Regional Integration in Asia - Web viewThe local context of the conditional cash transfer (CCT) is known as the Pantawid Pamilyang Pilipino Program (4Ps). It is a program under the

Externalities from the conditional cash transfer: evidence from Bataan, Philippines *

Mitzie Irene P. ConchadaSchool of Economics

De La Salle [email protected]

Marites M. TiongcoSchool of Economics

De La Salle [email protected]

Abstract

The Philippine government launched its poverty program Pantawid Pamilyang Pilipino Program (4Ps) in 2008 with the goal of assisting poor households address their short-term consumption needs. One of the conditions of the cash grant given to qualified poor families is to undergo regular check-ups: pre-natal and post-natal check-ups for pregnant women, regular monitoring of the weight and height of children ages 5 and below, and receive de-worming care among school-age children. These requirements are aimed at improving the health of the individual and eventually contribute to long-term outcomes such as increased labor productivity. Most of the studies done on assessing the impact of conditional cash transfers on health in Latin American countries point out to the positive effect of the program on the health of the beneficiaries. Similarly, an impact evaluation study of the program was conducted by Chaudhury et al. (2013) in the Philippines in selected provinces including Negros Oriental, Lanao del Norte, Mountain Province, and Occidental Mindoro and results of reveal that the program is successful in keeping children healthy. Similarly, this study focuses on the short-term impact of the program on health benefits on sample households in areas that received 4Ps and in areas that did not receive 4Ps in Bataan. Moreover, it investigates the effect on household members of a beneficiary family who are not directly involved in the program but who are mostly likely to benefit. The primary data set includes 228 households with 4Ps (distributed among 2009 to 2012 * We gratefully acknowledge financial support from the Angelo King Institute for Economics and Business Studies, De La Salle University for the on-going project on the Impact Evaluation of the CCT in Bagac, Bataan where the paper was lifted from.

entry groups), and 213 households without 4Ps. The evaluation uses propensity score matching, which compare health outcomes on beneficiaries and non-beneficiaries of 4Ps in Bataan. This evaluation study also intends to provide policy makers with a better understanding of the capacity of 4Ps to address chronic poverty and to improve the use of maternal and child health services.

JEL Classification Codes:

Keywords: poverty, impact evaluation, maternal and child health service, regression discontinuity

1. Introduction

1

The local context of the conditional cash transfer (CCT) is known as the Pantawid

Pamilyang Pilipino Program (4Ps). It is a program under the Department of Social Welfare

and Development (DSWD) and was started in 2007, with a pilot test on 6,000 households.

As of 2011, it was meant to cover about 2.3 million households. This program has two

primary objectives: reduce immediate poverty and reduce future poverty. The first

objective is attained by giving cash grants to be used to augment daily needs while the

second objective is achieved through the conditions that have to be met to be recipients of

the cash grants (i.e., household investments in education and health care) (Usui, 2011).

However, a variety of perspectives are available on the efficacy of this program in terms of

truly alleviating poverty.

In order to fully understand its short run effects, the motivations for the existence of

such a program must be evaluated. Prevalent in today’s society is the occurrence of

households investing less and less in education and health care of children due to financial

constraints. Hence, implementation of the CCT program not only in the Philippines but in

less developed countries all over the world. Given this backdrop, the study focuses on the

key question: what is the impact of the conditional cash transfer, particularly the Pantawid

Pamilyang Pilipino Program on improving health outcomes in Bagac, Bataan?

According to the second quarter report 2013 of the Department of Social Welfare

and Development (DSWD), there were over 3.8 million families covered in 143 cities and

1,484 municipalities in 79 provinces. Based on the distribution of households, the island

Luzon has the most number of households covered with 42 percent mainly because of the

magnitude of population (DSWD, 2013). On the other hand, the Mindanao island which

has the highest poverty incidence has a household coverage of 38 percent.

As of the second period of the year 2013, the total cash grant given to eligible and

compliant household beneficiaries amounted to PhP12.9 billion – PhP6.1 billion for

education and PhP6.8 billion allotted for health. Given the large amount of budget spent,

there is a need to delve into the effectiveness of the program especially on improving

outcomes such as the health of the children.

2

The only study on the impact of the 4Ps in the Philippines is that by Chaudhury,

Friedman, and Onishi (2013) using the survey done by the Department of Social Welfare

and Development (DSWD) in 2011. The results of the study echo that of the results of the

program in other Latin American countries, particularly in the area of education and health.

The survey conducted by the DSWD though was done in selected provinces namely Lanao

del Norte, Mountain Province, Negros Oriental, and Occidental Mindoro. Results of the

study show that 4Ps has a positive impact of school enrolment among young children.

Moreover, it was found to reduce stunting among young children (Chaudhury et al., 2013).

School enrolment among older children (high school) start to increase because of the

higher opportunity cost – that is the opportunity to be able to work and as a result the child

drops out of school. Thus, the 4Ps indirectly could contribute to lower incidence of child

labor as the children are required to be in school to continue to be part of the program. The

health of the child belonging to a poor family is also compromised because of the lack of

income to purchase healthier food items and the lack of opportunities to receive proper

health care.

Given the backdrop, the study aims to answer the following objectives:

a. Identify the factors that distinguish the CCT beneficiaries and non-

beneficiaries; and

b. Determine the impact of the CCT on health outcomes in Bagac and Pilar,

Bataan using the PSM method.

2. Review of Related Literature

Conditional Cash Transfer: Experiences and Lessons in Mexico

Conditional cash transfer programs originated from Latin America in the 1990s. It

started in Mexico as part of an integrated approach to poverty alleviation. Its main

objective was to improve human capital through education and health. Mexico was the

first country to design and implement a program aimed at providing transfers to the poor in

exchange for sending their children to school and health clinics for check-ups and

vaccinations, thus called the conditional cash transfer.

