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Randomized Experiments

11-07-15 2:02 PMMore Than 1 Billion People Are Hungry in the World - By Abhijit Banerjee and Esther Duflo | Foreign Policy

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More Than 1 Billion People AreHungry in the WorldBut what if the experts are wrong?

BY ABHIJIT BANERJEE, ESTHER DUFLO | MAY/JUNE 2011

For many in the West, poverty is almost synonymous with hunger. Indeed, the

announcement by the United Nations Food and Agriculture Organization in

http://www.foreignpolicy.com/the_food_issue

http://www.foreignpolicy.com/articles/2011/04/25/the_new_geopolitics_of_food

http://www.foreignpolicy.com/articles/2011/04/25/street_eats

http://www.foreignpolicy.com/articles/2011/04/25/how_food_explains_the_world

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2009 that more than 1 billion people are suffering from hunger grabbed

headlines in a way that any number of World Bank estimates of how many poor

people live on less than a dollar a day never did.

But is it really true? Are there really more than a billion people going to bed

hungry each night? Our research on this question has taken us to rural villages

and teeming urban slums around the world, collecting data and speaking with

poor people about what they eat and what else they buy, from Morocco to

Kenya, Indonesia to India. We've also tapped into a wealth of insights from our

academic colleagues. What we've found is that the story of hunger, and of

poverty more broadly, is far more complex than any one statistic or grand

theory; it is a world where those without enough to eat may save up to buy a TV

instead, where more money doesn't necessarily translate into more food, and

where making rice cheaper can sometimes even lead people to buy less rice.

But unfortunately, this is not always the world as the experts view it. All too

many of them still promote sweeping, ideological solutions to problems that defy

one-size-fits-all answers, arguing over foreign aid, for example, while the facts

on the ground bear little resemblance to the fierce policy battles they wage.

Jeffrey Sachs, an advisor to the United Nations and director of Columbia

University's Earth Institute, is one such expert. In books and countless speeches

and television appearances, he has argued that poor countries are poor because

they are hot, infertile, malaria-infested, and often landlocked; these factors,

however, make it hard for them to be productive without an initial large

investment to help them deal with such endemic problems. But they cannot pay

for the investments precisely because they are poor -- they are in what

economists call a "poverty trap." Until something is done about these problems,

neither free markets nor democracy will do very much for them.

But then there are others, equally vocal, who believe that all of Sachs's answers

are wrong. William Easterly, who battles Sachs from New York University at the

other end of Manhattan, has become one of the most influential aid critics in his

http://www.wfp.org/stories/number-world-hungry-tops-billion



books, The Elusive Quest for Growth and The White Man's Burden.

Dambisa Moyo, an economist who worked at Goldman Sachs and the World

Bank, has joined her voice to Easterly's with her recent book, Dead Aid. Both

argue that aid does more bad than good. It prevents people from searching for

their own solutions, while corrupting and undermining local institutions and

creating a self-perpetuating lobby of aid agencies. The best bet for poor

countries, they argue, is to rely on one simple idea: When markets are free and

the incentives are right, people can find ways to solve their problems. They do

not need handouts from foreigners or their own governments. In this sense, the

aid pessimists are actually quite optimistic about the way the world works.

According to Easterly, there is no such thing as a poverty trap.

This debate cannot be solved in the abstract. To find out whether there are in

fact poverty traps, and, if so, where they are and how to help the poor get out of

them, we need to better understand the concrete problems they face. Some aid

programs help more than others, but which ones? Finding out required us to

step out of the office and look more carefully at the world. In 2003, we founded

what became the Abdul Latif Jameel Poverty Action Lab, or J-PAL. A key part of

our mission is to research by using randomized control trials -- similar to

experiments used in medicine to test the effectiveness of a drug -- to understand

what works and what doesn't in the real-world fight against poverty. In practical

terms, that meant we'd have to start understanding how the poor really live

their lives.

