This document summarizes a thesis paper examining the socioeconomic impact of malaria control and eradication in Venezuela. It provides background on the malaria situation in Venezuela prior to a DDT campaign launched in 1945. Venezuela had the highest malaria mortality rate in Latin America before extensive control efforts. The campaign introduced DDT over four years between 1945-1948, varying by region. Exposure to the campaign during childhood is used to classify cohorts and analyze the impact on educational and economic outcomes like years of schooling, literacy rates, and income. Results found exposure in childhood led to better outcomes, indicating malaria negatively impacts development.

1. Hamlin 1
The Socioeconomic Impact of Malaria
Control and Eradication in Venezuela
Brittany Hamlin
Dr. Hyuncheol Kim, Thesis Advisor

2. Hamlin 2
The Socioeconomic Impact of Malaria Control and Eradication in
Venezuela
Brittany Hamlin
Cornell University
April 2015
Abstract
This paper examines the long-term socioeconomic impact of malaria exposure and
eradication in childhood. Research has shown that early life health conditions are an
important factor in human capital accumulation and adult outcomes. This report
investigates the effect of malaria eradication during one’s childhood on socioeconomic
outcomes. To investigate this relationship, this study uses the DDT campaign in
Venezuela to approximate the magnitude by which malaria eradication affects the
socioeconomic measures of years of schooling, literacy rates, and adult earned income.
The DDT campaign was introduced over a four-year period from 1945-1948 in
Venezuela, and the phase in nature of the campaign is utilized in the framework of this
analysis to measure differential exposure at distinct points of childhood. Pre-post
campaign cohorts are also compared to measure the broader socioeconomic impact of
eradication. To evaluate this, adults in the 1971, 1981, 1990, and 2001 censuses are
matched with the malaria death rates in their state of birth to measure relative malaria
burden. Cohorts born after the advent of the eradication campaign and those who were
exposed to malaria eradication earlier in childhood saw greater growth in socioeconomic
outcomes than cohorts who were not exposed or who were exposed at a later point in
childhood. Results indicate that malaria has a role in cross-regional socioeconomic
disparities.

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I. Introduction
Malaria has been a significant cause of morbidity and mortality across continents
and cultures for much of human history. Less than a century ago, the burden of malaria
was much more widespread than it is today. Most present-day economically developed
countries have either eliminated or severely limited the transmission of malaria within
their borders. However, many countries in the tropics, including most in South America,
Africa, and Southeast Asia, are still afflicted with malaria to this day. As Hoyt Bleakley
(2010) points out, countries in these regions also tend to be economically underdeveloped
in comparison to their counterparts that have successfully controlled malaria. Bleakley
introduces the question of whether high malaria burden depresses economic development
or whether the unfortunate circumstances of poor economics prevent these countries from
successfully controlling malaria and its vector, the mosquito. One methodology he and a
few others -- Cutler (2010), Lucas (2010), and Barreca (2010) -- have pinpointed as a
means of answering this question is to look at possible exogenous variation in malaria
within a country. With regard to malaria, national eradication and control campaigns are
an intervention that fits this criterion.
This paper examines the DDT intervention that occurred in Venezuela beginning
in 1945. Venezuela was not only the most malaria-afflicted country in this region at this
time but was also the first in this group to attempt eradication of malaria. The worldwide
DDT campaign against malaria would not take place for a full decade after the
implementation of Venezuela’s. Prior to the discovery and development of DDT, most
attempts to control malaria focused on preventing infection, through quinine, rather than
eliminating the vector, the mosquito (Griffing 2014). However, when it was discovered in

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1939 that the previously synthesized DDT was a remarkably effective insecticide, its
importance to malaria control and elimination was recognized. Prior to acquiring DDT in
1945 when it first became widely available for vector control, Venezuela had utilized
several anti-malarial activities. Because of these activities, the epidemiology and
distribution of malaria in Venezuela was well known, and the campaign using DDT was
implemented almost immediately (Gabaldón 1949).
There were two important components of this campaign that allow for the present
study. First, the sharp decline in malaria that occurred in a relatively short period of time,
in comparison to its long history in Venezuela, allows for two clear groups of individuals:
those who reached adulthood with a high malaria burden and those who were exposed to
the campaign and thus developed with significantly lower exposure to the parasite.
Second, Venezuela has a rich and varied geography with some areas ideal for the
breeding of mosquitoes, the malaria parasite vector, and other regions that were almost
entirely inhospitable to mosquitoes. Regions of Venezuela with relatively low malaria
burden thus act as a comparison, control group to those areas that were greatly affected
and positively impacted, to a greater extent than low burden areas, by the eradication
campaign. The comparison between these two groups also helps to control for national
trends in socioeconomic outcomes. Additionally, this rapid and effective intervention was
due to scientific innovation, an exogenous variable, rather than economic improvement.
These elements largely eliminate arguments of reverse causality and heterogeneous
interventions within the country. An important assumption in this analytical strategy is
that, prior to the advent of the malaria eradication program, there were no differential

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changes in socioeconomic outcomes between regions correlated with initial levels of
malaria.
Using these important characteristics of the intervention in Venezuela, this
paper’s goal is to determine in what ways a reduction in malaria burden during childhood,
through the DDT campaign, impacts socioeconomic outcomes in adulthood. Children
under five years of age are the most vulnerable to parasite infection because they have
not yet built any degree of immune resistance due to a lack of previous exposure. This
means that when the children are infected, the symptoms can be more acute and longer
lasting. Previous studies have indicated that early life malaria infection can have lifelong
socioeconomic consequences because essential cognitive and physiological development
occurs in the early years of life. The effects of malaria exposure on human capital
accumulation can have a number of mechanisms. First, in utero exposure to malaria,
through infection of the mother, can lead to low birth weight, anemia, and disruption of in
utero nutritional transmission, which can negatively impact lifelong growth and adult
success (Lucas 2010 and Barreca 2010). Additionally, studies by the World Health
Organization (WHO) in Africa have indicated that long-term infection in childhood can
negatively affect cognitive development, and in cases of cerebral malaria, can lead to
learning impairment and disability (WHO 2003). Finally, because the symptoms of
malaria (fever, headache, fatigue, and vomiting) can keep children from school, it affects
both the quantity and quality of their education during and after infection (WHO 2003).
Thus, reasonable measures of the socioeconomic impact of malaria burden during
childhood would include years of schooling, literacy rates, and earned income in
adulthood.

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In this paper, I utilize exposure to the DDT campaign as a means of classifying
cohorts into treatment (exposure to the campaign in childhood) and control groups
(exposure in adulthood or none at all) to ascertain possible educational and economic
effects of childhood exposure to the DDT campaign. Cohorts that reached adulthood (in
this study, the age of 18) prior to the introduction of the campaign would have no
childhood exposure to the eradication efforts and thus experienced a malaria-burdened
childhood. On the other hand, there is a cohort that was born after the advent of the DDT
campaign that spent their full eighteen years of childhood with exposure to the control
efforts and a significantly lower malaria burden than their older counterparts. In the
middle, there is a cohort (individuals who had not yet reached adulthood when the
campaign began) that had partial exposure to the campaign, and thus lived a portion of
their childhood with high malaria burden and a portion with low and/or no burden. These
three separate cohorts allow for a variety of analyses with similar characteristics.
In this analysis, I utilize the pre-campaign malaria burden in their states of birth in
combination with their year of birth with relation to the start date of the DDT campaign,
which varied between four separate years depending on birthplace. The phase-in nature
of the malaria control and eradication efforts in Venezuela is an essential aspect of the
econometric analysis. This design allows for differential exposure to the campaign at
varying years and ages depending on state of birth. An individual who was born close to
or after the initiation of the intervention in a state that had high pre-campaign malaria
burden would, logically, witness greater benefits in cognitive and physiological
development than older cohorts due to a sharp decrease in malaria intensity. With this
analytical construct in mind, cohorts can be compared in a number of different ways: pre

7. Hamlin 7
and post campaign birth as well as exposure to eradication at varying ages (0-18) of
childhood. In order to do these analyses, I use census microdata of both males and
females to construct cohorts based on birth year and birthplace. This allows me to
identify age of exposure to the campaign as well as pre-eradication malaria intensity
during development. Additionally, age of exposure during childhood is interacted with
pre-campaign malaria burden in their state of birth to examine the impact of malaria
reduction at differing ages of childhood.
In this particular study of Venezuela, the main results for every analytical strategy
indicate that exposure to the campaign in high burden areas in childhood led to more
years of schooling, increases in literacy rates, and a larger earned income in adulthood.
Section V presents a basic pre-post campaign comparison of cohorts and mimics the
approaches used by Bleakley (2010) and Lucas (2010). The results of this analysis show
that a childhood fully exposed to the nation-wide campaign (being born after the start
date) in areas of high malaria burden leads to larger growth in socioeconomic outcomes.
The results for years of schooling and literacy rates are not sensitive to a number of
controls, while adult earned income shows mixed results. This is perhaps due to the
economic downturn in Venezuela in the 1980s, when the post campaign cohorts were in
the peak of their adulthood (McCaughan 2005). The other analysis, which is presented in
Section VI, utilizes the differential start dates of the campaign based on year of birth and
state of birth. It seeks to identify the effects of exposure at specific years of childhood
based on malaria reduction. Results of this analysis show that the earlier one is exposed
during childhood, the greater the gains in years of schooling and adult earned income,
while literacy rates show the opposite trend. The results in the trends for all three

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measures of socioeconomic outcomes remain consistent when subjected to a variety of
different controls.
The results of both of these forms of analysis indicate that exposure to the DDT
efforts during childhood had a positive impact on years of schooling, literacy rates, and
earned adult income. This means that childhood malaria has a large detrimental effect on
early life development and subsequent economic success as an adult. In the case of the
Venezuelan cohorts, experiencing a 10% reduction in malaria in the first year of
childhood increases years of schooling by .154, literacy by 1.9%, and earned adult
income by 5.3%. These results are consistent with those of other studies examining the
effect the DDT campaign in various countries.
My approach is unique in that it examines the impact of exposure to the malaria
eradication campaign at different ages of childhood, in addition to the cross-cohort
longitudinal analysis of the other three studies, and examines both educational and
economic outcomes. This paper contributes to the literature as it analyzes the relative
importance of exposure to eradication at different points in time during early childhood.
This paper is organized in the following manner. Section II provides background
on pre-campaign malaria burden and the structure of the proceeding DDT campaign in
Venezuela. Section III discusses literature related to and influential in this study. Section
IV describes how the data in this study was organized and defined to create the cohorts
and variables of interest. Section V introduces the preliminary form of analysis, which
evaluates pre and post campaign cohorts to evaluate differential socioeconomic growth.
Section VI discusses the novel evaluation strategy of this paper, using the phased in
framework of the campaign to analyze contrasting exposure to malaria eradication efforts

9. Hamlin 9
at each age of childhood. Section VII concludes the paper and discusses broad
implications.
II. Background on the DDT Campaign in Venezuela
Malaria was a constant and continual burden in Venezuela in the late 1800s and
early 1900s. In fact, before the 1940s Venezuela had the highest rate of malaria mortality
in Latin America (Griffing 2014). Death rates were especially high in the years between
1905-1945, before extensive efforts to protect the population of Venezuela were
implemented. These efforts were largely created, organized, and implemented by the
Director of Mariology in Venezuela from the 1930s-1970s, Arnoldo Gabaldón. His
contributions to the control and eradication of malaria were essential not only to
Venezuela’s success but also to the global fight against malaria.
For the purpose of understanding malarial distribution and prevalence in
Venezuela, Gabaldón organized Venezuela into three regions with separate ecology,
shown in Figure 1. These three regions were Costa Cordillera, the northern mountainous
coastline; Los Llanos, the central grasslands; and Guayana, the largely tropical forest.
Costa Cordillera contained the bulk of the population during this time, 77%, even though
it was only 18% of the total area of Venezuela. Fortunately, malaria was not abundant in
this region compared to the other two due to a lack of large valleys and plains.
Nevertheless, certain regions of Costa Cordillera, mostly the western two thirds of this
area, were susceptible to severe epidemics due to changing populations of mosquito
vectors (Gabaldón 1949). The epidemics in this region followed a five- year cycle due to
the weather pattern called El Nino. A. darlingi and A. albimanus were the two most

