Externalities from the conditional cash transfer: evidence from Bataan,
Philippines *
Mitzie Irene P. Conchada
School of Economics
De La Salle University
mitzie.conchada@dlsu.edu.ph
Marites M. Tiongco
School of Economics
De La Salle University
marites.tiongco@dlsu.edu.ph
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
* 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.
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
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
1. Introduction
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 nonbeneficiaries; 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.
3
The program in Mexico was first implemented in 1997 and was called Progresa but
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
4
higher for boys aged 12 to 13, there is about 15 to 20 percent decrease in the probability of
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;
5

3 to 5 year old children must attend day care or pre-school classes at least
85 percent of the time;

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 014 years or pregnant
women
Php 300/month/child
aged 6-14 (up to 3
children)
has children aged 614
Education grant**
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
6
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.
Table 2. Characteristics of CCT in Latin America and the Philippines
Latin
America
Phils.
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
Yes
No
Characteristics
Targeted
toward
households
poor
Size of cash transfer changes
with age and gender of
children
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
7
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
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.
8
The expected outcomes of the 4Ps are increased food consumption, increased
investment on education measured by the increase in number of children enrolled in preschool, 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
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
9
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).
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 non4Ps community that has the same income class classification and similar socio-economic
10
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
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
11
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)
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 (nonbeneficiaries) 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
12
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 + 𝑍𝛽 )√𝑑𝜎 2 /(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;
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.
 z  z 
2
 n  d
e