Take, for example, Pak Solhin, who lives in a small village in West Java,

Indonesia. He once explained to us exactly how a poverty trap worked. His

parents used to have a bit of land, but they also had 13 children and had to build

so many houses for each of them and their families that there was no land left

for cultivation. Pak Solhin had been working as a casual agricultural worker,

which paid up to 10,000 rupiah per day (about $2) for work in the fields. A

recent hike in fertilizer and fuel prices, however, had forced farmers to

economize. The local farmers decided not to cut wages, Pak Solhin told us, but to

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But what if the poor are not, in general, eating too little food? What if, instead,

they are eating the wrong kinds of food, depriving them of nutrients needed to

be successful, healthy adults? What if the poor aren't starving, but choosing to

spend their money on other priorities? Development experts and policymakers

would have to completely reimagine the way they think about hunger. And

governments and aid agencies would need to stop pouring money into failed

programs and focus instead on finding new ways to truly improve the lives of

the world's poorest.

Consider India, one of the great puzzles in this age of food crises. The standard

media story about the country, at least when it comes to food, is about the rapid

rise of obesity and diabetes as the urban upper-middle class gets richer. Yet the

real story of nutrition in India over the last quarter-century, as Princeton

professor Angus Deaton and Jean Drèze, a professor at Allahabad University and

a special advisor to the Indian government, have shown, is not that Indians are

becoming fatter: It is that they are in fact eating less and less. Despite the

country's rapid economic growth, per capita calorie consumption in India has

declined; moreover, the consumption of all other nutrients except fat also

appears to have gone down among all groups, even the poorest. Today, more

than three-quarters of the population live in households whose per capita

calorie consumption is less than 2,100 calories in urban areas and 2,400 in rural

areas -- numbers that are often cited as "minimum requirements" in India for

those engaged in manual labor. Richer people still eat more than poorer people.

But at all levels of income, the share of the budget devoted to food has declined

and people consume fewer calories.

What is going on? The change is not driven by declining incomes; by all

accounts, Indians are making more money than ever before. Nor is it because of

rising food prices -- between the early 1980s and 2005, food prices declined

relative to the prices of other things, both in rural and urban India. Although

food prices have increased again since 2005, Indians began eating less precisely

Notes from: “Using Randomization in Development Economics Research: A Toolkit” (Duflo, Glennerster, Kremer 2008)

Randomization is now integral part of development economics

Over last ten years has grown to dominate the field

Diverse topics: education, health, technology adoption, micro-credit…

Randomized evaluations in developing countries – relatively cheap

Working with local partners allows for flexibility to researchers

Why Randomize?

Problem of causal inference

What is causal impact of specific program requires a counterfactual

At a given point in time, an individual is either exposed to the program or not

Comparing same individual over time does not work because other factors which affect outcomes have changed --- cannot get reliable estimate of program’s impact

Cannot obtain impact of program on a given individual – can obtain average impact of program on a group of individuals by comparing them to similar group of individuals who were not exposed to program

Need a comparison group – group of people who in absence of treatment would have had similar outcomes to those who received treatment

Reality- individuals exposed to treatments differ from those not exposed

programs placed in specific areas (poorer)

individuals screened for participation

decision to participate is voluntary, creating self-selection

Any difference between groups can be attributed to both impact of program and pre-existing differences (selection bias) – cannot decompose

Randomized evaluation removes selection bias

N individuals selected from population (not a random sample)

Divide N individuals into treatment and control randomly

Average treatment effect is difference in empirical means across two groups

Had the treatment not occurred – their outcomes would have been in expectation the same (selection bias equal to zero)

Provides and unbiased estimate of the impact of the program in the sample under study – estimate is internally valid

Many ways in which the assumptions in the simple framework may fail when randomized evaluations are implemented in the field in developing countries

Practicalities

Partners: governments, NGOs, private companies

Implementing real-world programs and interested in finding out whether they work or how to improve them

Partner is always in charge of implementing program

Government programs:

Meant to serve entire eligible population -- pilot programs run before

Most famous example in development: PROGRESA (grants to women) – randomized evaluation by government of Mexico --- due to budgets constraints could not implement in all communities at once

Randomized evaluations in collaboration with governments still relatively rare (require cooperation at high political levels – difficult to generate consensus for implementation)

Recent spread of randomized evaluations owes much to move towards working with non-governmental organizations (NGOs)

NGOs not expected to serve entire population

Even small organizations can substantially affect budgets for households, schools, or health clinics

Often eager to work with researchers to assess effectiveness of operations

For profit firms also started getting interested in randomized evaluations, goal is to understand their business and better serve their clients

Karlan et al. worked with private lender in South Africa – able to test models of lending

Design

Sample size and other design choices will affect the power of the experiment

Tradeoffs between how expensive data collection is compared to treatment – determines size of comparison group (data collection cheap if using administrative data)

Control Variables:

Controlling for covariates affected by the treatment would bias the estimate of the treatment effect by capturing part of the impact

Information on covariates should be collected in the baseline surveys (pre-treatment value of the outcome is of special interest)

Can reduce sample size requirement needed – e.g. controlling for baseline test scores in evaluations of education interventions greatly improves precision of estimates

Practical design choice is whether to randomize the intervention at the level of the individual, family, village, district etc.

Conducting baseline survey – in theory randomization renders baseline surveys unnecessary since it ensures treatment and comparison group are similar in expectation

Still want to collect:

Control variables will reduce sample size requirements

Check randomization was done appropriately

Notes from: “The Experimental Approach to Development Economics” (Banerjee and Duflo 2009)

Veritable explosion of randomized experiments in development economics

Rising tide of criticism

(most found in Heckman 1992 – mainly commenting on field of labour economics)

Promise of experiments:

Surprisingly positive results

o (Miguel/Kremer 2004) showed that deworming treatment (costs 49 cents/child per year) can reduce abesenteeims from by school by one-quarter

o In terms of increasing attendance – deworming is 20 times as effective as hiring an extra teacher, even though both work in the sense of generating statistically significant improvements

o Economic intuition would not have helped us come to this conclusion

o NGOs were equally uniformed about this comparison

Multiple treatment experiments can be informative

o Duflo, Kremer, Robinson (2010) reflects an iterative process

o succession of experiments on fertilizer use were run over a period of several years

o each set of results prompting the need to try out a series of new variation in order to better understand results of previous one

Theoretical Motivation

o Experiments designed to assess whether there is a demand for commitment products (Ashraf, Karlan, and Yin 2006) – came from theoretical motivation

o Karlan and others – experiments emerging as powerful too for testing theories

Biggest Advantage:

Experiments may be that they take us into terrain where observational approaches are not available

Objections raised by critics best viewed as warnings against over-interpreting experimental results

Also concerns about what experiments are doing to development economics as a field

Generalizability

Environmental Dependence - Core element of generalizability – would the same result occur in a different setting?

Effect is not constant across individuals – likely vary systematically with covariates?

Concern of implementer effects and compliance – smaller organization (NGO) – estimated treatment effect reflects unique characteristics of implementer

e.g. some NGOs refuse to randomize

Randomization Issues

Fact that there is an experiment going on might generate selection effects that would not arise in non-experimental setting (being part of an experiment and being monitored influences participants)

Villagers not used to private organization going around offering them things

Necessary that individuals are not aware that they are excluded from program (difficult when randomization is at individual level, easier if randomization is at village level)

Equilibrium Effects

Program effects from small study may not generalize when program is scaled up

e.g. :

Vouchers to go to private school

Students end up with better education and higher incomes

Scale up program to national level

Crowding in private schools (collapse of public schools)

Returns to education fall because of increased supply

Experimental evidence overstates returns to vouchers program

Notes from: “Instruments, Randomization, and Learning about Development” (Deaton 2010)

Effectiveness of development assistance is topic of great public interest

Much public debate among non-economists takes it for granted that, if the funds were made available, poverty would be eliminated -- Amongst economists, it is mixed.