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important vectors of malaria in Venezuela, which will be discussed in more detail later.
In contrast, these two vectors were not meaningfully present in the eastern portion of
Costa Cordillera due to its lack of rainfall, and thus this region experienced a low malaria
burden.
Figure 1. The Three Geographic and Political Zones of Venezuela
Notes: Zone A: Costa-Cordillera; Zone B: Los Llanos; Zone C: Guayana. Source: Gabaldón 1949
Los Llanos, the most malarious region of Venezuela, was historically the area
most affected by endemic, and rarely epidemic, malaria. It offered significant breeding
grounds, i.e. ponds and lagoons, for the Anopheles vector due to the intersection of many
rivers surrounded by forest, which frequently flooded during the rainy season (Gabaldón
1949). The incidence of malaria was not consistent across this region due to differing
ecologies. The southwest of Los Llanos, near the Apure River in the state of Apure, had

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little to no endemic or epidemic malaria. In contrast, the southern portion of Los Llanos
had consistently moderate endemic malaria while the northern portion was frequently
saddled with hyperendemic (a high and consistent incidence of) malaria.
Finally, there was the Guayana region of Venezuela, which was sparsely
populated except for a few large cities. While it was geographically the largest region, it
was mostly dense tropical forest, and thus only contained about 3% of the total
Venezuelan population during this time. A. darlingi was moderately endemic in the
southern portion of Guayana which has a savannah plateau and was almost entirely
absent in northern regions due to a lack of suitable breeding grounds for the mosquito
vector (Griffing and Gabaldón 1949).
As previously mentioned, the mosquito species of A. darlingi and A. albimanus
were the two most important vectors of malaria in Venezuela during the first part of the
20th
century. Arnoldo Gabaldón also identified A. punctipennis as a less important, but
still present, carrier of the parasite in the mountainous portions of Venezuela (Gabaldón
1949). Nevertheless, both the species mentioned above were vectors of the disease in
Costa-Cordillera. This is not the case in the other two; A. darlingi was almost exclusively
responsible for infection in Llanos and Guayana. A. albimanus was, at some points,
present in the eastern portion Llanos but not for extended periods of time. A. darlingi is,
unfortunately, a more effective vector of the malaria parasite than A. albimanus, with a
sporozoite rate of 0.9 vs. 0.6, respectively, as calculated by Gabaldón (Gabaldón 1949).
With this pattern of distribution and prevalence mapped out and recognized,
efforts to combat the debilitating impact of this disease were begun in the mid 1930s,
with the passing of the Law on the Defense Against Malaria in 1936. During this year,

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Gabaldón established and became head of the Malaria Division of the Ministry of Health
and Social Assistance (Griffing 2014). This can, effectively, be considered the beginning
of Venezuela’s efforts to control malaria. Between 1937-1941, stations were established
in malarious states around the country and infected citizens were provided a seven-day
course of quinine tablets (Gabaldón 1983). This measure helped prevent death, but was
completely ineffective at stopping infection because it was provided after the person was
already sick. Gabaldón and his colleagues recognized that in order to eradicate malaria,
infection had to be stopped altogether. Because DDT had yet to be developed at this time,
they began a series of sanitary engineering projects in urban areas. This concentrated on
the elimination of standing water, the primary breeding ground of the mosquito vector,
through drainage projects. However, more rural areas were still left largely untreated
(Gabaldón 1983). These efforts continued with limited results until Gabaldón was able to
procure DDT from the United States in 1945 and the DDT campaign to combat malaria
began.
From the beginning, the goal of the program was a nation-wide eradication
campaign and began with few preliminary tests and a mostly trial and error approach
(Gabaldón 1951). New zones were incorporated every year, and by 1950 the entire
country was being sprayed. The trial and error system evolved so that during 1946
spraying was repeated every 3 months, in 1947 and 1948 every 4 months, and in 1949
every 6 months, with the dose of DDT doubled during this time (Gabaldón 1951). The
DDT squads were unable, in the initial years, to reach all areas of the affected regions in
Venezuela because they were very difficult and expensive to access. Despite this,

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Gabaldón maintains in all of his published work that the most affected and heavily
populated areas were sprayed and malaria was significantly reduced (Figure 2).
Figure 2. Progress of the DDT Spraying Campaign in Venezuela: 1946, 1947, 1948
Notes: Progress of the DDT spraying program in Venezuela for three years: 1946, 1947 and 1948. Black
dots represent spraying at the county level. Regions were sprayed largely based on pre-campaign malaria
burden, so that the most infected regions saw the earliest exposure. Source: Gabaldón 1949.

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As previously mentioned, the two most significant vectors of malaria in
Venezuela were A. darlingi and A. albimanus, both of which are significantly affected by
DDT. Almost immediate reduction in malaria incidence was observed in areas sprayed
during this time. Gabaldón generalizes the results of the effects of the DDT campaign in
Malaria Eradication in Venezuela by stating, “in some of the study districts, those with
median and low endemicity, we found no more cases after the 3rd
year. In those with high
endemicity it took longer, about 5 years, to reach zero cases” (Gabaldón 1951). The
results were slightly different in certain parts of Venezuela where the responsible vectors
were A. emilianus and A. nuneztovari; eradication progressed at a slower pace. This was
labeled refractory malaria and required both DDT spraying and the distribution of quinine
to control (Gabaldón 1951). Nevertheless, the long-term effects of DDT spraying were
significant. For example, the main vector in central and north-central Venezuela, A.
darlingi, was completely eradicated within the first eight years of DDT spraying. North-
central Venezuela, in particular, previously had one of the highest endemicity rates in all
of Venezuela but was declared malaria free by the WHO in 1961 (Griffing 2014).
Notwithstanding significant accomplishments toward the control and eradication
of malaria in Venezuela, the country itself was never officially declared malaria free.
Gabaldón acknowledged that malaria eradication in certain areas, namely northern Costa-
Cordillera along the border with Colombia, Apure and Delta Amacuro in Los Llanos, and
Bolivar and Amazonas in Guyana, was either unfeasible economically or was unfeasible
due to migration. By 1954, malaria had been eliminated or was declining across 30% of
the malarious zone. Malaria reached its lowest prevalence in 1959 (911 cases in all of
Venezuela) with 68% of the malarious zone free of the disease; malaria eradication in this

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zone was acknowledged and confirmed by the WHO in 1961. By 1971, the malaria-free
region of Venezuela had increased to 77% of the malarious zone. Despite the clear
positive impact of the malaria control and eradication campaign in Venezuela, the DDT
campaign was officially ended in 1965 (Griffing 2014). Throughout the 70s and 80s, the
number of malaria cases fluctuated but remained low. Unfortunately, since the mid
1980’s, malaria cases have started to increase and it has, once again, become an
unfortunate problem in Venezuela.
Figure 3. Sharp decline in malaria mortality following onset of campaign in 1945
Notes: Malaria Death Rates Per 100K in Venezuela. The DDT campaign formally began in 1945, and a
sharp decline in malaria mortality is seen as a result. Source: Gabaldón 1946
Outcomes for the three socioeconomic measures, years of schooling, literacy
rates, and adult earned income, have been plotted to visualize the trends during the period
of interest. For each year of birth, 1900-1980, in each state, median outcomes of the three

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socioeconomic outcomes were calculated. These medians were then averaged for the two
separate classifications of malaria intensity to create the general average in that category
for each birth year. These calculations were then plotted against birth year. This model
allows one to examine broad changes in socioeconomic outcomes based on either high or
low malaria intensity. The results are presented in Figure 4.
Figure 4: Malaria Intensity and Differential Socioeconomic Growth
Panel A. Years of Schooling
Panel B. Literacy Rates

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Panel C. Ln (Income)
Notes: This figure plots average socioeconomic outcomes in high and low malaria intensity regions based
on year of birth. The highly malarious regions include Anzoategui, Barinas, Carabobo, Cojedes, Monagas,
Portugesa, and Yaracuy. The less malarious states include Apure, Federal District, Falcon, Lara, Merida,
Nueva Esparta, and Trujillo. The independent variable is year of birth and the dependent variable is average
socioeconomic outcome.
Results from this graphical representation of socioeconomic outcomes evidence
clear trends in the differential growth between the two malarious regions. Socioeconomic
measures in highly malarious states were consistently below those of low malarious states
until the advent of the campaign. After the nation-wide campaign, years of schooling,
literacy rates, and adult earned income are almost identical in the two malarious regions.
The trend results for adult earned income are not as clear as years of schooling and
literacy rates. However, it is still evident that income was growing faster in malarious
states, and during the economic downturn income surpassed that of low malarious states.
Thus, this represents the greater growth in socioeconomic measures over the same time
period for highly malarious states.

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III. Related Literature
In the fifteen years since the UN declared malaria reversal and eradication a
Millennium Development Goal, there have been a number of studies that seek to identify
the socioeconomic impact of malaria. While I have largely mentioned those that take
longitudinal, historical, or cross-cohort approaches in order to assess the long-term
socioeconomic effects of malaria, there are many studies that have looked at the
immediate effect of malaria infection on lost wages, depressed worker productivity, and
school absenteeism.
Dillon et al. (2014), for example, presents a randomized control trial with
Nigerian sugar cane workers; treatment for malaria increased labor supply and
productivity. Additionally, Leighton and Foster (1993), Brooker et al. (2000), and Clarke
et al. (2008) used randomized trials to measure the effects of malarial infection and
treatment on school attendance and cognitive ability. While studies such as these are able
to estimate the immediate consequences of infection, early life health is an important
determinant of human capital over the course of a lifetime (Gallup and Sachs 2001).
Longitudinal cross-cohort studies allow the researcher to determine life-long effects of
early-life malaria exposure.
The previously mentioned studies by Bleakley (2010) and Lucas (2010) use
similar approaches to the one I utilize. Bleakley explores four separate countries, the
United States, Colombia, Brazil, and Mexico in his analysis, while Lucas examines
Paraguay and Sri Lanka. Both studies examine cohorts born before and after the
campaign, the relative burden of malaria in their area of birth, and a variety of
socioeconomic outcomes. The benefit of using multiple countries in their analysis is that

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they can relate the results to possibly form more general conclusions. Bleakley’s
approach is more technical, as he utilizes a panel analysis by constructing year-of-birth
and state-of-birth cohorts that exist in multiple censuses. His analysis only includes
males, and while it finds mixed results on schooling and literacy, his evaluation supports
a very clear impact of malaria burden on adult economic success. Cutler et al.’s (2010)
similarly designed study in India also finds a clear impact of malaria eradication on
economic success but not on educational outcomes. In contrast, Lucas (2010) looked at
only women in Paraguay and Sri Lanka, two very malaria endemic countries, and focused
on educational outcomes. She found that malaria eradication increased female education
and literacy rates in the cross-cohort comparison.
Additionally, there are several longitudinal studies that take modified approaches
to the ones discussed above. Hong (2007, 2011) and Barreca (2010) used an instrumental
variable approach to estimate malaria burden using environmental factors. Hong uses
climate and elevation as instruments for potential malaria risk. By looking at US Union
Army records, he estimates that potential early-life malaria risk decreased Union soldiers’
height and increased their risk of infection during wartime as well as increased their
likelihood of having chronic diseases and being disabled in old age. Barreca (2010) also
used an instrumental variable approach utilizing environmental factors but concentrated
on in utero exposure. He creates an interaction term using hot and rainy weather
conditions, which in the right combination create ideal breeding grounds for mosquitoes,
and uses this to estimate potential malaria risk at time of birth in the United States. His IV
approach indicates that those who had higher risk of malaria at their time of birth had
lower levels of educational attainment.

