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DISCUSSION PAPERS IN ECONOMICS
Working Paper No. 06-02
Providing a Healthier Start to Life: The Impact of
Conditional Cash Transfers on Infant Mortality*
Tania Barham
Department of Agriculture and Resource Economics
U.C.Berkeley
May 2005
Center for Economic Analysis
Department of Economics
University of Colorado at Boulder
Boulder, Colorado 80309
© January 2006 Tania Barham,
Providing a Healthier Start to Life: The Impact of
Conditional Cash Transfers on Infant Mortality*
Tania Barham
Department of Agriculture and Resource Economics
U.C. Berkeley †
May, 2005
Abstract
In this paper, I evaluate the impact of Mexico's conditional cash transfer program, Progresa, on
infant mortality.
While studies on other aspects of Progresa make use of a randomized
treatment and control evaluation database performed in 506 communities, this database lacks
sufficient sample size to measure the effect on infant mortality. Instead, I use vital statistics data
to determine municipality-level, rural infant mortality rates and create a panel dataset covering
the period 1992-2001. I take advantage of the phasing-in of the program over time both between
and within municipalities to identify the impact of the program. I find that Progresa led to an 11
percent decline in rural infant mortality among households treated in Progresa municipalities.
Reductions are as high as 36 percent in those communities where, prior to program interventions,
the population all spoke some Spanish and had better access to piped water.
*
I would like to thank Elisabeth Sadoulet, Alain de Janvry, and Paul Gertler for their invaluable support of this
work. I am also grateful to Jean Lanjouw, Guido Imbens, and students and professors in the UC Berkeley
development community whom have provided so many useful comments. In addition, I am thankful to all those in
the Department of Health Economics at the Mexican National Institute of Public Health, Mexican Ministry of
Health, IMSS-Oportunidades and Oportunidades who graciously provided me with the data and their institutional
knowledge. Finally, this work was made possible by the financial support of the Institute of Business and Economic
Research at UC Berkeley and UC MEXUS.
†
PRELIMANARY DRAFT- please do not cite. Comments are welcome. Please direct correspondence to
[email protected]
1
Introduction
Every year more than 10 million children die from preventable diseases such as
malnutrition and intestinal infections in developing countries (World Bank, 2003). The majority
of these deaths take place during infancy, before the child reaches the age of one. 1
Consequently, finding effective policies to reduce mortality among infants is a key part of the
development agenda. This is evidenced by the selection of infant mortality as one of the targets
of the Millennium Development goals (World Bank, 2003). Conditional cash transfer programs
are a new type of social investment tool designed, amongst other goals, to improve the health of
children, but which may also lead to important reductions in infant mortality.
However,
empirically establishing causality between the implementation of conditional cash transfers and
infant mortality is difficult because the death of an infant is a relatively rare event. Thus, large
sample sizes are a requirement for accurate estimation. Even large household surveys commonly
do not have a sufficient number of observations to examine infant mortality. In 1997, Mexico
implemented one of the first, largest, and most innovative conditional income transfer programs,
Progresa. 2 Owing to its extensiveness, Progresa provides an opportunity to test the causality of
conditional cash transfers on the infant mortality rate (IMR). 3
In this paper, we use non-
experimental methods that exploit the phasing-in of Progresa over time throughout rural Mexico
to examine if this new policy tool reduced the rural IMR in Mexico.
1
According to the World Bank's World Development Indicators, the 2002 mortality rate for children (the probability
that a child dies before reaching the age of five per 1000 live births) in low and middle income countries was 88,
while the infant mortality rate (the probability that a child dies before the age of one per 1000 live births) was 60.
2
Progresa stands for Programa de Educatió n, Salud y Alimentació n. This program is now known as
Oportunidades.
3
The infant mortality rate is defined as the number of children in a given year who die before the age of one per
1000 live births in the same year.
Progresa differs from typical income transfer programs since the cash transfers to
beneficiary households are made conditional upon household members engaging in a set of
actions designed to improve their health, nutrition and education status. The aim of the program
is to build the human capital of young children and thereby break the intergenerational
transmission of poverty. Previous research on Progresa has taken advantage of a randomized
treatment and control evaluation database to investigate if the program improved various aspects
of children's health. 4 This research has shown that the nutritional status of children improved
and the number of days a mother reported her child ill decreased for treatment households as
compared to those from similar families that did do not receive the transfer (Behrman and
Hoddinott, 2001; Gertler and Boyce, 2001; Gertler, 2004). These findings indicate that there are
some important nutritional benefits of conditional cash transfers, but most other indicators of
children's health used in these studies rely on parent's recall and perceptions of good health
which have potential reporting biases. This paper therefore focuses on infant mortality, which is
a broader and more objective measure of children's health. 5
In addition, the sample size in the Progresa randomized treatment and control database is
too small to accurately estimate the impact of the program on infant mortality. This paper
resolves the sample size problem by constructing a panel data set of 2,399 municipalities 6 from
1992 to 2001 and uses a non-experimental research design. The treatment effect of Progresa on
4
The evaluation database is a panel of household surveys that contains information on the treatment and control
households both before and after the intervention.
5
The IMR is commonly used as a primary indicator of children's health, especially in developing countries. This is
partly due to inadequate information systems to gather data on child morbidity in many countries, and because
obtaining objective measures of children's health that does not rely on parent's recall or perceptions of good health is
difficult. In addition, infants are especially susceptible to many common diseases. Thus, their death rate serves as
an indicator of the overall health of children in areas that suffer from high rates of preventable diseases (Lederman,
1990).
6
In the 2000 Census there were 2445 municipalities in Mexico with an average population of 40,000 people and an
average size of 800 square kilometers. They are often compared to the size of a county in the US.
3
rural infant mortality is identified using the phasing-in of the program over time in rural Mexico.
This leads to a variation in the intensity of treatment indicator -- the percent of the rural
population covered by the program -- both within and between municipalities. The econometric
model employs municipality and time fixed effects, and includes variables associated with the
program phase-in rule to control for program timing bias. The analysis also explicitly controls
for changes in the supply of health care in rural areas. Additionally, the identification strategy
takes advantage of the fact that Progresa was not provided in urban areas prior to 2000, and uses
the urban IMR to test whether unobservable municipal time-variant variables are biasing the
results. Using these techniques, we find that the program led to a reduction of approximately 2
deaths per 1000 live births among program participants. From an average IMR of 18, this is an
11 percent reduction. Reductions in infant mortality were even higher in Progresa areas where,
prior to the program, houses had better access to piped water, fewer sewage systems, and in areas
where the population spoke some Spanish. 7
Furthermore, robustness checks show that the
program had no spurious impact on urban infant mortality, and also show that the impact is not
the result of an endogenous increase in the number of live births.
With the exception of Progresa, there is very little evidence at this time of the causal
impact of conditional or unconditional cash transfer programs on children's health outcomes or
mortality in other developing countries. Results from the Colombian conditional cash transfer
program show that while the number of episodes of acute diarrhea decreased among children less
6 years of age, there was no improvement in nutrition (Rawlings, 2004). In contrast, the
conditional cash transfer program in Nicaragua led to a significant reduction in malnutrition
7
Mexico has a large indigenous population and there are areas where this population does not speak Spanish.
4
(Maluccio & Flores, 2004). Studies on the effect of increasing the amount and coverage of the
social pension program in South Africa for the elderly black population found that income
transfers also led to nutritional improvements among girls (Duflo, 2003; Case, 2001). The
present study therefore makes an important contribution to the literature on health impacts of
cash transfer programs by investigating a different and important children's health indicator,
infant mortality. It is also the first study to use government administrative data to investigate
outcomes of conditional cash transfer programs that could not have been studied otherwise.
The remainder of the paper is organized as follows. Section 2 describes the Progresa
program including the targeting mechanism and the phase-in rule. A description of the data is
provided in section 3. The identification strategy, including an explanation of the sources of
variation in the treatment variable and the empirical model is presented in section 4. Results are
provided in section 5 and section 6 concludes.
2
The Rural Progresa Program
2.1
Background
Adopted in 1997, Progresa aims to break the intergenerational transmission of poverty by
improving the human capital of poor children in Mexico. The program targets the rural poor
reaching nearly 2.5 million rural households by 2000. The Progresa model is extremely popular
throughout the Latin American region and has been adopted by Argentina, Colombia, Honduras,
Jamaica, and Nicaragua.