The program in Mexico was first implemented in 1997 and was called Progresa but

3

was changed to Opportunidades and eventually became the benchmark for other poverty

alleviation programs in Central and South American countries (Gantner, 2007). The

program was designed to replace many earlier subsidy and poverty alleviation programs

and was so complex that it needed the support of several government agencies. The

program initially started with 300,000 families with a budget of USD5.8 million and was

gradually phased over in the next years targeting the poorest of the poor. The phasing of

the program allowed the government to evaluate the program’s impact by comparing the

beneficiaries and non-beneficiaries with the same characteristics (Gantner, 2007).

The program was deemed to be generally successful in improving conditions of the

poor in its initial impact evaluation thus it was continued but with some modifications.

There was an improvement in school attendance and showed lower incidence of illnesses

among children. Food expenditure of poor households also increased with the program

allowing family members to consume more quality food and increase calorie intake

(Gantner, 2007).

Several studies have been done on the impact of the program in Mexico. The

estimation of the impact of CCTs on child labor and school participation was attempted by

Skoufias and Parker (2001) in their study of the Mexican CCT called the Education,

Health, and Nutrition Program, or Progresa. The study by Skoufias and Parker (2001) on

the program notes outcomes that provide valuable insight on aspects such as time allotted

for both leisure and work on top of the usual aspects such as the incidence of child labor

and levels of school participation. It was found that school participation increased while

time spent on labor decreased for children whose families received the benefits of

Progresa.

Another study by Rawlings & Rubio (2003) further supports the claim that CCT

promotes human capital accumulation among poor households in Mexico. This is evident

in the increase in the enrollment rates and attendance rates of the mentioned countries.

Through the CCT program in Mexico, it is estimated that the impact in enrolment rates for

girls ranged from 7.2 to 9.3 percentage points and 3.5 to 5.8 percentage points for boys.

The program is also effective in decreasing the situation of child labor. In Mexico, the

probability of working among aged 8 to 17 reduced by 10 to 14 percent. The effect is

higher for boys aged 12 to 13, there is about 15 to 20 percent decrease in the probability of

4

working and the girls also show a significant decrease in the probability of working.

The last effect of CCTs can be measured in terms of promoting gender equality.

According to the study of Molyneux (n.d.), by awarding the cash transfers to the women,

CCTs, in a way, promotes gender equality and women empowerment. Oportunitades

claims to ‘promote equal access of women to its benefits’. One of Progresa’s objectives is

to empower the beneficiary mothers and daughters, it also aims to give the women the

power to decide as a household member. They believe that through women empowerment,

families will have a better quality of life.

Pantawid Pamilyang Pilipino Program

Due to the effectiveness of the poverty program in Latin American countries in

achieving the desired outcomes, other developing countries have implemented the same

program and was tailor fit to suit the needs of the stakeholders. Such is the case of the

Philippines.

The local context of the conditional cash transfer is called Pantawid Pamilyang

Pilipno Program or is also known as 4Ps. The 4Ps was implemented as an aid to provide

assistance to extremely poor households to improve their health, nutrition, and education

particularly children aged 0 to 14. According to the DSWD website, the selection process

is done through the National Household Targeting System for Poverty Reduction using the

proxy means test, which determines the socio-economy category of the families by

evaluating certain proxy variables such as ownership of assets, housing type, household

head’s education, family’s livelihood and access to water and sanitation facilities.

According to DSWD, beneficiaries should comply with the following conditions:

pregnant women must avail pre- and post-natal care and be attended by a

trained health professional during childbirth;

parents must attend Family Development Sessions;

0 to 5 year old children must receive regular preventive health check-ups

and vaccines;

3 to 5 year old children must attend day care or pre-school classes at least

85 percent of the time;

5

6 to 14 year old children must enroll in elementary or high school and must

attend at least 85 percent of the time; and

6 to 14 years old children must receive de-worming pills twice a year.

It provides an education grant of Php300 per child per month up to a maximum of 3

children or Php3,000 per year. Households eligible for this grant must have children aged 5

years old and/or aged 6-14 years old that attend school at least 85 percent of the time. The

program also provides health and nutrition grant of Php500 per month or Php6,000 per

year per household. Households eligible for are those with children 0 to 14 years of age

and/or pregnant women. Table 1 shows the summary of the various grants and the

corresponding amount.

Table 1. Pantawid Pamilya Grant Package

Type of grant Amount Eligibility

Health grant*Php 500/month per

beneficiary household

has children aged 0-14 years or pregnant

women

Education grant** Php 300/month/child aged 6-14 (up to 3

children)

has children aged 6-14

has children aged 3-5 years and less than three children aged

6-14 years*for 12 months of the year**for 10 months of the year

The conditional cash transfer program in the Philippines has adopted the best

practices in Latin American countries such as Brazil and Mexico. Though the program in

the Philippines is still in its first and second stage of implementation, the results have yet to

be evaluated. Both developing countries have implemented this program to address

poverty issues that affect human capital particularly in the areas of education and health.

The table below summarizes the similarities and differences between the program

in Latin America and the Philippines.

6

Table 2. Characteristics of CCT in Latin America and the Philippines

Characteristics

Latin Americ

a Phils.Targeted toward poor households Yes Yes

Female heads are recipients of cash transfer

Yes Yes

Program include nutrition and education program

Yes Yes

Cash transfers vary with number of children

Yes Yes

Size of cash transfer changes with age and gender of children

Yes No

Though the Pantaiwid Pamilyang Pilipino Program has the same objective of

alleviating current poverty, it falls short of addressing the increasing opportunity cost as

the child gets older. While in most Latin American countries the transfer for education

increases especially when the child is in high school, the Philippines does not observe this

practice. The transfer for each child is the same whether the child is in grade school or in

high school. An older child, one who is in high school, has a higher opportunity cost than

a grade school student because he is more likely to get a job and earn income.