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Two studies, Acemoglu and Johnson (2007) and Acemoglu et al. (2003), looked
at outcomes due to reduction in infectious disease burden (including malaria) across
countries, while the other studies have been within a country. They take the approach that
while reduction in disease does increase life expectancy, this does not necessarily
translate into economic growth and an increase in income per capita equivalent to the
growth seen in low disease burdened countries.
My methodology in this paper is most similar to that of Bleakley (2010), Lucas
(2010), and Cutler et al. (2010). My approach is unique in that it examines the impact of
exposure to the malaria eradication campaign at different ages of childhood, in addition
to the cross-cohort longitudinal analysis of the other three studies, and examines both
educational and economic outcomes.
IV. Data
To estimate the long-term educational and economic impact of exposure to
malaria eradication in early life, I utilize the micro-level census data obtained from the
Integrated Public Use Microdata Series (IPUMS). IPUMS is an organization dedicated to
the collection and distribution of census data from countries around the world. I analyze
the data from four separate Venezuelan censuses: 1971, 1981, 1990, and 2001.
My analysis uses an individual’s state of birth rather than state of current
residence, as malaria burden during early development is the factor of interest in this
study. Furthermore, only native-born individuals were included in the study, as it would
be difficult to track malaria burden in their previous country of residence. Therefore, this
design takes on the form of intention to treat because undocumented migration between

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states or between countries is possible. For Venezuela, birthplace is categorized by state
of birth. Two states, Amazonas Federal Territory and Amacuros Delta Federal Territory,
are excluded from the analysis, as pre-campaign malaria rates are largely unrecorded.
Furthermore, the current state of Vargas, which is only part of the later censuses, is
combined with individuals from the Federal District, as they were one territory in earlier
censuses.
The base sample consists of both males and females in the IPUMS dataset, over
the age of eighteen for the census years 1971-2001, which includes individuals with years
of birth ranging from 1872- 1983. I consider both males and females in my analysis
because while females were not, perhaps, as active in the labor force, Lucas (2010)
showed that they represent an important cohort in educational analysis.
To measure labor productivity, the log of adult earned income was used, a
variable that was present in all four censuses. The outcome of hours per week was also
considered to measure labor productivity. Unfortunately this variable was organized into
five-hour categories, and, as Thomas et al. (2003) evidenced, alleviating morbidity results
in modest gains in hours worked per week (approximately twenty minutes in his study),
and thus results would be largely insignificant. Years of schooling was collected in all
four censuses, and in this particular study ranged from zero to eighteen years. Finally,
literacy rates were classified as either ability to read and write or not. Lucas (2010) was
able to collect data on highly literate vs. minimally literate, but this type of data was not
available for this study. All of these variables are based on self-report, due to the nature
of collection.

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Malaria data was collected from a variety of sources. Malaria mortality by state
and year was collected and published by Arnolodo Gabaldón in Tijeretazos Sobre
Malaria (1946), Clippings about Malaria, up until the year 1945, the advent of the DDT
campaign. Later malaria statistics were sourced from a variety of other publications by
Gabaldón, including Gabaldón (1949, 1951, 1954, 1983), and a publication by the CDC
(Griffing 2014).
A number of other variables are utilized as controls and checks in this study.
These are used to control for individual, household, and regional differences that might
affect or correlate with early life development, access to education, and income. A more
thorough description of these variables can be found in the Appendix, and summary
statistics can be found in Table 1. Summary statistics separated by level of malaria
burden can also be found in the Appendix.
Childhood exposure was determined using two important characteristics. There
were four years in which the DDT campaign was started, based on state of birth: 1945-
1948. Carabobo, one of the most heavily infected regions, began spraying in 1945 and
spraying was expanded largely based on need until 1948. The two excluded regions,
Amazonas Federal Territory and Amacuros Delta Federal Territory, were sprayed at a
later date, but for the purpose of this study, spraying had reached every state by the end
of 1948. The timing of spraying at a county level cannot be precisely determined. Thus, if
spraying began in an individual’s state in a specific year, it is considered treated, once
again adopting an intention to treat design.

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Table 1. Summary Statistics: Educational, Employment, Population, and Household
Characteristics
Notes: A series of individual and household descriptors used as control variables in all regressions.
Represents a 10% sample from each of the four censuses: 1971, 1981, 1990, and 2001. Source: IPUMS.
TOTAL WOMEN MEN
N= 6,214,894 N= 3,097,374 N= 3,117,520
Education Mean SD Mean SD Mean SD
% no schooling 0.224 0.479 0.229 0.481 0.219 0.478
Years of Schooling 5.100 4.12 5.167 4.178 5.027 4.070
% literate 0.801 0.399 0.799 0.401 0.803 0.398
% less than primary completed 0.516 0.500 0.508 0.500 0.524 0.500
% primary completed 0.343 0.475 0.341 0.474 0.345 0.475
% secondary completed 0.139 0.346 0.149 0.356 0.129 0.335
% university completed 0.007 0.081 0.005 0.073 0.008 0.088
Employment
% employed 0.412 0.494 0.235 0.424 0.589 0.492
% self-employed 0.278 0.448 0.164 0.370 0.326 0.469
% inactive 0.543 0.499 0.741 0.437 0.342 0.475
% disabled 0.019 0.136 0.015 0.120 0.023 0.150
Ln (Earned Income) 6.722 1.888 6.596 1.660 6.775 1.973
Hours worked per week:
% 1-14 hours 0.050 0.219 0.068 0.252 0.042 0.201
% 15-29 hours 0.082 0.275 0.133 0.340 0.059 0.236
% 30-39 hours 0.091 0.288 0.125 0.331 0.076 0.265
% 40-49 hours 0.558 0.497 0.512 0.500 0.579 0.494
% 49+ hours 0.218 0.413 0.161 0.368 0.243 0.429
Population Characteristics
Age 23.638 18.75 24.025 19.048 23.234 18.441
% under 18 0.465 0.499 0.460 0.498 0.470 0.499
% male 0.502 0.500
% single/ never married 0.635 0.481 0.605 0.488 0.665 0.471
% native 0.945 0.278 0.947 0.223 0.943 0.232
Household Characteristics
rural 0.217 0.382
urban 0.783 0.412
electricity 0.878 0.327
water supply 0.781 0.414
sewage 0.717 0.451
toilet 0.843 0.364
% with >1 family 0.101 0.302
% with no mother 0.491 0.500
% with no father 0.604 0.489
% no children 0.671 0.470
% no children under 5 0.852 0.355

24. Hamlin 24
The initial analysis is one modeled off of that of Bleakley and Lucas. Cohorts
born on or after the start year in their specific state of birth are assigned potential
childhood exposure and given a 1, with the other cohorts receiving a 0 based on timing of
birth. This variable is also interacted with a measure of pre-campaign malaria in their
state of birth. Pre-campaign malaria intensity is measured as the natural log of the
average malaria deaths per 100 thousand in the eighteen years prior to the campaign.
Section VI analyzes cohorts using this method.
The second form of analysis, and the one that will be the major focus of this
paper, assigns potential exposure to individuals for every year of their childhood. It
utilizes the phase-in design of the eradication campaign; the structure of the campaign
frames it so different birth cohorts are exposed at different ages depending on state of
birth. Cohorts born on or after the start year in their state of birth are assigned potential
exposure to the campaign at age of 0, and every subsequent year until 18, and given a 1
for all ages 0 to 18. Individuals born in a state a year prior to the start year are given a 0
for potential exposure at age 0, but a 1 for potential exposure at age 1 and every
subsequent year until 18. Potential exposure to the malaria eradication campaign was
assigned in this manner for all ages 0-18. This exposure variable was later interacted with
the previously mentioned pre-campaign malaria intensity to measure the relative impact
of the eradication campaign. Section VI considers cohorts using this method.
V. Analysis of Cohorts Using Pre-Post Comparisons
In this section, I compare socioeconomic outcomes across cohorts while
separating through two channels: their year of birth in relation to the start date of the

25. Hamlin 25
malaria campaign and by the level of pre-eradication campaign malaria burden in place
of birth. To get an initial sense of the differences in socioeconomic outcomes based on
malaria burden, I have included Figure 5. These graphs shows clear and basic evidence of
the effect of malaria control: regions that had a lower malaria intensity experienced
smaller gains in educational and economic outcomes than the more infected regions.
In Venezuela, there were four years in which the DDT campaign was
implemented, based on state of birth: 1945-1948. Carabobo, one of the most heavily
infected regions, began spraying in 1945 and spraying was expanded largely based on
need until 1948. The timing of spraying at a county level cannot be precisely determined,
so if spraying began in an individual’s state in a specific year, it is considered treated,
adopting an intention to treat design. Using this structure, states were assigned a start year
as follows; 1945, Carabobo; 1946, Yaracuy; 1947, Anzoategui, Barinas, Cojedes, Federal
District, Monagas, Portugesa, and Trujillo; 1948: Apure, Falcon, Lara, Merida, and
Nueva Esparta. The states included in the analysis are those grouped as highly malarious
or low malarious regions. Highly malarious regions were classified as the states in the top
tercile of intensity, using the previously explained measure of malaria burden, while low
malarious regions were classified as the bottom tercile, with the middle tercile being
excluded for clarity.
For each year of birth, 20 years prior to the start year to 20 years after the start
year in the specific state of birth, median results of the three socioeconomic outcomes
were calculated. These medians were then averaged for the two separate classifications of
malaria intensity to create the general average in that category for each birth year with
relation to start year. These calculations were then plotted against birth year. This model

26. Hamlin 26
allows one to examine general changes in socioeconomic outcomes based on either high
or low malaria intensity with regard to the advent of the eradication campaign.
Median socioeconomic outcomes in high burdened states start lower than low
burdened states and remain lower until the advent of the campaign. For years of
schooling and literacy rates, graphical analysis indicates that the average outcomes of
individuals in high malaria areas begin to catch up to and match those in low malaria
areas beginning around the start of the eradication efforts. The malaria control and
eradication campaign was implemented between 1945-1948 in Venezuela, and malaria
death rates dropped to nearly zero within 3-5 years of initial spraying in an area. This
would mean that cohorts born about 3-5 years after the start year should experience the
full benefits of the campaign. This is evidenced in the graphical trends. Cohorts born
during the campaign years in high burdened states begin to have average outcomes equal
to those of their low burdened counterparts: average years of schooling and literacy rates
are almost identical in the two malarious areas five years after the start year.
Figure 5 – Socioeconomic Outcomes with Relation to Start Year
Panel A. Years of Schooling

27. Hamlin 27
Panel B. Literacy Rates
Panel C. Ln (Income)
Notes: This figure plots average socioeconomic outcomes by birth year with relation to the start year and
by malaria intensity. The dependent variable is the average socioeconomic outcome and the independent
variable is year of birth. The averages for the twenty years prior to and after the start year are plotted. The
zero line represents the start year for the four possible options. 1945, Carabobo; 1946, Yaracuy; 1947,
Anzoategui, Barinas, Cojedes, Federal District, Monagas, Portugesa, and Trujillo; 1948: Apure, Falcon,
Lara, Merida, and Nueva Esparta. The highly malarious regions include Anzoategui, Barinas, Carabobo,
Cojedes, Monagas, Portugesa, and Yaracuy. The less malarious states include Apure, Federal District,
Falcon, Lara, Merida, Nueva Esparta, and Trujillo

28. Hamlin 28
The trend results for adult earned income are different, but still significant.
Because of the economic downturn in the 1980s, which would affect all cohorts born
after the start year, there is not a consistent upward trend. However, the graphical
analysis does show that the gap in adult earned income is narrowed between high and low
malarious states in the years following the eradication campaign. Earned income for
highly malarious states surpasses that of low malarious states, at one point, during the
large drop in income seen in the later birth years and remains almost identical during the
economic downturn. This could, perhaps, be interpreted as regional growth during a
national decline. Previously high malarious areas could have been realizing the full
impact of malaria reduction on the growth of their income just as the national income was
in decline.
More thorough results, broken down and plotted for each state, are presented in
the Appendix. The results of the graphical analysis performed above are quantified and
presented in Table 2. This brief analysis only allowed for two classifications of malaria
levels, “high” or “low,” and classifies each cohort as either born eighteen years pre-
eradication or post-eradication (this is explored further in the discussion of Regression 2).
Nevertheless, it shows clear differences in socioeconomic gains between high and low
malaria areas within in Venezuela.

