metadata: authors: 0: author: Wagstaff, Adam
1: author: Manachotphong, Wanwiphang
count: Thailand
docna: 0: docna: Universal health care and informal labor
            markets : the case of Thailand
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Labor Markets,Educational Sciences,Rural Labor Markets
teratopic: Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development
abstracts: cdata!: This paper explores the possibility that
            universal health coverage may inadvertently result in
            distorted labor market choices, with workers preferring
            informal employment over formal employment, leading to
            negative effects on investment and growth, as well as
            reduced protection against non-health risks and the income
            risks associated with ill health. It explores this
            hypothesis in the context of the Thai universal coverage
            scheme, which was rolled out in four waves over a 12-month
            period starting in April 2001. It identifies the effects of
            universal coverage through the staggered rollout, and gains
            statistical power by using no less than 68 consecutive labor
            force surveys, each containing an average of 62,000
            respondents. The analysis finds that universal coverage
            appears to have encouraged employment especially among
            married women, to have reduced formal-sector employment
            among married men but not among other groups, and to have
            increased informal-sector employment especially among
            married women. The largest positive informal-sector
            employment effects are found in the agricultural sector.
geo_reg_key: 80475,80451,80471
repnme: repnme: Universal health care and informal labor
            markets : the case of Thailand
display_title: Universal health care and informal
            labor markets : the case of Thailand
listing_relative_url: /research/2012/07/16459393/universal-health-care-informal-labor-markets-case-thailand
topicv3: Informal Sector m1326258 1168,Labor Markets m1326488 855,Education m1327048 470,Health m1504661 3480,Universal Health Coverage m1627075 4756
publishtoextweb_dt: 2012-07-01T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development
no_of_pages: 38
model_extractions: WPS6116 Policy Research Working Paper 6116 Universal Health Care and Informal Labor Markets The Case of Thailand _Adam Wagstaff Wanwiphang Manachotphong_ The World Bank Development Research Group Human Development and Public Services Team July 2012
Policy Research Working Paper 6116

# **Abstract**

This paper explores the possibility that universal health coverage may inadvertently result in distorted labor market choices, with workers preferring informal employment over formal employment, leading to negative effects on investment and growth, as well as reduced protection against non-health risks and the income risks associated with ill health. It explores this hypothesis in the context of the Thai universal coverage scheme, which was rolled out in four waves over a 12-month period starting in April 2001. It identifies the effects of universal coverage through the staggered rollout, and gains statistical power by using no less than 68 consecutive labor force surveys, each containing an average of 62,000 respondents. The analysis finds that universal coverage appears to have encouraged employment especially among married women, to have reduced formal-sector employment among married men but not among other groups, and to have increased informal-sector employment especially among married women. The largest positive informal-sector employment effects are found in the agricultural sector.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at awagstaff@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Universal Health Care and Informal Labor Markets: The Case of Thailand

## by

## Adam Wagstaff<sup>a</sup> and Wanwiphang Manachotphong<sup>b</sup>

a Development Research Group, The World Bank, Washington DC, USA b Faculty of Economics, Thammasat University, Bangkok, Thailand

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Universal health coverage; informal labor markets; informality.

JEL codes: I1, I38, J08, J3.

**Acknowledgements:** We are grateful to Pummarin Hmokthong at the Thai National Village and Urban Community Fund Office for providing us with the village fund data, to Pornpan Kaewsringam at the Thai National Statistics Office for helping us with the Labor Force Survey data, and Sutayut Osornprasop for helpful comments. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
2

# I. **INTRODUCTION**

The developing world – and some of the developed world too – is in the midst of a major push toward universal health coverage (cf. World Health Organization 2010). Since everyone in almost every country has some coverage through the partially tax-financed network of public facilities that are accessible by everyone, the agenda in practice is about reducing the gap in de facto coverage between the section of the population relying on public facilities and the sections of the population covered by more generous and more explicit coverage schemes. The latter group typically includes civil servants who receive health coverage at the taxpayer’s expense as a perk of their job, and formal-sector workers, many (if not most or all) of whom are required to enroll in a contributory scheme with contributions linked to earnings, split between the employee and employer, and supplemented by subsidies from tax-payers. While the details of the arrangements vary from country to country, a common emerging pattern is for governments to use general revenues to provide more generous – and sometimes more explicit – coverage to at least some individuals not covered by schemes for civil servants and formal-sector workers and their dependents. In some countries this has involved setting up a third scheme (Mexico and Thailand are examples<sup>1</sup> ), while in others the hitherto “uncovered” individuals are brought into an existing scheme (Vietnam is an example<sup>2</sup> ). In some cases, everyone without formal coverage is included in the new program at no cost to the individual (Thailand is an

> 1 On Mexico’s reform, see Frenk et al. (2006). On Thailand’s, see Damrongplasit and Melnick (2009). 2 On Vietnam’s reform, see Lieberman and Wagstaff (2009).
3 example); in others only some are given the full subsidy, and the rest are expected to contribute at least part of the cost (this is the case in Mexico and Vietnam).

The goal of these initiatives, of course, is to narrow gaps in coverage and hence improve people’s health and their protection against the financial consequences of ill health. But the question arises as to whether these well-intentioned initiatives may have unintended negative side-effects. If they do, one would want to know how large they are relative to the benefits of the policies, and whether these benefits could be achieved at lower cost through some alternative policy. Levy (2008) has argued that one unintended consequence of such policies is that they provide people with an incentive to work in the informal sector rather than the formal sector, since people can obtain similar if not identical health coverage without making any additional specific health contribution. (They have to non-payroll pay taxes, of course, but they have to do this anyway.) Inadvertently therefore universal health coverage may skew employment and investment away from the formal sector, and – according to Levy – reduce economic growth. This informalization of the labor force may also leave workers with less protection against the income losses associated with health shocks if the universal coverage program covers health care costs but not sickness absence, disability benefits, and so on. It may also leave them with less protection against the financial consequences of non-health shocks (e.g. unemployment, and old age) insofar as the various social insurance programs are bundled together.

