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Child Health Insurance and Household Labour-Related Outcomes: An

Experiment from the Central Philippines

Dean Gerard Dulay & Miguel Garcia Barretto

Barccelona GSE & Universitat Pompeu Fabra

I. INTRODUCTION

How does health insurance targeted to young children affect the labor supply decisions, wages,

borrowing and income of their low-income parents? We use experimental data (henceforth QIDS

data) collected from 2005-2010 in the Central Philippines, where randomly selected district

hospitals were given an “access” intervention – which provided all children of randomly

selected treatment households 5 years of age and below with 100% Medical Insurance over a

period of 3 years, from 2005 – 2008. We first discuss the literature on children’s health, health

insurance and labor supply. We also address how our study contributes to this literature.

The current relevance that policymakers attach to expanding and improving health care

coverage of children and the poor in both developed and developing countries has been justified

by recent academic work on the impacts of early health insults and restrictive budget constraints

on current and future health related and socioeconomic characteristics of these children. Godfrey

& Barker (2000) find that early life health has consequences on old age chronic disease, while

Almond (2006) finds effects of health in early life on wages in later life. School attainment of

sicker children compared to that of less sick children might also be lower through reduced school

participation and reduced cognitive development (Case & Paxson, 2008). This may result lower

productivity and wages in later life (Maccini & Young, 2009; Bleakley, 2007). On the other hand,

Case & Paxson (2008) have found intergenerational consequences. Shorter women tend to give

birth to smaller children, an intergenerational trap where the effects of malnutrition in the

mother exhibit persistence across generations. Also, poor people are less likely to obtain

adequate medical care because of very restrictive budget constraints.

One way that policymakers have sought to improve the quality of health services and

expand its access amongst the most vulnerable was to introduce nationwide health insurance

programs. Currie and Gruber (1996) and Hanratty (1999) have found that increased use of health

insurance reduced the use of medical services among children and reduced child mortality.

Examining the Philippines using our dataset, Quimbo et. al. (2008) find that children who are

introduced to the treatment are less likely to be wasted and CRP – Positive.

Now we turn to the robust literature on health insurance, Gruber and Madrian (2004)

survey over 90 papers on health insurance, labor supply and job mobility and draw several

empirical regularities from their appraisal of the existing literature. We expound on their

relevant points here. First, work on the relationship between dependents’ labor force entry

decision and health insurance has been studied extensively. Buchmueller and Valleta (1999) and

Schone and Vistnes (2000) show that the introduction of health insurance decreases the

probability of said dependent working in a full time job with health insurance, increases the

probabilities of the dependent working in a full time job without health insurance and a part

time job. These papers, along with Olsen (1998) show that health insurance reduces dependents’

labor force participation rate. This result holds in the context of a developing country, Taiwan,

where Chou and Staiger (2001) estimate that the introduction of the Taiwanese government of

health insurance led to a decrease in the labor participation rate of married women. All in all,

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there is strong support that health insurance affects individual labor supply decisions, in

particular of dependent spouses.

Second, there does not seem to be much evidence that health insurance is not a major

determinant of the labor supply and welfare exit decisions of low income mothers. Yelowitz

(1995) and Meyer and Rosenbaum (2005) exploit variation in Medicaid expansion to show that

this expansion did not lead statistically significant, but very small, increases in the labor force

participation rate of low income mothers. Decker (1993) finds an insignificant effect of Medicaid

in the late 1960s and early 1970s on the labor force participation rate. Perreira (1999) looks at

Medicaid in California and saw a small significant effect. Overall, the literature seems to imply

that health insurance has a small effect on the labor force participation rate of low-income

mothers. Third, the literature seems to indicate that health insurance plays an important role in

the job mobility decision. Gruber and Madrian (1994) use an exogenous source of non-

employment based health insurance, the continuation of coverage mandates in the US, and show

that continued health insurance coverage increased job mobility by 10%. Cooper and Monheit

(1993) use a two stage estimation procedure and find that being likely to gain health insurance

increases turnover by 28 – 52%.

However, there has been no discussion on the welfare implications of the previously

mentioned empirical results. This should be seen as the obvious next step in the analysis of the

effects of health insurance on labor supply outcomes. We address welfare concerns in our paper,

but will not propose any analytically rigorous means for discussing welfare.

