA and R package for all estimations. It is apparent that the mean tendency of marginal effects under OLS in Equation (2) is a constant. In contrast, the marginal effects across the conditional distribution under quantile regression in Equation (4) may hardly be identical. The more parsimonious θ is used in the estimation of quantile regressions, the more complete picture of the conditional distribution will be described. Hence, quantile regression is to introduce an extra dimension in estimation that substantially extends the insights of the traditional mean and median regressions. In the discussion of NHI treatment effect, if the distribution functions of saving of injection and non-injection groups differ in location only, it is a location shift and the quantile treatment effects, i.e., the horizontal distance between the two distribution functions, are constant across savings. Therefore, mean tendency under OLS model is adequate because the treatment effects are homogenous. Alternatively, if the two distributions differ in shape other than location, it is a scale shift or location-scale shift in which the horizontal difference between the two distribution functions vary across savings. We have to rely on quantile regression to illustrate the heterogenous treatment effects. In the context of quantile treatment effect under difference-in-differences estimator, there are conceptually four distribution functions involved in which two for control group and two for treatment. Within each group, two distributions belong to before and after NHI implementation, respectively. Deviation of distributions over time of the control group is attributed solely to economic impact and that of the treatment group, impacts from both NHI implementation and economic factors. Quantile treatment effects of NHI can then be separated out from the double differences of the four distribution functions: the first is to calculate a pair of within-group distances across savings and the second, the difference between the two groups across savings. If the double difference are fairly stable across the conditional distribution, it is suggested that crowd-out effects will be about the same for higher savers and lower savers. Reversely, if NHI implementation dictates the shape of saving distributions of the treatment group, crowd-out effects will not be homogenous thus higher savers will behave differently than lower savers. 10

3 Data and Empirical Model 3.1 Universal Health Insurance in Taiwan Experiencing the rapid economic development over the 70s and 80s, Taiwan government has introduced two comprehensive welfare policies since the mid 80s: one is the enactment of Labor Standard Law in 1984 followed by subsequent enforcement regulations, the other is the introduction of NHI, an universal health insurance, in 1995 which substantially expanded the medical insurance coverage to almost everyone. Before 1995, there were three major health insurance programs in Taiwan: Labor Insurance, Government Employees Insurance and Farmers Health Insurance launched in 1950, 1958 and 1985, respectively. Within them, Labor Insurance covers the private sector employees of compliant enterprises in certain industries. Government Employees Insurance ensures public sector employees (including retirees) initially and then the coverage gradually extended to spouses in 1982, to parents in 1989, and to children by 1992. Farmers Health Insurance covers agricultural and fishery workers since 1985 and then extended to their household members in 1989. Besides the three major programs, military servants and veterans have received free medical care from military hospitals that are similar to Government Employees Insurance. For low income households, there was free health insurance program like Medicaid in the States since 1991 but the coverage are less than 1% of total households. Evidently, coverage of the three major social insurance programs depends on the insureds working status. In 1994, 78% of adults (over 20 years old) as well as 57% of total population were covered by these social insurance programs. Remaining 43% of the uninsured population were mainly the under age, students, elderly and nonworking adult population of non-public sector headed households (Cheng and Chiang, 1997). After the implementation of NHI, the coverage rate soon boosted to be 89.5% (19,123,278/21,357,431) by the end of 1995, and reached 97.8% (22,276,672/22,770.383) in 2000 (DGBAS, 2007). Up to 1994, around 85% of hospitals and 70% of clinics were contracted with the social insurance program. Along with the implementation of NHI, Bureau of National Health Insurance (BNHI) became the monopsony of medical care services, and contracted medical care institutions increased to around 96.5% of hospitals and 89.5% of clinics in 1997 (Chou et al, 2003). Based on the three pre-existed social insurance programs, NHI coverage extends generously to include teeth, chronical and serious ills. Even the catastrophic injuries and illnesses of the insured occurred outside Taiwan are covered by NHI. There are six categories of the insured, within each category insurance premiums varies 11