Furthermore, Latin American countries pay a higher transfer to females to

encourage greater school attendance. School attendance for females in these countries is

very low compared to males. This is one way of empowering women and giving them

more opportunities to develop their capacity and become more productive. In the case of

7

the Philippines, transfer for education is the same for both male and female. Both males

and females receive the same amount of transfer.

To date, the 4Ps program was expanded to include children aged 15 to 18 years old

to ensure that they at least finish high school. There were a total of 709,644 children

registered as part of the expansion coverage. Moreover, it has also included homeless and

street families which were not covered in the original 4Ps program. This is called the

Modified Conditional Cash Transfer (MCCT). Based on the second quarter report of the

Department of Social Welfare and Development (2014), there are 2,325 households

covered in by the MCCT. This year, it also plans to include indigenous communities in

geophysically isolated and disadvantaged areas (DSWD, 2014).

3. Analytical Framework and Data

Evaluation Design

The core of the program is based on encouraging poor mothers to seek preventive

health services and to invest in the education and nutrition of their children by providing a

cash incentive for their healthy behavior (with healthy behavior representing performance).

The targeted beneficiaries/communities must be near schools and health centers, and

should have basic services such as electricity and potable water.

The expected outcomes of the 4Ps are increased food consumption, increased

investment on education measured by the increase in number of children enrolled in pre-

school, elementary and secondary schools, increased investment on health care which can

be measured by the increase in number of pregnant women getting pre-natal, postnatal care

and whose child birth is in a health facility and attended by health professionals, and

number of children 0-5 years old availing of preventive services and immunization.

In establishing the baseline, we used the Community Based Monitoring System

CBMS) data collected in 2008 for Bagac and the year 2009 for Pilar as baseline data. The

municipality Bagac served as the treatment group while Pilar was the control group. The

impact evaluation survey was conducted in April-May 2014 (4 years after the 4Ps was

8

implemented in Bagac). Data collected included information at the individual, household,

and community levels.

To assess the impacts of the 4Ps child nutrition and health outcomes, data height,

weight, mortality rate for children under five (0-4 years), children’s dietary intake,

morbidity, vaccination full coverage (access and use of preventive health services), percent

of healthy years of life lost due to communicable (respiratory infections, measles, tetanus)

and non-communicable diseases (mental retardation, congenital abnormalities), and

general health and disability conditions (self-reported by parents) were collected. In terms

of maternal health outcomes, the following data will be collected: mothers’ anthropometry

and knowledge on childcare, fertility rate, maternal mortality ratio, frequency of preventive

health check-ups, and maternal health status.

All of our impact estimates are based on the difference in differences. We define

“before” and “after” as follows: For the 2008/2009 entry group, we use the date of the first

payment in each municipality as a cutoff between the before and after periods. For

indicators measured at or after birth, we considered the birth preprogram if it occurred

before the payment date and post program if it occurred after the payment date. To break

up the control group into before and after periods as well, we used the median start date

among the 2009 entry group, November 1, as the cutoff between the before and after

periods. For prenatal care indicators, we defined the cutoff period slightly differently,

whereby the woman must be at least two months pregnant by the time of the initial

municipality-specific payment date as a cutoff. We used two months as the threshold

because by this time women are likely to be aware of the pregnancy and thus there is

potential for behavior change such as initiating a health clinic visit or prenatal care.

Focused group discussions were also conducted to assess the extent of community

driven development (including cash generating activities), women empowerment (role in

household decision-making, reduction of domestic violence and allocation of time use for

household chores and childcare), and the happiness and well-being of the sample

households (both treatment and control groups).

9

Randomization and matching-based design

To ensure the appropriate counterfactual is established, we randomly sampled

communities with and without CCTs that share similar socio-economic conditions. On

average, the provinces/communities have to be comparable on the basis of observable

characteristics (such as level of education, poverty threshold/distribution, distance to health

centers, distance to schools, access to electricity, distance to bank (Land Bank), and

others), hence we use existing data sources such as the CBMS socio-economic survey data

for Bagac, collected in 2008 and for Pilar in 2009).

Since CCTs have already been implemented purposively to selected poorest

barangays and municipalities based on a ranking system, we have to establish the control

group using non-experimental methods. It is desired that the control groups (non-4Ps) be

selected from neighboring municipalities that share similar socio-economic conditions. In

other words, it is important to control for potential endogeneity of program placement

using a combined geographic and household propensity score model (Abadie, Drukker,

Herr & Imbens, 2004). With adequate community and household information (using

baseline survey data from CBMS), we matched the community of Bagac to Pilar, a non-

4Ps community that has the same income class classification and similar socio-economic

conditions. This is to fully control for time-varying bias due to unobservable

characteristics, and avoid spillover effects on non-4Ps communities.

Important factors considered in using this approach are the choice of comparison

sample, the choice of matching variables, the extent of outcome variables, and the process

by which household beneficiaries were selected into the program. After selecting the

control group community, the poor households were randomly selected within Bagac and

Pilar using power calculations.

The selection of the poorest households was based on a ranking system using Proxy

Means Tests (it takes the household’s assets as a proxy or estimator for its means or

purchasing power), which means that households above the cutoff point are not eligible for

the 4Ps. Since the households just above the cutoff point are similar to the ones that are

10

just below it, except that they do not receive the cash transfers, we evaluate the impacts of

4Ps using propensity score matching (PSM).