2
  2

 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
13
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
Mean
Attendance, elementary
Attendance, secondary
Prop visiting health facility
Food share
per capita expenditure (total
family exp/famsize)--APIS
Source: authors' own
calculations
S.D.
Treated
Control
power
alpha
MDE_single
side
0.925
0.686
0.108
0.653
0.959
0.747
0.149
0.714
230
230
230
230
230
230
230
230
0.9
0.9
0.9
0.9
0.05
0.05
0.05
0.05
0.262
0.204
0.041
0.195
5841.85
4434.31
230
230
0.9
0.05
1211.560
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
14
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 socioeconomic characteristics, and electricity consumption.
If a household meets all the
requirements mentioned, they are considered non-poor.
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
1
2
3
4
5
6
7
8
9
10
mean
2,001.84
6,536.20
10,751.03
14,797.19
19,358.30
24,408.86
30,900.55
40,361.29
58,036.40
176,039.35
N
770
771
768
776
801
759
744
768
770
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
15
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
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
Hwater**
Htoilet
Hwtv
Hwvcd**
Hwwmach**
Hwaircon**
Hwcar**
Hwphone*
Hwcomputer**
Hwmicrow*
Hwelec**
Hmksft
Hnagri**
_cons
Coef.
.168990
.073064
.103976
.323286
.349172
.403495
.323941
.170401
.502040
.160955
-.171805
.281413
-.271912
-.471435
P>|z|
0.002
0.260
0.262
0.000
0.000
0.000
0.000
0.072
0.000
0.063
0.093
0.300
0.009
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
16
seasonal and very minimal. The aforementioned coefficients are significant at least at the
10% level.
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) >= .5
True 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
Table 7. Predictive accuracy for probability cut-off of .60
-------- True -------Classified
|
Non-poor (D)
Poor (~D) | Total
-----------------------+---------------------------------------------+----------Not eligible (+) | 2849
1121
|
3970
Eligible (-)
| 1910
1931
|
3841
-----------------------+---------------------------------------------+----------Total
| 4759
3052
|
7811
Classified + if predicted Pr(D) >= .6
True 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%
----------------------------------------------------------------------------------
Table 8. Predictive accuracy for probability cut-off of .70
-------- True -------Classified
|
Non-poor (D)
Poor (~D) | Total
-----------------------+---------------------------------------------+----------Not eligible (+) | 1478
350
|
1828
Eligible (-)
| 3281
2702
|
5983
-----------------------+---------------------------------------------+----------Total
| 4759
3052
|
7811
Classified + if predicted Pr(D) >= .7
True D defined as nonpoor2 != 0
---------------------------------------------------------------------------------Sensitivity
Pr( +| D) 31.06%
18
Specificity
Pr( -|~D) 88.53%
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%
Table 9. Predictive accuracy for probability cut-off of .80
-------- True -------Classified
|
Non-poor (D)
Poor (~D) | Total
-----------------------+---------------------------------------------+----------Not eligible (+) |
749
124
|
1828
Eligible (-)
| 4010
2928
|
5983
-----------------------+---------------------------------------------+----------Total
| 4759
3052
|
7811
Classified + if predicted Pr(D) >= .8
True 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
19
time, which implies that exclusion is less than 10 percent. According to Reyes (2006), this
leakage can be reduced by the other criteria such as income and electricity. These socioeconomic 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(𝐶𝐶𝑇𝑖 = 1) = 𝐹(𝛽1 + 𝛽2 𝑎𝑔𝑒𝑖 − 𝛽3 ℎ𝑖𝑔ℎ𝑒𝑑𝑢𝑐𝑖 − 𝛽4 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑𝑖 +
𝛽5 𝑔𝑒𝑛𝑑𝑒𝑟𝑖 + 𝛽6 𝑓𝑎𝑚𝑠𝑖𝑧𝑒𝑖 + 𝜀𝑖 )
The equation above describes the relationship of the independent variable which
are the characteristics of the household to whether they are beneficiaries or nonbeneficiaries 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
21
could mean that there are more dependents in the family. Having more dependents in the
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
beneficiary or nonbeneficiary
441
1
0.5003
0
1
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_x
weight of child whose mom
received pre-natal
218
7
8.8060
1
79
upp45a_x
check-up of baby after
delivery
246
1
3.6555
0
30
37 13.5841
3
81
CCT
outcomes
independent
age2_x
higheduc2_x
Famsize
wrk3mos2_x
hmsex2_x
Source: authors'
own calculations
age
highest educational
attainment
family size
employment
sex
401
22
344
2
0.8035
1
4
441
126
389
5
2
1
1.6706
0.8159
0.3489
1
1
1
10
6
2
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)
Variable
Description
Obs
Mean
Std.
Dev.
Min
Max
outcome
upp13a_x
number of times mother
received pre-natal check-up
134
6
3.0700
2
36
upp14a_x
frequency of prenatal care
101
2
0.9786
1
3
upp34a_x
weight of child whose mom
received pre-natal (lbs.)
110
6
2.6580
1
18
upp45a_x
check-up of baby after
delivery
126
1
0.9699
0
5
209
35
14.1929
3
81
163
2
0.8603
1
4
213
46
195
5
2
1
1.6218
0.7727
0.3890
1
1
1
10
3
2
independent
age2_x
higheduc2_x
famsize
wrk3mos2_x
hmsex2_x
age
highest educational
attainment
family size
employment
sex
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
23
the 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)
Variable
Description
Obs
Mean
Std.
Dev.
Min
Max
outcome
upp13a_x
number of times mother
received pre-natal check-up
123
7
4.9517
1
40
upp14a_x
frequency of prenatal care
104
2
0.9005
1
3
upp34a_x
weight of child whose mom
received pre-natal (lbs.)
108
8
12.1604
2
79
upp45a_x
check-up of baby after
delivery
120
2
5.0831
0
30
192
39
12.5704
4
70
181
2
0.7160
1
4
228
80
194
5
2
1
1.6237
0.8433
0.2980
2
1
1
10
6
2
independent
age2_x
higheduc2_x
famsize
wrk3mos2_x
hmsex2_x
age
highest educational
attainment
family size
employment
sex
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
=
LR chi2(5) =
Prob > chi2 =
Pseudo R2
=
Log likelihood = -66.134834
CCT
Coef.
age2_x
higheduc2_x
famsize
wrk3mos2_x
hmsex2_x
_cons
Std. Err.
-0.0000903
-0.1130282
0.1126967
-0.0752647
-0.8099164
0.9777482
z
0.0120
0.1584
0.0748
0.1601
0.4002
0.9596
P>z
-0.01
-0.71
1.51
-0.47
-2.02
1.02
0.994
0.476
0.132
0.638
0.043*
0.308
107
8.1
0.1506
0.0577
[95%
Conf.
Interval]
-0.0236
-0.4236
-0.0340
-0.3891
-1.5944
-0.9030
0.0234
0.1975
0.2594
0.2386
-0.0253
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
Model
Lower limit Upper limit
Outcomes
0.27140642 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
0
1
Density
2
3
Figure 1. Propensity score distribution with common support
0
.2
.4
.6
psmatch2: Propensity Score
.8
The next step was to perform balancing tests to see whether the beneficiaries and nonbeneficiaries 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
CCT
Inferior of block
of pscore
0
0.2
0.4
0.6
0.8
7
10
20
1
4
6
56
2
11
16
76
3
38
68
106
Total
26
1 Total
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
ATT
Std.
Error
t-stat
number of times mother received pre-natal check-up
bootstrapped SEs
68
68
14
14
2.351
2.351
0.995
1.066
2.364
2.206
frequency of prenatal care
bootstrapped SEs
68
68
14
14
0.675
0.675
0.305
0.305
2.216
2.212
weight of child whose mom received pre-natal
bootstrapped SEs
68
68
10
10
4.647
4.647
2.622
4.55
1.772
1.021
check-up of baby after delivery
bootstrapped SEs
68
68
13
13
1.133
1.133
0.372
0.592
3.047
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
Propensity Score
Untreated
.6
.8
Treated
Figure 3. Histogram of matched sub-samples along common support: frequency of prenatal care.
.2
.4
Propensity Score
Untreated
.6
.8
Treated
Figure 4. Histogram of matched sub-samples along common support: weight of new born
baby.
28
.2
.4
Propensity Score
Untreated
.6
.8
Treated
Figure 5. Histogram of matched sub-samples along common support: check-up of new
born baby.
0
.2
.4
Propensity Score
Untreated
.6
.8
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.
67
ATT
22
21.299
Std. Err. t-stat
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 prenatal 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
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