Progresa is unique in that it combines two traditional methods of poverty alleviation: cash
transfers and free provision of health and education services. These programs are linked by
5
conditioning the cash transfers on children attending school and family members obtaining
sufficient preventative health care. Therefore, the income transfer not only relaxes the household
budget constraint, but also provides an increase utilization of health and education services.
While the program was first introduced in rural areas, it expanded into urban areas in 2000. This
study focuses on the rural program.
The health component of Progresa was designed to address many recalcitrant health
issues in rural Mexico. For instance, the program targets infants, children, and pregnant and
lactating women in an effort to ensure a healthier start to live. In addition, the cash transfers are
conditional on the household's participation in four important health activities:
1. growth monitoring from conception to age 5;
2. regular preventative health check-ups for all family members, including prenatal care,
well baby care and immunizations;
3. mother's attendance at health, hygiene and nutrition education programs; and
4. children ages 0-2 and pregnant and lactating women taking nutritional supplements.
Adequate prenatal care, medical assistance at birth, immunizations and good breastfeeding practices -- all aspects of the Progresa program -- are known to be important for proper
in-uterine growth of a child and for reducing the probability of infant death (Murata et. al., 1992;
Costello and Manandhar, 2000; World Bank, 2003). Thus, we may expect the program to reduce
infant mortality. In fact, research has shown that programs in the US that target poor families
and are similar to Progresa in terms of the type of health interventions, but do not provide an
income transfer, have led to reductions in infant mortality (Currie and Gruber, 1996; Devaney et.
al., 1990).
Since it was expected that health care utilization would rise as a result of the program,
Progresa coordinated with other government ministries responsible for health care delivery to
ensure an adequate supply and quality of health care in program areas. In addition, the program
6
used mobile clinics and foot doctors to reach many marginalized communities that did not have
access to permanent health clinics.
2.2
Targeting and Program Phase-In
Progresa used a two-stage process to identify eligible beneficiary households in rural
areas (Skoufias et. al., 1999). In the first stage, rural localities 8 were selected. Localities with
2,500 inhabitants or less are denominated as rural. 9 In order to meet the program's objectives,
localities where chosen based on a number of attributes. Localities were first ranked by a
marginality index 10 and only those with a high marginality 11 were included in the program.
Next, localities were screened to ensure access to primary and secondary schools as well as to a
permanent health care clinic.12
Finally, the program used population density data and
information on the proximity of localities to each other to determine the geographic isolation of
the locality. This information was used to identify groups of localities where the maximum
benefit per household in extreme poverty would be reached. As a result, any locality with less
8
A locality is a cluster of inhabited houses that can vary in size from 1 dwelling to over a million and has an average
population size of 489. Localities are grouped into municipalities. The 2000 census recorded that there were
199,391 localities in 2,445 municipalities in Mexico. This leads to an average of 80 localities in a municipality with
the range from 1 to 1630. A municipality is approximately 100 times larger than a locality with an average
population of 40,000 as compared to 489 in 2000. The average population in rural areas of a municipality is 10,306,
while the mean population of a rural locality is 125.
9
Of the 199,391 localities in the 2000 census 196,350 were rural. The average number of people living in a rural
locality is 126.
10
This index is constructed using the principal components method. The variables that make up the index include:
literacy rate; percent of dwellings with running water, drainage, and electricity; average occupants per room; percent
of dwellings with a dirt floor; and percent of labor force working in the agriculture sector.
11
The marginality index was divided into quintiles based on the degree of marginality (for details, see de la Vega,
1994). A grade of 5 indicates a high level of poverty and a grade of 1 a low level of poverty. Only those localities
with a marginality grade of 4 or 5 were considered.
12
A locality was considered to have access to a health care clinic if the clinic was either in the locality or in a
neighboring locality at most 15 kilometers away (Skoufias et. al., 1999).
7
than 50 inhabitants or that was determined to be geographically isolated was excluded from the
program.
While the exact program phase-in rule is not clearly documented, the general criteria are
known (Skoufias et. al., 1999). For logistical and financial reasons, the program was phased-in
over time starting with 2,578 localities in 7 out of 32 states in 1997 (Figure 1). In 1998, the
program was greatly expanded, reaching almost 34,000 localities and all but two states. In this
year, the requirement that localities must have access to a permanent health clinic was relaxed.
In 1999, localities that were eligible but not yet included and some localities which were
previously excluded due to geographical isolation were also incorporated into the program.
Once localities were selected, beneficiary households in each community were identified.
A census, called the Encaseh, was taken of all households in the program localities. This census
collected
information
on
household
income
and
characteristics
that
captured
the
multidimensional nature of poverty. Using these data, a welfare index was established and
households were classified as poor or non-poor. Only the poor became eligible for benefits.
Lastly, the list of potential beneficiaries was presented to a community assembly for approval.
As a result, a different percent of the rural population is covered by the program in each locality.
Recertification of eligibility began in 2000.
2.3
The Randomized Experiment
A prominent feature of Progresa is the randomization of 506 program localities in seven
states into treatment and control groups. Eligible households in treatment villages received
benefits immediately, while eligible household in control villages became part of the program
about 2 years later. A baseline survey was performed in October 1997 and six follow-up surveys
8
were taken at approximately 6 month intervals. The design was created in order to ensure
rigorous evaluation of the program impacts. The delay in the implementation of the program in
control villages was justified since the government lacked sufficient funds to provide the
program nationally from the outset. While many studies on Progresa take advantage of these
data, there are only two deaths of children under age one in the control areas in the postintervention period. For this reason, we use vital statistics data and a different identification
strategy to study the program impacts on IMR as explained in the following sections.
3
The Data
We construct infant mortality rates using 1992-2001 vital statistics data. The mortality
data are from a nation-wide database containing information on every registered death in Mexico
and were provided by the Mexican Ministry of Public Health. While these data are available at
the municipality level, they do distinguish whether the death occurred in a rural or urban locality
within that municipality. The live birth data are publicly available for every municipality in
Mexico from the Mexican Statistical Agency, INEGI, except for the state of Oaxaca in the year
2000. 13 These data are provided annually by municipality and size of the locality where the
mother who gave birth resided. The rural and urban infant mortality data are constructed by
linking these two databases by municipality. 14
13
While the urban and rural breakdown of the number of live births is missing for Oaxaca, the total number of births
is available from INEGI. To fill in the missing values for the number of rural births in 2000, we calculated the
average of the ratio of rural to total birth for 1999 and 2001 in Oaxaca. We multiply this ratio by the total number of
births in 2000. We used a similar process to determine the number of urban births.
14
Values for municipal rural infant mortality rates greater than 240 were set to missing. These values were removed
from the analysis because they are outliers. Removal of these values affected a total of 58 observations or less than
0.3 percent of the data.
9
The intensity of treatment indicator is the percent of rural households in a municipality
receiving Progresa benefits. It is determined using Progresa administrative data and INEGI
census data. Progresa provided administrative data on the number of households registered for
the program in December of each year. This information is available for each locality from the
inception of the program in 1997 to 2001 (Figure 1). However, we aggregate these data to the
municipality level since the infant mortality rate is only available at this level. Using INEGI
census data on the number of rural and urban households in a municipality for 1990, 1995 and
2000, we linearly interpolate the number of households for each year between 1992 and 2001.
Thus, the percent of rural households receiving program benefits is simply the ratio of the
number of beneficiary households to the total number of households in rural areas of a
municipality. 15
A variety of municipality characteristics are used as control variables in the analysis. The
marginality index is publicly available at the locality and municipality levels on the CONAPO
website for 1990, 1995 and 2000. Health supply data are not publicly available; we collected
them from the Ministry of Health and IMSS-Oportunidades at the locality level. Data on other
municipality characteristics were obtained from the INEGI 1995 Conteo 16 and 2000 Census and
are also at the locality level. Here again, this locality data is aggregated to the municipality level.
Lastly, the INEGI 1990 Census is used to provide information on some locality characteristics.
Using these data, a municipality-level, panel dataset was constructed from 1992-2001.
However, municipality boundaries were redefined during this time period. In order to make a
15
Approximately 2 percent of all positive values of the treatment variable are greater than one. These values are set
to missing.
16
The Conteo is a shorter version of the Census.
10
consistent panel of municipalities from 1992-2001, municipalities which were split in a
particular year are amalgamated. This results in a balanced panel of 2,399 municipalities.
4
Identification Strategy
4.1
Sources of Variation
The objective is to estimate the treatment effect of Progresa on rural infant mortality.