29. Hamlin 29
Table 2: Differences in Means of Socioeconomic Outcomes by Malaria Burden
Notes: This table presents average socioeconomic outcomes for pre and post campaign cohorts, classified
by malaria burden. The pre-eradication period is before 1945 for Carabobo; 1946 for Arugua, Sucre,
Yaracuy; 1947 for Anzoategui, Barinas, Bolivar, Cojedes, Federal District, Guarico, Mirands, Monagas,
Portugesa, Trujillo, Zulia; 1948 for Apure, Falcon, Lara, Merida, Nueva Esparta, Tachira. The highly
malarious regions include Anzoategui, Barinas, Carabobo, Cojedes, Monagas, Portugesa, and Yaracuy. The
less malarious states include Apure, Federal District, Falcon, Lara, Merida, Nueva Esparta, and Trujillo.
A less restrictive version of this analysis allows malaria burden to vary by state.
The first regression is modeled off of the approach taken by Lucas (2010) in her study of
Sri Lanka and Paraguay. This classifies cohorts into those born during and after the
campaign start year or born a year or more before the advent of malaria eradication. This
separates cohorts into two categories for analysis: those who spent their entire life
exposed to eradication efforts and those who experienced a minimum of one year of
childhood with high malaria intensity due to lack of exposure. The first equation is, thus,
a standard difference-in-differences specification:
Years of schooling Pre-eradication Eradication Increase
Highly Malarious 2.088 7.026 4.938
(0.035) (0.016)
Less Malarious 2.563 7.182 4.618
(0.024) (0.014)
Difference 0.320
Literacy Pre-Eradication Eradication Increase
Highly Malarious 0.499 0.919 0.421
(0.005) (0.001)
Less Malarious 0.570 0.917 0.347
(0.003) (0.001)
Difference 0.074
Ln (Income) Pre-eradication Eradication Increase
Highly Malarious 6.215 6.723 0.507
(0.039) (0.010)
Less Malarious 6.441 6.653 0.211
(0.023) (0.010)
Difference 0.296

30. Hamlin 30
(1) Yijc = α + β (malariajpre * prec ) + Xijc Γ + εijc
in which Yijc is a socioeconomic outcome of an individual i in region j, who is a member
of cohort c. Malariajpre is the pre-eradication malaria intensity in the state of birth of the
individual, while prec is a dummy variable indicating membership in the pre-eradication
birth cohort. Xijc are a series of individual and household controls, and α is a constant. β is
the coefficient of interest and represents the effect on socioeconomic outcomes due to a
log change in malaria burden. If exposure to the campaign increases socioeconomic
outcomes, then cohorts born before the eradication campaign in states with higher pre-
eradication malaria should have lower educational attainment, lower literacy rates, and a
smaller earned income than those born after the DDT campaign in the same state.
States in Venezuela with higher malaria burdens before the eradication campaign
saw greater benefits from the vector control than states with lower malaria burden. These
results are found in columns 1 and 2 of Table 3. The first column of Table 3 uses only
basic specifications with no controls, while column two utilizes the full set of individual
and household controls. All of the columns use the natural log of malaria mortality per
100 thousand as the indicator of pre-campaign malaria burden in the state of birth.
Malaria mortality per 100K effectively was reduced to zero, or close to zero, within three
to five years of spraying in each state.
The estimates for the impact of malaria are slightly depressed using a full set of
controls, but still remain significant for all three socioeconomic outcomes. The exception
is ln (income) using the basic specifications from Regression 1. This is mostly likely due

31. Hamlin 31
to the previously mentioned economic downturn in the 1980s, but this trend is reversed
when a full set of individual and household level controls is used.
These results indicate that exposure to the malaria campaign had a positive and
substantial effect on the number of years of schooling, literacy, and adult earned income.
Being born prior to the advent of the campaign in areas of high malaria burden was
disadvantageous to socioeconomic outcomes. Based on the estimates in column 2, a 10%
decrease in malaria burden, would translate into .771 additional years of school, a 5.6%
increase in literacy rates, and a 9.0% increase in adult earned income.
Column 3 and 4 of Table 3 are calculated using a different specification to define
pre and post campaign birth, and is modeled off of the one used by Bleakley (2010). In
this regression, being born during or after the start year of the DDT campaign in one’s
state is still considered “post”. However, with this second model, I attempt to look at the
effect of living one’s full childhood with no campaign exposure as compared to one with
complete exposure. Therefore, only the individuals who were born eighteen years prior to
the start date in their state are given a one for the dummy variable “pre”, while those born
in between are excluded from the analysis. For cohorts born in Carabobo, for example,
where the campaign started in 1945, only individuals born after 1945 and before 1927 are
included and compared in the analysis. For this specific section, the outcome variables
represented in the table are cross-cohort differences (born after minus born 18 years
before) in the measures associated with a percentage drop in malaria burden. The second
equation, is an ordinary least squares approach:
(2) Yijc, post – Yijc, pre = α + β Malariajpre + Xijc Γ + εijc

32. Hamlin 32
in which, once again, Yijc is a socioeconomic outcome of individual i in region j, who is a
member of cohort c. The subscript of ‘post’ refers to being born after the start of the DDT
campaign, and ‘pre’ indicates being born, and having reached adulthood (age 18) prior to
the advent of the campaign. Malariajpre is the pre-eradication malaria intensity in the state
of birth of the individual. Xijc are a series of individual and household controls, and α is a
constant. β is the coefficient of interest, and represents the socioeconomic effect due to a
log change in malaria burden, either pre or post campaign.
Again, areas in Venezuela with high malaria intensity before the DDT campaign
saw greater benefits from the vector control than states with lower malaria burden. These
results are found in column 3 and 4 of Table 3. The third column of Table 3 uses only
basic specifications with no control, while column four utilizes the full set of individual
and household controls. The estimates for the impact of malaria are slightly depressed
using a full set of controls, except for additional years of education, which was elevated,
but still remain significant for all three socioeconomic outcomes. The analysis of this
regression equation indicates that the higher the malaria burden pre-campaign, the greater
the socioeconomic gains in that particular region following control and partial
eradication.
Gains in socioeconomic outcomes are roughly similar to those in the previous
analysis. The results can be interpreted as full childhood exposure to the eradication
campaign in regions of high malaria burden confers an additional .122 years of schooling,
1.7% increase in literacy rates, and 1.0% increase in earned income per log decrease in
malaria burden.

33. Hamlin 33
Table 3- Cross Cohort Differences in Socioeconomic Outcomes in Venezuela
Notes: This table reports the estimates of the malaria coefficient of Regression (1) and (2). The units of
observation are Venezuelan states. The control group can be interpreted as regions of Venezuela with
relatively low malaria burden. The independent variable is membership in the post cohort interacted with
pre-campaign malaria intensity and the dependent variable is change in socioeconomic outcome. Robust
standard errors are in brackets. For Regression (1), membership in the pre cohort is defined as a birth year
at least one full year before start date. For Regression (2), the pre cohort is defined as a birth year at least
18 years prior to start date. The dependent variable can be interpreted as cross cohort differences between
exposed and unexposed cohorts.
***significant at the 1 percent level
** significant at the 5 percent level
* significant at the 10 percent level
Panel A. Regression (1) Regression (2)
Dependent Variable: (1) (2) (3) (4)
Years of schooling
Born Post Campaign 0.351*** 0.220*** 0.031*** 0.122***
(0.001) (0.002) (0.006) (0.006)
Controls: N Y N Y
Observations: 5,288,297 4,948,878 4,307,714 4,043,760
R2
: 0.022 0.290 0.032 0.417
Panel B.
Dependent Variable: (1) (2) (3) (4)
Literacy
Born Post Campaign 0.030*** 0.056*** 0.023*** 0.017***
(0.001) (0.001) (0.001) (0.003)
Controls: N Y N Y
Observations: 5,564,805 5137825 4,484,956 4,159,161
R2
: 0.018 0.178 0.031 0.253
Panel C.
Dependent Variable: (1) (2) (3) (4)
Ln (Income)
Born Post Campaign -0.015*** 0.090*** 0.033*** 0.010**
(0.001) (0.001) (0.006) (0.006)
Controls: N Y N Y
Observations: 1,711,203 1,580,370 1,253,631 1,160,525
R2
: 0.001 0.089 0.002 0.118

34. Hamlin 34
VI. Analysis of Cohorts Using Years of Childhood Exposure
The next stage of the analysis focuses on the impact of exposure to the campaign
at differing ages of childhood. Regression 1 effectively assumes that the positive
consequences of exposure to control and eradication is concentrated in the first year of
life, while Regression 2 focuses on the effect of a high malaria burden through ones
entire childhood as compared to one lived largely malaria free. In this section, I analyze
the differential impact of exposure to the malaria control and eradication campaign at
each year of childhood. I compare changes in socioeconomic outcomes by birth year
cohort in relation to the start date of the eradication campaign in their state, later
interacted with birthplace pre-campaign malaria intensity, in order to asses the
contribution of the eradication campaign to socioeconomic gains at different stages of
childhood.
The phase-in structure of the DDT campaign is essential in this analysis as it
separates cohorts into year of birth exposure cohorts based on state of birth. This means
cohorts born in the same year in different states can have different exposure variables.
These can be utilized to assess the impact of the campaign at specific years of birth.
Potential exposure to the DDT campaign is assigned to individuals for every year of their
childhood. Cohorts born on or after the start year in their state of birth are assigned
potential exposure to the campaign at age zero and every subsequent year until eighteen
and given a one for all ages 0-18. Individuals born in a state a year prior to the start year
are given a zero for potential exposure at age zero, but a one for potential exposure at age
one and every subsequent year until eighteen. Potential exposure to the malaria
eradication campaign was assigned in this manner for all ages 0-18. Individuals born after

35. Hamlin 35
the advent of the campaign would receive a one for all years of childhood while those
born eighteen years prior to the campaign would receive zeros for all years of childhood.
This exposure variable was later interacted with the previously mentioned pre-campaign
malaria intensity to measure the relative impact of the eradication campaign.
The first regression I present is actually a series of regressions, each run
separately, building on the regression before it. This form of the regression does not rely
on malaria burden in the state of birth. The counterfactual, or control group, in this
analysis is not low malaria burdened regions but is instead individuals who were not
exposed to eradication efforts at that particular age and thus were not treated. As
previously discussed, malaria eradication had a larger impact in highly malaria-burdened
regions, but this will be explored more with Regression 4. Thus, consider the OLS
regression model:
(3) Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + β4 Exp3ijc + β5 Exp4ijc +
β6 Exp5ijc + Xijc Γ + εijc
in which Yijc is a socioeconomic outcome of individual i in region j, who is a member of
cohort c. Exp0ijc is a dummy variable indicating if an individual was exposed to the
eradication campaign at birth, Exp1ijc represents an individual exposed at one, and so
forth up until age five, in this particular equation. Coefficients were calculated for all 18
years of childhood but only ages 0-5 are presented in the table, while ages 0-18 are
presented later in graphical form. Xijc are a series of individual and household controls,
and α is a constant. β is the coefficient of interest for all ages and represents
socioeconomic outcomes associated with exposure during that year of life.