from 0% (mandatory military service men, veteran and low income household members), 30% (employees) to 100% (employer and self-employed). Private enterprises and the government share the premium partially for private and public sectors employees in between 10% and 70% (BNHI, 2007). Insurance benefits offered by these social insurance programs were similar, including outpatient visit, inpatient care and prescription drugs. Under the co-payment system of NHI, the patients have to pay registration fees (from NT$50 to NT$150) and partial prescription drug costs for outpatient visits. 4 Out of pocket expenditure of each physician visit is around NT$200. Co-payment of general hospitalization cares ranges from 5% to 30%, and is free for catastrophic injuries and illness. For detailed elaboration of the evolution of Taiwan s social insurance system, refer to Cheng and Chiang (1997), Chou, et al. (2003) and Peabody (1995). Because the Public Employees Insurance had provided nearly full coverage as that of NHI, presumably NHI would bring little effects on government employees household saving because their precautionary motive is virtually intact. On the contrary, NHI would supposedly mitigate the motive for precautionary saving of non-government employees households due to the following reasons. First, Labor Insurance covers no family members. Second, compliance rate of private sector enterprises is not 100%. Third, according to Labor Standard Law, not all industries were covered under Labor Insurance by 1994. 5 After the implementation of NHI, private sector households received extensive insurance coverage similar to that of public sector employees households. 6 The unique social experiment of NHI implementation in Taiwan that exogenously lower medical expense risks on identifiable groups enables us to estimate the crowd-out effects of health insurance on household precautionary saving. 7 In addition to the crowd-out effect based on primarily dichotomous grouping of public/private sector households, we further try to differentiate the quantile treatment effects with respect to various household characteristics. First is the stratification of households of different risk reduction after NHI. According to the combination of head and spouse s 4 Exchange rate between USandNT is around 1:30 in mid-90s. 5 This is related to the regulation and enforcement of Laobr Standard Law, see Kan and Lin (2006) for detailed discussion. 6 Due to the pre-existed partial coverage of Labor Insurance for the non-public sector households before 1995, the overall effect of NHI on household precautionary saving tends to be underestimated in this study. 7 Although there is no selectivity concern in the participation of NHI, choice of public or private sector might be endogenous. Because the joint decision of the head and spouse s sectoral choice within a household is not yet clarified, we temporally ignore this endogeneity as Chou et al. (2001). 12

working status, four groups can be identified. Both heads of public sector is of group 1. One in public sector and the other is either in private sector, unemployed or out of labor force is of group 2. 8 Both of private sector is of group 3. One in private sector and the other is either unemployed or out of labor force is of group 4. 9 Within them, the insurance coverage of group 1 households is almost indifferent before and after NHI and they are qualified as the control group. Coverage for households of group 2 is similar to that of group 1 except the public sector head s parents in law were not covered prior to NHI. Hence group 2 is defined as the quasi-control (Chou et al., 2003). Households of groups 3 and 4 were only moderately covered prior to NHI. The last three groups of households are defined as treatment groups and supposedly the crowd-out effect is larger for groups 3 and 4 than that for group 2. We also investigate the existence of heterogenous crowd-out effects of NHI on saving over household s income distribution (income quintile ) and life cycle span (head age s stratification). 