We now address how our work will contribute to the literature. First, we use

experimental data to tease out the causal effect of the health insurance intervention on labor

supply and wages. Most previous work has been non-experimental in nature; therefore we do

not need to deal with the usual concerns that plague causal identification. Second, we add to the

literature on health insurance and labor outcomes in developing countries. Third, we consider

the “indirect” effects of health insurance. Most previous work has focused on the effect of health

insurance on outcomes directly related to the recipient of the insurance – for example, the health

outcomes of a child if the child is the recipient of the insurance. This paper examines how

expanded health insurance aimed at increasing children’s health “indirectly” affects the labor

supply decisions and wages of the household heads. Fourth, the variation in pre-treatment

insurance status allows us to test for heterogeneous treatment effects. Specifically, a quarter of

the households in the sample had insurance pre-treatment, while three-fourths of the remaining

households did not. Therefore we will be able to analyze how labor supply decisions and wages

are affected for our subsample that goes from standard health insurance coverage to expanded

health insurance coverage, and the remaining majority of our sample, that goes from no

insurance coverage to expended insurance coverage. Details on the program will be expounded

upon in the succeeding sections. Finally, this paper expands on the results of Quimbo et. al.

(2008). They look at health outcomes in the short run. We look at labor outcomes in the long run.

The remainder of the paper proceeds as follows: Section II clarifies the institutional

setting and the data; in Section III we present our results. Section IV discusses the implications of

our results. In Section V we talk about possible extensions to this work. Section VI concludes.

II. INSTITUTIONAL SETTING AND DATA

The Institutional Setting. In 1999, the Philippines’ Department of Health (DOH) sought to

improve the performance of the health sector by launching the National Health Sector Reform

Agenda (HSRA) to improve the administration and financing of health services. The goals of the

agenda were twofold: (1) increasing access to personal health services, especially for the poor,

and (2) improving the quality of care delivered at hospitals. Motivating such a response is the

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current health and disease environment. Infant mortality stands at 4 and 36 deaths per 1000 in

urban and rural areas, under 5 national mortality is 30 and 52 per 1000 in urban and rural areas

and 25% of all deaths of children under 5 are pneumonia (2003 NDHS). 30% of under 5 are

stunted, 27% are underweight and 5.3% wasted. Current levels of medical service use remain

very low with only 43% of children with acute respiratory disease and 29% of children with

diarrhoea taken to hospital in rural areas.

In lockstep with these governmental efforts, the University of California San Francisco’s

Institute for Global Health and UPEcon Foundation collaborated with the DOH and the

Philippine Health Insurance Corporation (PhilHealth), the governmental financial institution

tasked to implement the health sector reforms. PhilHealth is the country’s social health insurance

program, covering 30% of total charges and around 79% of the population of the country

(disproportionally more formal sector employees, retirees and pensioners, self-employed out of

pocket payers and the poor who are subsidized by local government units). The country’s poor

depend on publicly provided health services such as government hospitals (40% of total

hospitals), and rural health units and primary health facilities. 49% of total health care

expenditure in 2005 amounted to out of pocket expenditures (Philippine National Health

Accounts 2005) and only 30% of the poor can afford to make such expenditures themselves

(Peabody, 2005). The goal of this collaboration was to test the efficacy of health sector initiatives

in improving health-related child outcomes.

The plan of the study, named the Qualitative Improvement Demonstration Study (QIDS)

was to collect data via experimental design. Randomization was done at the district hospital

level of 30 hospitals in the Visayas, one of the three main island groups in the Philippines. It

covers one third of the geographical area and consists of 20 million people living in relatively

isolated islands. Most of its population is poor, rural, with farming and fishing are primary

means of subsistence. Two different policy interventions were randomly assigned to these

hospitals, with 10 out of 30 serving as the control group. The first intervention was labeled the

“access intervention”. This intervention expanded the (PhilHealth) insurance benefits of children

less than or equal to 5 years of age to 100%. PhilHealth insurance normally covers 75% of the

expenses. This was done by automatically treating cases of these children as “intensive” cases.