36. Hamlin 36
There is never a case where an individual is potentially exposed to the eradication
campaign at age 0 and not exposed to the campaign at any other year of childhood.
Therefore, when I run Regression 3 to determine the differential impact of exposure at
age 0, only the dummy Exp0ijc is included in the regression, creating the model:
Yijc = α + β1 Exp0ijc + Xijc Γ + εijc. This is because when Exp0 takes on the value of 1,
there is no other values the rest of the age exposure dummies (Exp1 - Exp18) can
represent other than 1, and thus do not need to be controlled for. As a further example,
when evaluating the differential impact for exposure to the campaign at age 2 for the first
time (i.e. born two years before the advent of the campaign, and experienced the first two
years with no exposure), I include only the dummies Exp0ijc, Exp1ij, and Exp2ijc in the
regression to create: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc+ Xijc Γ + εijc. This is
because while Exp0 and Exp1 can take on values of 1 or 0 depending on birth year, if
Exp2 takes on the value of 1, all dummies from Exp3 - Exp18 must take on the value of 1
as well and do not need to be controlled for in the regression. This approach is furthered
explained in the Appendix. The results for Regression 3 are presented in Table 4. The
first column of Table 4 uses only basic specifications with no control, while column two
utilizes the full set of individual and household controls.
The estimates for the impact of exposure to the eradication campaign are
somewhat depressed using a full set of controls, but still remain significant and the trend
remains the same for all three socioeconomic outcomes. The exception is ln (income)
using the basic specifications from Regression 3. This is mostly likely due to the
economic downturn in the 1980s, but this trend is reversed when a full set of individual
and household level controls is used.

37. Hamlin 37
These results show that exposure to the malaria eradication campaign in the early
years of life, regardless of pre-campaign malaria intensity, had a positive effect on years
of schooling, literacy rates, and adult earned income. The results can be interpreted as
follows: cohorts exposed to the campaign at the age of 0, as compared to their counterpart
cohorts who were not exposed, obtained .817 additional years of schooling, a 4.5%
increase in literacy rates, and a 10.3% increase in adult earned income. These results are
in line, but lower than the results obtained in Regression 1 and Regression 2. This could
be due to the inclusion of individuals born in states with low malaria burden, and thus
smaller gains in education and economic outcomes. This will be accounted for in
Regression 4.
The results from column 2, representing the differential impact of exposure to the
campaign during the first five years of childhood, reveal separate trends for all three
measures of socioeconomic outcomes. For years of schooling, the later one is exposed,
the less benefit one receives from exposure; an individual exposed before the age of 1
gains .817 years of school, while an individual exposed at 5 gains a fraction of that, .599
years. This trend supports the fetal origins hypothesis: environmental and health
circumstances have a greater effect on long-term development the younger the age. In
contrast to this, the trend in literacy rates is fairly constant. Based on the results from this
regression, it does not seem to matter at what age of childhood one is exposed to the
campaign; an increase in literacy is still seen ranging from 4.5% - 5.2%. Finally, the
movement in adult earned income shows the opposite trend. It appears that while
exposure to the campaign does increase earned income, the effect is greater the later one
is exposed. This is in contrast to the results of Bleakley’s (2010) study of economic

38. Hamlin 38
outcomes in South American countries. This could be partially explained by the
previously mentioned economic downturn in Venezuela during the 1980s (i.e. cohorts
who were exposed later were born earlier and lived a smaller portion of their life in this
economic recession) and will be explored further in Regression 4.
The regression results for the full eighteen years of childhood were calculated and
can be found in the Appendix. The calculated coefficients were plotted as the
independent variable against age of exposure in childhood as the dependent variable in
order to visualize the varying trends of age of exposure on outcomes. These results are
presented in Figure 6.
Regression 3 is represented in these graphs by the upper level fits, entitled “no
interaction term”, while Regression 4 is represented by the lower line. The graphs, with
the inclusion of all coefficients up until the age of eighteen, evidence that the general
trend seen in the tables for ages zero through five generally hold for all years of
childhood. Exposure to the campaign as age increases has a mitigating positive impact for
years of schooling, a positive even and consistent impact for literacy rates, and a positive,
growing impact for earned income. Although the results for ln (income) are not
consistent, especially when compared to those of Bleakley (2010), they can be partially
explained by the 1980 recession. The results in the trend for literacy, however, are
counterintuitive and not as easily explained. The results indicate that exposure has a
significant positive impact on literacy rates, but this impact should be mitigating,
especially considering that literacy is often something picked up early on in life as
opposed to at the ages of seventeen or eighteen.

39. Hamlin 39
The second specification of a similar form to that of Regression 3 uses the
previously defined dummy variables interacted with pre-campaign malaria burden in their
state of birth (the natural log of the 18 year average of malaria mortality per 100K). This
separates cohorts based on the age at which they were exposed to the campaign as well as
on the malaria intensity in their birthplace. I compare changes in socioeconomic
outcomes by birth year cohort in order to assess the contribution of the eradication
campaign, with relation to pre-campaign intensity, to socioeconomic gains at different
stages of childhood.
Membership in exposure cohorts was carried out in the exact same manner as
described above: birth year on or after start year receiving a 1 for exposure at 0 and every
subsequent year of childhood, birth year a year before the start year receiving a zero for
exposure at 0 but a 1 for exposure at 1 and every subsequent year of childhood, and so
on. The equation I present is, once again, actually a series of equations, each run
separately, building on the equation before it. The counterfactual, or control group, in this
form of analysis is both low malaria burdened regions as well as individuals who were
not exposed to eradication efforts at that particular age, and thus were not treated. Thus
consider the differences in differences approach:
(4)
Yijc = α + β1 Exp0ijc + β2 (Exp0ijc * malariajpre) + β3 Exp1ijc +
β4 (Exp1ijc * malariajpre) + β5 Exp2ijc + β6 (Exp2ijc * malariajpre) + β7 Exp3ijc
+ β8 (Exp3ijc * malariajpre) + β9 Exp4ijc + β10 (Exp4ijc * malariajpre) +
β11 Exp5ijc + β12 (Exp5ijc * malariajpre) + Xijc Γ + εijc

40. Hamlin 40
in which, once again, Yijc is a socioeconomic outcome of individual i in region j, who is a
member of cohort c. Exp0ijc is a dummy variable indicating if an individual was exposed
to the eradication campaign at birth. Exp1ijc represents an individual exposed at one, and
so forth up until age five, in this particular equation. Coefficients were calculated for all
18 years of childhood but only ages 0-5 are presented in the table, while ages 0-18 are
presented above in graphical form. Malariajpre is the pre-eradication malaria intensity in
the state of birth of the individual. Xijc are a series of individual and household controls,
and α is a constant. β in front of the interaction terms (the even numbered βs) are the
coefficients of interest for all ages and represent differential changes in socioeconomic
outcomes due to a log change in malaria intensity dependent on exposure during that year
of childhood.
In a similar vein to that of Regression 3, this function was run multiple times for
each potential year of childhood exposure. Again, this is because there is never a case
where an individual is potentially exposed to the eradication campaign at age 0 and not
exposed to the campaign at any other year of childhood. This means that when the
analysis using Regression 4 is executed to determine the differential impact of exposure
at age 0, only the dummy Exp0ijc and the interaction term (Exp0ijc * malariajpre) are
included in the regression, creating the model:
Yijc = α + β1 Exp0ijc + β2 (Exp0ijc*malariajpre) + Xijc Γ + εijc. This is because when Exp0
takes on the value of 1, there is no other values the rest of the age exposure dummies
(Exp1 - Exp18) can represent other than 1. The coefficient of the interaction term, β2 in
this example, captures the effect of exposure to the campaign at age 0 based on pre-
campaign malaria burden. This approach was taken for all ages.

41. Hamlin 41
As a further example, when evaluating the differential impact for exposure to the
campaign for the first time at age 2, I include only the dummies Exp0ijc, Exp1ij, and
Exp2ijc as well as the interaction term (Exp2ijc * malariajpre), in the regression to create:
Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β5 Exp2ijc+ β6 (Exp2ijc * malariajpre) + Xijc Γ + εijc. This
is because while Exp0 and Exp1 can take on values of 1 or 0 depending on birth year, if
Exp2 takes on the value of 1, all dummies from Exp3 - Exp18 must take on the value of
one as well and do not need to be controlled for in the regression. Additionally, only the
interaction term using the Exp2 dummy is utilized for two reasons. One, the interaction
terms for Exp0 and Exp2 will take on the value of 0 when these dummies are held
constant at 0, and two, this β is the coefficient of interest as it represents the effect of
initial exposure to the campaign at age 2 based on pre-campaign malaria burden. This
approach is furthered explained in the Appendix. The results for Regression 4 are
presented in Table 4. The third column of Table 4 uses only basic specifications with no
control, while column four utilizes the full set of individual and household controls.
The estimates for the impact of exposure to the eradication campaign are not
sensitive to using a full set of controls, and remain significant, with a similar trend for all
three socioeconomic outcomes. These results show that exposure to the malaria
eradication campaign in areas of high intensity pre-campaign malaria in the early years of
life has a positive effect on years of schooling, literacy rates, and adult earned income,
regardless of age of exposure. The results can be interpreted as follows: for every log
change in the malaria burden, cohorts exposed to the campaign at the age of 0, in
comparison to those who were not, experienced .154 additional years of schooling, a
1.9% increase in literacy rates, and a 5.3% increase in adult earned income. These results

42. Hamlin 42
are in line, and slightly larger than the results obtained in Regression 1 and Regression 2.
This could be due to the greater effect of malaria eradication at younger ages of exposure,
versus the average over an 18-year period.
Table 4 – Effect of Differential Exposure to the Eradication Campaign During
Childhood
Panel A. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Years of schooling
Exposure at:
0 1.486*** 0.817*** 0.185*** 0.154***
(0.004) (0.013) (0.005) (0.009)
1 1.875*** 0.512*** 0.173*** 0.151***
(0.019) (0.032) (0.005) (0.009)
2 1.879*** 0.607*** 0.158*** 0.150***
(0.019) (0.034) (0.005) (0.011)
3 1.820*** 0.566*** 0.145*** 0.146***
(0.020) (0.035) (0.005) (0.012)
4 1.840*** 0.597*** 0.130*** 0.135***
(0.024) (0.037) (0.005) (0.012)
5 1.731*** 0.599*** 0.114*** 0.132***
(0.021) (0.038) (0.005) (0.013)
Controls: N Y N Y
Observations: 5,288,297 870,868 5,288,297 870,868
Panel B. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Literacy
Exposure at:
0 0.126*** 0.045*** 0.021*** 0.019***
(0.001) (0.001) (0.001) (0.001)
1 0.160*** 0.049*** 0.021*** 0.019***
(0.002) (0.003) (0.001) (0.001)
2 0.155*** 0.049*** 0.021*** 0.020***
(0.002) (0.003) (0.001) (0.001)
3 0.154*** 0.049*** 0.021*** 0.020***
(0.002) (0.003) (0.001) (0.001)
4 0.155*** 0.049*** 0.020*** 0.021***
(0.002) (0.003) (0.001) (0.001)
5 0.151*** 0.052*** 0.020*** 0.021***
(0.002) (0.003) (0.001) (0.001)
Controls: N Y N Y
Observations: 5,564,805 900,308 5,564,805 900,308