Propensity score matching, as developed by Rosenbaum and Rubin (1983), is a

statistical technique that tries to estimate the effect of an intervention or treatment given

certain covariates that predict receiving the treatment. The PSM technique is used for

observational data to estimate the impact of an intervention and helps answer the question

“what is the treatment effect on the treated.” In the case of this study, we answer the

question what is the impact of the conditional cash transfer program on health outcomes of

the beneficiaries. Moreover, the PSM technique establishes the counterfactual, that is what

would have happened to the beneficiaries had they not received the conditional cash

transfer. Through the PSM a proper counterfactual can be found by matching a beneficiary

to a non-beneficiary with similar pre-intervention characteristics (also called covariates).

For the households with matched characteristics, each has an equal chance of becoming a

beneficiary or non-beneficiary (Capuno, 2013).

For observational studies such as this, the assignment of treatments to subjects is

not randomized and the PSM thus mimics an experiment by creating a sample of units that

received the treatment that is comparable on all observed covariates to a sample of units

that did not receive the treatment. The propensity scores generated are used to match a

beneficiary to a non-beneficiary, which is better than using covariates to match the two

groups because the latter has too many dimensions which may result in the failure of

common support (Capuno, 2013).

It is also possible to look at differences in trends differentiated between poor and

non-poor communities and thus conditions are quite different between these communities.

Given these differences, averaging outcomes in a community hides household-level

heterogeneity that potentially affects outcomes of the 4Ps. Impacts may also differ across

individuals. To reassess the average effect, appropriate econometric methods were used to

correct for bias arising from the correlations between the program placement and

individual and community characteristics that affect both the initial level and dynamic

changes of school attendance and preventive health care use. When outcomes can be

measured in changes, we will estimate the impact as the “difference in differences” (DID)

11

in the outcome between the treatment and comparison group. With DID, the effect of any

unobserved time-invariant differences between the treatment and comparison groups will

be removed thus estimates are less subject to selection bias.

Sample Selection Process

In determining the sample size for the treatment (beneficiaries) and control (non-

beneficiaries) group, we used estimates of statistical power used in assessing the impacts of

4Ps on household welfare (measured by per capita total consumption expenditure as proxy

for income). We worked backwards to determine the minimum change in household

welfare between 4Ps beneficiaries and non-4Ps that could be identified at the given sample

size for evaluating the 4Ps. Other outcomes for consideration in calculating the sample

size are proportion to visiting health facility and food share.

We followed the standard practice to find a sample size that would give an 80-85

percent change (i.e., the power of the test based on a randomized trial; note that accounting

for the effect of matching will increase the power of the evaluation design) of rejecting the

null hypothesis of zero change in household welfare (and other outcomes).

Power calculation and sample size calculation

The power calculation provides the minimum detectable effect (MDE), which

refers to the smallest actual program impact that can be statistically identified, for a given

sample. On the other hand, sample size calculation gives the minimum sample size in

order to detect a given level of actual program impact. The team will follow Duflo et al.

(2007) to calculate the minimum detected effect for the proposed sampling strategy. The

formula is as follows:

MDE= (Zα /2+Z β )√d σ2/ (na(1−a))

Where:

d = 1+ ρ(m − 1) is the design effect due to intra-cluster correlation, where is the intra-cluster correlation of households belonging to the same community, and m is the average number of households per community.

n is the total number of households;

12

a represents the proportion of the total sample that will be allocated to the intervention group;

σ2 is the baseline variance of the continuous indicator; α is the significance level to be used in the statistical tests; and 1 − β is the power of the test.

Closely related to the last formula, we used the following formula to compute the

sample size for the given level of actual program impact e (Duflo et al. 2007). The other

parameters have the same notations as explained earlier.

n=d {zα

2

+ zβ

e }2

σ2

a (1−a )

Since the power calculation and sample size calculation are closely related, as we

can see from the two formulas, our explanation will focus on the sample size calculation.

We take the following steps using the Annual Poverty Income Survey (APIS 2008):

key outcome variables were identified: probability of visiting health facility, food share, and per capita total expenditure

the variance (σ2) was obtained and within community correlation ( ) of the key outcome variables from APIS 2008.

α and 1-β were assigned the conventional values (α=5% or 10%, 1-β=80% or 90%). α indicates the chances of wrongly rejecting the null hypothesis of no program impact when the null hypothesis is true. β indicates the chances of wrongly accepting the null hypothesis when it is not true. So the lower α and β are, the larger the sample size is required.

e is the expected program impact which can be roughly determined by the estimation results from previous literatures. The smaller e is, the larger the sample size will be needed to detest the impact.

Our sampling size and strategy (m, n and a) is then determined based on the above parameters.

The Using the APIS 2008 to determine the MDE that would aid in randomly

selecting the participants in both Bagac and Bataan, and results show the following:

Table 3. Sample size calculation using 90% power

13

Mean S.D. Treated Control power alphaMDE_single

side

Attendance, elementary 0.925 0.959 230 230 0.9 0.05 0.262Attendance, secondary 0.686 0.747 230 230 0.9 0.05 0.204Prop visiting health facility 0.108 0.149 230 230 0.9 0.05 0.041Food share 0.653 0.714 230 230 0.9 0.05 0.195per capita expenditure (total family exp/famsize)--APIS 5841.85 4434.31 230 230 0.9 0.05 1211.560Source: authors' own calculations

The sample size was computed in Stata using the formula mentioned above, given a

power of 90%. To get the optimal representative of the treatment and control group, there

should be on the average 230 households each. From the master list of Bagac

municipality, 228 households were randomly selected. On the other hand, we used the

2009 CBMS data to randomly select households to serve as the control group and we were

able to get 213 households.