Mexico – where Levy served as deputy finance minister – is the country where this issue has been researched and debated the most, although there has been some work undertaken in other Latin American countries (see Aterido et al. (2011) for a review) and
4 in Europe and central Asia (Wagstaff and Moreno-Serra 2009). Aterido et al. (2011) provide a review of the Mexican literature and other Latin American studies. They also provide a new set of estimates for Mexico whose _Seguro Popular_ (SP) or People’s Insurance provides explicit health coverage to the majority (55%) of the Mexican population not covered by the formal-sector health insurance program (the latter covers dependents as well as workers) (see e.g. King 2007; King _et al._ 2009). SP was launched in 2001 as a pilot program and was subsequently rolled out across the country over a period of several years; only in 2012 does the government expect to achieve 100 percent coverage of households not covered by the formal-sector or social security program. The staggered rollout of SP provides a credible way of identifying the impacts of the program on informality. Studies to date have found some effect, but the magnitude is very small. The study by Aterido et al. is the most recent and arguably the most credible, not least because it uses the longest time period, and exploits the panel nature of the data to eliminate individual- or householdspecific effects. Aterido et al. find that SP reduced the probability of being in the formal sector by just half of one percent.

In this paper we explore the labor impacts of another universal health coverage program, namely the Thai Universal Coverage (UC) or 30-Baht scheme (cf. e.g. Pannarunothai _et al._ 2004). Like _Seguro Popular_ , Thailand’s UC scheme has become one of the world’s most discussed universal coverage initiatives: the two reforms have in effect become the poster children of universal coverage reforms, with the architects of the systems engaging in extensive international public relations exercises to promote their reform model. The Thai reform is an especially interesting one from a labor market perspective. The Thai UC scheme differs from _Seguro Popular_ in three respects, all of which
5 are likely to make for larger informality effects. First, there are minimal copayments and no joining fee in Thailand’s UC scheme – everyone is covered automatically with a 100 percent subsidy. By contrast, in _Seguro Popular_ households with the means to pay are supposed to contribute, so opting out of the formal sector scheme does not eliminate contributions.<sup>3</sup> Second, the relative generosity of the two schemes differs. Both are less generous than the social security scheme, but the gap is wider in Mexico: in 2000, spending per enrollee in _Seguro Popular_ was just 66 percent that of the social security program; by contrast, on a per-beneficiary basis Thailand’s UC scheme spends 85 percent of the amount spent by the social security scheme.<sup>4,5</sup> Third, the two countries’ social security schemes treat dependents differently: Mexico’s covers dependents while Thailand’s does not. Thus in Mexico once one household member is in the formal sector, there is no incentive – from a health coverage perspective – for additional members to seek a formal-sector job. In Thailand, by contrast, at least prior to the UC reform, the health coverage rules gave each household member an incentive to seek a formal-sector job. Health coverage rules prior to the reform thus created an incentive for Mexican households to limit the number of formalsector workers to one, but for Thai households to keep adding formal-sector workers. The informalizing effect of the Thai reform ought therefore to have been greater – a larger fraction of workers will have been encouraged to formalize prior to the reform, but discouraged from formalizing after the reform.

> 3 It needs to be acknowledged that in practice contributions are less than would be expected from application of the means-testing rule to household survey data, and are a good deal lower for most workers than the social security contribution.

> 4 The Thai scheme excludes a few high-cost interventions (e.g. renal transplants) but for the most part the schemes are fairly comparable in terms of their generosity.

> 5 One feature of the Mexican SP program that may limit the disincentive to formalize is that it provides access to a different network of health facilities – sometimes these may be more accessible for a household than the facilities operated by the formal-sector health scheme.
6 We use an informal expected-utility framework that captures the risk of medical care expenses associated with informal employment prior to UC to explore the possible effects of the UC reform: we conclude that UC could cause unmarried individuals to prefer the informal sector over the formal sector, and may cause a married couple to favour having both of them working in the informal sector in preference to having the husband work in the formal sector and the wife not working. We explore empirically the impacts of the UC scheme on the probabilities of working, working in the informal sector, and working in the informal sector. Our identification strategy comes from the staggered rollout of the UC scheme over a period of 12 months, with six provinces adopting the program in April 2001, 15 provinces doing so in June 2001, 55 provinces and 13 districts in Bangkok following in October 2001, and the final set of Bangkok’s districts adopting UC between November 2001 and April 2002. We estimate the labor market effects of UC by linking an individual’s labor market status at a particular date to their length of exposure to the UC “regime”, which will depend on the survey date and the person’s province of residence. Our statistical power comes from the fact that we have an average of 62,000 respondents at each survey date, and 68 survey dates most of which are just one month apart. The accuracy of our estimates is enhanced by the fact that 17 of the 68 surveys we use predate the launch of UC: this allows us our regressions to capture the pre-reform trends in our labor market outcomes. We control for the potentially confounding effects of the Thai Village Fund that was also rolled out from 2001 onwards.

We find that UC appears to have encouraged employment especially among married women, to have reduced formal-sector employment among married men but not among other groups, and to have increased informal-sector employment especially among married
7 women. We see the largest positive informal-sector employment effects in the agricultural sector. These results are consistent with our hypothesis that by eliminating the medical expenditure risks associated with informal-sector employment, UC has encouraged couples to work in the informal sector in a rural setting rather than live in the city and rely on earnings from a single breadwinner’s formal-sector job. Our results suggest that UC increased informal-sector employment overall by two percentage points in the year of UC adoption rising to just under 10 percentage points after three years. This effect is, as expected, larger than that found for _Seguro Popular_ . We end with some thoughts on the policy implications.