It is important to mention here that (1) at baseline, roughly a quarter of the households in

both treatment and control districts had the PhilHealth insurance prior to the intervention, and

(2) the insurance was conditional on household heads receiving the treatment. Therefore, in all

treatment districts in our sample included heads with 75% health insurance, children 5 years of

age and below with 100% health insurance, and children 6 years old and above with 75% health

insurance, and in control areas one quarter of the households had all its members having health

insurance at 75%, while in the remaining 75%, no one in the household had any health insurance.

The second intervention was a “bonus” intervention, but since we will not be focusing on this

intervention we will not discuss it further.

The Data. The data collection was as follows: In the first round (baseline) a facility survey was

conducted on 30 facilities and 150 physician vignettes. The facility survey gathered data on

structural variables, staffing; clinical practice variables, and the cost, price and availability to

households. A patient exit survey was conducted on 1495 kids with one of two tracer conditions

– diarrhea and pneumonia. A subset of these kids was from hospitals awarded the “access”

intervention and another mutually exclusive subset was from hospitals that received the “bonus”

intervention. The remaining kids were from control hospitals. The patient exit survey collected

subjective and objective health measures, provider characteristics, perceived satisfaction, and the

total cost of care, as well as socioeconomic and health status information. These same kids were

given a follow-up Household survey 4-10 weeks after the Patient Exit survey, and were given the

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same Household survey 2 years after the first Household survey was conducted. The household

survey collected information on the health status of the children in the household, as well as

detailed information on the socioeconomic status and health status of the household and its

members.

For the purposes of this study we will focus on the “access” intervention and use the

Household dataset and the Patient Exit dataset. To test for randomization at baseline, we use

data in the Patient Exit survey. We will use the information from the household datasets to test

for any behavioral, labor supply effects brought about by the intervention. Therefore, we use a

panel dataset where we obtain information at baseline from the patient exit survey, assess

“short-run” child health and household expenditure outcomes using the first round of the

household survey 4- 10 weeks later, and assess “long-run” child health and household

expenditure outcomes 2 years later using the second round of the household survey.

The manner by which the experiment was conducted raises specific concerns. First, the

baseline was collected upon exit from the hospital, raising concerns that if residents in treatment

areas were made aware of the existence of the treatment prior to arrival at the hospital, they may

alter their behavior before arrival at the hospital, rendering treatment and control no longer

comparable. Second, there is a possible selection into to the treatment. In other words, can we

make any claim that our results can be generalized to the population of the Central Philippines?

To alleviate these concerns we run a set of t-tests of differences in means on the entire sample.

We also run the same tests on those who had PhilHealth prior to the program, in treatment and

control, and those who did not have PhilHealth prior to the program, in treatment and control

groups. Finally, we run the test of differences in means between our sample and on a sample of

random households in the Central Philippines. The random household survey was taken at

baseline. This will allow us to test the external validity of our results.

We now address issues of compliance, attrition and take-up. First, do index children

assigned to treatment go to treatment hospitals and do children assigned to control hospitals go

to control hospitals? Tracking the district of residence of the household and the district hospitals

that these households go to for treatment, Quimbo et. al. (2008) note that children who belong to

a given district receive treatment in the district hospital of their district of residence. They also

infer that distance between district hospitals leads to proper compliance. Regarding attrition,

from patient exit to the first household survey 4-10 weeks later, attrition was 10.93% in treatment

districts and 15.44% in control districts. There is differential attrition between the two groups.

However, turning to overall attrition, from baseline, until the second household survey 2 years

later, attrition in treatment districts is 12.78% and attrition in control is 15.95%, and this is not

differential. A couple of points here: (1) attrition is not large, so in this sense it is not a major

concern of this paper, (2) the treatment seemed to play a role in attrition. We are unsure how the

treatment affected attrition. Perhaps the poorer households migrated out of the Visayas to

Manila looking for better opportunities. At any rate, this is a consideration in the eventual

interpretation of the results. Take-up was 90.5%. In other words, 9.5% of patients who were

asked to participate in the study agreed to participate.