43. Hamlin 43
Notes: This table reports the estimates of the malaria coefficient of Regression (3) and (4). The
units of observation are Venezuelan states. Robust standard errors are in brackets. Both equations
are a series of regressions, each run separately, building on the regression before it. Regression (3)
does not rely on malaria burden in the state of birth; the counterfactual is individuals who were not
exposed to eradication efforts at that particular age and thus were not treated. The independent
variables for Regression (3) are a series of dummies reflecting possible exposure to the eradication
campaign during that year of childhood and the dependent variable is change in socioeconomic
outcome. The control group for Regression (4) is both unexposed cohorts as well as cohorts in low
malaria burdened states. The independent variable for Regression (4) is a series of dummy
variables for exposure at age interacted with pre-campaign malaria intensity. The dependent
variable can be interpreted as change in socioeconomic outcome due to log decrease in malaria
intensity at a particular age.
***significant at the 1 percent level
** significant at the 5 percent level
* significant at the 10 percent level
The results from column 4, representing the differential impact of exposure based
on pre-campaign malaria burden during the first five years of childhood, reveal two
different trends for the socioeconomic outcomes. In a similar vein to the results of
Regression 3, for years of schooling, the later one is exposed, the less benefit one
receives from exposure: an individual exposed before the age of 1 gains .154 years of
school for each log decrease in malaria intensity, while an individual exposed at 5 gains
Panel C. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Ln (Income)
Exposure at:
0 -0.148*** 0.103*** 0.041*** 0.053***
(0.003) (0.004) (0.003) (0.003)
1 0.139*** 0.140*** 0.042*** 0.053***
(0.009) (0.009) (0.003) (0.003)
2 0.165*** 0.161*** 0.043*** 0.053***
(0.009) (0.010) (0.003) (0.003)
3 0.193*** 0.143*** 0.042*** 0.053***
(0.010) (0.010) (0.003) (0.003)
4 0.223*** 0.172*** 0.041*** 0.054***
(0.010) (0.011) (0.003) (0.003)
5 0.230*** 0.158*** 0.039*** 0.053***
(0.10) (0.011) (0.004) (0.003)
Controls: N Y N Y
Observations: 1,711,203 900,310 1,711,203 900,310

44. Hamlin 44
only a portion of that, .132 years. However, in contrast to this, the trend in literacy rates
and ln (income) is fairly constant across the first five years of life. Based on the results
from this regression, it does not seem to matter at what age of childhood one is exposed
to the campaign; increases in literacy and ln (income) is constant across the early years of
childhood.
The coefficients of interest for the full eighteen years of childhood were
calculated for Regression 4 and can be found along with the results of Regression 3 in the
Appendix. These calculated coefficients were also plotted as the dependent variable
against age of exposure in childhood as the independent variable and are presented along
side the results of Regression 3 in Figure 6.
Regression 4 is represented in these graphs by the lower level fits, entitled
“interaction term”. The graphs, with the inclusion of all coefficients up until the age of
eighteen, evidence that the general trend seen in the tables for ages zero through five
generally hold for all years of childhood, although literacy rates seem to increase at a
faster rate past the age of five. Exposure to the campaign as age increases with relation to
pre-campaign malaria burden has a mitigating positive impact for years of schooling, a
positive, even and consistent impact for ln (income), and a positive but growing impact
for literacy rates.

45. Hamlin 45
Figure 6 – Relationship Between Age of Exposure and Socioeconomic Outcome
Panel A. Years of Schooling
Panel B. Literacy Rates

46. Hamlin 46
Panel C. Ln (income)
Notes: This figure plots that coefficients calculated for each age of childhood (0-18) for Regression (3) and
Regression (4). The dependent variable is change in socioeconomic outcome. The independent variable for
the top plot (Regression 3) is exposure at a particular age to the eradication campaign and the independent
variable for the bottom plot (Regression 4) is exposure at an age of childhood interacted with pre-campaign
malaria burden. Lines of best fit have been plotted for both relationships.
VII. Applications and Conclusion
Countries located in malaria rich areas, most notably the tropics, have historically
been socioeconomically underdeveloped in comparison to their malaria free counterparts.
The question this study seeks to address is whether high malaria burden depresses
economic development or whether the unfortunate circumstances of poor economics
prevent these countries from successfully controlling malaria and its vector, the mosquito.
Through the analysis of the impact of the exogenous variable that was the nation-wide
DDT campaign commencing in Venezuela in the 1940s, this study concludes that
socioeconomic growth is depressed in areas with high malaria burden.

47. Hamlin 47
These findings can be explained through the hindrance of human capital
accumulation due to early life exposure to high malaria burden. This was substantiated in
a number of analytical ways. First, pre-post comparisons were executed to determine the
differential impact of malaria eradication based on pre-campaign malaria intensity. The
results of this analysis suggest that full childhood exposure to the eradication campaign
conferred .122-.220 additional years of schooling, 1.7- 5.6% increase in literacy rates,
and 1.0- 9.0% increase in earned income per log decrease in malaria intensity. The results
of the second form of analysis validated these findings. Exposure to the eradication
campaign in early life caused positive increases in socioeconomic attainment. Per log
decrease in malaria intensity, exposure in the first years of life equated to .132-.154
additional years of schooling, 1.9-2.1% increase in literacy, and a 5.3% increase in adult
earned income.
In considering the broad implications of these results, there are a few important
matters that come to attention. The first is that cohorts in Venezuela could have been
subject to selective mortality. This means that members of the older cohorts who survived
to the time of the census are a selective sample. It is possible that they were physically
healthier, which could also translate into high socioeconomic outcomes. However,
differential mortality by income level or educational attainment would negatively bias the
result. Additionally, selective mortality prior to the eradication campaign could have
resulted in the weakest members of society not surviving to adulthood, with these
members surviving after malaria burden was reduced. This would also result in
downward bias.

48. Hamlin 48
Another consideration is that the application of DDT could have reduced the
burden of other vector-borne diseases, as DDT does not only kill mosquitoes. Gabaldón
observed that fly populations also decreased in response to DDT, thereby decreasing
morbidity due to diarrhea and enteritis. However, he also notes that flies rapidly
developed resistance to the insecticide within the first few years of spraying (Gabaldón
1949). Furthermore, in the pre-campaign period, from 1905-1945, deaths due to malaria
at one point accounted for as much as 10% of all deaths, and there was no pathogen, not
even the influenza, that caused more death during this period. Because the incidence of
other diseases was small relative to that of malaria, the increases in socioeconomic
outcomes can largely be attributed to malaria control and eradication.
In terms of applications of these results in the present day, it is important to look
at the vector and source of malaria. The primary vectors in Venezuela were A. darling
and A. albimanus. The major vector in Africa, which currently has a larger malaria
burden than any other region in the world, is A. gambiae, which is a more efficient and
effective vector than those from Venezuela. Additionally, there were two primary forms
of malaria in Venezuela, Plasmodium vivax and Plasmodium falciparum, both with about
equal prevalence. P. vivax is less powerful and deadly strain than P. falciparum, the strain
that is more common in Africa. Selective mortality and childhood effects would be larger
in an area where the more deadly and debilitating form of malaria is more common, thus
eradication might have even greater socioeconomic results in these areas.
Nevertheless, the implications of this study are clear. Malaria is currently one of
the leading causes of morbidity and mortality worldwide but especially in developing
countries. Control and eradication of this widespread disease leads to socioeconomic

49. Hamlin 49
gains: increasing years of schooling, literacy rates, and adult earned income.
Furthermore, it validates that early-life health is an important determinant in human
capital accumulation and long-term socioeconomic success.
Conclusion: malaria is bad

50. Hamlin 50
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Gabaldón, Arnolodo. Malaria eradication in Venezuela: doctrine, practice, and
achievements after twenty years. Am J Trop Med Hyg. 1983; 32: 203–211.

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Gabaldón, Arnoldo. "Nation-wide Malaria Eradication Projects in the Americas. II.
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eradication: north-central Venezuela.Am J Trop Med Hyg. 1954;3: 793–807 .
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Gallup, John Luke; Sachs, Jeffrey D. The Economic Burden of Malaria. The American
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Griffing SM, Villegas L, Udhayakumar V. Malaria control and elimination, in Venezuela,
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Hong, Sok Chul. Doctoral dissertation. University of Chicago; 2007. Health and
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52. Hamlin 52
Schoolchildren: A Cluster Randomised, Double-Blind, Placebo-Controlled Trial.”
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The author wishes to acknowledge the statistical offices that provided the underlying data
making this research possible: National Institute of Statistics, Venezuela.

53. Hamlin 53
APPENDIX
Part A. Control Variables for the Venezuelan Sample
Access to Electricity – indicates whether the household had access to electricity.
Access to Sewage – indicates whether the household has access to a sewage system or
septic tank.
Access to Toilet – indicates whether the household had access to a toilet and, in most
cases, whether it was a flush toilet or other type of installation.
Access to Water Supply – describes the physical means by which the housing unit
receives its water. The primary distinction is whether or not the household had
piped (running) water.
Age – gives age in years as of the person’s last birthday prior to or on the day of
enumeration.
Current States of Residence – identifies the household’s state or capital district within
Venezuela, which are the major administrative levels of the country.
Employment Disability – indicates if the respondent was economically inactive because
of disabilities.
Employment Status – indicates whether or not the respondent was part of the labor force –
working or seeking work – over a specified period of time.
Hours Worked Per Week – indicates the number of hours the respondent worked per
week at all jobs, categorized into intervals.
Location of Father – indicates whether or not the person’s father lived in the same
household
Location of Mother – indicates whether or not the person’s mother lived in the same
household
Marital Status- describes the person’s current marital status according to law or custom.
Nativity- indicates whether the person was native- or foreign-born.
Number of Children in Household – provides a count of the person’s own children living
in the household with her or him.