Ideally, we would compare the IMR in treated rural localities with the counterfactual ─the IMR
had Progresa not been available in the locality. Since the counterfactual is never observed, we
would take advantage of the phasing-in of the program over time and use rural localities yet to be
treated as the comparison group. Since infant mortality is not available at the locality level, we
instead investigate the impact of the program on municipality-level, rural IMR. Similar to
localities, new municipalities came onto the program over time between 1997 and 2001 (Figure
2) leading to variation in the intensity of treatment across municipalities over time. Therefore,
municipalities yet to be treated can be used as comparison municipalities. The identifying
assumption in this case is that the changes in infant mortality observed in the comparison group
are the same as in the treatment group had they not received the program. Although it is not
possible to test this assumption, we can test that the pre-intervention trends in infant mortality are
the same between municipalities that joined the program in different years. If the trends are the
same in the pre-intervention period, they are likely to have been the same in the post-intervention
period in the absence of the program.
We test that the pre-intervention trends in rural IMR, IMR r , between municipalities that
joined the program in different years are the similar. Two sets of dummy variables are used
11
ENTERk and YEARj, where k=1998-2001 and j=1991-1996. ENTERk takes on the value 1
if the first program locality of municipality m was phased-in during year k ,
otherwise.
YE ARj
and is zero
are year dummy variables for 1991-1996 (years prior to the program
introduction). Using data prior to 1997, the equation used to test the difference in trends is:
r
IMRmt
= β0 + ∑ β jYEARjt + ∑ ∑ θjkYEARjt ∗ ENTERkm + umt
j
j
k
(1)
If the  's are not significantly different from zero, then the pre-intervention trends do
not statistically differ between municipalities entering the program in subsequent years. Results
are reported in Table 1. With the exception of the group of municipalities that joined the
program in 2001 and those municipalities that have no Progresa, the results show that the preintervention trends in the rural IMR are not significantly different from municipalities that
entered the program in 1997. Municipalities that joined the program in 2001 and those that do
not have Progresa will therefore not be included in the comparison group.
Not all Progresa localities within a municipality were phased-in to the program during the
same year. As a result, the program intensity also varies over time within a municipality. For
example, Table 2 shows that there were 2,424 Progresa localities in 1997. In 1998, the number
of Progresa localities in those same municipalities almost doubled to 4,705. This variation in
program intensity within a municipality over time is another source of variation used to identify
the program impact.
Results may be biased if Progresa localities that were phased-in during different years
within a municipality are not similar. One way to reduce this bias is to control for the program
phase-in rule. Since localities that joined the program in 1997 had better access to permanent
12
health care clinics than those that joined the program later, we control for changes in the supply
of health care in rural municipalities, as well as the percent of Progresa localities with access to a
permanent health care clinic.
Furthermore, localities with lower population densities were
phased-in during 1999. While we do no know the density of rural areas of the municipality we
can control for the density as a whole for the municipality.
Ideally, we would also test that the pre-intervention trends in rural IMR are the same
between localities that were phased-in to the program in the various years. Since these data do
not exist, instead we examine if locality characteristics in the pre-program period (1995 or 1990),
and the change in locality characteristics between 2000 and the pre-program period are the same
across phase groups. To the extent that the level and change in locality characteristics are
correlated with the trends in rural IMR, their similarity across phase groups is an indication that
the trends in rural IMR are also likely to have been similar in these localities.
Table 3 presents the difference in locality characteristics across phase groups in the preintervention period. The means for localities that were incorporated into the program in 1997
(phase group 1997) are reported in the first row. The difference in the locality characteristics
between phase group 1997 and each of the other phase groups are reported in subsequent rows.
These differences in these means are almost all significant. With the exception of the percent of
population with dirt floors in 1990 and localities that where brought into the program in 2001,
means are within 10 percentage points. While these differences are arguably small, there is
concern that they could bias the results. The trends in the infant mortality rate between phase
groups may be more likely to be determined by the changes in locality characteristics rather than
their level. Table 4 presents the change in mean locality characteristics between 2000 and 1995
for localities that were phased-in during 1997 in the first row. The subsequent row show how
13
this change differed between the 1997 phase-in group and those localities the joined the program
in later years. Now the majority of the differences in the changes between phase group 1997 and
each of the other groups are not significant (Table 4). In order to account for these differences in
the observables, these variables are included as covariates. If the findings do not vary when these
variables are included, it is hoped that similar changes in the unobservables would also not bias
the results. However, locality observables must be aggregated to the municipality level in order
to be included in the analysis.
In addition, inclusion of municipality fixed effects controls for biases due to differences
in time-invariant variables across municipalities arising from non-random program placement
(Rosenzweig and Wolpin, 1986). The estimate of the treatment effect will be unbiased if there
are no unobserved time-varying municipality characteristics or trends that are correlated with the
intensity of treatment variable. If this is the case, the urban infant mortality rate should not be
affected since the program targeted rural areas. However, if there were important omitted
municipality time trends correlated with the treatment variable, we would expect to find an
impact of the program on urban infant mortality due to the unobservables. Therefore, in the
results section we also present results for urban IMR to test if there are municipality time trends
that could be biasing the results. Lastly, we will also present a validity check where we include a
time trend for each municipality to account for further variation over time between
municipalities resulting from to these unobservables.
4.2
Graphical Analysis
Due to the variation in the intensity of treatment both between and within municipalities
over time, it is difficult to show the treatment effect graphically. However, graphs can provide
14
suggestive evidence. In Figures 3-5, trends in average municipality rural IMR are provided for
three groups of municipalities, based on the year the program was first offered in the
municipality. Only municipalities that entered the program in 1997, 1998 and 1999 are shown
on the graphs. Municipalities that entered in 2000 are not displayed since there are just 12
observations. Those that joined in 2001 are also excluded since the pre-intervention trend for
this group is statistically different from the other municipalities. Trends in urban IMR over the
same time period are presented in Figure 6. Finally, since program intensity varies between
municipalities, trends in rural IMR are also presented only for municipalities that had an average
program intensity of 30 percent or more over the program period (Figure 7).
If Progresa is successful, there should notice a break in the trend in rural IMR soon after
the program entered the municipality. However, since the program intensity increased over time
within a municipality, these breaks may not be visible in the first year of the program. Mean
municipality program intensity by year for each of the three groups is presented in Table 5. The
first group of municipalities began to receive the program in 1997. Only 24 percent of rural
households in these municipalities were covered by the program in that year. In 1998, the
program was greatly expanded covering 55 percent of rural households in these same
municipalities. Thus, there may be a larger impact of the program in 1998 rather than 1997 for
this group. Figure 3 demonstrates that this is indeed the case for the municipalities that entered
the program in 1997. The break in the trends for the two other groups occurs the year the
program entered the municipalities. We verify that these breaks are not due to general trends in
the municipalities by presenting a similar graph for urban IMR. As expected, there are no breaks
in the trend in urban IMR the year the program entered the municipalities.
15
4.3
Empirical Model
We develop the empirical model by first considering a cohort of infants that dies in year
t , in municipality m . Whether an infant dies, ( D  1 , during that year depends on (i)
whether the infant was born in a household registered for Progresa benefits or not that year, and
if the infant's mother was registered for the program during her pregnancy , H t ,
H t−1 ,
H t−2 ;
(ii) mother and household characteristics, I , and; (iii) municipality characteristics such as the
supply of health care or the quality of the environment (both time-varying and time-invariant),
X . Time fixed effects are included to control for time trends. Assuming a linear relationship,
t−j
Pr(Dimt = 1) = αt + ∑ β j H imt
+ ∑ φg I imtg + ∑ φp Xmtp + εimt ,
j
g
p
(2)
where im t indexes infant i born alive in municipality m in year t , and j  0 − 2 . Year
fixed effects are represented by  t , and  im t is the error term, which is assumed to have a zero
mean and be orthogonal to the independent variables.
There are a number of variables in equation individual that are not observed in the data.
The indicator variable H im t (if child im t is from a program household or not) does not exist at
the individual level in the dataset, however, the probability of treatment at the municipality level
does. This probability is the percent of live births to beneficiary households in municipality m
in year t , and is the same for all infants in the municipality. Thus, we use this value in lieu of
the individual H im t . Also, mother and household characteristics of the infant are not available
in the Mexican vital statistics.