Here we test if the randomization was done correctly. If this is the case, the treatment

and controls groups do not differ systematically and are therefore comparable. This means that a

difference estimator will be able to establish causal effects of the intervention on a variable of

interest. To check if the randomization was indeed done correctly, we run a set of differences in

means on several variables in the Patient Exit dataset. We should notice that the p-values we

obtain are unable to reject the hypothesis that the means are not statistically significantly

different between treatment and control. To run these tests of equality we use the following

variables: the hemoglobin level of the index child, the CRP level of the index child, the folate

level of the index child, the height of the index child (in centimeters), the weight of the index

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child (in kilograms), the age of the index child (in years), the total income of the household in the

past 12 months, the years of education of the mother, the years of education of the father, the

earnings of the primary income earner (per month), the percentage of PI earners with a spouse,

the type of job of the PI earner, the employment status of the PI earners spouse, the probability

that the child had a fever prior to hospitalization, and a subjective rating of the overall health of

the child.

We reiterate here our earlier concerns: (1) the baseline survey was conducted upon

patient exit. This is means that if patients were somehow made aware of the existence of the

treatment, they may plausibly change their behavior before admission into the hospital, and (2) a

participation effect may be in play, i.e. those individuals who choose to go to the hospital may be

substantively different from the general population.

To address these concerns we do the following: first, we run the test of differences in

means on the full sample. This is standard in the randomized control trial literature. If

households who had heard about the treatment adjusted their behavior accordingly, we would

then see systematic differences between treatment and control groups. Next, to check the

external validity of our results (whether the results of this study can be generalized to the rest of

the Central Philippines, we run the test of differences in means between households that

participated in the study and a random household survey taken at baseline.

Table 1. Characteristic Differences of Treatment and Control Groups

Variable Mean Treatment Mean Control P-Value Diff.

Hemoglobin 11.49656 11.46994 0.9185 0.02662

CRP -72.22675 -69.54633 0.3258 -2.68042

Folate** 213.562 191.0956 0.0069 22.4664

Height (cm) 76.30171 76.69507 0.6243 -0.39336

Weight 9.405745 9.425361 0.9131 -0.019616

Age of child (years) 1.233546 1.177551 0.4388 0.055995

Total HH Income (year) 59190.64 52295.87 0.1276 6894.77

Index Children in PhilHealth 0.2675159 0.2857143 0.529 -0.0181984

Years of Educ. (mother)** 8.980728 8.523013 0.0313 0.457715

Years of Educ (father) 8.458797 8.08172 0.1112 0.377077

Primary Income Earner 2.002128 2.010246 0.7887 -0.008118

PI Earnings 3994.709 3636.759 0.2235 357.95

PI Earner with Spouse 0.1740977 0.2061224 0.2066 -0.0320247

Spouse Emp. Status 0.163482 0.1591837 0.8565 0.0042983

Child had fever 0.7091295 0.6734694 0.2322 0.0356601

Overall Health 2.869936 2.848049 0.6676 0.021887

Sex 1.420382 1.434694 0.6543 -0.0100674

Years Child's School Completion** 8.980728 8.523013 0.0313 0.4050914

Spouse 1.825532 1.796407 0.2068 0.0272443

Mother as PIE 0.0764331 0.0816327 0.7656 -0.0051471

Father as PIE 0.8598726 0.8408163 0.4085 0.0165838

Grandparent as PIE 0.0530786 0.0632653 0.5009 -0.0078444

Other Relatives as PIE 0.0042463 0.0081633 0.4415 -0.0038273

Housekeeping 0.0084926 0.0102041 0.7833 -0.0016786

Unemployed 0.0127389 0.0081633 0.4852 0.0044542

3/26 s.s diff at 5%, sample 961

Note: *p<0.05; **p<0.01; *** p<0.001

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Table 2. Characteristic differences between Follow-up Group and Random Households

Variable

Mean Follow-up

Group

Mean Random

Household Diff. p-value

(N = 985) (N = 1496)