Macro perspective: can foreign assistance raise growth and eliminate poverty?

Micro perspective: what sorts of projects are likely to be effective? Should aid focus on roads, electricity, schools, health clinics?

Answer – we don’t know – how should we go about finding out?

Frustration with Aid organizations

Particularly the World Bank

Allegedly failing to learn from its projects and to build up a systematic catalogue of what works and what does not

Movement toward randomized controlled experiments:

Esther Duflo:

“ randomized trials can revolutionize social policy during 21st century just as they revolutionized medicine during the 20th”

---- Lancet editorial headed “ The World Bank is finally embracing science”

Deaton argues:

under ideal circumstances randomized evaluations of projects are useful for obtaining convincing estimates of the average treatment effect of a program or project

This focus is too narrow and too local to tell us “what works” in development and to design policy or to advance scientific knowledge about development processes

Argues that work needs to be refocused – not answer which projects work but why

Randomization in the Tropics

Under ideal conditions an RCT can estimate certain quantities of interest with minimal assumptions

Originally laid out by Neyman in 1920s:

What we observe in the data:

E(Y1|T=1) – E(Y0|T=0) [*]

What we would like to know is effect of being treated on the treated:

E(Y1-Y0|T=1)

[*] rewritten as:

[E(Y1|T=1) – E(Y0|T=1)] + [E(Y0|T=1) – E(Y0|T=0)]

second term is = 0 by randomization

given expectation is a linear operator we have: E(Y1-Y0|T=1)

Difference in means between treatment and control groups is estimate of the treatment effect on the treated

Since the treatment and control groups are random, is an estimate of the treatment effect for all

Result depends on randomization and linearity of expectations

RCT is only informative of the mean of the treatment effects

Cannot identify anything else in the distribution --- e.g. median which could be of equal interest to policy makers

Also would like to know the fraction of population for which the treatment was positive (cannot know this either)

So if very few are receiving large benefits, everyone else is hurting – could not know this

In practice, researchers who run RCTs present results other than the mean:

Yi = b0 + b1Ti + b2Xi + b3Ti*Xi + ei

Xi are baseline controls

Interaction between Xi and Ti – can see if treatment affected some groups more than others

These estimates depend on more assumptions than trial itself

One immediate charge is data mining

Sufficiently determined examination of any trial will eventually reveal some subgroup for which treatment effect was significant

Such analysis does not share the special features of an RCT – must be assessed same way we assess any other non experimental or econometric study

Also no (econometric) guarantee that another RCT run just on that subgroup will yield same result

Also relevant are assumptions – regarding variances between treatment and control group

Bigger question:

RCTs allow investigator to induce variation that might not arise nonexperimentally – but are these the relevant ones?

RCTs of “what works”

even when done without error of contamination

unlikely to be helpful for policy or move beyond the local

unless they tell us something about why

RCTs are not targeted or suited to these questions

Actual policy will always be different than experiments:

General equilibrium effects that operate on large scale

Outcomes are different when everyone is covered by treatment rather than a few

Experimental subjects are not representative of population

Small development projects at village level do not attract attention of corrupt politicians

Scientists or experimentalists more careful than government implementers

Transporting successful experiments?

Mexico’s PROGRESA program

Conditional cash transfer program paid to parents if children attend schools and clinics

Now in 30 other countries

Is this a good thing?

Cannot simply be exported if countries have

Pre-existing anti-poverty programs with conditional transfers

No capacity to meet increased demands of education and health care

No political support

Combination of mechanism and context that makes for scientific progress

Much interest in RCTs, and instrumental variables, and other econometric techniques that mimic random allocation

comes from skepticism of economic theory

impatience with its ability to deliver structures that seem helpful in interpreting reality

Internal versus external validity:

Contrast between the rigor applied to establish internal validity and the looser analysis to render it policy relevant

To do this typically use some theory or some other information from observables – both go against simplicity of RCTs

Applied and theoretical economists have never been so far apart

Failure to reintegrate is not an option

Otherwise no chance of long term scientific progress extending from the RCTs.