16
Given the lack of individual-level data and because mortality is identified at the
municipality level, equation individual is aggregated to the rural municipality level as follows:
∑Dimt
i ∈I m
where
t −j
= N mt αt + ∑ β j PBmt
+ N mt ∑ φp Xmtp + ∑εimt ,
j
p
(3)
i ∈I
N m t is the population of the infants (<1 year old) born alive in the rural areas of
municipality m in year t and I m is the set of infants born alive in municipality m . The
dependent variable is now the number of deaths among infants born alive in a municipality in a
given year, and the treatment variable, PB m t , is the number of live births in municipality m in
year t to Progresa households in year t − j . To make comparisons across municipalities,
equation summation is normalized by the number of live births in each municipality. At the
municipality level, the equation is written as follows:
1
N mt
∑Dimt = αt + ∑ βj
j
i ∈I
t−j
PBmt
ε
+ ∑ φp Xmtp + ∑ imt .
N mt
p
i ∈I N mt
(4)
The database provides information on the number of program households not the number
of births to Progresa households, PB . Assuming that the fertility rate remains constant over the
t−j
period of the program (1997 - 2001), we redefine
PB mt
N mt
to be the ratio of the number of
beneficiary households over the total number of households in rural areas of the municipality in a
given year. This is the intensity of treatment or program intensity variable, referred to as
Intensity . Municipality fixed effects are also added to equation normalize to control for timeinvariant municipality characteristics that could be correlated with both infant mortality and
program intensity due to program placement bias.
The estimation equation is
17
r ,t − j
r
r
IMRmt
= αt + τm + ∑ β j Intensitymt
+ ∑ φp Xmtp
+ umt ,
j
where the
r
p
(2.5)
superscript is added to emphasize that these data are for rural areas of the
municipality. The dependent variable is now labeled IMR r since it is a measure of the rural
infant mortality rate. Heteroskedasticity and serial correlation maybe both be present in the error
term. Thus, the regressions are weighted by the number of rural households 17 and robust
standard errors that are corrected for serial correlation 18 are used. The estimate of the treatment
effect of Progresa on the treated is measured by the  's, while the average treatment effect can
be calculated by multiplying the impact on the treated by the average of the Intensity.
5
Results
5.1
General Impact of the Program
We start by estimating the treatment effect of Progresa on the rural IMR. Columns 1
through 5 of Table.6 present different specifications for estimating this impact. The adjusted R 2
is the same for each of the specifications, and the lag of the treatment variable, program
intensity, consistently provides the only significant result. Therefore, the specification depicted
in column 5, which includes only the lag of program intensity as an explanatory variable, is the
primary estimation of the treatment effect.
This result shows that among the treated the
probability of an infant dying is reduced by almost 2 deaths per 1000 live births on an average of
18 deaths, or 11 percent. At the municipality level, the percent of rural households covered by
17
While the equations suggest weighting by the number of live births, this variable suffers from under-reporting in
Mexico so the number of rural households is used because it provides a more consistent weight.
18
The correction for serial correlation is made by clustering the standard errors at the municipality level.
18
the program reached an average of 47 percent. Therefore, the average treatment effect is a 5
percent reduction in the rural IMR.
5.2
Spillover Effects
Reduction in infant mortality among the treated may be overestimated due to the inability
to exclude non-eligibles (non-poor in a locality) from benefiting from the improved health
supply or due to program spillover effects. While cash transfers are only provided to
beneficiaries, improvements in the health supply associated with the program could potentially
lead to mortality reduction in the non-eligible group. Furthermore, program beneficiaries may
inform those not in the program of the health gains they experienced from increased health care
utilization or share their knowledge from the health education session. These health spillover
effects could also generate lower infant mortality rates among the untreated.
Bobonis and Finan (2002) study health spillover effects and find no indication of such
effects on the incidence of illness or on self-reported health indicators for children. This provides
partial evidence that spillover effects may not be a concern. However, it may be that women's
health behaviors during pregnancy and their child's infancy are not related to behaviors that
affected the children's health outcomes mentioned above. While this question can be investigated
further using the randomized treatment and control evaluation database, the average treatment
affect reported in this paper provides a lower bound on the impact of the program on the treated.
5.3
Validity Checks
Although the model controls for time-invariant unobserved municipal heterogeneity, it
cannot control for unobserved time-varying municipality factors that may be correlated with the
19
treatment variable and infant mortality. We take advantage of the fact that Progresa mainly
operated in rural localities before 2001 and test whether the program had a significant impact on
urban IMR. 19
If there are indeed municipal-level omitted variables, program intensity might
also impact urban IMR due to these unobservables. Table 6, column 6, shows that the program
had no significant impact on urban IMR, thereby providing some evidence that unobservables
are not biasing the results.
A further concern is that during program implementation there was an expansion of
health care in rural communities. To control for possible biases, information on per capita health
care infrastructure and personnel are included in the regression equation. Although many of
these regressors are likely to be endogenous, if their inclusion does not influence the coefficient
on the lag of the program intensity, we gain some confidence that health care supply is not
correlated with the phasing-in of the program. The results in columns 1 to 3 of Table 7
demonstrate that the program impact remains unchanged.
During the first three years of the program, two criteria for choosing localities were
relaxed. After 1997, the condition that beneficiaries had to have access to permanent health
clinic no longer applied as mobile clinics and foot doctors also provided health care in many
areas. Also, in 1999, localities that had a lower population density and were isolated from other
Progresa localities were incorporated in the program. We include a variable defined as the
percent of rural Progresa localities with access to permanent health clinic in a given year to take
into account the first change in the phase-in rule. The addition of this control has almost no
effect on the estimate of the impact and is not significantly different from zero (Table 7, column
19
There are some semi-urban localities that joined the program before 2000. The program did expand to urban
localities in 2000 but this should not affect our analysis.
20
4). Additionally, we control for the density of the municipality and the inclusion of this variable
also does not change the estimate of the impact (Table 7, column 5).
We also control for all other observable time-varying municipality characteristics and
individual municipality time trends (Table 8). The municipality characteristics are generated
from the locality census data and are the municipality means made by aggregating data for
localities that received Progresa benefits before 2001. 20 The results do not differ if municipality
characteristics for the rural areas or the municipalities as a whole are used. Columns 1-8 clearly
show that adding the available covariates does not affect the estimate of the impact. However,
once a time trend is added for each municipality to account for the trends in unobservables, the
impact of the program on infant mortality is higher.
Progresa leads to a reduction of
approximately 3 deaths per 1000 live births, or 17 percent among the treated. This estimate is
still inside the 95 percent confidence interval for the impact of the program with the individual
time trends in column 5 of Table 7. However, the result suggests that omitted other time-varying
municipal characteristics may result in an under-estimate of the effect of Progresa on infant
mortality.
Finally, as discussed earlier, the means and changes in means of locality characteristics
across phase-in groups were arguably small but significantly different. Using data on 1995
locality characteristics, we estimate the municipality mean by aggregating the data only for
localities that received Progresa for that particular year. So, as localities are phased-in, the
municipality mean will change to reflect the difference in pre-intervention characteristics of the
phase-in groups. 21 Results are presented in Table 9 and demonstrate that the point estimate of
20
At present, the locality data is only available for 1995 and 2000. Therefore, we linearly interpolate between these
points to generate data for the missing years.
21
The municipality mean is set to zero in the time period before Progresa is available in a municipality.
21
the treatment effect varies from -1.6 to -2.6. However, none of these values are significantly
different from the comparable program impact of -1.86 in column 5 of Table 7.
5.4
Under-Reporting of Births and Death
Under-reporting of both births and deaths is common in rural Mexico. The fact that the
urban municipality IMR is higher than the rural municipality IMR is partly a reflection of this
phenomenon. As long as the under-reporting does not change in a manner that is correlated with
the lag of program intensity the estimates will be unbiased. However, one might be concerned
that mothers in program areas may be more likely to register their child's birth in hopes of
receiving a cash transfer in the future. Or, more babies may be born alive due to increased
prenatal care utilization or improved mother's health. Thus, it is possible that the program
impact is a result of an increase in the number of registered live births rather than a reduction in
mortality. To investigate if this is the case, the impact of Progresa on the number of registered
lives births per 1000 population in a municipality is also examined.
Results in Table 10
demonstrate that the treatment variable, the lag of program intensity, had no impact on the
number of live births per 1000 population. Thus, the estimate of the program impact is not the
result of an endogenous increase in the number of births 22 .
5.5
Heterogeneity of the Treatment Effect
Data from 1995 is used to examine if the program impact varies by pre-intervention
characteristics of Progresa areas within the municipalities.23 Findings from Table 11 highlight
22
Skoufias, 2001 reports a similar result.
Since the 1995 Conteo data is available at the locality level, it is possible to calculate the characteristics of just the
localities that eventually receive Progresa in a municipality.