Years of Father's Schooling* 8.267876 8.639777 -0.371901 0.0115

Years of Mother's Schooling** 8.749226 9.153898 -0.404672 0.0028

Primary Income Earner 2.009165 2.024733 -0.015568 0.4653

Work Status of PI Earner*** 2.689093 1.079019 1.610074 0.0000

Main Activity of PI Earner 1.070552 1.079019 -0.008467 0.6512

Wages of PI Earner* 3826.973 4282.472 -455.4985 0.0214

Spouse*** 1.809766 1.049565 0.760201 0.0000

Is Spouse Working*** 1.831906 1.729577 0.102328 0.0000

PI Spouse's Work Status 2.422078 2.5125 -0.090422 0.5015

Spouse's Main Activity 1.131579 1.199501 -0.067922 0.2546

Spouse Earning at Primary Job*** 3325.693 2260.737 1064.956 0.0003

Total Income per Month* 4161.013 4662.355 -501.3426 0.0215

Total Value of Bonuses, Tips, etc.* 1685.958 2810.886 -1124.928 0.0308

Total Value of Other Things Received 1319.409 1662.49 -343.0811 0.3714

Total Extra Money, 12 Months 2278.069 2755.905 -477.8358 0.1603

Total Income from Family Friends, 12 Mos.** 1567.816 2870.648 -1302.832 0.0021

Total Income, 12 Mos.** 55811.29 64280.52 -8469.229 0.0028

Note: *p<0.05; **p<0.01; *** p<0.001

Table 3. Attrition Rates on Households during the intervention

Full Sample Insured Uninsured

HH 1 HH 2 HH 1 HH2 HH 1 HH 1

Group Base %Att. Base %Att. Base %Att. Base %Att. Base %Att. Base %Att.

Treatment 540 10.93 540 12.78 141 8.51 141 10.64 399 11.78 399 16.94

Control 583 15.44 583 15.95 164 12.20 164 13.41 419 16.71 419 13.53

Diff. 4.51 3.17 3.68 2.78 4.93 3.41

S.E. (0.020)** (0.021) (0.0347) (0.037) (0.024)** (0.025)

Note: Base = Baseline sample size; % Att. = percent who attrited; HH1 = household surveyed in

first period, HH2 = household surveyed in second period.

Looking to the results, Table 1 shows the differences in means for the full sample on

personal, health, and income characteristics. Results show that only folate levels are statistically

significantly different at 95% confidence. This is evidence that the randomization was done

correctly. Notice that the above result alleviates the concern that residents of the districts in

treatment areas may have heard of the program and adjusted their behavior before they took

part in the intervention. First, the randomization suggests balance. Second, if those in treatment

groups adjusted their behavior, we may see statistically significant differences in quickly

adjustable behavior. None of our results suggest this.

Next, we check if the sample that is in both treatment and control groups is

systematically different from the Visayan (Central Philippines) population as a whole. We report

results for another set of difference in means in Table 2. We see that the sample is systematically

different from the general Visayan population. This means we should be careful to extrapolate

our results to the Central Philippines as a whole. This result is not, however, surprising, since the

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tables show that the households in the sample are poorer than the average Visayan household.

This makes sense because district hospitals are for less-well off households. Therefore we believe

these results are more externally valid if we look at the less well-off segment of the Visayan

population.

To summarize the results of our checks for randomization, we test for balance in the full

sample and we see that the randomization was done correctly. Then, households that participate

in the experiment are systematically different from the population of the Central Philippines.

Therefore we cannot make any general claims on how this intervention will affect health

outcomes for children and household expenditures in general equilibrium.

III. RESULTS

Tables 4.1 and 4.2 report the results from our empirical analysis. We first focus our results on the

primary income earner. In particular, does the treatment affect the PI earner’s labor supply

decisions or wages? We find no effects on the monthly earning of the primary income earner,

either 2 months post-treatment or 2 years post-treatment. Next, we check for any changes in the

type of employment the primary income earner chooses to undertake. First, we check if the

treatment makes him more or less likely to be working for a private firm, by running a linear

probability model on a binary variable equal to 1 if the PI earner is employed by a private firm

and 0 otherwise. We see no statistically significant effects here. However, turning to the

probability that the PI earner is self-employed, we see that the joint test of the treatment and

interaction terms is strongly significant and positive, which seems to indicate that those who are

uninsured pre-treatment are more likely to be self-employed as a result of the treatment.

Turning to the labor force participation decision, we see that in the long run there is a

statistically significant and negative effect of the treatment on the probability of the PI earner

working to earn a living, and this effect is differential in pre-intervention insurance status. For

the pre-treatment uninsured, the treatment increased the likelihood that the PI earner would

work to earn a living, but among those insured the likelihood actually decreased. Although

significant, the effects are very small, -3 and 2 percent for pre-treatment insured and uninsured,

respectively.