# II. **THAILAND’S UNIVERSAL COVERAGE SCHEME**

Before the introduction of Universal Coverage (UC) in 2001, Thais were either not covered for their health care expenses (more than 25 percent of the population fell into this category) or were covered by one or more of the schemes operating at the time: see Hanvoravongchai and Hsiao (2007) and Table 1. The largest of the pre-UC schemes was the Medical Welfare Scheme (WHS) which provided tax-financed cover for various poor and vulnerable groups, including the poor, the elderly, children below the age of 12, secondary school students, the disabled, war veterans, and monks. These groups comprised around 33 percent of the population. The next largest scheme was the Health Card Scheme (HCS) – a public voluntary insurance program for nonpoor households who were ineligible for the WHS. This accounted for around 12 percent of the population. The third largest scheme was the Civil Servants Medical Benefits Scheme (CSMBS) in which health insurance was
8 provided as a fringe benefit to current and retired civil servants and their dependents. This scheme covered around 11 percent of the population in 2000. The final scheme was the Social Security Scheme (SSS). This scheme aimed to cover employees of establishments with more than 10 workers, but not their dependents, and was – and still is – financed through a payroll tax (1.5 percent paid by the employer, 1.5 percent paid by the employee) and a subsidy (the government also pays 1.5 percent). Around 10 percent of the population was covered through this scheme in 2000. Outlays per enrollee varied considerably across the schemes: the CSMBS recorded the highest at Baht 2,106; the SSS recorded the second highest at Baht 1,558; and the HC and MWS recorded much lower outlays per enrollee of just Baht 534 and Baht 363 respectively.<sup>6</sup> Frustrated with the low coverage of the HC scheme and with the resultant large number of Thais without coverage<sup>7</sup> , Thailand’s government decided to introduce a UC scheme to cover people not covered by the SS and CSMBS schemes. The scheme was to be funded largely by general revenues, with a minimal copayment of 30 baht (about USD 0.75 at the time) fixed-fee per visit (hence the initial name – the Thirty Baht Scheme). Once it was rolled out, the UC scheme covered around 70 percent of the Thai population. Table 2 summarizes the main features of the UC scheme and compares it with the CSMBS and SSS schemes. The government budgeted as much as Baht 1,309 per enrollee in the UC scheme, a dramatic increase on the per-enrollee outlays of the HC and MWS schemes, and equal to 85 percent of the expenditure per enrollee in the SSS. On paper at least all three schemes provide comprehensive medical coverage to their members, and the UC and SS schemes are

> 6 This masks the cross-subsidies from the public hospitals to the last two schemes.

> 7 Srithamrongsawat (2002) also found that those who joined the health card scheme (HCS) had a significantly higher utilization rate than those under the social security scheme (SSS), hence suggesting that there was an adverse selection problem.
9 similar in terms of their coverage of maternity benefits (both cover them), annual physical checkup (neither does), and prevention and promotion (both cover health education and immunization). UC and SS enrollees are similarly restricted in their choice of provider, being required to choose a contracted hospital or its network; the contracted providers differ, however, between the two schemes. In both schemes, registration with a regular provider is required, and enrollees in both schemes have access to both public and private providers, though in practice most UC contracted providers have been Ministry of Public Health hospitals. One difference between the UC and SS schemes is that the latter but not the former provides certain cash benefits payable in the event of sickness, disability and death.

The UC scheme was rolled out in four phases. Phase 1 started in April 2001 and involved six of Thailand’s provinces. Phase 2 started in June 2001 and involved a further 15 provinces. Phase 3 started in October 2001 and involved the remaining 55 provinces and 13 districts in Bangkok. The rest of Bangkok's districts gradually began implementation between November 2001 and April 2002.

# III. **HYPOTHESIZED LABOR MARKET EFFECTS OF UNIVERSAL COVERAGE**

We explore in this section the possible labor supply effects of UC using a simple expected utility framework. We argue that UC could cause unmarried individuals to prefer the informal sector over the formal sector, and may cause a married couple to prefer both working in the informal sector to having the husband working in the formal sector and the wife not working.
10 Consider first the case of an unmarried individual. If the person works in the informal sector, they get an income _II_ but face a risk _p_ of incurring medical costs equal to _M_ . In the example in Figure 1, it is assumed for simplicity that _p_ =0.5. The expected utility associated with informal-sector employment is _EUI_ in Figure 1. If the individual works in the formal sector, they get an income _IF_ , and in the event of illness all their medical bills are covered by insurance. Their (certain) utility associated with formal-sector employment is _UF_ which in Figure 1 exceeds _EUI_ ; the individual therefore chooses the formal sector even though the income associated with formal-sector employment is lower than that associated with informal-sector employment. After UC, the risk associated with medical care costs is eliminated, and the individual can attain _U_ ( _II_ ) with certainty by working informally, which is what they choose to do. This example illustrates that by eliminating the risk of medical care expenses UC may cause unmarried individuals to switch from formal-sector to informal-sector employment.

Consider next the case of a married couple. If both work in the informal sector, they get an income _II_ , _I_ but face a risk (assumed to be 0.5 as before) of incurring medical costs equal to 2 _M_ . The expected utility associated with husband and wife working in the informal sector is therefore _EUI_ , _I_ in Figure 2. An alternative is that the husband works in the formal sector, though this may require the couple live in an urban location and the wife may end up not working, either because of limited employment opportunities or because of limited childcare and eldercare. In this case the couple gets an income equal to _IF_ , _N_ . If illness strikes, all the husband’s medical expenses are covered by insurance, but the wife’s are not. The couple’s income in the event of the wife falling ill is equal to _IF_ , _N_ - _M_ , and the couple’s expected utility from the husband being in the formal sector and the wife not working is
11 equal to _EUF_ , _N_ , which in this example exceeds _EUI_ , _I_ . The couple therefore chooses to have the husband in formal-sector employment and the wife not working even though this option yields a lower income than that associated with them both in informal employment. After UC, the risk associated with medical care costs is eliminated, and the couple can attain _U_ ( _II_ , _I_ ) with certainty by both working informally, which is what they decide to do. This example illustrates that by eliminating the risk of medical care expenses UC may cause a couple to prefer to have both working in the informal sector rather than having the husband in the formal sector and the wife not working.