Proxy means test score using CBMS

Since the proxy means test score used in identifying the poor and non-poor

households was not available from the Department of Social Welfare and Development

(DSWD), we computed for our own scores based on the work of Reyes (2006) on

alternative means testing options. Using the CBMS in Pilar, Bataan for the year 2009, we

employed the alternative means testing options. The CBMS is a an enumeration of all

households in a particular municipality and includes core indicators of poverty such as

health, nutrition, shelter, water and sanitation, basic education, income, employment, and

peace and order (Reyes, 2006). This method was used to choose the non-poor in Pilar,

Bataan instead of using DSWD’s own proxy means test score.

Based on the study of Reyes (2006), there are three areas by which means testing

options can be carried out and these include income, ownership of assets and socio-

economic characteristics, and electricity consumption. If a household meets all the

requirements mentioned, they are considered non-poor.

14

On income, each household’s total income is divided by number of household

members and compared with the per capital threshold for Pilar, Bataan which is available

in from the National Statistical Coordination Board (NSCB). The per capital poverty

threshold in Pilar for 2009 is Php16,800. All households that fall above the poverty

threshold are considered non-poor and vice versa.

Table 4. Average income per capita

quantiles of total income per capita mean N

1 2,001.84

770

2 6,536.20

771

3 10,751.03

768

4 14,797.19

776

5 19,358.30

801

6 24,408.86

759

7 30,900.55

744

8 40,361.29

768

9 58,036.40

770

10 176,039.35

769

Source: Authors’ own caculations

Based on our computations, all households belonging to the 5th income decile and

above are considered non-poor. The rest are poor and will not be included in the sample.

The second category is ownership of assets and socio-economic characteristics.

Reyes (2006) explained that ownership of assets and socio-economic characteristics differ

from one income class to the other. Ownership of certain assets can distinguish the poor

from non-poor. Using the CBMS for Pilar, the following variables were used in

determining the poverty status of the household: access to water, type of toilet, type of

housing material, access to electricity, household head engaged in the agriculture, and

15

other appliances such as tv, vcd, washing machine, aircon, car, phone, computer, and

microwave oven.

These variables were combined into a composite index that can be used to

distinguish the poor from the non-poor. A logit regression was performed to determine

which of the variables are significant in determining poverty status based on income

(Reyes, 2006).

Table 5. Results of logit regression model

nonpoor1 Coef. P>|z|

Hwater** .168990 0.002Htoilet .073064 0.260Hwtv .103976 0.262Hwvcd** .323286 0.000Hwwmach** .349172 0.000Hwaircon** .403495 0.000Hwcar** .323941 0.000Hwphone* .170401 0.072Hwcomputer** .502040 0.000Hwmicrow* .160955 0.063Hwelec** -.171805 0.093Hmksft .281413 0.300Hnagri** -.271912 0.009_cons -.471435 0.089

Source: Author’s own calculations

Based on the results, owning certain appliances such as a vcd, washing machine, air

conditioner, computer and microwave show a positive correlation to being non-poor.

Moreover, owning a car and phone most likely to lead the household to being non-poor. If

the household works in the agriculture, it is most likely to be poor. Owning an electricity,

however, does not mean that the household is non-poor. On the other hand, working in the

agriculture will most likely make the household poor since income from the sector is

seasonal and very minimal. The aforementioned coefficients are significant at least at the

10% level.

16

Reyes (2006) also did some sensitivity and specificity tests to measure the

probability of the family being classified as non-poor and if the family classified as poor is

really poor. We employ the same method in our study and yield the following results:

Table 6. Predictive accuracy for probability cut-off of .50

-------- True --------Classified | Non-poor (D) Poor (~D) | Total-----------------------+---------------------------------------------+----------- Not eligible (+) | 3842 1958 | 5800 Eligible (-) | 917 1094 | 2011-----------------------+---------------------------------------------+----------- Total | 4759 3052 | 7811

Classified + if predicted Pr(D) >= .5True D defined as nonpoor2 != 0-----------------------------------------------------------------------------------Sensitivity Pr( +| D) 80.73%Specificity Pr( -|~D) 35.85%Positive predictive value Pr( D| +) 66.24%Negative predictive value Pr(~D| -) 54.40%-----------------------------------------------------------------------------------False + rate for true ~D Pr( +|~D) 64.15%False - rate for true D Pr( -| D) 19.27%False + rate for classified + Pr(~D| +) 33.76%False - rate for classified - Pr( D| -) 45.60%-----------------------------------------------------------------------------------Correctly classified 63.19%-----------------------------------------------------------------------------------


17


Classified + if predicted Pr(D) >= .6True D defined as nonpoor2 != 0----------------------------------------------------------------------------------Sensitivity Pr( +| D) 59.87%Specificity Pr( -|~D) 63.27%Positive predictive value Pr( D| +) 71.76%Negative predictive value Pr(~D| -) 50.27%----------------------------------------------------------------------------------False + rate for true ~D Pr( +|~D) 36.73%False - rate for true D Pr( -| D) 40.13%False + rate for classified + Pr(~D| +) 28.24%False - rate for classified - Pr( D| -) 49.73%----------------------------------------------------------------------------------

Correctly classified 61.20%----------------------------------------------------------------------------------



Classified + if predicted Pr(D) >= .7True D defined as nonpoor2 != 0----------------------------------------------------------------------------------Sensitivity Pr( +| D) 31.06%Specificity Pr( -|~D) 88.53%

18

Positive predictive value Pr( D| +) 80.85%Negative predictive value Pr(~D| -) 45.16%----------------------------------------------------------------------------------False + rate for true ~D Pr( +|~D) 11.47%False - rate for true D Pr( -| D) 68.94%False + rate for classified + Pr(~D| +) 19.15%False - rate for classified - Pr( D| -) 54.84%----------------------------------------------------------------------------------Correctly classified 53.51%