We also run a linear probability model on the likelihood that the PI earner is

unemployed. We see that there are no statistically significant effects. We can see that the change

in the probability of employment may be due to the increase in the likelihood that the PI earner

may choose to do housekeeping or job hunting. We then check for the effect on the treatment on

the likelihood that the PI earner’s spouse is working. We find a small, positive and statistically

significant effect on those households who are uninsured prior to treatment. Evidence suggests

that the treatment led to an increased probability that the spouse entered the labor force for this

subsample.

On the work status of the PI earner’s spouse, we see no effect of the treatment on the

likelihood that the spouse works for a private firm to earn a living. We do, however, see a short

run decrease in the probability that the spouse is unemployed, for those how are uninsured pre-

treatment. This effect is very small. Analyzing the spouse’s monthly earnings, we see that there is

a long run increase in the PI earner’s spouse’s monthly earnings, but only for the uninsured. We

see this as an interesting contrast to the PI earner’s earnings, where our tests how no statistically

significant effects.

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Table 4.1 Difference in Difference Results Labor Supply Outcomes due Health Insurance Intervention

(a) PIE

Earnings

(b) If PIE works

in private firm

(c) If PIE

self-employed

(d) PIE working

to earn income

(e) PIE

unemployed

(f) Spouse works

for a living

(g) Spouse works

for firm

Variables Short Long Short Long Short Long Short Long Short Long Short Long Short Long

Treatment 690.6 253.6 -0.0123 -0.0511 0.0283 0.00233 0.00927 -0.0310 0.00906 0.00611 -0.00509 -0.00509 -0.00541 0.0393

(524.2) (374.5) (0.0598) (0.0551) (0.0565) (0.0516) (0.0232) (0.0197) (0.00869) (0.00817) (0.0161) (0.0160) (0.0363) (0.0278)

No Prior

Insurance

805.4* -57.86 0.000055 -0.0937** 0.0470 0.0150 0.0172 -0.0360** 0.00449 0.00604 -0.0215 -0.0213 0.00576 0.0176

(421.5) (365.5) (0.0484) (0.0460) (0.0481) (0.0473) (0.0190) (0.0142) (0.00387) (0.00500) (0.0155) (0.0155) (0.0291) (0.0206)

Treatment x No

Prior Insurance

-871.0 -246.8 -0.00754 0.0939 0.0545 0.0624 -0.0191 0.0495** -0.0131 -0.00528 0.0255 0.0254 -0.0267 -0.0446

(659.2) (434.9) (0.0713) (0.0645) (0.0685) (0.0635) (0.0265) (0.0242) (0.0100) (0.0105) (0.0201) (0.0202) (0.0424) (0.0317)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant -1,840 3,363* -0.122 -0.394** 0.705*** 0.900*** 0.980*** 0.993*** -0.0249 -0.00137 -0.0404 0.0223 0.127 0.0118

(2,002) (1,801) (0.218) (0.169) (0.212) (0.175) (0.0626) (0.0444) (0.0300) (0.0279) (0.0436) (0.0396) (0.117) (0.0620)

Observations 863 864 867 869 867 869 852 850 867 869 867 869 867 869

R-squared 0.379 0.648 0.092 0.252 0.127 0.201 0.013 0.052 0.013 0.020 0.869 0.869 0.024 0.081

Note: Short refers to short-term effects while long for long-term effects; Robust standard errors in parentheses; *** p< 0.01, ** p<0.05, * p<0.1

Table 4.2 Continuation on Difference in Difference Results Labor Supply Outcomes due Health Insurance Intervention

(h) Spouse

unemployed

(i) Spouse’s

earnings

(j) PIE + Spouse

earnings

(k) Total value

bonuses, tips

(l) Total value

things received

(m) Earnings

other work

(n) Money from

family, friends

(o) Income for 12

months

Variables Short Long Short Long Short Long Short Long Short Long Short Long Short Long Short Long

Treatment 0.00109 0.000414 504.7 38.06 655.8 639.8 780.1 1,611* 70.94 2,351** -2,282* 1,494 -322.1 835.4 7,309 12,550**