23
22
that the program was more successful at reducing infant mortality in municipalities where
Progresa areas had better access to piped water, less access to sewage systems, and where all the
population spoke some Spanish. The treatment effect does not vary due to differences in the
percent of households with electricity 24 or the percent of the population 15 years of age or older
that are literate.
In particular, program impacts are higher in municipalities where at least 75 percent of
households in Progresa localities had access to piped water prior to the intervention.
Approximately a third of the Progresa municipalities fall into this group. The treated in these
municipalities experienced a reduction in infant mortality of approximately 5 deaths per 1000
live births, while those in areas with less access to piped water only experienced a reduction of
1.7 deaths. Given that the mean rural IMR over the sample period for the group of municipalities
with better access is 19 as compared to 17 in areas with less access, this represents a decline in
infant mortality of 28 and 10 percent respectively. The average percent of beneficiary rural
households in municipalities in 1999 for these same groupings is 40 as compared to 46.
Therefore, the average treatment effect of the program resulted in a 4 percent reduction in rural
IMR in those municipalities where access to piped water is lower and a 12 percent decline in
those municipalities with better access to piped water 1995.
The program also led to a much greater reduction in rural IMR in Progresa localities
where the population over four years of age all spoke some Spanish. This is the case for 57
percent of the municipalities in the estimation sample. In particular, the rural IMR for the treated
declined by 6 deaths per 1000 live births, on an average rural IMR of 17, or 33 percent. The
average intensity of treatment in these municipalities reached 35 percent, so for these
24
This is significant at the 10.5 percent level.
23
municipalities as a whole the infant mortality rate declined by 13 percent. In contrast, the rural
infant mortality rate declined by 2 deaths per 1000 live births in areas where some of the
population in Progresa areas only spoke an indigenous language. The mean rural IMR was 18
and the program intensity reached 53 percent in these areas. Therefore the rural IMR fell by 11
percent among the treated and 6 percent on average in these municipalities.
Lastly, the reductions in rural IMR mainly took place in the three quarters of the
municipalities where less than 30 percent of the households in Progresa localities had some type
of sewage system prior to program implementation. The decline in infant mortality among the
treated in these areas is similar to the main impact of the program at 2 deaths per 1000 live
births, or 11 percent. 25
The treated in those municipalities with better access to sewage
experienced almost no decline in their infant mortality as a result of the program. However, the
average rural IMR was also lower in these areas prior to the program at 17 as compared to 19.5
in areas with less access to sewage. This may seem contradictory to the results from piped
water, but less than 35 percent of the municipalities had Progresa areas with both good access to
piped water and sewage systems.
6
Discussion
The conditional cash transfer program, Progresa, led to a significant decline in infant
mortality in rural Mexico. Findings suggest that the program resulted in an 11 percent reduction
of the infant mortality rate among the treated. While we cannot test if there are spillover effects
using the present dataset, their possible presence may lead to an over-estimation of the impact.
25
Approximately 40 percent of the observations fall into the group with better electricity access. The mean IMR for
this group is 19 as compared 17 in areas with less access, and the intensity of treatment is 40 percent in areas with
more access as compared to 46.
24
The average treatment effect, which is a 5 percent reduction in the rural infant mortality rate in
municipalities where some of the population received Progresa, is on the other hand a lower
bound on the estimate of the impact on the treated. Given that on average the rural IMR fell by
less than 1 percent each year between 1992 and 1996, these are large declines in infant mortality.
Program effects were even greater in areas where, prior to the program, Progresa
localities had better access to piped water, and a population that spoke some Spanish. In
particular, infant mortality declined by 28 and 33 percent among the treated in Progresa areas
that had more piped water and not only indigenous language speakers respectively. The declines
in infant mortality also mainly occurred in Progresa areas where fewer houses had a sewage
disposal system prior to the program. The municipalities that had a relatively high level of
sewage disposal, experienced little reduction in their mortality rate, though the mortality rate was
lower in these areas before the program.
Unfortunately, it is somewhat difficult to interpret these results since these variables
could be proxies for a number of different attributes. It is often argued that piped water is
correlated with clean water; if this is the case, these findings highlight that there is an association
between having safe drinking water prior to the program and more substantial reductions in the
rural IMR from conditional cash transfers in Mexico. Also, if the presence of a sewage system is
a proxy for a sanitary environment, larger reductions in rural IMR are also associated with areas
that were less sanitary prior to the program. This may be a result of the health education
component of Progresa. However, these are just hypotheses and these data cannot provide
further evidence. They would be interesting questions to examine further using the nutrition
information from the randomized treatment and control database.
25
We presented evidence on the internal validity of these results. We showed that the
program did not lead to a reduction in the urban IMR which might have been the case if the
phasing-in of the program over time was correlated with other municipality trends. We also
controlled for the change in the supply of free health care in rural areas. This is important since
Progresa worked closely with other ministries to ensure an adequate supply of health care. In
addition, we provided evidence that the reduction in infant mortality is not the result of an
endogenous increase in the number of live births.
It is also of interest to policy makers to understand the mechanisms that led to this
reduction in infant mortality in Mexico. Extensions of this work will examine this question by
taking advantage of the randomized treatment and control database to explore the kinds of health
behavior changes that occurred as a result of Progresa. For example, among other factors we
will explore: if treated babies weighted more at birth than non-treated babies; if treated mothers
received more prenatal care, were more likely to have their delivery attended by a medical
attendant, or had better knowledge of how to make oral rehydration salts; and, if treated families
were more likely to make home improvements leading to a more sanitary environment.
26
7
References
Alderman, H. 1986. The Effects of Income and Food Price Changes on the Acquisition of Food
by Low-Income Households, International Food Policy Research Institute, Washington,
D.C.
Alderman, H. 1993. "New Research on Poverty and Malnutrition: What are the Implications for
Research and Policy?" in M. Lipton and J. Van der Gaag eds., Including the Poor, The
World Bank, Washington, D.C.
Attanasio, O. and R., Victor. 2000. "Consumption Smoothing in Island Economies: Can Public
Insurance Reduce Welfare?" European Economic Review, 44(7), 1225-58.
Behrman, J. and J. Hoddinott. 2001. Program Evaluation with Unobserved Heterogeneity and
Selective Implementation: The Mexican Progresa Impact on Child Nutrition, Penn
Institute of Economic Research Working Papers no. 35.
Behrman, J., and A. Deolalikar. 1987. "Will Developing County Nutrition Improve with
Income? A Case Study for Rural South India," Journal of Political Economy, 95(3), 108138.
Behrman, J. and P. Todd. March 1999. Randomness in the Experimental Samples of Progresa.
International Food Policy Research Institute, Washington, D.C.
Bobonis, G and F. Finan. 2002. Do transfers to the Poor Increase the Schooling of the
Nonpoor? The Case of Mexico's Progresa. Mimeo, University of California at Berkeley.
Case, A. October 2001. Does Money Protect Health Status? Evidence from South African
Pensions. NBER working paper w8495.
Case, A., D. Lubotsky, and C. Paxson. June 2001. Economic Status and Health in Childhood:
The Origins of the Gradient, NBER working paper w8344.
Chay K., M. Greenstone. July 2001. The Impact of Air Pollution on Infant Mortality: Evidence
From Geographic Variation in Pollution Shocks Induced by a Recession. Mimeo,
University of California at Berkeley.
Conapo. 2001. La Poblacion de Mexico en el Nuevo Siglo. Mexico City.
Cook, C., K. Selig, B. Wedge, and E. Gohn-Baube. 1999. "Access to Barriers and the Use of
Prenatal Care by Low-Income, Inner-City Women," Social Work, 44(2), 129-39.
Costello, A. and D. Manadhar eds. 2000. Improving Newborn Infant Health in Developing
Countries. Imperial College Press, London.
27
Currie, J. and J. Gruber. 1996. "Saving Babies: The Efficacy and Cost of Recent Changes in the
Medicaid Eligibility of Pregnant Women", The Journal of Political Economy, 104(6),
1263-1296.
Currie, J. 1994. "The Welfare and the Well-Being of Children: The Relative Effectiveness of
Cash and In-Kind Transfers," Tax Policy and the Economy, 8, 1-43.
Cutler, D. and L. Sheiner. 1997. Managed Care and the Growth of Medical Expenditures,
National Bureau of Economic Research Working Paper 6140.
Cutler, D., M. McClellan, J. Newhouse. 2000. “How Does Managed Care Do It?” Rand Journal
of Economics, 31, 526-548.