3.2 Survey of Family Income and Expenditure of Taiwan Taiwanese households had long been exhibiting extraordinarily high saving rates (Deaton and Paxson, 1994; Gersovitz, 1988) over the entire 80s and early 90s. Figure 1 shows that the household saving rates in Taiwan were less than 20% in the 70s and 80s. Economic growth led an increase of household saving rates to peak at 30% in the early 90s and fell to around 25% by late 90s, then further decreased after year 2000. The evident drop of saving rate after the mid-90s coincides with the implementation of NHI in 1995. Reform of social insurance program plus substantial variation of household saving rates make Taiwan an excellent laboratory to study the impact of regime switch of welfare policy on household saving. Data used in this study is the Survey of Family Income and Expenditure (SFIE) conducted annually since the early 60 s by Directorate-General of Budget, Accounting and Statistics (DGBAS), a central government agency in charge of national surveys and statistics. Raw data of SFIE are available on tape from 1976 to current. Sample households range from around 10,000 in the late 70s to 15,000 since the early 80s and onward. Figure 1 is about here 8 As long as one of the couple is under Public Employees Insurance, his/her spouse and immediate relatives (parents and children) are all covered no matter his/her spouse is working or not. The only uninsured in this type of household is the natal parents of the non-public sector employed spouse. 9 Stratification rule here is similar to that of Chou et al. (2001) except some minor modification. 13

Samples in the SFIE are not of panel type but new sampling every year. Modules in SFIE include individual basis socio-demographic and socio-economic characteristics along with detailed income and outlays of each income earner within the households, and the categorical consumption expenditures of the household in total. Unlike most budget surveys that contains fairly limited messages of household characteristics, SFIE provides detailed information on each household member s income sources (including earnings and unearned income) and job attributes that are crucial for precise estimation of permanent income and exact identification of treatment/congrol groups in the following analysis. We restrict the sample to non-farm households with heads aged 20-70 years old to eliminate headship selection (Deaton and Paxson, 1994). 10 waves of SFIE, from year 1990 to 2000 with 1995 excluded, are used with 5 waves before and after NHI, respectively. 10 Table 1 shows the general statistics of households characteristics prior, and posterior, to 1995. Over the 90s, household real income increased moderately from 967,929 to 1,209,437. 11 The reverse is true for saving rates that it was over 20% before 1995 but dropped to 11.5% after 1995. Saving rates of the four types of head-spouse working status combination show similar trend in saving reduction over time. Saving rates of groups 1 and 2 lowered for more than 8% while that of groups 3 and 4, over 12%. Over income quintile, higher income households tend to save more. The proportional relationship between income and saving, however, shift downward systematically for all groups after 1995. Saving rates of five income quintile households range in between 21% and 26% in 1990-1994, then sharply decreased to 10% to 14% in 1996-2000. Saving rates over the household s life cycle are inverse-u shape that younger and elderly households save more as stressed by Deaton and Paxson (1994). Similarly, saving rates of all age groups show a structural change in the mid-90s. Table 1 is about here With regard to changing household characteristics over the 90s, the ratio of public sector headed households lowered from 20% in 1990 to 17% in 2000. The decrease of official public sector employees is attributed to the trend of privatizing and expending outsourcing of the public agencies and state enterprises. Increasing head age reflects delayed marriage age, prolonged life expectancy and less popular of elder-parents and adult-child coresidence in Taiwan over the past decades. Decreasing ratio of male headed 10 Chou et al. (2001) employ 1990 to 1999 waves of SFIE in analysis. 11 All pecuniary measures are deflated by CPI (2000 = 100) to be real. 14

households indicates the increase of economic independency of the female as well as the instability of marriage. Consequently, percentage of intact couple households reduced from over 80% in the early 90s to be less than 75% in the late 90s. Number of income recipients in the household were fairly stable at around 1.7 persons over the time span while household size shrank from 4.2 to 3.5. Successful birth control policy since the 70s sharply reduced birth rate that leads to sizable change of household composition with significant reduction in young dependency ratio (aged 0-19). Accompanying all these transitions is fast urbanization in the island. By 2000, almost 90% of the households reside in urban and suburban areas. In sum, the household in Taiwan have undergone comprehensive changes in the patterns of income, saving, head s attributes and family background over the 90s. These dramatic changes of the household characteristics make it possible to have detailed examination of the relationship between health insurance and precautionary saving. To exploit the differentials of household saving across groups over time, difference-in-differences estimators are specified in next section. 