RCTs that are not theoretically guided are unlikely to have more than local validity

sk children around the world why they are not in school and you will get many answers: cost, distance, lack of facilities. Very few of

them will mention worms—soil-transmitted hel-minths (STHs) and schistosomes. Until recently few experts would have mentioned worms as a key barrier to schooling either.

Four hundred million children of school-age are chronically infected with intestinal worms. In-fected children suffer listlessness, diarrhea, ab-dominal pain and anemia. These parasites are so widespread that some societies do not recognize infection as a medical problem. Symptoms of worms, such as blood in the stool, are considered a natural part of growing up. So even though safe, cheap, and effective oral medication that can kill 99 percent of worms in the body is available and the World Health Organization (WHO) recom-mends mass deworming of school-aged children, only 10 percent of at-risk children get treated.

OCTOBER 2007

Policy Briefcase No. 4

Abdul Latif Jameel Poverty Action LabMIT Department of Economics

E60-275

30 Memorial Drive

Cambridge, MA 02142

Voice: 614 324 3852

Email: [email protected]

www.povertyactionlab.org

Mass Deworming: A Best-Buy for Education and Health

For more details on this study

see Miguel and Kremer (2004)

and Kremer and Miguel (2007)

available at

www.povertyactionlab.org

A This Briefcase (based on Miguel and Kremer, 2004; and Kremer and Miguel, 2007) reports the results of a randomized impact evaluation of a de-worming program in western Kenya. The results show that school-based mass deworming—where every child in a school is treated—is the most cost-effective way to increase school participation (of all the alternatives that have been rigorously evaluated). It is also one of the most cost-effective ways to improve health that we know of.

Similar educational benefits were found when intestinal worms were eradicated from the southern states of the U.S. in 1915 (Bleakley, 2007). Follow-up work found that attempts to make the program self-sustaining—through health education and user fees—led to its col-lapse. Only long-term funding of a school-based program sustained the benefits.

Summary

What was done About 30,000 children in 75 primary schools in rural Kenya were treated en masse in schools with drugs for hookworm, whipworm, roundworm, and schistosomiasis (bilharzia).

Key Impacts Reduced the incidence of moderate-to-heavy infections by 25 percentage points.

Reduced school absenteeism by 25 percent, with the largest gains among the youngest pupils.

School participation in the area increased by at least 0.14 years of schooling per treated child.

There was no evidence that deworming increased test scores.

Cost Effectiveness

Cost: 50 cents per child per year

Health: US$5 for every Disability Adjusted Life Year (DALY) saved

Education: US$3.50 for each additional year of school participation

Take Action Nowwww.dewormtheworld.org

11-07-20 1:08 AMOur Story | dewormtheworld

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Our Story

Over 400 million children are infected with parasitic worms. Although the harm they cause to children’s health and educationhas been recognized since the 1980s, deworming was not widespread due to more urgent health sector priorities. However,over two decades later, new groundbreaking research changed how the education sector viewed school-based deworming.

There were three key findings. First, researchers showed that the health impacts of deworming were significantly greater thanpreviously estimated, due to the spillover effects of treatment. Second, they illustrated that mass deworming drastically improvedschool participation. In fact, it is one of the best returns on investment of any intervention evaluated to increase school attendance.Finally, they conclusively demonstrated that deworming through schools is an efficient and effective way to treat large numbers ofchildren.

Investigators have also since followed up on this research to show the long run impacts of deworming, which result in increasedearnings and workforce participation of adults who received two to three additional years of treatment during school.

This evidence was a breakthrough. School-based deworming was globally recognized as a ‘best buy’ for development, and thebenefits and cost-effectiveness of school-based deworming were now clear to both the health and education sectors. However,additional barriers remained, and millions of children continued to go without treatment. Some countries needed access to drugs, whileothers needed technical assistance and capacity building. In addition, policies needed to be developed or strengthened in order tosupport school-based deworming programs.