Classified + if predicted Pr(D) >= .8True D defined as nonpoor2 != 0----------------------------------------------------------------------------------Sensitivity Pr( +| D) 15.74%Specificity Pr( -|~D) 95.94%Positive predictive value Pr( D| +) 85.80%Negative predictive value Pr(~D| -) 42.20%----------------------------------------------------------------------------------False + rate for true ~D Pr( +|~D) 4.06%False - rate for true D Pr( -| D) 84.26%False + rate for classified + Pr(~D| +) 14.20%False - rate for classified - Pr( D| -) 57.80%----------------------------------------------------------------------------------Correctly classified 47.07%----------------------------------------------------------------------------------

Based on the results, the higher the cut-off rate, the more the poor are classified as

poor (specificity). At the 0.80 cut-off, the poor are correctly classified as poor 96% of the

time, which implies that exclusion is less than 10 percent. According to Reyes (2006), this

19

leakage can be reduced by the other criteria such as income and electricity. These socio-

economic characteristics and income were used to randomly select the non-poor from Pilar

to serve as the control group.

Propensity score matching

The propensity score generated through the PSM method is the probability of an

entity, the household is used in this study, being part of a group that receives the

conditional cash transfer intervention given a set of observed covariates or characteristics

(Cabuay et al., 2013). Suppose that we have a binary treatment T, an outcome Y, and

vector of variables X. The propensity score is defined as the conditional probability of

treatment given X as shown by equation below. It reduces the information in the covariates

into one number or propensity score.

p(x)=Pr(T=1|X=x)

Let Y(0) and Y(1) denote the potential outcomes under controland treatment

respectively. Then, the designation of treatment is conditionally unconfounded if treatment

is independent of potential outcomes conditional on X. This can be represented compactly

by

T ¿

Y(0), Y(1) | X

where¿

denotes statistical independence. On the other hand, if uncomfoundedness holds,

then the equation above holds. Moreover, Pearl (2009) has shown that a “backdoor”

provides an equivalent definition of unconfoundedness.

T ¿

Y(0), Y(1) | p(X)

In carrying out the PSM in this study, the following steps were implemented. First,

covariates (independent variables) that simultaneously influence participation into the

program as well as the outcome were selected. The covariates and outcome were then used

in the probit model that would help estimate the propensity score.

Since the study was not able to have access to pre-treatment data, we used time

invariant observable variables in the study. We used household characteristics such as

20

gender of the household head (1 if male, 0 otherwise), highest educational attainment of

the household, age of the household head, employment status of the household head, and

family size. The treatment variable shall be whether the household is a beneficiary or not

of the conditional cash transfer. The model was run against several health outcome

variables such as the number of time the mother received pre-natal care, weight of the

newborn baby, and the number of times a newborn baby went for check-up.

The model is described as:

Pr (CCT i=1 )=F ¿)

The equation above describes the relationship of the independent variable which

are the characteristics of the household to whether they are beneficiaries or non-

beneficiaries in Bagac and Pilar, Bataan.

The following independent variables are socio-economic characteristics that affect

the beneficiaries and non-beneficiaries. But since they have similar characteristics, the

quasi experiment is expected to yield results of the counterfactual, i.e. what would have

happened had the beneficiaries not received the CCT.

The first independent variable is age of the household head. As the household head

gets older, he or she is assumed to know better on how to take care of the health of the

children as the individual gains more experience with age. The second variable is the

highest educational attainment of the household head. According to human capital theory,

the higher the educational attainment, the higher chances of getting a job and earning an

income to meet the needs of the family, including the health requirements. This variable

takes on the value of 1 if the highest educational attainment is grade school, 2 if high

school, 3 if college, 4 if post-graduate, and 5 if technical vocation. The next variable is

whether the household head is employed or not. This variable takes on a value of 1 if the

household head is employed, 0 otherwise. The next variable is the gender of the household

head, 1 if male, 0 otherwise. If the household head is a female, the chances of the family

experiencing poor health is lower because she has income to supplement the family income

to attend to the needs of the family. The last variable is family size. A larger family size

could mean that there are more dependents in the family. Having more dependents in the

21

family means that income is spread thinly over the members and could result in less budget

allocated to health.

Descriptive statistics

The propensity score model was computed using various outcomes: (1) the number

of times the mother availed of pre-natal check-ups; (2) the frequency of pre-natal care

(once or more than once); (3) the weight of the child upon birth whose mother received

pre-natal care; and (4) number of times the newborn baby had check-ups. The table below

describes the variables that were used in the study.

Table 10. Descriptive statistics for beneficiaries and non-beneficiaries

Variable Description Obs Mean Std. Dev. Min Max

dependent

CCT beneficiary or non-beneficiary 441 1 0.5003 0 1

outcomes

upp13a_x number of times mother received pre-natal check-up 257 7 4.0730 1 40

upp14a_x frequency of prenatal care 205 2 0.9431 1 3

upp34a_xweight of child whose mom received pre-natal 218 7 8.8060 1 79

upp45a_xcheck-up of baby after delivery 246 1 3.6555 0 30

independentage2_x age 401 37 13.5841 3 81

higheduc2_x highest educational attainment 344 2 0.8035 1 4

Famsize family size 441 5 1.6706 1 10wrk3mos2_x employment 126 2 0.8159 1 6hmsex2_x sex 389 1 0.3489 1 2Source: authors' own calculations

22

There were a total of 441 households who were interviewed, 228 from Bagac

(treatment group/beneficiaries) and 213 from Pilar (control group/non-beneficiaries). The

following are the results of the outcome variables. On the average, mothers in both Bagac

and Pilar have availed of pre-natal check-ups at least 7 times. For Bagac, the average is 7

times while in Pilar, the average is 6 times. On the frequency of pre-natal care, the

households in Bagac get pre-natal care more often than those in Pilar. On the weight of the

baby upon delivery, the average weight for Bagac is 8.4 pounds and the average weight in

Pilar is 6.3 pounds. On the number of times the newborn had check-ups during the first 6

months, the average in Bagac is 2 times, and only once in Pilar.