De la Vega, S. 1994. Construccion de un Indice de Marginacion. Masters thesis in Statistics and
Operations Research, UNAM, Mexico City, Mexico.
Devany, B., L. Bilheimer, J. Shore. 1990. The Savings in Medicaid Costs for Newborns and
Their Mothers from Prenatal Participation in the WIC Program, Mathematic Policy
Research Inc., Washington, D.C.
Duflo, E. 2003, "Grandmothers and Granddaughters: Old-Age Pensions and Intra-household
Allocation in South Africa", The World Bank Economic Review, 17(1): 1-25.
Gertler, P. 2000. Final Report: The Impact of Progresa on Health. International Food Policy
Research Institute, Washington D.C.
______2004. "Do Conditional Cash Transfers Improve Child Health? Evidence from
PROGRESA's Control Randomized Experiment," American Economic Review, 94(2),
336-341.
Gertler, P. and S. Boyce. 2001. An Experiment in Incentive Based Welfare: The Impact of
Mexico's Progresa on Health. Mimeo, University of California at Berkeley.
Heckman J. and V. Holtz. 1989. "Choosing Among Alternatives Non-Experimental Methods for
Estimating the Impact of Social Programs: The Case of Manpower Training", Journal of
the American Statistical Association, 84(408), 862-72.
Lederman R. 1990. "Infant Morality and Prenatal Care," in J.N. Natapoff and R.R. Wieczorek
eds., Maternal-Child Health Policy: A Nursing Perspective, Springer Publishing
Company, New York.
Legovini, A. and F. Regalia. 2001. Targeted Human Development Programs: Investing in the
Next Generation, Inter American Development Bank Sustainable Development
Department Best Practices Series.
28
Maluccio, J. and R. Flores. 2004. Impact Evaluation of a Conditional Cash Transfer Program:
The Nicaraguan Red De Proteccion Social. International Food and Policy Research
Institute, Washington D.C.
Meyer D. 1995. "Natural and Quasi-Experiments in Economics", Journal of Business &
Economics Statistics, 151-161.
Murata, P., E. McGlynn, A. Siu, R. Brook. 1992. Prenatal Care: A Literature Review and
Quality Assessment Criteria, Rand, Santa Monica, CA. USA
Oportunidades. 2003. Información General [on-line]. [Accessed on August 5, 2003].
<http://www.progresa.gob.mx/informacion_general/15072003
/historico%20de%20Cobertura.htm>
Rawling, L. and G. Rubio. 2003. Evaluating the Impact of Conditional Cash Transfer Programs:
Lessons from Latin America. World Bank Policy Research Working Paper 3119, The
World Bank, Washington DC.
Rosenzweig, M. and K. Wolpin. 1986. "Evaluating the Effects of Optimally Distributed Public
Programs: Child Health and Family Planning Interventions," The American Economic
Review, 76(3), 470-482.
Schultz, P. 2001. School Subsidies for the Poor: Evaluating the Mexican Progresa Poverty
Program, Yale Economic Growth Center Discussion Paper: no. 834.
Skoufias, E., and S. Parker. 2001. "Conditional Cash Transfers and Their Impact on Child Work
and Schooling: Evidence from the PROGRESA Program in Mexico," Journal of the
Latin American and Caribbean Economic Association, 2(1), 45-86.
Skoufias, E. December 2001. PROGRESA and its Impacts on the Human Capital and Welfare of
Households in Rural Mexico: A Synthesis of the Results of an Evaluation by IFPRI.
International Food Policy Research Institute, Washington D.C.
Skoufias, E., B. Davis, S. Vega. December 1999. Targeting the Poor in Mexico: An Evaluation
of the Selection of Households into OPORTUNIDADES, International Food Policy
Research Institute, Washington, D.C., USA
Strauss, J., and D. Thomas. 1997. "Health and Wages: Evidence on Men and Women in Urban
Brazil," Journal of Econometrics, 77(1), 159-185.
Strauss, R. 2000. “Adult Functional Outcome of These Born Small for Gestational Age:
Twenty-six-year Follow Up on the 1970 British Birth Cohort”, JAMA, 283(5):625-32.
29
Szilagyi, P. 1998. “Managed Care for Children: Effect on Access to Care and Utilization of
Health Services,” The Future of Children, 8(2):39-59.
The World Bank. 2003. Millennium Development Goals [on-line]. [Accessed on October 21].
Available from the World Wide Web <http://ddpext.worldbank.org/ext/MDG/gdmis.do>.
30
Tables and Figures
Figure 1: Trends in the Number of Progresa Beneficiary Families and Localities.
Rural Localities
3000
70
2500
60
50
2000
40
1500
30
1000
20
500
10
0
Number of rural localities in
1000s
Number of Rural
beneficiaries in 1000s
Rural Beneficiaries
0
1997
1998
1999
2000
2001
Year
Figure 2: Number of New Program Municipalities by Year.
1400
1318
1200
Number of Municipalities
8
1000
800
644
600
400
200
119
118
12
0
1997
1998
1999
Year
31
2000
2001
Figure 3: Trends in Rural IMR for Municipalities That Enter the Program in 1997.
18
Rural IMR
Upper and Lower CI
IMR
16
14
12
10
8
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Figure 4: Trends in Rural IMR for Municipalities That Entered the Program in 1998.
Rural IMR
Upper and Lower CI
18
IMR
16
14
12
10
8
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
32
Figure 5: Trends in Rural IMR for Municipalities That Enter the Program in 1999.
20
Rural IMR
Upper and Lower CI
18
IMR
16
14
12
10
8
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Figure 6: Trends in Urban IMR by Year Municipality Entered Program.
24
22
1997
1998
1999
IMR
20
18
16
14
12
10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
33
Figure 7: Trends in Rural IMR by Municipality Entry Date.
18
1997
1998
1999
IMR
16
14
12
10
1992
1993
1994
1995
1996
1997
1998
1999
2000
Year
Note: Only municipalities with an average program intensity of at least 30% included.
34
2001
Table 1: Difference in Pre-Intervention Trends in Rural Infant Mortality Rate by Date Municipality Entered Program.
Year
Municipalities
that entered in
1997
Mean IMR 1990 = 21.17
1991
1992
1993
35
1994
1995
1996
-3.704
[0.903]
-3.758
[0.863]
-4.605
[0.892]
-4.624
[0.908]
-4.519
[0.871]
-4.609
[0.905]
Difference in IMR between municipality by entry date compared to 1997
No Progresa
1998
1999
2000
2001
5.813
[6.765]
-3.436
[4.660]
-5.882
[4.626]
-10.010**
[4.346]
-12.081***
[4.192]
-10.494**
[4.194]
0.99
[0.999]
-1.809*
[0.952]
-1.289
[0.979]
-0.822
[0.996]
-0.54
[0.960]
-1.45
[0.991]
0.462
[1.349]
-1.065
[1.305]
-0.495
[1.301]
0.31
[1.330]
-1.182
[1.324]
-1.07
[1.344]
17.876
[22.539]
16.823
[12.120]
-3.135
[10.327]
-5.713
[11.242]
2.781
[12.304]
20.293
[29.969]
3.793
[2.534]
2.415
[2.612]
-0.148
[2.435]
2.221
[2.354]
5.315**
[2.557]
-2.145
[2.204]
Notes:
1. Standard errors in brackets.
2. * significant at 10%; ** significant at 5%; *** significant at 1%.
3. See equation 1 for the specification of the equation corresponding to these results.
4. 1990 was the year left out and municipalities that entered in 1997 was the group of municipalities left out.
5. Column 2, is the decrease in the rural IMR between 1990 (21.17) and the other years for municipalities that entered in 1997.
6. Column 3, is the difference in the decrease in rural IMR between municipalities that entered in 1997 and those that never received Progresa.
7. Columns 4-7 show the difference in the decrease in rural IMR between municipalities that entered in 1997 and those that entered in later years.
Table 2: Number of New Program Localities Between 1997-2001 by the Date the Municipality Started the Program.
Year the Municipality
Entered the Program
1997
1998
1999
2000
2001
1997
2,424
1998
4,705
28,261
Year
1999
5,560
35,222
16,726
2000
5,538
440
240
46
2001
5,927
9,413
2,548
23
376
36
Table 3: Differences in Means of Pre-Program Locality Characteristics, by phase group.