There is one factor that will likely limit – but not eliminate – the reduction in the incentive to be in the formal sector versus the other two labor market states, namely that the social security scheme likely provides greater access to private providers (cf. Table 2), which is likely to be considered a plus.

# IV. **METHODS**

While a national program, UC was phased in, as explained above, over a period of nine months, with one set of provinces implementing the policy in April 2001, a second set two months later, a third set four months later, and the final set three months afterwards. We use this staggered rollout to identify the impacts of UC. From January 2002 (the date the fourth and last wave of provinces implemented UC), we have an exposure difference of nine months between the first-wave provinces and the last-wave provinces. Assuming the effects of UC were similar across provinces, we should see changes appearing first in the first-wave provinces, the next set of changes two months later in the second-wave
12 provinces, and so on. In other words, we can estimate the impacts of UC by linking labor market outcomes of individual _i_ at time _t_ to the amount of time individual _i_ has been exposed to the UC “regime” at time _t_ . Exposure will vary depending on when the survey was done, but also on which of the four groups of provinces the person lives in. Clustering at the provincial level will, of course, be an issue from the point of view of statistical power.

However, with a very large number of respondents at each survey date (62,000 on average), and a very large number of survey dates (68, of which a majority – 51 – are after the start of the rollout of UC and a month apart), we should have sufficient statistical power to detect any effect that increased exposure to the UC “regime” has on our outcomes of interest.

When linking labor market outcomes to exposure to UC, we need to be careful to control for variables that may be correlated with UC rollout. Household- and individuallevel variables are unlikely to cause omitted variable bias; however, their inclusion does help to improve precision (cf. e.g. Angrist and Pischke 2009, p.237) so we include the obvious individual- and household-level covariates. Rather, our concern with omitted variable bias lies with events and/or programs that may have also had labor market effects and whose timing in a specific location coincides with the introduction of UC. The confounder we are most worried about is the Village Fund (VF) program, which was also launched in 2001 and whose rollout could conceivably have coincided – at least in some areas – with the rollout of UC. The VF aimed to stimulate local economies by providing to every village and urban community one million baht (around $22,500) in seed money for a locally-run micro-credit association (cf. Boonperm _et al._ 2012). Villagers could add this fund to their existing village’s micro-credit institution, or if the village did not have one at
13 the time, the money could be used to set one up. According to the socio-economic survey collected by the Thai national statistical office in 2004, 50 percent of VF borrowers used the loan to fund agricultural activities, 30 percent used the loan to fund daily expenses, while 14 percent used the loan to fund non-farm business (Boonperm _et al._ 2012). Given this, we expect that the VF may have had a positive impact on the number of informal and – to a lesser extent –formal jobs. We discuss below how we try to capture the effects of the VF in our estimations so that our UC impact estimates are not biased.

Let _yipt_ be our outcome of interest for individual _i_ in province _p_ at quarter _t_ . Our estimating equation takes the form: where _Xipt_ is a vector of covariates at the household- and individual-level, the _UC_  _pt_ and _VF pt_  are a series of dummies capturing the UC and VF policies defined in a way that we explain below,  _p_ and  _t_ are province- and period-specific effects (each quarter is allowed its own fixed effect), and _eit_ is an error capturing unobservable variables and noise. Our interest is in the  which capture the effect of UC (we discuss their interpretation below). In our estimation of eqn (1), we heed Angrist and Pishcke’s (2009 p.94 ff) advice and use OLS rather than a limited dependent variable model to estimate the marginal effects of interest. We adjust standard errors for clustering at the province level, since this is the level of variation in the UC program.
14 We capture UC through a series of dummies that reflect the quarters until or after UC implementation.<sup>8</sup> Thus _UC_<sup></sup> equals 1 at time _t_ in province _p_ if UC has been in force exactly  quarters. In the quarter of implementation, _UC_<sup></sup> =1 and _UC_<sup></sup> =0 for  ≠0. In the quarter after implementation, _UC_<sup></sup> =1 and _UC_<sup></sup> =0 for  ≠1. In the quarter before implementation, _UC_<sup>_-_</sup> =1 and _UC_<sup></sup> =0 for  ≠-1; however, we omit this UC dummy. In the quarter before this, _UC_<sup>_-_</sup> =1 and _UC_<sup></sup> =0 for  ≠-2. If UC has a causal effect on _y_ , we would expect the  for quarters before UC to be zero, and the  for quarters after UC to be non-zero, and not necessarily equal to one another; the effect of UC may build up over time, for example, reflected in a growth of the  with time since UC implementation. We control for the effects of the VF program in the same way.

We also estimate a version of eqn (1) with constraints imposed on the  similar to the constraints imposed by Bosch and Campos-Vázquez (2010) in their analysis of Mexico’s _Seguro Popular_ . We constrain the  and the  in each quarter to be the equal to one another, giving UC and VF variables that capture years (rather than quarters) to or since UC and VF implementation. Like Bosch and Campos-Vázquez, we constrain the  and the  to be the same for 3+ years before UC/VF implementation and for 3+ years after UC/VF implementation. Bearing in mind that we omit the UC dummy for the period prior to UC implementation, and that we have quarterly data prior to UC and monthly data thereafter, the constrained version of eqn (1) includes six dummies: the first equalling one when the quarter is three or more years before implementation ( _UC_<sup>-3</sup> ); the second equalling one when the quarter is two years before UC implementation ( _UC_<sup>-2</sup> ); the third equalling one when the month is between zero and 12 months after implementation ( _UC_<sup>0</sup> ); the fourth equalling one when the month is between 12 and 24

> 8 This specification is similar to that used by Campos-Vázquez (2010), and is in effect a mix of the models discussed by Wooldridge (2002 p.317) and Angrist and Pischke (2009 p.237).
15 months of implementation ( _UC_<sup>1</sup> ); the fifth equalling one when the month is between 24 and 36 months of implementation ( _UC_<sup>2</sup> ); and the sixth equalling one when the month is 36 months or more after implementation ( _UC_<sup>3</sup> ). If UC has a causal effect on _y_ , we would expect  and  to be zero, and at least some of  ,  ,  and  to be nonzero, depending on the time profile of UC impacts.