(0.00103) (0.000617) (345.5) (718.4) (548.9) (400.3) (1,150) (973.8) (379.6) (935.3) (1,323) (1,147) (1,382) (1,671) (7,209) (5,779)

No Prior

Insurance

0.00804 0.0000991 -145.5 -1,358** 622.0 314.4 -100.8 -1,033** -181.0 -652.7** -613.3 -1,182* 2,501* 1,854 13,980** 806.6

(0.00571) (0.000404) (253.3) (609.2) (463.8) (359.0) (944.1) (400.5) (284.1) (296.5) (1,101) (622.7) (1,399) (1,372) (6,542) (4,576)

Treatment x No

Prior Insurance

-0.00841 0.00265 -593.5 665.6 -909.5 -692.7 -1,771 -1,010 -0.691 -531.3 1,496 806.5 -1,632 -1,333 -15,317* -9,314

(0.00602) (0.00270) (401.6) (860.1) (677.1) (456.9) (1,350) (995.3) (453.0) (993.6) (1,434) (1,270) (1,759) (2,065) (9,237) (6,327)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant -0.00114 -0.00341 409.9 559.7 -785.6 2,706 -2,931 -2,653 1,425 -1,073 -930.7 -969.3 -808.7 -18,774* -13,463 9,865

(0.00795) (0.00378) (1,081) (2,430) (1,999) (1,772) (2,295) (2,150) (1,079) (1,487) (4,302) (2,697) (10,497) (10,782) (30,412) (15,832)

Observations 867 869 567 166 863 858 848 841 855 848 857 852 860 853 864 861

R-squared 0.013 0.013 0.178 0.462 0.396 0.628 0.181 0.193 0.042 0.043 0.050 0.064 0.101 0.116 0.390 0.662

Note: Short refers to short-term effects while long for long-term effects; Robust standard errors in parentheses; *** p< 0.01, ** p<0.05, * p<0.1

9

Looking at the joint monthly wages of the PI income earner and his/her spouse, we see a

long run increase in these joint wages. This result is positive and statistically significant, and

there are no differential effects. However, these effects are small. We also see a long run increase

in the total value of all commissions, tips and extra payments made to the PI earner and his/her

spouse in the past month. This effect is for both pre-treatment insured and uninsured

households, and there is no differential effect conditional on pre-treatment insurance status.

Similarly, we see similar results for the total value of things received by the couple as payment

for their work. These include food, housing and transportation and communication. We also see

a decrease in lending from family and friends, which we proxy for overall borrowing, for those

who were uninsured prior to the treatment. We also see an increase in the long run in annual

income for the household. There is no differential effect between pre-treatment insurance

statuses.

IV. DISCUSSION

Our results show that the health insurance intervention did not always directly alter the family

income earner’s wage rates, but it did significantly improve the quality of life among the

households. This is shown by overall improvements such as more valuables and bonuses earned

the increase in the opportunity for spouses to work, and the decreased dependency on

borrowings from other family members, among others. It seems apparent that households with

no prior insurance have benefitted more with the intervention. We discuss here in detail and

provide plausible explanations.

First, to those without any prior insurance, we observed that the primary income earner’s

earnings from his or her main job increased in the short-term. The plausible source of increased

earnings may not come from employment under fixed wages, but through self-employment.

This is supported by the fact that the probability the primary income earner’s employment status

in a private firm decreases in the long run and that for both short-term and long-term periods,

the probability that both insured and uninsured groups would opt themselves to seek self-

employment. This is further corroborated with the fact that in the long-term both insured and

uninsured households would be less likely to earn their income from working. The fact that

there is no effect on unemployment status on the household simply indicates a structural change

of labor from fixed wages to more flexible earnings from self-employment opportunities.

Furthermore, it seems that in the long-term spouses’ working status decreased. Our

results show that unemployment status among spouses did not increase indicating that

households have opted themselves out from the labor force and into the household. This would

indicate that prior to the insurance intervention, spouses work simply to cover the costs for their

ailing children. However, the presence of the health insurance indicates that spouses,

particularly mothers, need not work anymore. As a corollary, the impact of the health insurance

decreased the earnings of formerly uninsured spouses.