Table 11. Descriptive statistics for non-beneficiaries (Pilar)


outcome



upp34a_xweight of child whose mom received pre-natal (lbs.) 110 6 2.6580 1 18




famsize family size 213 5 1.6218 1 10wrk3mos2_x employment 46 2 0.7727 1 3hmsex2_x sex 195 1 0.3890 1 2

The average age of the household head is 37 years old and is usually a male. This

supports the theory that families in the rural areas in the Philippines are mostly headed by

the male and is usually young. On the other hand, the highest educational attainment of the

23

household head is high school level and is usually employed on a part-time basis, i.e.

engaged in agricultural and fishing activities. The average family size for both

beneficiaries and non-beneficiaries is 5, but Pilar has an average family size of 4 members.

Table 12. Descriptive statistics for beneficiaries (Bagac)


outcome



upp34a_xweight of child whose mom received pre-natal (lbs.) 108 8 12.1604 2 79




famsize family size 228 5 1.6237 2 10wrk3mos2_x employment 80 2 0.8433 1 6hmsex2_x sex 194 1 0.2980 1 2

The number of household beneficiaries and non-beneficiaries was reduced to 68

and 14 respectively because of the matching that was done afterwards.

Results and analysis

Taking after the discussion above, the covariates or independent variables that

simultaneously affect participation and outcome were selected to estimate the propensity

score by running a probit regression model. After running the probit regression for

propensity score matching with common support, the results show that only one variable

appeared to be significant. The variable sex of the household head is significant at the

24

90% confidence level. If the household head is female, the family is most likely to be poor

and thus satisfy one of the conditions of being part of the CCT program. The household

head being the female denotes that the family income earned by the father is not enough to

support the family thus the mother has to engage in the labor market even in part-time jobs.

Table 13. Probit regression for PSM with common support

Number of obs = 107LR chi2(5) = 8.1Prob > chi2 = 0.1506

Log likelihood = -66.134834 Pseudo R2 = 0.0577

CCT Coef. Std. Err. z P>z[95% Conf. Interval]

age2_x -0.0000903 0.0120 -0.01 0.994 -0.0236 0.0234higheduc2_x -0.1130282 0.1584 -0.71 0.476 -0.4236 0.1975famsize 0.1126967 0.0748 1.51 0.132 -0.0340 0.2594wrk3mos2_x -0.0752647 0.1601 -0.47 0.638 -0.3891 0.2386hmsex2_x -0.8099164 0.4002 -2.02 0.043* -1.5944 -0.0253_cons 0.9777482 0.9596 1.02 0.308 -0.9030 2.8585

After performing the probit regression, the next test was to see whether there are

matched beneficiaries and non-beneficiaries using estimated propensity score along a

common support. Based on the table below, there is relatively a large overlap between the

treated and control observations.

Table 14. Area of common support

ModelLower limit Upper limit

Outcomes0.2714064

2 0.84776837

The graph below shows the propensity score distribution showing common support.

This implies that the distribution between both groups is similar between the average

propensity score and the mean of X to satisfy the balancing property.

25

Figure 1. Propensity score distribution with common support

01

23

Den

sity

0 .2 .4 .6 .8psmatch2: Propensity Score

The next step was to perform balancing tests to see whether the beneficiaries and non-

beneficiaries have balanced covariates. The test reveal the actual number of households

per inferior block of pscore, most of which are from the block .6 and this leads to a total of

106 households. Results reveal that the balancing property has been satisfied and that the

area of common support has been selected.

Table 15. Inferior bound, number of treated and control

CCTInferior of block of pscore 0 1 Total

0.2 7 4 110.4 10 6 160.6 20 56 760.8 1 2 3

Total 38 68 106

26

The last step done was to estimate the average treatment effect on the treated (ATT)

using the beneficiaries and matched non-beneficiaries. The difference in outcomes

between the beneficiaries and matched non-beneficiaries were computed. Using the

Nearest Neighbor Matching was used for the standard errors as well as the boostrapped

standard errors. The bootstrap method was used to create valid standard errors. The

default number of repetitions for sampling that was used in the data was 100 repetitions.

The results are summarized in the table below.

Table 16. Average treatment effect on the treated of all outcomes

Outcomes Treated Control ATTStd. Error t-stat

number of times mother received pre-natal check-up 68 14 2.351 0.995 2.364 bootstrapped SEs 68 14 2.351 1.066 2.206

frequency of prenatal care 68 14 0.675 0.305 2.216 bootstrapped SEs 68 14 0.675 0.305 2.212

weight of child whose mom received pre-natal 68 10 4.647 2.622 1.772 bootstrapped SEs 68 10 4.647 4.55 1.021

check-up of baby after delivery 68 13 1.133 0.372 3.047 bootstrapped SEs 68 13 1.133 0.592 1.912

The number of control observations used in the Nearest-Neighbor matching was

only 14, 10 and 13 for the outcomes number of times mother received pre-natal check-up,

frequency of prenatal care, weight of child whose mom received pre-natal, and check-up of

baby after delivery respectively.

27

Figure 2. Histogram of matched sub-samples along common support: number of times

mother received pre-natal check up.

0 .2 .4 .6 .8Propensity Score

Untreated Treated

Figure 3. Histogram of matched sub-samples along common support: frequency of pre-

natal care.

.2 .4 .6 .8Propensity Score

Untreated Treated

Figure 4. Histogram of matched sub-samples along common support: weight of new born

baby.

28

.2 .4 .6 .8Propensity Score

Untreated Treated

Figure 5. Histogram of matched sub-samples along common support: check-up of new

born baby.