# V. **DATA**

Our data are from Thailand’s Labor Force Survey (LFS) conducted by the National Statistical Office of Thailand (NSO). The data were collected quarterly from 1985 to 2000, and then monthly from January 2001 (UC was launched in the first provinces on April 2001) until the present. Each round of the survey covers all provinces and all industries in the entire country. The sample size is between 300,000 and 460,000 persons per year, with some rotation across surveys. The LFS gives a weight variable which allows us to gross up to the population. In this paper, we use the data from the start of 1997 to the end of 2005. This time frame covers the four years and three months prior to the earliest implementation of UC, the nine months of staggered implementation across the remaining provinces, and four years after the last province acquired UC. This gives us a sample size of 4.7 million individuals. Our effective sample size in terms of UC impacts is, of course, much smaller because the program operates at the province level, and there are only 76 provinces. Offsetting this is the fact we have data for 74 periods: 12 quarters covering the
16 period 1997-2000, the three months of 2001 up to the launch of UC in April, and then 57 months during and after UC implementation.<sup>9</sup> Our outcomes of interest are whether the individual is working, and if so whether they are in the formal sector or informal sector. LFS respondents are asked whether they worked during the previous week, and if not whether they received a salary or wage and whether they have a job to return to. If the individual was not working and had no job to return to, we classified them as not working. Respondents who said they were employed were asked about their type of employment, the categories being: (i) employer; (ii) selfemployed; (iii) unpaid family helper; (iv) government employee; (v) government enterprise employee; and (vi) private employee. We classified (i)-(iii) as informal sector, and (iv)-(v) as formal sector. As for (vi), the private employees, we classified those who earned monthly salary as formal and those who earned hourly or daily wage as informal.

Our UC “treatment” variable is constructed from the province of residence of the respondent, the timing of the UC implementation in the individual’s province, and the date (month and year) of the LFS. We construct variables corresponding to the numbers of months, quarters, half years, and years since implementation of UC in the individual’s province as of the LFS in question. From these we construct the dummy variables in eqn (1).

Our VF variable is constructed from the province of residence of the respondent, the timing of the arrival of the first VF funds, and the date (month and year) of the LFS. We define the VF variable analogously to the UC variable so if, for example, the UC variable is

> 9 We have only the first and the third quarters of LFS 1997 and LFS 1998.
17 defined as years from UC implementation, the VF variable is also be defined as years from VF implementation<sup>10</sup> . In practice, pinning down the date of VF implementation was not straightforward. We were able to obtain from the National Village and Urban Community Fund Office the date that each village registered with the Thai government. However, we could not identify the date that each village actually received its 1-million baht fund. What we do know is that, on average, villages received their money within 2-3 months of registration. We therefore assume that the “implementation” date of VF is 3 months after the registration date. We define the VF variable at the provincial level because we do not know the identity of the village in our LFS data; we define the VF variable as the proportion of villages in the province that had received the first tranche of VF money. The first fund was transferred in July 2001; by the end of 2002, about 73,941 villages (or 93.79 percent of all villages) received the money (Satsanguan 2006). Our VF variable – unlike the UC variable – exhibits variation within each of the blocks of provinces that comprise the four waves of the UC rollout; this extra geographic variation helps us separate the effects of UC from the effects of the VF program.

Table 3 shows the descriptive statistics of our dependent variables and covariates. The statistics are calculated from the total sample size of 4,770,735 individuals over the time period 1997-2005. For each variable, we report their population-weighted mean and standard deviation. As discussed earlier, the dependent variables include whether the individual is working, whether they work in the formal or informal sector, and which industrial sector they are in. About 67 percent of all Thai population age 15 and above

> 10 The value of this village fund variable would be equal to zero for municipal areas because villages are in non-municipal areas only.
18 were working, while about 21 percent were in the formal sector.<sup>11</sup> In terms of industrial sectors, about 28 percent were in agriculture. The manufacturing, commerce and service sectors each employed about 11 percents of workers; while the construction sector employed about 4 percent of workers. Our covariates, also reported in Table 3, include province-level minimum wage, non- municipal area dummy, number of children under 6 years old in the household, number of children between 6-15 years old in the household, and number of elderly older than 64 years old in the household. The covariates also include the individual gender, age and education level.

The minimum wage in Thai Baht per day is obtained from the Thai Ministry of Labor. The Ministry of Labor reconsiders the minimum wage at least once a year. This reconsideration is done at the province level. Thus, minimum wage could potentially control for the trend of living cost, labor scarcity and the relative attractiveness of jobs in each province. The non-municipal area dummy variable, on the other hand, should highly correlate with the industrial sector the worker is in. For example, agricultural workers are more likely to live in non-municipal areas. The number of children under age 6, the number of children ages 6-15, and the number of elderly older than 64 are to control for the claims on resources of the household.

As for individual-specific covariates, we include gender, age and education. For the ease of analysis but without much loss of generality, we group age into 15-29 years old, 3049 years old, 50-64 years old and over 64 years old. We also group education attainment based on the highest level of education achieved. The categories include: less than primary

> 11 We categorize workers who were covered by the Civil Servant Medical Benefit Scheme (CSMBS) and the Social Security Scheme (SSS) as formal. This 21 percentage approximation is consistent with Na Ranong et al. (2004) as shown in Table 1.
19 education; primary education; some secondary education; completed secondary education; vocational education; and university education and above.

# VI. **RESULTS**

We focus in our presentation of our results on the impacts of UC. The signs of the coefficients of the non-UC variables in our regressions are broadly as expected. Education increases the likelihood of being in the formal sector, rural residents are less likely to be in the formal sector, and having school-age children reduces the likelihood of people being in the formal sector, as does having elderly household members. The Village Fund apparently increased the likelihood of working, but interestingly more in the formal sector than the informal sector.