3.3 Empirical Specification of Saving Function under Difference-in- Differences In the empirical studies of household saving behavior, saving is often defined as the annual change of net wealth holding in panel data analysis (Gruber and Yelowitz, 1999). If panel data are not available or when the data set contains no module of wealth holding, saving is equal to the difference between income and consumption (Dynan et al., 2004). It is suggested that if saving is defined as income minus consumption, incorrectly measured income will bias toward the same side with savings and result in spurious correlation (Dynan et al., 2004). Thus, correct measurement of income is crucial in the estimation of saving function. Because SFIE provides no information of wealth holding, saving is defined as the difference between income and consumption in this study. Based on a twostage estimation procedure, a permanent income proxy is estimated rigorously at the first stage to alleviate the problems of transitoriness and measurement error of household income (Kazarosian, 1997). Similar to the setting of Chou et al. (2003), household permanent income in this study is defined as the sum of head s and spouse s permanent income. In the regression model of head s and spouse s permanent income, dependent variable is the logarithm of total household income and the covariates include either head s and spouse s educational level and unearned income, gender of head, public sector worker (private sector as reference), 15

age and age squared, existence of spouse, number of earners, household size, ratios of household members aged under 20 and in between 20 and 64 with the ratio of aged over 65 as reference group. In addition, two urbanization control (urban and suburban, rural as reference) plus five regional dummies (eastern and remote islands as reference) are also included. Head s and spouse s permanent incomes are estimated separately every year by OLS with Huber/White estimator of variance. The results show that the adjusted R squared measures are all above 0.5 with F test statistics way above 10.3, the problem of weak instrument (Staiger and Stock, 1997) is surely not a concern here. In the income quintile treatment effect model, the index of income quintile is estimated within a 3 stage setup that 1st stage is the estimation of household permanent income, 2nd stage use the estimated income quintile to regress on the real income quintile. 3rd stage is to employ the estimated income quintile in the quantile treatment model. Detailed procedure of these estimates are available from the authors. As mentioned above, excluding negative saving households may subject to selection problem. Sheiner (1995) insists the inclusion of both positive and negative net worth households in analysis because the household with positive saving contain as much information about asset accumulation as those without. In this study, negative saving households account for a considerable proportion (18.71%) of total sample and the exclusion of the one fifth sample would inevitably suffer from serious selectivity. Both positive and negative saving households are therefore included in the following analysis. To avoid the problem of undefined negative saving when taking logarithm, we follow Deaton and Paxson (1994) to define saving as the difference between the logarithm of income (lny) and logarithm of consumption (lnc). This definition is in effect an approximation of saving rate, i.e., (Y C)/Y. Refer to Chen et al. (2007) for detailed elaboration. It is noted that the validity of the difference-in-differences estimator depends crucially on the choice of a control group who nets out the impact of all other factors on the trends as that under consideration (Bertrand et al., 2002). In Taiwan, households with heads employed by public or private sector seem to face identical economic environment except the NHI benefits over the entire 90s. 12 With the identification of head s and spouse s employment statuses and the implementing time of NHI, we are able to construct a set of standard covariates in a difference-in-differences estimator including an indicator of NHI affected private sector headed households (public sector headed households as references), an indicator of SFIE waves after the implementation of NHI (prior to NHI as references) 12 Although wage structures of the two sectors are not the same, the two stage estimates of household permanent income may mitigate the effects from sectoral wage differentials. 16