Despite the decrease in income, our results do not indicate any overall decrease in

income. This would indicate that the earnings gained by the primary income earner are

compensated by the spouse’s decision to focus on her work at home. Added evidence that would

support this claim is the increase in bonuses or tips—external earning opportunities that could

potentially be due to their self-employment earnings. Increased productivity in the household

has allowed them the opportunity of acquiring more valuable things at home. Furthermore, we

are also seeing that households are now borrowing less. Finally, we also see that the treatment

group that had prior insurance is enjoying increases in annual income.

Given all the evidence, it is clear that the health insurance program improved overall

household productivity. It can be said that raising sickly children with the impending threat of

10

no insurance is costly; the intervention actually made the household greater leeway to use their

productive capacities to more profitable enterprises.

V. EXTENSIONS

Our paper has analyzed the effects of a health insurance intervention on the labor supply

outcomes and wages of households. Certain data limitations and the necessity that academic

papers be relatively narrow in scope prevent us from considering, at this point, several

interesting extensions to this work. First we look at what else we could have done had we had

access to more data, because all this data is in principle currently available but not in our

possession.

It would be of interest to look at the effects of the insurance intervention on human

capital of children. Work by Chetty (2006) has shown that insurance has significant welfare

implications even in the absence of consumption smoothing if the means of achieving some level

of consumption smoothing are attained in ways that involve costly welfare-reducing decisions,

and the introduction of insurance prevents the necessity of these measures as a means of

consumption smoothing. For example, households may achieve consumption smoothing by

making their children work on a farm instead of going to school. The introduction of the

insurance may shift this decision in favor of the school and not the farm, and this has large

welfare implications. This means, of course, that we should examine how health insurance

affects consumption smoothing. We believe this would be extremely interesting because it is also

a spillover effect of the health insurance, because the insurance may then affect the human

capital of older children who are not the direct beneficiaries of the insurance.

Next, it would be interesting to look at health outcomes, and spillovers of these outcomes

within and across households and districts, in the long run, The paper that uses this dataset,

Quimbo (2008), looks at the effects of the treatment on the probability of children being wasted

and CRP-positive 4 – 10 weeks later. It would make sense to look at different measures of health

outcomes in the longer term, to check for the robustness of these results. In fact, one of us is

already undertaking this project. Still looking at health outcomes, it is plausible that expanded

health insurance to children 5 years of age and below has intra-household health spillovers – not

only do these kids get healthier, but children 6 years old and above may get healthier too. This

could happen in many ways – increased health-consciousness within the household as a result of

the insurance or income effects leading to changes in spending patterns that benefit the health of

the entire household, some combination of the two, or some other reason are all plausible. We

could also consider inter-district spillovers. Better health within the household may lead to better

health of children in control districts who are exposed, maybe in school or at play, to these

treated children. The framework we would use to analyze this would be to run the regressions

controlling for population density and the number of treated kids at some distance j from district

hospital h. This is as an identification strategy similar to Miguel and Kremer (2004) in their

evaluation on the effects of a deworming program in rural Kenya.

Turning to non-health related outcomes, we could then look at expenditures and

examine how income and substitution effects determine either an increase in total expenditures

or shifts in the relative amounts spent on certain goods vis-à-vis other goods, since the price of

health services for treated households is effectively 0. Again, one of us is working on this topic.

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VI. CONCLUSION

This paper sought to identify the effects of a health intervention that provided 100% medical

insurance to children 5 years of age and below in treatment districts in the Central Visayas. All

results tend to support a story that the intervention affected households primarily by increasing

the productivity of the working members of the household, and most often effects were only

apparent in the long run. Most public policy tends to be evaluated by the direct effects of the

intervention. For example, health interventions to infants are only evaluated as cost effective or

not based only on the degree by which the policy increases infants’ health. This work adds

support to the idea that any claim related to the welfare effects of public policy should consider

not only these direct effects, but also spillover effects, both on direct outcomes (in this case,

possible health spillovers to those not directly part of the program), and indirect outcomes (as in

our paper, the effects of child insurance on household expenditures). Therefore, any holistic

analysis of welfare effects requires a holistic assessment of the many dimensions by which public

policy affects constituents.

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