Table 4 reports the estimates of the impacts of UC on the probability of working, and the probabilities of being employed in the formal and informal sectors. The latter are not conditional on working, so in the case of formal-sector employment the other states are not employed and informally employed, while in the case of informal-sector employment the other states are not employed and formally employed. The coefficients are the estimates of the  in eqn (1) with the restrictions placed on the  as explained in section IV. The estimates show the “impact” of UC three and two years prior to its implementation (these coefficients ought not to be significantly different from zero), and the impacts of UC zero, one, two and three years after implementation.
20 Figure 3, Figure 4 and Figure 5 show the corresponding estimates and 95% confidence intervals for the more flexible version of equation (1) where the  are unrestricted. In all cases, we control for the effects of the village fund, as well as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban), and province and year. There are some significant “effects” of UC prior to UC implementation which ought not to be evident if the effects we are estimating are causal relationships – unless they capture anticipatory effects. However, the number of significant prior “effects” in Table 4 is relatively few – just five out of 24 (20%). By contrast, there is a fairly high rate (just under 60%) of significant effects of UC after UC implementation, suggesting we can be reasonably confident we are estimating genuine causal effects.

Table 4 and to a lesser extent Figure 3 suggest that UC increased employment, especially among married women. This is consistent with our hypothesized effects of UC in section IV, namely that UC offers married couples the possibility of returning from the city where only the man may work (due to limited job opportunities or limited childcare) to the countryside where informal employment is available without the risk of medical expenses, and childcare may be available through elderly parents (or less necessary because the home is also the place of employment). Despite the positive effect of UC on the probability of working, Table 4 suggests that UC reduced formal-sector employment, though only significantly so among men. The effect appears only with a lag, and is somewhat larger for married men. This gender-specific result and the larger effects among married men are also consistent with our hypothesized effects of UC in section IV. These effects are also apparent in the more general specification in Figure 4. Finally, Table 4 suggests that UC had a
21 positive effect on informal employment. The effect increases with the length of “exposure” to UC, beginning at around 1-3% in the year of implementation, and rising to 8-14% three years after implementation. These effects – which are also evident in Figure 5 – are evident for both men and women, and are statistically significant among all four groups two and three years after implementation, and statistically significant among three of the four groups one year after implementation. The effects on informal employment are most pronounced among married women – consistent with our hypothesized effects of UC prompting a return of families from the city to the countryside.

Table 5 shows the effects of UC on form- and informal-sector employment for the sample as a whole. For the sample as a whole we find a negative but insignificant impact of UC on formal-sector employment, but a positive and significant impact of UC on informalsector employment starting at two percentage points in the year of implementation rising to 11 percentage points three years after. The formal and informal effects are not the mirror image of the other because the zero-coded category in each case includes not just the other category but also those not working. Also shown in Table 5 are the estimates of the effect of UC on formal- and informal-sector employment by sector. We see significant effects on formal-sector employment only in the case of the construction sector, and the reductions implied are very small. By contrast, we see sizeable significant positive impacts of UC on informal-sector employment, but only in the case of agriculture with an effect starting at three percentage points in the year of implementation rising to 14 percentage points after three years.
22

# VII. **CONCLUSIONS**

Our analysis exploits the phased rollout of the Thai Universal Coverage policy to estimate the labor market effects of the policy. We control for the effects of the Village Fund scheme which began to be rolled out around the same time; we are able to separate out the effects of the two programs through the spatial and temporal differences in program rollout. Our results point to labor market effects: UC appears to have encouraged employment especially among married women, to have reduced formal-sector employment at least among married men, and to have increased informal-sector employment especially among married women. We see the largest positive informal-sector employment effects in the agricultural sector. These results are consistent with our hypothesis that by eliminating the medical expenditure risks associated with informal-sector employment, UC has allowed couples to switch from a situation where the breadwinner works in an urban formal-sector job and the spouse co-locates but does not work to having both working in informal-sector jobs in a rural setting.

Our effects suggest that UC increased informal-sector employment by two percentage points initially rising to just under 10 percentage points after three years. This effect is larger than that found in recent work for Mexico’s _Seguro Popular_ universal health coverage reform – there the negative effects on formal-sector employment were around half of one percent. However, as indicated in the Introduction, there are some reasons to expect a larger effect in Thailand. The gap in the benefit package in Mexico means that in Figure 1 the individual still faces some risk in an informal sector job even after the SP reform. So while SP reduces _M_ it does not reduce it to zero. _U_ ( _II_ ) is not attainable and the
23 Mexican informal-sector worker has an expected utility after the reform that will exceed _EUI_ but may fall short of _UF_ . The fact that the Mexican informal-sector worker has to pay to join the SP scheme means that with insurance the individual’s income in the state where illness does not occur is less than _II_ ; this further reduces the likelihood that the informalsector job – even after the SP reform – dominates the informal-sector job. Finally, as mentioned in the Introduction, the fact that social security provides health coverage to all household members in Mexico but only to the worker in Thailand means that Thai households had an incentive prior to the UC reform to get additional household members into formal-sector jobs, while in Mexico the incentive was to get just one household member into the formal sector. This made for a bigger pool of workers in Thailand who would be incentivized by the UC reform to switch out of the formal sector.

Thailand’s UC scheme – like Mexico’s _Seguro Popular_ scheme – does appear to have encouraged growth in informal-sector employment, although the negative effects on the formal sector in Thailand appear to have been very small. Formal-sector employment is not, of course, an end its own right, but nonetheless having a health insurance program that biases employment growth toward the informal sector may well be considered unattractive. Several policy options suggest themselves. Some of these are likely to be considered unacceptable to Thai policymakers. One such option would be to deliberately encourage a per-capita funding gap between the UC scheme and the SS scheme, so that relying on the UC scheme has a “price” associated with it, in the form of a less generous benefit package. Another option would be to levy a contribution on UC enrollment, perhaps linked to the household’s living standards through a “proxy means” test. This is also unlikely to be considered unacceptable by the Thai government, and evidence from Mexico
24 suggests that collecting means-tested contributions is expensive and hard to enforce. Successful enforcement might deter the very people the government is seeking to cover from enrolling. A more palatable option would be to merge the two schemes at some point in time and delink the payroll tax from health care entitlements, either by scaling back the payroll tax so it raises just enough to finance the occupational pension, or by treating what is currently the health part of the payroll tax as just a tax on labor that helps to fund government programs.
# Table 1: Health coverage of the Thai population in 2000

|Type of Health Coverage|Number<br>(millionpersons)|Percentage of<br>Population|
|---|---|---|
|Medical Welfare Scheme (WHS)|20|33%|
|Health Card Scheme (HCS)|7-8|12%|
|Civil Servant Medical Benefit Scheme (CSMBS)|~7|11%|
|Social Security Scheme (SSS)|6|10%|
|Private Health Insurance|~5.9|~10%|
|No coverage|>15.5|>25%|
|Eligible for more than 1 type of coverage*|?|?|
|Total|61.5|100%|

Source: Na Ranong et al. (2004).