and the interaction between the two. Other than the standard covariates, explanatory variables of the saving function are similar to those in first stage permanent income except heads educational levels and unearned income that serve as identifications. In addition, annual GDP growth rates and a time trend are also added to manage the idiosyncratic year effects and trend effects. A baseline conditional quantile saving function of the difference-in-differences estimator is specified as: S i = β θ 0 + α θ 1 P RI i + α θ 2 NHI i + α θ 3 (P RI i NHI i ) + x β θ + e θ i, (6) where i indexes individual household. θ indexes specific quantiles over the distribution. S i is the saving rate (lny-lnc) observed for household i. PRI is an indicator variable for treatment group with coefficient α 1. NHI is an indicator variable for the waves of SFIE after the introduction of NHI with coefficient α 2. x is a set of covariates that contains the determinants of household saving other than the difference-in-differences controls with coefficient β. e is a random error term. Note that the net effects of NHI on saving is to take the second derivatives of saving on NHI and PRI, consequently. The first derivative (ds/dnhi) is α θ 2 + balphaθ 3 NHI i, meaning the difference of saving of the household before and after 1995. The second derivative ds 2 /dnhi dp RI is to net out the common time trend of the control group. Accordingly, the coefficient α θ 3 measures the the differentiated impact of NHI on household saving in between treatment and control groups across the conditional quanitle the quantile treatment effect. It is straightforward to extend the baseline model to incorporate further stratification by simply adding extra sets of treatment controls. Beyond the baseline model, we additionally separate out a quasi-control group from the control group and divide the treatment group into two groups according to head and spouse s sectoral combination. All these three treatment groups are to compare with the control group simultaneously within a model. 13 Model setups of the extended model are similar to that in baseline model and explanation of the coefficients applies as well. After the examination on the heterogenous quantile treatment effects across the conditional distribution of saving (left hand side dependent variable), we then turn to explore the possibility of heterogenous marginal propensity to save along the distribution of income and head age (Attanasio and Brugiavini, 2003). 14 To accommodate the extension 13 The grouping here is similar to that of Chou et al. (2003) but they estimate the treatment effects within each pair of the four groups. 14 Attanasio and Brugiavini (2003) consider both head s age and birth cohort in the difference-in- 17

on the categorization of dependent variables, the controls of difference-in-differences estimator of equation (6) have to interact with each of the group, respectively. As an example, k k k S i = β θ 0+ α θ 1j (P RI i K j )+ α θ 2j (NHI i K j )+ α θ 3j (P RI i NHI i K j )+z β θ +e θ i, j=1 j=1 j=1 (7) where K j is the category of dependent variables, j=1,...,k. z is similar to the vector of x except the specific dependent variable is left out to interact with difference-in-differences terms. Given each θ, we are interested in the various marginal effects, α θ 3j over the distribution of household income or head age. Economic meaning of the coefficient is the marginal effect of NHI on saving of the specific treatment group, e.g., lower income households, in comparison with that of the control group of the same income group. 4 Empirical Results 4.1 Unconditional Treatment Effects of Difference-in-Differences Estimators Following the concept of difference-in-differences estimator, we first conduct unconditional estimates to have an overview of the household saving behavior in Taiwan. Table 2 shows the 5-years average saving rates before and after 1995. Four panels from top to bottom are total sample, four types of head-spouse sectoral combination grouping, five income quintile groups and five head age groups, respectively. Total sample estimates indicate that both public sector headed (control group) and private sector headed (treatment group) households both reduced saving rates after 1995 and the within-group decline of saving of the latter (-11.9%) is larger than that of the former (-8.6%). The between-group difference of saving decline (-3.4%)is attributed to the implementation of NHI on the household s precautionary saving. Analogously, for the head-spouse grouping, saving rates of all groups decreased after 1995. In comparison with group 1 (the control group), group 2 has little change of saving while group 3 and 4 show evident decline of saving that are consistent with the notion that households lower their precautionary saving after receiving health insurance. In consequence, negative impact of NHI on household saving are prevalent at all sub-sample income quintile groups and head age differences model because the social welfare benefit of Italy s welfare reform depends on the insured s birth year. NHI coverage in Taiwan is identical to all age of insured so birth cohort of head is irrelevant. 18