37
Percent of
Average MarginalPercent of Households With
ization
number of
Workers Indigenous Illiterates
Dirt
Dirt
Piped Sewage Electricity
occupants in Grade
in the
speakers
floor
floor water
(1995)
(1995)
(1995)b
c
a
a
household
primary
(1990)
(2000)
(1995)
(00-95)
(1995)
(1995)
sector
(1990)
Mean for Phase Group 1997
76.4
22.7
27.1
5.4
4.5
71.9
50.2
41.4
13.2
65.2
[0.6]
[0.7]
[0.4]
[0.0]
[0.0]
[0.8]
[0.6]
[1.0]
[0.6]
[1.1]
Differences in Means Between other Groups and Phase Group 1997
Phase 1998 - Phase 1997
2.8***
-3.9***
1.5***
0.1***
0.1***
-13.5*** 1.6** -4.7***
-2.9***
-5.5***
[0.6]
[0.8]
[0.4]
[0.0]
[0.0]
[0.9]
[0.7]
[1.0]
[0.6]
[1.2]
Phase 1999 - Phase 1997
-2.4***
-6.1***
-0.7*
-0.2***
-0.4*** -28.3*** -5.4*** 3.9***
5.7***
-3.0**
[0.7]
[0.8]
[0.4]
[0.0]
[0.0]
[0.9]
[0.7]
[1.1]
[0.6]
[1.2]
Phase 2000 - Phase 1997
0.2
-5.3***
-0.6
-0.2***
-0.5*** -32.0*** -8.0***
1.9
6.1***
-2.8
[1.5]
[1.2]
[0.8]
[0.1]
[0.1]
[1.9]
[1.5]
[2.5]
[1.4]
[2.2]
Phase 2001 - Phase 1997
2.3***
-5.5***
2.1***
-0.2***
-0.1*** -33.7*** 1.4** -7.9***
1.3**
-20.4***
[0.7]
[0.8]
[0.4]
[0.0]
[0.0]
[0.9]
[0.7]
[1.1]
[0.6]
[1.2]
Observations
53624
63771
63771
Notes:
a. Percent of population over 4 year olds.
b. Percent of population over 14 year olds.
c. The marginalization grade ranges from 0 to 5 with 5 being the most marginalized.
1. Standard errors in brackets.
2. * significant at 10%; ** significant at 5%; *** significant at 1%.
3. Time and municipality fixed effects included.
63771
64213
64328
62023
63771
63771
63771
Table 4: Change in Mean Locality Characteristics Between 2000 and Pre-Program Time Period, by phase group.
Percent of
Workers Indigenous
In the
Speakers
Primary
(00-95)a
Sector (0090)
Mean for Phase Group 1997 -10.292
-0.406
[0.640]
[0.229]
Iliterates
(00-95)b
-2.595
[0.264]
Percent of Households With
Average MarginalDirt Floor
Piped
Sewage Electricity
Number of
ization
(00-90)
Water
(00-95)
(00-95)
Occupants
Grade
c
(00-95)
in a
(00-95)
Household
(00-95)
-0.461
-0.368
-21.838
7.921
6.936
11.947
[0.022]
[0.017]
[0.897]
[0.845]
[0.720]
[0.961]
38
Differences in the change between other Phase Groups and Phase Group 1997
Phase 1998 - Phase 1997
0.305
0.326
-0.377
0.012
0.002
[0.666]
[0.238]
[0.273]
[0.023]
[0.018]
Phase 1999 - Phase 1997
1.058
0.264
0.329
0.056**
0.368***
[0.694]
[0.244]
[0.287]
[0.025]
[0.019]
Phase 2000 - Phase 1997
1.745
-0.032
-0.658
0.081
0.345***
[1.533]
[0.521]
[0.576]
[0.063]
[0.049]
Phase 2001 - Phase 1997
3.034***
0.332
0.323
0.114***
0.245***
[0.744]
[0.250]
[0.304]
[0.026]
[0.019]
Observations
58039
68043
68043
Notes:
a. Percent of over 4 year olds.
b. Percent of over 14 year olds.
c. The marginalization grade ranges from 0 to 5 with 5 being the most marginalized.
1. Robust standard errors in brackets.
2. * significant at 10%; ** significant at 5%; *** significant at 1%.
3. Time and municipality fixed effects taken out.
68043
68859
14.346***
[0.945]
21.426***
[0.983]
22.513***
[1.944]
33.007***
[1.031]
0.07
[0.871]
-4.926***
[0.905]
-6.218***
[2.234]
-5.154***
[0.941]
1.745**
[0.744]
0.679
[0.773]
-0.828
[1.647]
-1.158
[0.792]
0.853
[1.000]
-3.413***
[1.018]
-3.552**
[1.714]
-1.221
[1.049]
67661
68043
68043
68043
Table 5: Mean Municipality Program Intensity by the Year the Municipality
Entered the Program.
Year the Municipality
Entered the Program
1997
1998
1999
1997
0.24
1998
0.55
0.34
Year
1999
0.59
0.46
0.30
2000
0.55
0.44
0.29
2001
0.57
0.49
0.36
Notes
1. Program intensity is defined as the proportion of rural household receiving Progresa benefits in
December of a given year.
39
Table 6: Impact of Progresa on IMR.
Program intensity
[1]
-0.812
[0.736]
Lag of program intensity
[2]
0.169
[0.682]
-1.909**
[0.873]
Lag of lag of pogram intensity
Rural IMR
[3]
0.1
[0.679]
-2.164***
[0.820]
0.202
[0.787]
Lag of program intensity squared
Year 1993 (=1)
Year 1994 (=1)
Year 1995 (=1)
Year 1996 (=1)
Year 1997 (=1)
Year 1998 (=1)
Year 1999 (=1)
Year 2000 (=1)
Year 2001 (=1)
Observations
Adjusted R2
Mean of dependent variable
Municipality fixed effects
[4]
[5]
-4.868** -1.898**
[2.304]
[0.865]
-0.365
[0.270]
0.100
[0.298]
0.177
[0.310]
-0.527*
[0.290]
-1.161***
[0.294]
-1.162***
[0.371]
-2.283***
[0.450]
-2.752***
[0.474]
-3.521***
[0.508]
-0.366
[0.270]
0.100
[0.298]
0.177
[0.310]
-0.527*
[0.290]
-1.180***
[0.294]
-1.429***
[0.363]
-2.185***
[0.468]
-2.316***
[0.564]
-3.194***
[0.566]
-0.366
[0.270]
0.100
[0.298]
0.177
[0.310]
-0.527*
[0.290]
-1.179***
[0.295]
-1.418***
[0.363]
-2.094***
[0.461]
-2.334***
[0.585]
-3.147***
[0.647]
3.861
[2.776]
-0.366
[0.270]
0.101
[0.298]
0.178
[0.310]
-0.527*
[0.290]
-1.176***
[0.294]
-1.331***
[0.303]
-1.798***
[0.451]
-1.869***
[0.598]
-2.764***
[0.598]
18891
0.59
17.5
Y
18852
0.59
17.5
Y
18818
0.59
17.51
Y
18956
0.59
17.5
Y
Urban IMR
[6]
0.38
[1.388]
-0.366
[0.270]
0.101
[0.298]
0.178
[0.310]
-0.527*
[0.290]
-1.176***
[0.294]
-1.361***
[0.304]
-2.110***
[0.406]
-2.249***
[0.534]
-3.170***
[0.514]
-1.392***
[0.284]
-1.750***
[0.361]
-1.406***
[0.348]
-2.757***
[0.401]
-3.013***
[0.374]
-4.440***
[0.414]
-5.045***
[0.446]
-5.213***
[0.731]
-5.867***
[0.946]
18956
0.59
17.5
Y
12164
0.56
19.07
Y
Notes:
1. Standard errors in brackets. Standard errors are robust and clustered at the municipality level.
2. * significant at 10%; ** significant at 5%; *** significant at 1%
3. All regressions are weighted by number of rural/urban households in municipality.
4. Program intensity is defined as the proportion of rural household receiving Progresa benefits in December of a given year.
5. IMR=infant mortality rate, it is the number of deaths before the age of 1 per 1000 live births.
40
Table 7: The Impact of Progresa on IMR Controlling for Health Supply.