54. Hamlin 54
Number of Children Under 5 in Household – provides a count of the person’s own
children under age five living in the household with her or him.
Number of Families in Household – indicates the number of families within each
household.
School Attendance – indicates whether or not the person attended school at the time of
the census or within some specified period of time prior to the census.
Sex – reports the sex (gender) of the respondent.
State of Birth – indicates the province within Venezuela in which the person was born.
Urban/ Rural Status – whether the household was located in a place designated as urban
or as rural
Year – gives the year in which the census was taken.
Year of Birth – indicates the year in which the individual was born
Part B. Socioeconomic Outcome Variables
Literacy – indicates whether or not the respondent could read and write in any language.
A person is typically considered literate if he or she can both read and write. All
other persons are illiterate; including those who can either read or write but
cannot do both.
Natural Log of Earned Income – reports the person’s total income from their labor (from
wages, a business, or a farm) in the previous month or year
Years of Schooling – indicates the highest grade/level of schooling the person had
completed, in years. Only formal schooling is counted.
Part C. Summary Statistics By Malaria Burden
In this section, the formerly presented summary statistics in Table 1 are broken
down by malaria burden. As previously explained, highly malarious regions were
classified as the states in the top tercile of intensity, using the previously explained

55. Hamlin 55
measure of malaria burden, while low malarious regions were classified as the bottom
tercile, with the middle tercile being excluded for clarity.
Table A. 1. Summary Statistics for High Malaria Burden States: Educational,
Employment, Population, and Household Characteristics
High Malaria Burden TOTAL WOMEN MEN
N= 1,438,206 N= 715,656 N= 733,550
Education Mean SD Mean SD Mean SD
% no schooling 0.2324 0.482 0.236 0.484 0.229 0.480
Years of Schooling 4.925 4.041 5.023 4.120 4.827 3.968
% literate 0.792 0.406 0.791 0.407 0.794 0.405
% less than primary completed 0.531 0.499 0.520 0.500 0.541 0.498
% primary completed 0.342 0.474 0.340 0.474 0.343 0.475
% secondary completed 0.123 0.329 0.136 0.343 0.110 0.313
% university completed 0.004 0.066 0.004 0.062 0.005 0.071
Employment
% employed 0.399 0.490 0.224 0.417 0.575 0.494
% self-employed 0.260 0.448 0.147 0.370 0.305 0.469
% inactive 0.552 0.497 0.754 0.431 0.350 0.477
% disabled 0.016 0.127 0.013 0.111 0.020 0.140
Ln (Earned Income) 6.601 2.009 6.527 1.685 6.631 2.127
Hours worked per week:
% 1-14 hours 0.051 0.220 0.070 0.255 0.042 0.201
% 15-29 hours 0.088 0.283 0.142 0.349 0.064 0.245
% 30-39 hours 0.096 0.295 0.125 0.333 0.082 0.275
% 40-49 hours 0.556 0.497 0.502 0.500 0.580 0.494
% 49+ hours 0.209 0.406 0.159 0.365 0.231 0.421
Population Characteristics
Age 22.280 18.090 22.629 18.354 21.935 17.817
% under 18 0.490 0.499 0.484 0.498 0.497 0.499
% male 0.50 0.500
% single/ never married 0.652 0.476 0.616 0.486 0.687 0.463
Household Characteristics
rural 0.237 0.382
urban 0.763 0.425
electricity 0.857 0.350
water supply 0.680 0.414
sewage 0.676 0.468
toilet 0.832 0.374
% with >1 family 0.080 0.646
% with no mother 0.456 0.500
% with no father 0.571 0.489
% no children 0.696 0.470
% no children under 5 0.853 0.355

56. Hamlin 56
Table A. 1. Summary Statistics for Low Malaria Burden States: Educational,
Employment, Population, and Household Characteristics
Notes: A series of individual and household descriptors used as control variables in all regressions.
Represents a 10% sample from each of the four censuses: 1971, 1981, 1990, and 2001. The highly
malarious regions include Anzoategui, Barinas, Carabobo, Cojedes, Monagas, Portugesa, and Yaracuy. The
less malarious states include Apure, Federal District, Falcon, Lara, Merida, Nueva Esparta, and Trujillo.
Source: IPUMS.
Low Malaria Burden TOTAL WOMEN MEN
N= 2,273,158 N= 1,135,479 N= 1,137,679
Education Mean SD Mean SD Mean SD
% no schooling 0.219 0.479 0.227 0.481 0.211 0.478
Years of Schooling 5.237 4.197 5.289 4.252 5.184 4.140
% literate 0.805 0.396 0.800 0.400 0.810 0.392
% less than primary completed 0.505 0.500 0.498 0.500 0.512 0.500
% primary completed 0.342 0.474 0.339 0.473 0.345 0.475
% secondary completed 0.146 0.353 0.157 0.364 0.135 0.341
% university completed 0.007 0.083 0.006 0.077 0.008 0.088
Employment
% employed 0.423 0.494 0.248 0.432 0.601 0.490
% self-employed 0.2533 0.448 0.138 0.370 0.303 0.469
% inactive 0.534 0.499 0.729 0.444 0.337 0.472
% disabled 0.020 0.140 0.015 0.123 0.025 0.155
Ln (Earned Income) 6.714 1.874 6.616 1.643 6.757 1.966
Hours worked per week:
% 1-14 hours 0.049 0.215 0.046 0.246 0.041 0.198
% 15-29 hours 0.080 0.272 0.128 0.334 0.058 0.235
% 30-39 hours 0.092 0.289 0.126 0.331 0.076 0.266
% 40-49 hours 0.567 0.495 0.526 0.499 0.588 0.492
% 49+ hours 0.211 0.408 0.156 0.363 0.237 0.425
Population Characteristics
Age 24.473 19.101 24.892 19.405 24.056 18.783
% under 18 0.444 0.499 0.438 0.498 0.451 0.499
% male 0.502 0.500
% single/ never married 0.630 0.483 0.603 0.489 0.656 0.475
Household Characteristics
rural 0.229 0.382
urban 0.771 0.420
electricity 0.874 0.331
water supply 0.775 0.414
sewage 0.725 0.447
toilet 0.831 0.374
% with >1 family 0.107 0.302
% with no mother 0.474 0.500
% with no father 0.595 0.489
% no children 0.676 0.470
% no children under 5 0.857 0.355

57. Hamlin 57
Part D. Pre-Post Graphical Analysis By State
This continuation of the initial graphical analysis also uses the classifications of
high and low intensity states, but the average outcome per birth year in each state is
plotted in order to validate trends in these areas. For each year of birth, 1900-1980, in
each state, median outcomes of the three socioeconomic outcomes were calculated, as
well as the average across all states in each malaria intensity classification. These
calculations were then plotted against birth year. This model allows one to visualize the
relationship between socioeconomic outcome and birth year relative to the eradication
campaign. Recall that cohorts born well before 1945 would be too old to experience
childhood benefits of the campaign, while cohorts born well after the campaign would
have significantly less malaria infection. The results are presented in Figure A.1.
Figure A.1 - Socioeconomic Outcomes By Birth Year and Pre-Campaign Intensity
Panel A. Years of Schooling

58. Hamlin 58
Panel B. Literacy Rates
Panel C. Ln (Income)
Notes: This figure plots median socioeconomic outcomes in for each state in high and low malaria intensity
regions based on year of birth. The highly malarious regions include Anzoategui, Barinas, Carabobo,
Cojedes, Monagas, Portugesa, and Yaracuy. The less malarious states include Apure, Federal District,
Falcon, Lara, Merida, Nueva Esparta, and Trujillo. The independent variable is year of birth and the
dependent variable is median socioeconomic outcome. Lines of best fit have been plotted for the roughly
three exposure periods in relation to the eradication campaign: 1900-1930, no childhood exposure; 1930-
1955, partial childhood exposure; 1955-1980, full childhood exposure

59. Hamlin 59
The patterns of estimates are broadly consistent with the childhood exposure
model explored in Section VI, and with the greater gains in socioeconomic outcomes
explored in Section V and earlier in this section. Cohorts born in states with a higher pre-
campaign malaria burden had, on average, lower initial magnitudes in years of schooling,
literacy rates, and earned income before the campaign. Exposure to the campaign in
childhood, especially in highly malarious states, increases the rate of growth (i.e. the
slope in these graphs) of socioeconomic outcomes. As you may recall, the majority of
states in Venezuela had been sprayed at least once by DDT by the end of 1947.
Additionally mortality rates reached approximately zero in states 3-5 years after spraying.
Thus, the period of interest in the differential growth in socioeconomic outcomes lies
from approximately 1930-1952.
In this graphical analysis, it can be shown that while socioeconomic outcomes
were rising prior to the advent of the campaign, growth rate increased as cohorts became
partially exposed in the 1930s and continued to increase as cohorts spent a greater
percentage of their childhood with a low malaria burden. This is consistent with the
childhood exposure model explored in Section VI; partial exposure to the DDT campaign
confers measurable, but fractional benefits to the middle cohorts. Moreover, this trend is
mostly clearly represented in the highly malarious states. The growth rate in
socioeconomic outcomes of cohorts born between ≈ 1930-1955 is greater in highly
malarious states than less malarious states, this is represented in the graphs by the plotted
slope during this time period. This means, as has been previously evidenced, that the
malaria control and eradication campaign had a greater positive impact on socioeconomic
outcomes in highly malarious regions than in less malarious regions.

60. Hamlin 60
These graphs are particularly important in that they assist in visualizing the
relationship between timing of the eradication campaign in childhood and relative benefit
to socioeconomic outcomes. The results from this graphical analysis show that the
benefits from malaria control and eradication are not disproportionally concentrated in
the first years of life. Exposure at any point of childhood will earn socioeconomic
benefits, although these benefits build as percent exposure grows larger. This means that
cohorts exposed to the eradication campaign for their full childhood will experience an
increase in socioeconomic outcomes, but the magnitude of this increase will not be
disproportionally larger than that of cohorts exposed from age 1 on. This result was also
confirmed in Section VI. This trend disqualifies an in utero hypothesis of early life
malaria infection.
Part E. Methods Regarding Regression (3)
Here I further explain and expand upon the method used in Regression 3 to obtain
the coefficients for each year of childhood. This analysis is used to determine the relative
importance of the malaria eradication campaign at different points in time during early
childhood and later childhood. Recall that the initial regression equation I presented was:
(3) Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + β4 Exp3ijc + β5 Exp4ijc + β6 Exp5ijc +
Xijc Γ + εijc
in which Yijc is a socioeconomic outcome of individual i in region j, who is a member of
cohort c. Exp0ijc is a dummy variable indicating if an individual was exposed to the
eradication campaign at birth, Exp1ijc represents an individual exposed at one, and so

61. Hamlin 61
forth up until age five, in this particular equation. Coefficients were calculated for all 18
years of childhood and are presented below in Table A.3. Xijc are a series of individual
and household controls, and α is a constant. β is the coefficient of interest for all ages and
represents socioeconomic outcomes associated with exposure during that year of life.
A series of dummy variables were constructed for possible exposure to the
campaign ages 0-18. If an individual was born on or after the start year of the campaign,
he/she was given a 1 for possible exposure at age 0 and every subsequent year of
childhood. There is never a case where an individual is potentially exposed to the
eradication campaign at age 0 and not exposed to the campaign at any other year of
childhood, as spraying did not stop for any prolonged period of time once commenced in
an area. As a further example, an individual born 3 years prior to the advent of the
campaign in their state of birth, would receive a 0 for possible exposure at age 0, 1, and 2,
but a 1 at age 3 and every subsequent year of childhood. This method was carried out for
all years 0-18. This analysis does assume that an individual did not move from a region
that had previously been sprayed or to a region that had not yet been sprayed within the
first couple years of life. However, this is most likely not the case as cross-state migration
was not particularly common in the first half of the 20th
century in Venezuela.
To determine the differential impact of possible exposure to the campaign at age 0,
only the dummy Exp0ijc is included in the regression. This forms the regression:
Yijc = α + β1 Exp0ijc + Xijc Γ + εijc
This is because when Exp0 takes on the value of 1, there are no other values the rest of
the age exposure dummies (Exp1 - Exp18) can represent other than 1. Additionally, the
measure of interest is the β associated with a one-unit increase (i.e. no exposure vs.