* For example, those who covered by CSMBS from being a parent or a child of a civil servant would be covered by CSMBS but also automatically covered by WHS.
Table 2: Characteristics of different health insurance schemes

|**Characteristics**|**Government Employees**|**Social Security Scheme**|**Universal Health Coverage**|
|---|---|---|---|
||**(CSMBS)**|**(SSS)**|**(UC)**|
|Population Coverage|Civil Servant, pensioners and their<br>dependents (parents, spouse,<br>children)|Formal sector private employee|The rest of Thai population who<br>are not eligible for CSMBS and<br>SSS.|
|**Benefits**||||
|Ambulatory services|Public Only|Public & Private|Public & Private|
|Inpatient services|Public & Private (emergency only for<br>private)|Public & Private|Public & Private|
|Choice of provider|Free choice|Contracted hospital or its<br>network with referral line,<br>registration required|Contracted hospital or its<br>network with referral line,<br>registration required|
|Cash benefit|No|Yes|No|
|Maternity benefits|Yes|Yes|Yes|
|Annual Physical checkup|Yes|No|Yes|
|Prevention Health Promotion|No|Yes|Yes|
|Services not covered|Special nurse|Private bed, special nurse|Private bed, special nurse,<br>eye glasses|
|Copayment|Yes<br>(inpatient at private hospital only)|Maternity,<br>emergency services|30-baht/visit*|
|**Financing**||||
|Source of funds|General tax|Employee & Employer|General tax|
|Financing body|Comptroller General Department,<br>Ministry of Finance|Social Security Office|National Health Security Office|
|Expenditure per capita (in 2006)|8,785|1,738|1,659|
|Per capita tax subsidy (in 2006)|8,785 (plus administrative cost)|579 (plus administrative cost)|1,659 (plus administrative cost)|

Source: Sakunphanit (2006). * The 30-baht copayment was eliminated in 2006.
# Table 3: Descriptive Statistics

||Mean|Std. Dev.|
|---|---|---|
|_Work status (of all population age_≥_15)_|||
|By employment|||
|Work during the survey week or have a job to return to|0.69|0.46|
|By formality|||
|Formal|0.18|0.38|
|Informal|0.50|0.50|
|By industry|||
|Agriculture|0.29|0.45|
|Manufacturing|0.11|0.31|
|Construction|0.04|0.19|
|Commerce|0.11|0.31|
|Service|0.11|0.31|
|All other|0.04|0.21|
|_Determinants of work status_|||
|Province-level minimum wage (THB/day)|140.53|13.77|
|Non-municipal area|0.67|0.47|
|Female|1.50|0.50|
|Education - primary|0.62|0.49|
|Education - some secondary|0.17|0.38|
|Education - secondary|0.10|0.30|
|Education - vocational|0.05|0.21|
|Education - university|0.06|0.23|
|No. of children (age ≤ 5)|0.95|1.04|
|No. of children (age 6-14)|0.58|0.80|
|No. of elderly (age > 64)|0.31|0.60|
|Age (15-29)|0.36|0.48|
|Age (30-49)|0.40|0.49|
|Age (50-64)|0.16|0.36|
|Age (>64)|0.08|0.28|
|N (unweighted sample size)<br>|4,770,735||

Source: Thai Labor Force Survey (1997 - 2005) Notes: N = unweighted sample size (individual respondents) for 32 quarters from 1997-2005. Mean and Std. Dev. are calculated using population weight given by the survey.
28

# Table 4: Estimates of impact of UC on employment

|||||Ye|ars since im|plementatio|n||
|---|---|---|---|---|---|---|---|---|
||||-3|-2|0|1|2|3|
|Probability of|inl ml|coef|-0.053*|-0.026|0.024**|0.038**|0.050*|0.070*|
|working|sge ae|t|-1.72|-1.46|2.35|1.98|1.84|1.91|
||id l|coef|-0.010|-0.007|0.003|0.008|0.015|0.016|
||marre mae|t|-1.09|-1.12|0.72|1.10|1.39|1.05|
||il fl|coef|-0.012|-0.020*|0.011|0.023*|0.036**|0.075***|
||snge emae|t|-0.76|-1.91|1.37|1.74|2.02|2.71|
||id fl|<sup>coef</sup>|-0.060*|-0.025|0.025**|0.061**|0.080**|0.116**|
||marre emae|<br>t|-1.68|-1.09|2.15|2.41|2.30|2.37|
|Formal<br>|<sup>sinle male</sup>|coef|-0.021*|-0.011|-0.009|-0.018|-0.026|-0.030*|
|employment|<sup>g</sup>|t|-1.82|-1.18|-1.10|-1.26|-1.59|-1.75|
||id l|coef|0.006|0.004|-0.002|-0.015|-0.033*|-0.032|
||marre mae|t|0.37|0.35|-0.25|-1.14|-1.71|-1.64|
||il fl|coef|-0.009|-0.011**|0.002|0.001|-0.005|-0.001|
||snge emae|t|-1.40|-2.11|0.54|0.26|-0.55|-0.12|
||id fl|<sup>coef</sup>|-0.015|-0.001|0.003|-0.003|-0.009|-0.004|
||marre emae|<br>t|-1.44|-0.15|0.43|-0.28|-0.70|-0.23|
|Informal<br>|<sup>il l</sup>|coef|-0.030|-0.013|0.034**|0.058**|0.078**|0.102**|
|employment|<sup>snge mae</sup>|t|-0.86|-0.67|2.41|2.08|2.16|2.14|
||id l|coef|0.006|0.008|0.014|0.040*|0.069**|0.074*|
||marre mae|t|0.17|0.34|1.20|1.74|2.10|1.80|
|||coef|0.004|-0.003|0.011|0.026|0.046**|0.082***|
||single female|t|0.20|-0.27|1.19|1.62|2.02|2.75|
|||<sup>coef</sup>|-0.043|-0.022|0.024|0.067**|0.093**|0.125**|
||married female|<br>t|-1.05|-0.88|1.56|2.08|2.09|2.06|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Results are from estimates of eqn (1) with the coefficients on UC and VF constrained as described in section IV. The regressions include – in addition to the UC dummies – the VF dummies, province- and quarter-specific fixed effects, as well as as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban). Standard errors are adjusted for clustering at the province level.
29 Table 5: Estimates of impact of UC on employment, by sector