Lag of program intensity
[1]
-1.898**
[0.865]
[2]
-1.790**
[0.887]
[3]
-1.836**
[0.888]
18956
0.59
17.5
Y
Y
18940
0.58
17.51
Y
Y
Y
18940
0.58
17.51
Y
Y
Y
Y
% of Progresa localities with free health clinic
Population density
Observations
Adjusted R2
Mean of dependant variable
Year effects
Municipality fixed effects
Health infrastructure
Health personnel
Rural IMR
[4]
[5]
-1.920** -1.889**
[0.864]
-0.867
0.011
[0.023]
0.365
[1.11]
18956
0.59
17.5
Y
Y
18940
0.59
17.5
Y
Y
[6]
-1.883**
[0.888]
0.014
[0.024]
-2.922
[2.577]
Urban IMR
[7]
0.168
[1.387]
0
[0.020]
-5.489
[5.929]
18940
0.58
17.51
Y
Y
Y
Y
12164
0.56
19.07
Y
Y
Y
Y
Notes:
1. Standard errors in brackets. Standard errors are robust and clustered at the municipality level.
2. * significant at 10%; ** significant at 5%; *** significant at 1%
3. All regressions are weighted by number of rural/urban households in municipality.
4. Program intensity is defined as the proportion of rural household receiving Progresa benefits in December of a given year.
5. IMR=infant mortality rate, it is the number of deaths before the age of 1 per 1000 live births.
6. Health clilnic information for SSA and IMSS-SOL only. This is health infrastructure for the uninsured.
7. Health infrastructure variables are all per 1000 population and include the number of: rural clinic rooms, mobile clinics, hospitals, walking health teams
8. Health personnel variables are all per 1000 population and included the number of: doctors, residents, and nurses in contact with the patient in rural areas.
41
Table 8: The Impact of Progresa on IMR Controlling for Municipality Characteristics and Time Trends.
Lag of program intensity
Rural IM R
[1]
[2]
[3]
[4]
[6]
[7]
[8]
[9]
-1.919** -1.968** -1.970** -1.869** -1.855** -1.899** -1.825** -3.06***
[0.898] [0.898] [0.898] [0.901] [0.910] [0.885] [0.890]
[0.96]
Municipality characteristics for localities that eventually receive Progresa benefits
Percent of households with :
Piped w ater
0.007
[0.020]
Electricity
0.066
[0.040]
Sew age
-0.014
[0.018]
Percent of:
Rural population >4 that
0.118
speaks an indigenous language
[0.145]
Rural population >14 that is
illiterate
Average num ber of occupants in
rural households
Observations
18804
Adjusted R 2
0.58
M ean dependent variable
17.55
Year effects
Y
M unicipality fixed effects
Y
Health supply controls
Y
Individual m unicipality tim e trends
N
0.003
[0.021]
0.070*
[0.041]
-0.013
[0.018]
-0.030
[0.017]
-0.016
[0.0030]
-0.017
[0.018]
0.132
[0.161]
-0.032
[0.037]
1.78
[1.692]
0.113
[0.131]
0.787
[1.990]
-0.033
[0.152]
-2.681
[1.700]
18804
0.58
17.55
Y
Y
Y
N
18804
0.58
17.55
Y
Y
Y
N
0.07
[0.120]
18804
0.58
17.55
Y
Y
Y
N
18804
0.58
17.55
Y
Y
Y
N
18804
0.58
17.55
Y
Y
Y
N
18804
0.58
17.55
Y
Y
Y
N
Notes:
1. Standard errors in brackets. Standard errors are robust and clustered at the m unicipality level.
2. * significant at 10%; ** significant at 5%; *** significant at 1%
3. All regressions are weighted by num ber of rural/urban households in m unicipality.
4. Program intensity is defined as the proportion of rural household receiving Progresa benefits in Decem ber of a given year.
5. IMR=infant m ortality rate, it is the num ber of deaths before the age of 1 per 1000 live births.
42
Urban IM R
[10]
[11]
-0.513
0.53
[1.480] [1.006]
18940
0.62
17.55
Y
Y
Y
Y
12037
0.56
19.04
Y
Y
Y
Y
12164
0.62
19.04
Y
Y
Y
N
Table 9: The Impact of Progresa on IMR Controlling for Municipality Characteristics in Progresa Areas.
Lag of program intensity
[1]
-2.623***
[0.911]
[2]
-2.074**
[0.922]
Rural IM R
[3]
[4]
-1.602*
-2.002**
[0.934]
[0.923]
Municipality characteristics for localities that receive Progresa benefits
Percent of households w ith :
Piped w ater
-0.027***
[0.006]
Electricity
-0.009
[0.006]
Sew age
0.012
[0.009]
Percent of:
R ural population >4 that
0.003
speaks an indigenous language
[0.006]
Rural population >14 that is
illiterate
Average num ber of occupants in
rural households
O bservations
Adjusted R 2
M ean dependent variable
Year effects
M unicipality fixed effects
Health supply controls
18940
0.58
17.55
Y
Y
Y
18804
0.58
17.55
Y
Y
Y
18804
0.58
17.55
Y
Y
Y
18804
0.58
17.55
Y
Y
Y
[6]
-2.117**
[0.895]
[8]
-2.602***
[0.989]
U rban IM R
[10]
0.684
[1.230]
-0.033***
[0.007]
0.009
[0.012]
0.019*
[0.010]
0.027**
[0.011]
-0.003
[0.014]
0.046*
[0.027]
-0.09
[0.075]
-0.003
[0.008]
0.037*
[0.020]
-0.158
[0.210]
0.004
[0.010]
-0.022
[0.026]
-0.283
[0.226]
18804
0.58
17.55
Y
Y
Y
18804
0.58
17.55
Y
Y
Y
12037
0.56
19.04
Y
Y
Y
[7]
-1.892**
[0.888]
0.016
[0.011]
18804
0.58
17.55
Y
Y
Y
Notes:
1. Standard errors in brackets. Standard errors are robust and clustered at the m unicipality level.
2. * significant at 10% ; ** significant at 5% ; *** significant at 1%
3. All regressions are weighted by num ber of rural/urban households in m unicipality.
4. Program intensity is defined as the proportion of rural household receiving Progresa benefits in Decem ber of a given year.
5. IMR=infant m ortality rate, it is the num ber of deaths before the age of 1 per 1000 live births.
43
Table 10: Impact of Progresa on the Number of Registered Live Births per 1000 Population.
Lag of program intensity
[1]
0.344
[1.273]
Rural
[2]
-0.124
[1.247]
Urban
[3]
-1.249
[0.785]
Observations
Adjusted R2
Mean dependent variable
Year effects
Municipality fixed effects
Health suppy controls
20922
0.49
31.63
Y
Y
N
20842
0.5
31.59
Y
Y
Y
12709
0.63
30.88
Y
Y
Y
Notes:
1. Standard errors in brackets. Standard errors are robust and clustered at the municipality level.
2 * significant at 10%; ** significant at 5%; *** significant at 1%
3. All regressions are weighted by number of rural/urban households in municipality.
4. Program intensity is defined as the proportion of rural household receiving Progresa benefits in December of a given year.
5. IMR=infant mortality rate, it is the number of deaths before the age of 1 per 1000 live births.
44
Table 11: Heterogeneity of the Impact of Progresa on IMR by Pre-Intervention Municipality Characteristics.
Lag of program intensity
[1]
-2.048**
[0.909]
[2]
-1.653*
[0.911]
Rural IM R
[3]
-1.759*
[0.924]
[4]
-1.945**
[0.908]
[5]
-1.962**
[0.900]
Interaction of the Lag of Program Intensity with an indicator variable that in 1995:
30-100% of households in Progresa villages
1.818*
have a sew age system
[1.020]
-3.630***
[1.081]
75-100% of households in Progresa villages
have piped water into household
-1.617
[1.000]
91-100% of households Progresa villages have
electricity in the houseold
0.179
[1.038]
80-100 % of over 15 year olds are literate in
Progresa villages
-3.715*
[2.152]
0 % of the population only speaks an
indigenous language in Progresa villages
Observations
Adjusted R 2
M ean dependent variable
Year effects
M unicipality fixed effects
Health suppy controls
Other municipality characteristics 1
18792
0.58
17.56
Y
Y
Y
Y
18792
0.58
17.56
Y
Y
Y
Y
18792
0.58
17.56
Y
Y
Y
Y
18792
0.58
17.56
Y
Y
Y
Y
Notes:
1. These municipality characteristics are an aggregation of the locality characteristics of Progresa areas only.
2. Standard errors in brackets. Standard errors are robust and clustered at the municipality level.
3. * significant at 10%; ** significant at 5% ; *** significant at 1%
4. All regressions are weighted by number of rural/urban households in municipality.
5. Program intensity is defined as the proportion of rural household receiving Progresa benefits in December of a given year.
6. IMR=infant mortality rate, it is the num ber of deaths before the age of 1 per 1000 live births.
45
18792
0.58
17.56
Y
Y
Y
Y
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