62. Hamlin 62
exposure) in the dummy Exp0. The five additional regressions that were run to determine
the coefficients of exposure for the first five years of childhood are included below:
Age 1: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + Xijc Γ + εijc
Age 2: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + Xijc Γ + εijc
Age 3: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + β4 Exp3ijc + Xijc Γ + εijc
Age 4: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + β4 Exp3ijc + β5 Exp4ijc + Xijc Γ +
εijc
Age 5: Yijc = α + β1 Exp0ijc + β2 Exp1ijc + β3 Exp2ijc + β4 Exp3ijc + β5 Exp4ijc + β6 Exp5ijc
+ Xijc Γ + εijc
This approach was taken because once a dummy exposure variable is given a value of 1
all subsequent age exposure variables must also equal 1 and should not be controlled for
in the regression. However, the exposure dummies for ages before that specific age must
be controlled for as they can take on the value of 1 or 0. By including them in the
regression, I am, hypothetically, holding them constant at 0 and thus measuring the
differential impact of initial exposure to the campaign at a specific age. The coefficient of
interest for all ages was the β associated with that age’s exposure dummy and is bolded in
each regression above. These coefficients were calculated for each age 0-18 in order to
create the graphs in Figure 6, and are presented in Table A.3.
Part F. Methods Regarding Regression (4)
Here I further explain and expand upon the method used in Regression 4 to obtain
the coefficients for each year of childhood. This analysis is used to determine the relative
importance of reduction of malaria burden, through exposure to the eradication

63. Hamlin 63
campaign, at different points in time during early childhood and later childhood. Recall
that the initial regression equation I presented was:
(4)
Yijc = α + β1 Exp0ijc + β2 (Exp0ijc * malariajpre) + β3 Exp1ijc + β4 (Exp1ijc * malariajpre) +
β5 Exp2ijc + β6 (Exp2ijc * malariajpre) + β7 Exp3ijc + β8 (Exp3ijc * malariajpre) + β9 Exp4ijc
+ β10 (Exp4ijc * malariajpre) +β11 Exp5ijc + β12 (Exp5ijc * malariajpre) + Xijc Γ + εijc
in which, Yijc is a socioeconomic outcome of individual i in region j, who is a member of
cohort c. Exp0ijc is a dummy variable indicating if an individual was exposed to the
eradication campaign at birth. Exp1ijc represents an individual exposed at one, and so
forth up until age five, in this particular equation. Coefficients were calculated for all 18
years of childhood but only ages 0-5 are represented in this equation. Malariajpre is the
pre-eradication malaria intensity in the state of birth of the individual. Xijc are a series of
individual and household controls, and α is a constant. β in front of the interaction terms
(the even numbered βs) are the coefficients of interest for all ages and represent
differential changes in socioeconomic outcomes due to a log change in malaria intensity
dependent on exposure during that year of childhood.
The possible exposure dummy variables are constructed in the exact same manner
as those in Regression (3). In a similar vain to that of Regression 3, this function was run
multiple times for each potential year of childhood exposure. Again, this is because there
is never a case where an individual is potentially exposed to the eradication campaign at
age 0 and not exposed to the campaign at any other year of childhood. To determine the
differential impact of reduction in malaria burden due to exposure to the campaign at age

64. Hamlin 64
0, only the dummy Exp0ijc and the interaction term (Exp0ijc * malariajpre) are included in
the regression creating the model:
Yijc = α + β1 Exp0ijc + β2 (Exp0ijc * malariajpre) + Xijc Γ + εijc.
This is because when Exp0 takes on the value of 1, there are no other values the rest of
the age exposure dummies (Exp1 - Exp18) can represent other than 1, and thus cannot be
controlled for in the regression Additionally, the measure of interest is the β in front of
the interaction term, as it represents the socioeconomic effect of a log decrease in malaria
burden if the individual was exposed to the campaign at age 0. The four additional
regressions that were run to determine the coefficients of exposure for the first five years
of childhood are included below:
Age 1: Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β4 (Exp1ijc * malariajpre) + Xijc Γ + εijc
Age 2: Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β5 Exp2ijc + β6 (Exp2ijc * malariajpre) + Xijc Γ
+ εijc
Age 3: Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β5 Exp2ijc + β7 Exp3ijc +
β8 (Exp3ijc *malariajpre) + Xijc Γ + εijc
Age 4: Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β5 Exp2ijc + β7 Exp3ijc + β9 Exp4ijc +
β10 (Exp4ijc * malariajpre) + Xijc Γ + εijc
Age 5: Yijc = α + β1 Exp0ijc + β3 Exp1ijc + β5 Exp2ijc + β7 Exp3ijc + β9 Exp4ijc +
β11 Exp5ijc + β12 (Exp5ijc * malariajpre) + Xijc Γ + εijc
This approach was taken because once a dummy exposure variable is given a
value of 1 all subsequent age exposure variables must also equal 1 and should not be
controlled for in the regression. The differential impact of a reduction in malaria burden
at 0 age due to exposure to the campaign (represented by β of the interaction term), but

65. Hamlin 65
not the subsequent years, is the calculation of interest. This is why the interaction terms
of the following years were not included in the analysis at the age of 0.
For calculating the coefficient of interest for the other ages of childhood the
exposure dummies for ages before that specific age must be controlled for as they can
take on the value of 1 or 0. By including them in the regression, I am, hypothetically,
holding them constant at 0, and thus measuring the differential impact of a reduction in
malaria burden due to initial exposure to the campaign at a specific age. Furthermore,
when these dummies are held constant at zero, the interaction terms in which they are
represented will also take on the value of 0, and thus are unnecessary to the regression.
The coefficient of interest for all ages was the β associated with that dummies interaction
term and is bolded in each regression above. These coefficients were calculated for each
age 0-18 in order to create the graphs in Figure 6, and are presented in Table A.3

66. Hamlin 66
Table A. 3 - Effect of Differential Exposure to the Eradication Campaign During
Childhood
Panel A. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Years of school
Exposure at:
6 1.554*** 0.530*** 0.100*** 0.128***
(0.021) (0.040) (0.005) (0.014)
7 1.555*** 0.540*** 0.089*** 0.123***
(0.021) (0.041) (0.006) (0.014)
8 1.565*** 0.544*** 0.075*** 0.112***
(0.022) (0.044) (0.006) (0.015)
9 1.483*** 0.426*** 0.059*** 0.095***
(0.023) (0.045) (0.006) (0.016)
10 1.368*** 0.387*** 0.050*** 0.087***
(0.023) (0.046) (0.006) (0.016)
11 1.175*** 0.344*** 0.041*** 0.086***
(0.023) (0.047) (0.006) (0.017)
12 1.100*** 0.214*** 0.037*** 0.087***
(0.024) (0.049) (0.006) (0.018)
13 1.094*** 0.320*** 0.035*** 0.082***
(0.024) (0.054) (0.006) (0.019)
14 1.043*** 0.315*** 0.027*** 0.081***
(0.025) (0.056) (0.006) (0.020)
15 1.018*** 0.375*** 0.017*** 0.067***
(0.025) (0.057) (0.006) (0.021)
16 0.888*** 0.437*** 0.007 0.047**
(0.025) (0.059) (0.007) (0.022)
17 0.843*** 0.260*** 0.009 0.063***
(0.025) (0.060) (0.007) (0.024)
18 1.018*** 0.159** 0.005 0.070***
(0.027) (0.063) (0.007) (0.025)
Controls: N Y N Y
Observations: 5,288,297 870,868 5,288,297 870,868

67. Hamlin 67
Table A. 3 - Effect of Differential Exposure to the Eradication Campaign During
Childhood (Continued)
Panel B. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Literacy
Exposure at:
6 0.137*** 0.048*** 0.020*** 0.022***
(0.002) (0.004) (0.001) (0.001)
7 0.138*** 0.046*** 0.020*** 0.023***
(0.002) (0.004) (0.001) (0.001)
8 0.150*** 0.058*** 0.020*** 0.023***
(0.002) (0.004) (0.001) (0.001)
9 0.147*** 0.059*** 0.020*** 0.024***
(0.002) (0.004) (0.001) (0.001)
10 0.141*** 0.045*** 0.020*** 0.025***
(0.002) (0.005) (0.001) (0.001)
11 0.125*** 0.047*** 0.020*** 0.025***
(0.002) (0.005) (0.001) (0.002)
12 0.121*** 0.043*** 0.020*** 0.026***
(0.002) (0.005) (0.001) (0.002)
13 0.124*** 0.048*** 0.021*** 0.026***
(0.002) (0.006) (0.001) (0.002)
14 0.119*** 0.041*** 0.020*** 0.026***
(0.002) (0.006) (0.001) (0.002)
15 0.112*** 0.043*** 0.020*** 0.027***
(0.002) (0.006) (0.001) (0.002)
16 0.095*** 0.032*** 0.020*** 0.027***
(0.002) (0.007) (0.001) (0.002)
17 0.093*** 0.052*** 0.021*** 0.028***
(0.002) (0.006) (0.001) (0.002)
18 0.121*** 0.053*** 0.021*** 0.030***
(0.002) (0.007) (0.001) (0.002)
Controls: N Y N Y
Observations: 5,564,805 900,308 5,564,805 900,308

68. Hamlin 68
Table A. 3 - Effect of Differential Exposure to the Eradication Campaign During
Childhood (Continued)
Notes: This table reports the estimates of the malaria coefficient of Regression (3) and (4). The units of
observation are Venezuelan states. Robust standard errors are in brackets. Both equations are a series of
regressions, each run separately, building on the regression before it. Regression (3) does not rely on
malaria burden in the state of birth; the counterfactual, or control group, is individuals who were not
exposed to eradication efforts at that particular age and thus were not treated. The independent variables for
Regression (3) are a series of dummies reflecting possible exposure to the eradication campaign during that
year of childhood and the dependent variable is change in socioeconomic outcome. The control group for
Regression (4) is both unexposed cohorts as well as cohorts in low malaria burdened states. The
independent variable for Regression (4) is a series of dummy variables for exposure at age interacted with
pre-campaign malaria intensity. The dependent variable can be interpreted as change in socioeconomic
Panel C. Regression (3) Regression (4)
Dependent Variable: (1) (2) (3) (4)
Ln (Income)
Exposure at:
6 0.222*** 0.162*** 0.038*** 0.051***
(0.010) (0.011) (0.004) (0.003)
7 0.269*** 0.185*** 0.037*** 0.049***
(0.011) (0.012) (0.004) (0.004)
8 0.294*** 0.184*** 0.037*** 0.047***
(0.011) (0.012) (0.004) (0.004)
9 0.279*** 0.179*** 0.033*** 0.045***
(0.012) (0.013) (0.004) (0.004)
10 0.276*** 0.172*** 0.032*** 0.046***
(0.012) (0.013) (0.004) (0.004)
11 0.267*** 0.186*** 0.030*** 0.046***
(0.012) (0.013) (0.004) (0.004)
12 0.259*** 0.171*** 0.030*** 0.045***
(0.013) (0.013) (0.005) (0.005)
13 0.280*** 0.180*** 0.029*** 0.045***
(0.013) (0.014) (0.005) (0.005)
14 0.278*** 0.176*** 0.026*** 0.043***
(0.014) (0.015) (0.005) (0.005)
15 0.296*** 0.200*** 0.025*** 0.041***
(0.014) (0.015) (0.005) (0.005)
16 0.249*** 0.195*** 0.023*** 0.041***
(0.014) (0.016) (0.005) (0.005)
17 0.308*** 0.177*** 0.027*** 0.044***
(0.014) (0.016) (0.005) (0.006)
18 0.367*** 0.204*** 0.027*** 0.044***
(0.015) (0.017) (0.006) (0.006)
Controls: N Y N Y
Observations: 1,711,203 900,310 1,711,203 900,310

69. Hamlin 69
outcome due to log decrease in malaria intensity at a particular age. Results for age 0-5 are reported in
Table 4.
***significant at the 1 percent level
** significant at the 5 percent level
* significant at the 10 percent level