|||||Ye|ars since im|plementati|on||
|---|---|---|---|---|---|---|---|---|
||||-3|-2|0|1|2|3|
|By Formality|Fl|coef|-0.008|-0.003|-0.000|-0.008|-0.019|-0.018|
||orma|t|-0.83|-0.36|-0.07|-0.81|-1.43|-1.33|
||Ifl|coef|-0.014|-0.006|0.021*|0.051**|0.075**|0.097**|
||norma|t|-0.46|-0.30|1.76|2.13|2.23|2.17|
|Formal sector|Ailt|coef|-0.004|-0.003|0.001|0.000|-0.003|-0.001|
|By Industry|grcuure|t|-0.54|-0.57|0.28|0.00|-0.37|-0.14|
||Mfti|<sup>coef</sup>|0.001|-0.001|-0.002|-0.007|-0.011|-0.012*|
||anuacurn|<br>t|0.20|-0.76|-0.68|-1.30|-1.51|-1.92|
||Ci|<sup>coef</sup>|0.000|0.001|0.001|-0.001|-0.001|-0.000|
||onstructon|t|0.27|0.97|0.91|-0.82|-0.91|-0.32|
||C|coef|0.001|0.001|0.000|0.002|0.001|0.001|
||ommerce|t|0.27|0.46|0.08|1.06|0.27|0.42|
||Si|coef|-0.002|0.001|0.002|0.000|-0.004|-0.003|
||ervce|t|-0.74|0.60|1.32|0.14|-1.45|-1.07|
||All h|coef|-0.004*|-0.001*|-0.002**|-0.003*|-0.001|-0.002|
||oter|t|-1.78|-1.70|-2.24|-1.84|-0.77|-0.69|
|Informal sector|Ailt|coef|-0.044|-0.019|0.026|0.063**|0.092**|0.129**|
|<br>By Industry|grcuure|t|-1.15|-0.84|1.59|2.02|2.17|2.42|
|||<sup>coef</sup>|0.011**|0.006**|-0.001|-0.004|-0.009|-0.015**|
||Manufacturing|<br>t|1.99|2.32|-0.47|-0.73|-1.25|-2.10|
|||<sup>coef</sup>|0.009**|0.005**|-0.003|-0.006|-0.009|-0.014*|
||Construction|t|2.03|1.97|-1.18|-1.50|-1.60|-1.85|
|||coef|0.006*|0.002|0.000|0.001|0.000|-0.004|
||Commerce|t|1.70|0.60|0.14|0.17|0.03|-0.57|
|||coef|0.003|-0.001|-0.000|-0.002|0.001|0.002|
||Service|t|0.88|-0.37|-0.25|-0.77|0.25|0.45|
||All th|coef|0.002|0.000|-0.001|-0.001|-0.000|-0.001|
||oer|t|1.06|0.39|-1.33|-0.81|-0.01|-0.73|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Results are from estimates of eqn (1) with the coefficients on UC and VF constrained as described in section IV. The regressions include – in addition to the UC dummies – the VF dummies, province- and quarter-specific fixed effects, as well as as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban). Standard errors are adjusted for clustering at the province level.
30 Figure 1: The effects of UC on the labor supply decision of an unmarried individual <!-- Start of picture text --> U ( II )<br>UF<br>EUI<br>II - M IF II<br><!-- End of picture text -->
31 Figure 2: The effects of UC on the labor supply decisions of a married couple <!-- Start of picture text --> U ( II , I )<br>EUF , N<br>EUI , I<br>II , I -2 M IF , N - M IF , N II , I<br><!-- End of picture text -->
32

# Figure 3: Impacts of UC on probability of working, by gender and marital status

<!-- Start of picture text --> Men (single) Men (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>Women (single) Women (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 f UC on prob. of workingEffect o 0<br>-.1 -.1<br>-.2 -.2<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Effect of UC on prob. of working<br>-.1 -.1<br>-.2 -.2<br><!-- End of picture text -->
33 Figure 4: Impacts of UC on formal employment, by gender and marital status <!-- Start of picture text --> Men (single) Men (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>Women (single) Women (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>.1 .1<br>0 plyment o m 0<br>-.1<br>-.1<br>Effect of UC on formal e<br>-.2<br>-.2<br>.1 .1<br>ymento<br>0 0<br>-.1 -.1<br>Effect of UC on formal empl<br>-.2<br>-.2<br><!-- End of picture text -->
34

# Figure 5: Impacts of UC on informal employment, by gender and marital status

<!-- Start of picture text --> Men (single) Men (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>Women (single) Women (married)<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since UC implementation<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>C on informal employment<br>-.1 0 f Effect U o -.1 0<br>-.2 -.2<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>-.1 Effect of UC on informal employment -.1<br>-.2 -.2<br><!-- End of picture text -->