groups. Crowd-out effects range from 2.4% to 5.9% for the former, 1.1 to 3.9% for the latter. In sum, if the discrepancy of decline in saving rates between NHI affected (treatment) and unaffected (control) groups reflects change of precautionary motive, the fact that private sector headed households reduced more of saving than public sector counterpart is consistent with the precautionary saving hypothesis. Based on the unconditional results, we then turn to the estimation of conditional quantile treatment effects. Table 2 is about here 4.2 Conditional Quantile Treatment Effects within Difference-in-Differences Framework Table 3 shows the regression results of baseline model of OLS and five representative conditional quantiles on the 0.1 th, 0.25 th, 0.5 th, 0.75 th and 0.9 th quantiles. It is clear that almost all covariates are statistically significant and most of them show heterogeneities across the conditional distribution of saving that are not effectively captured by OLS model. Other than the controls of NHI effects, saving rates of private sector headed households are lower than that of public counterpart. Both income and head ages affect saving behavior in quadratic terms. Male headed and intact couple households save less than female headed and single headed counterpart, respectively. Number of earner in a household is significantly correlated with saving rates while the reserve is true for household size. Ratio of household members aged under 20 and in between 20 to 64 in contrast to that of above 65 are adverse toward saving rates. It seems that the elderly tend to save more than the youth in Taiwan, as suggested in Deaton and Paxson (1994). Furthermore, urban and suburban households save less than the rural counterpart. It is noted that the coefficients under conditional quantiles are not able to be depicted by that of conditional mean. The determinants of Taiwan household s saving function show evident heterogeneities across the conditional distribution. Focusing on the coefficient of treatment effect (α 3 ), the result of OLS model indicates that the implementation of NHI crowd-outs household saving rate by 6.3% in average. The conditional quantile counterparts depict that in the left tail of saving distribution, NHI crowd-outs household saving rates by 4.5% while in the right tail, 8.0%. NHI impacts at around the median and the 3rd quartile (the 0.75 th quantile) are similar to that of average tendency. Figure 5-1 plots the treatment effect under the typical style that the three solid horizental lines represent the conditional mean within upper and lower 95% confidence intervals and the three dotted lines, the corresponding statistics 19

under conditional quantile over the distribution (Koenker and Hallock, 2001). It is clear that the crowd-out effects are monotonically increasing (in absolute magnitude) over the distribution, i.e., the higher savers reduce more of saving rates after the implementation of NHI. The estimated quantile treatment effects of the left tail (the 0.1 th and the 0.25 th ) and the right tail (the0.9 th ) are not within the range of the confidence interval of OLS estimates. It is suggested that the average treatment effect is insufficient to describe the heterogenous quantile treatment effect. Apparently, removal of medical care expenditure uncertainty favors higher savers more than lower ones in terms of consumption smoothing and welfare gain because NHI qualification in Taiwan is without testing. Table 3 is about here Figure 2 is about here Table 4 shows the results of quantile treatment effects of NHI on saving over the groups of head-spouse sectoral combination. Given group 1 as the control, the relative impacts of NHI implementation on saving are significantly heterogenous across the groups as well as the saving distribution. In terms of mean tendency, households of quasi-control group (group 2) have minor response (2.2%) to NHI implementation while households of group 3 and 4 significantly lower their saving by around 8%-9% in stead. These mean tendencies, nevertheless, represent only partial of the whole picture of NHI treatment effects. Comparisons between conditional mean and conditional quantile treatment effects are shown in Figures 3-1 to 3-3. Figure 3-1 indicates that NHI plays a significant role for the entire distribution thought the magnitude is not large. It implies that the quasi-control group behave only moderately different from that of group 1. Figure 3-2 and 3-3 show that the crowd-out effects on NHI on group 3 and group 4 are similar at both patterns and magnitudes that the two groups of private sector headed households do lower saving after NHI. Within them, the right tail (very high) savers significantly reduce more while the left tail (very low) savers are unable to dissave too much. Although the confidence intervals of conditional mean and conditional quantiles are largely overlapped, the quantile treatment effects of both the left and right tails actually diverge from the confidence interval of mean tendency. In terms of welfare gains of consumption smoothing, the right tail savers of group 3 and 4 benefit the most from NHI than the left tail counterparts. Table 4 is about here Figure 3-1 to 3-3 are about here 20