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

Hamlin 1

The Socioeconomic Impact of Malaria Control and Eradication in Venezuela

Brittany Hamlin

Dr. Hyuncheol Kim, Thesis Advisor

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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

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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

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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

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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

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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

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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

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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

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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.

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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

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(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

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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

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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.

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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

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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

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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.

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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.

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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

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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.

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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

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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.

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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

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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

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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

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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.

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Figure 6 – Relationship Between Age of Exposure and Socioeconomic Outcome

Panel A. Years of Schooling

Panel B. Literacy Rates

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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.

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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.

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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

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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

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REFERENCES:

Acemoglu, Daron, and Simon Johnson. 2007. “Disease and Development: The Effect of Life Expectancy on Economic Growth.” Journal of Political Economy, 115(6): 925–985.

Acemoglu, Daron, S. Johnson, J. Robinson, and Y. Thaicharoen. 2003. “Institutional Causes, macroeeconmic Symptoms: Volatility, Crisis and Growth.” Journal of Monetary Economics, 50, January 2003: pp. 49-123

Barreca, Alan. Technical Report. UC-Davis; 2007. The Long-Term Economic Impact of In Utero and Postnatal Exposure to Malaria.

Bleakley Hoyt. Disease and Development: Evidence from Hookworm Eradication in the American South. Quarterly Journal of Economics. 2007a; 122(1): 73–117.

Bleakley, Hoyt. 2010. “Malaria Eradication in the Americas: A Retrospective Analysis of Childhood Exposure.” American Economic Journal: Applied Economics, 2(2): 1–45.

Brooker, S., H. Guyatt, J. Omumbo, R. Shretta, L. Drake, and J. Ouma. 2000. “Situation

Analysis of Malaria in School-Aged Children in Kenya—What Can Be Done?” Parasitology Today, 16(5): 183–86.

Clarke, Sian et al. 2008. “Effect of Intermittent Preventive Treatment of Malaria on Health and Education in Schoolchildren: A Cluster Randomised, Double-Blind, Placebo-Controlled Trial.” Lancet, 372(9633): 127–38.

Cutler, D., Fung, W., Kremer, M., Singhal, M., & Vogl, T. (2010). Early-life Malaria Exposure and Adult Outcomes: Evidence from Malaria Eradication in India. American Economic Journal: Applied Economics, 2(2), 72-94.

Dillon, Andrew, Jed Friedman, and Pieter Serneels. Health Information, Treatment, and Worker Productivity : Experimental Evidence from Malaria Testing and Treatment among Nigerian Sugarcane Cutters, Volume 1. Working paper no. TF093306. The World Bank, 01 Nov. 2014. Web. 01 Apr. 2015.

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. Progress of the Malaria Campaign in Venezuela." Journal of the National Malaria Society 10.2 (1951): 124-41. Print.

Gabaldón Arnoldo. The nation-wide campaign against malaria in Venezuela. Trans R Soc Trop Med Hyg. 1949; 43:113–64.

Gabaldón Arnoldo, Berti Allan. The first large area in the tropical zone to report malaria eradication: north-central Venezuela.Am J Trop Med Hyg. 1954;3: 793–807 .

Gabaldón Arnoldo, Guia de Perez G. Mortality from malaria in Venezuela (in Spanish). Tijeretazos Sobre Malaria.1946; 10:191–237.

Gallup, John Luke; Sachs, Jeffrey D. The Economic Burden of Malaria. The American Journal of Tropical Medicine & Hygiene. 2001 Jan-Feb; 64(1, 2S): 85–96.

Griffing SM, Villegas L, Udhayakumar V. Malaria control and elimination, in Venezuela, 1800s–1970s. Emerg Infect Dis (Internet). 2014 Oct (April 1, 2015). http://dx.doi.org/10.3201/eid2010.130917.

Hong, Sok Chul. Doctoral dissertation. University of Chicago; 2007. Health and Economic Burden of Malaria in Nineteenth-Century America.

Leighton, Charlotte, and Rebecca Foster. 1993. “Economic Impacts of Malaria in Kenya and Nigeria.” Health Financing and Sustainability Project Major Applied Research Paper 6. http://www. healthsystems2020.org/files/765_file_hfsmar6.pdf.

Lucas, Adrienne M. 2010. “Malaria Eradication and Educational Attainment: Evidence from Paraguay and Sri Lanka.” American Economic Journal: Applied Economics, 2(2): 46–71.

McCaughan, Michael. The Battle of Venezuela. New York: Seven Stories, 2005. Print.

Minnesota Population Center. Integrated Public Use Microdata Series, International:

Version 6.3 (Machine-readable database). Minneapolis: University of Minnesota, 2014.

Otido, Christopher Crudder, Benson B. A. Estambale, and Simon Brooker. 2008. “Effect of Intermittent Preventive Treatment of Malaria on Health and Education in

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Schoolchildren: A Cluster Randomised, Double-Blind, Placebo-Controlled Trial.” Lancet, 372(9633): 127–38.

Thomas, Duncan; Frankenberg, Elizabeth; Friedman, Jed; Habicht, Jean-Pierre; Hakimi, Mohamme; Jaswadi, Nathan Jones; Pelto, Gretel; Sikoki, Bondan; Seeman, Teresa; Smith, James P.; Sumantri, Cecep; Suriastini, Wayan; Wilpo, Siswanto. Iron defficiency and the well-being of older adults: Early results from a randomized nutrition intervention. 2003 Apr. Unpublished manuscript

UN. (2013). The Millennium Development Goals Report 2013. New York: United Nations.

World Health Organization. Africa Malaria Report 2003. World Health Organization/UNICEF; Geneva, Switzerland: 2003.

The author wishes to acknowledge the statistical offices that provided the underlying data making this research possible: National Institute of Statistics, Venezuela.

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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.

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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

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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

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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

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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

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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

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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.

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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


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