0 .2 .4 .6 .8Propensity Score

Untreated Treated

All of the results are significant at the level of 90% except for the third outcome,

the weight of the child during birth. On the outcome on the number of times the mother

received pre-natal check-up, the ATT reveal that the likelihood that the mother will avail

of the pre-natal check-ups will increase by 2 times more compared to those who did not

receive the CCT. The same is true for the frequency of pre-natal care: it increases by .675

or at least 1 more if the household is a CCT beneficiary. On the weight of the child, the

weight of a new born baby increases by 5 percent (although only before the bootstrapping

29

was done because of the significance level) if the household is a CCT beneficiary.

Moreover, the number of times a new born baby increases by 1 percent if the household is

a CCT beneficiary.

Finally, a sensitivity test was performed to check the robustness of the model.

Based on the results, t-stat value is significant at 90% level, which mean that the results

generated earlier are reliable.

Table 17. ATT estimation with Nearest Neighbor Matching method

n. treat. n. contr. ATT Std. Err. t-stat

67 22 21.299 14.254 1.494

4. Conclusion

In this study, we used the Propensity Score Matching method to determine the

impact of the CCT on beneficiaries in Bagac and non-beneficiaries in Pilar, Bataan,

particularly in health outcomes such as the number of times the mother availed of the pre-

natal check-ups, its frequency, the weight of the new born child, and the number of times

the new born child had check-ups.

The covariates used in the study are pre-treatment characteristics which was helpful

in establishing the counterfactual. These covariates were the characteristics of the

household head such as the age, highest educational attainment, employment status, and

sex. The family size was also considered. The two underlying assumptions of PSM which

are conditional independence and common support were proven and this served as a robust

check for the model.

Results reveal that the average treatment effect on the treated improves the health

outcomes related to pre-natal, post-natal care, as well as the health of the new born child

for the household beneficiaries in Bagac, Bataan. There is a significant difference in

Bagac and Pilar in terms of the health outcomes. One of the significant results (at least

before bootstrapping) was on the weight of the new born. Since the mother beneficiaries

are required to undergo pre-natal check-ups, the positive effect created is the improvement

in the weight of the new born. New born babies of mother beneficiaries are 5% heavier (in

30

terms of pounds) than non-beneficiaries. This is an indication that the pre-natal health care

received is effective in improving the health of the mother and baby which contribute to an

increase in weight. Babies who are born underweight are most likely to be sickly, thus

affecting their productivity and performance when they grow older. As shown in the

results, the CCT helps in improving the weight of the new born through the pre-natal care

that the mother receives.

Investments in human capital, particularly health, have short term and long term

effects. This, however, has to be supplemented by other welfare enhancing strategies such

as improving the supply side factors such as the barangay health care units, among others.

Though there is a health center for each barangay, the supplies available as well as trained

personnel is sometimes not sufficient for the needs of the households in the barangay.

31

5. References

Abadie, Albert, David Drukker, Jane Leber Herr, and Guido W. Imbens. 2004.“Implementing Matching Estimators for Average Treatment Effect in Stata. The Stata Journal 4 (3rd Quarter, 2004), 290–311.

Chaudhury, N., J. Friedman, and J. Onishi. (2013). Philippines Conditional Cash Transfer Program Impact Evaluation 2012. The World Bank.

de Brauw, A., & Peterman, A. (2011). Can conditional cash transfers improve maternal health and birth outcomes? Evidence from El Salvador’s Comunidades Solidarias Rurales. International Food Policy Research Institute

Department of Social Welfare and Development (2013). Retrieved from http://www.dswd.gov.ph

Gantner, L. (2007). Case Study #5-1, "PROGRESA: An Integrated Approach to Poverty Alleviation in Mexico". In: Per Pinstrup-Andersen and Fuzhi Cheng (editors), "Food Policy for Developing Countries: Case Studies." 11 pp.Retrieved from: http://cip.cornell.edu/dns.gfs/1200428168

J. Capuno, CA Tan, Jr. and VM Fabella (2013). Do piped water and flush toilets prevent child diarrhea in rural Philippines? Asia Pacific Journal of Public Health

Molyneux, M. (n.d.) Conditional cash transfers: A ‘pathway to women’s empowerment’?. Retrieved from http://www.pathwaysofempowerment.org/PathwaysWP5-website.pdf

Rawlings, L. & Rubio, G. (2003). Evaluating the impact of conditional cash transfer programs: Lessons from Latin America. Retrieved from http://info.worldbank.org/etools/docs/library/79646/Dc%202003/courses/dc2003/readings/ccteval.pdf

Reyes, C. (2006). Alternative means testing options using CBMS. PIDS Discussion Paper Series No. 2006-22.

Rosenbaum, P.R. & Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal

Skoufias, E., & Parker, S.W. (2011). Conditional cash transfers and their impact on child work and schooling: Evidence from the PROGRESA program in Mexico. Economia.

Usui, N. (2011). Searching for effective poverty interventions: Conditional cash transfers in the Philippines. Asia Development Bank.

32

http://info.worldbank.org/etools/docs/library/79646/Dc%202003/courses/dc2003/readings/ccteval.pdf

http://info.worldbank.org/etools/docs/library/79646/Dc%202003/courses/dc2003/readings/ccteval.pdf

http://www.pathwaysofempowerment.org/PathwaysWP5-website.pdf

http://www.pathwaysofempowerment.org/PathwaysWP5-website.pdf

http://cip.cornell.edu/dns.gfs/1200428168

http://www.dswd.gov.ph/

Documents

Regional Integration in Asia - Web viewThe local context of the conditional cash transfer (CCT) is known as the Pantawid Pamilyang Pilipino Program (4Ps). It is a program under the