Recognizing the huge opportunity to impact the lives of millions of children, economists Michael Kremer and Esther Duflo shared theevidence with fellow members of the Young Global Leaders Education Task Force, who promptly launched the Deworm the WorldInitiative in January 2007 at the World Economic Forum Annual Meeting in Davos, Switzerland.

The Deworm the World Initiative is operated as a partnership between Innovations for Poverty Action and Partnership for ChildDevelopment. Working together, the Initiative has reached 20 million children in 27 countries by supporting the launch of newcountry programs and enabling the continued activity of existing ones.

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1

Notes on “Worms: Identifying impacts on education and health in the presence of treatment externalities” (Miguel

and Kremer)

Paper looks at relationship between health and education Another example of a policy experiment Intestinal worms infect more than ¼ world’s population – reinfection rate is rapid Look at impact of deworming program in rural Kenyan primary schools

2

Large literature on positive correlation between health and economic outcomes Results suggest causal link running from health to education Also find treatment externalities - role for treatment subsidies? Big contribution of the paper is that they are able to capture externality effects

3

Intestinal Worm Infections Hookworm and roundworm infect 1.3 billion people around the world Whipworm affects 900 million Schistosomiasis affects 200 million Heavy infections can lead to iron-deficiency anemia, protein-energy malnutrition, abdominal pain, listlessness Schistosomiasis has more severe consequences – affects liver and spleen

4

Low-cost single-dose oral therapies can kill the worms Reducing hookworm, roundworm, and schistosomiasis by 99% Single-dose treatments are only moderately effective against severe whipworm infections Reinfection rate is rapid – worm burden often returns to >80% original level within a year

drugs must be taken every 6 months

5

WHO has endorsed mass school-based deworming programs in high infected areas Eliminates cost of individual parasitological screening Cost is then 49 cents per person per year in Africa Standard practice is not to treat girls because side effects of drugs can cause birth defects School-aged children likely account for the bulk of helminth transmission Less likely to use latrines and have poor hygiene practices

6

Worms spread through contact with contaminated feces Children in particular defecate in the “bush” Worms spread through contact with contaminated water In area understudy children bathe in lake In absence of reinfection disease rates should fall rapidly, worms have short life span (1 to 3 years)

7

Educational impact of de-worming programs is key issue for poorest countries deciding to make them a priority Need to do careful analysis of this issue

8

Primary School Deworming Project (Busia, Kenya) Project in southern Busia • western Kenya • poor, densely-settled farming region • highest worm infection rates in Busia district

75 project schools (30 000 students) Schools randomly divided into 3 groups of 25 Health intervention phased in over several years

9

Group 1: received free deworming treatment in 1998 & 1999 Group 2: 1999 Group 3: 2001 In 1998: Group 1 treatment schools Groups 2 and 3: comparison schools In 1999: Group 1 and 2 treatment schools Group 3 comparison school

10

Baseline characteristics: prior to treatment: • groups similar on most demographic, nutritional, and

socioeconomic characteristics, • despite randomnization – Group 1 pupils worse off along

some dimensions

166 E. MIGUEL AND M. KREMER

TABLE I

1998 AVERAGE PUPIL AND SCHOOL CHARACTERISTICS, PRE-TREATMENTa

Group 1 Group 2 Group 3 Group 1 − Group 2 −(25 schools) (25 schools) (25 schools) Group 3 Group 3

Panel A: Pre-school to Grade 8Male 0�53 0�51 0�52 0�01 −0�01

(0�02) (0�02)Proportion girls <13 years,

and all boys0�89 0�89 0�88 0�00 0�01

(0�01) (0�01)Grade progression

(= Grade − (Age − 6))−2�1 −1�9 −2�1 −0�0 0�1

(0�1) (0�1)Year of birth 1986�2 1986�5 1985�8 0�4** 0�8***

(0�2) (0�2)Panel B: Grades 3 to 8Attendance recorded in school

registers (during the four weeksprior to the pupil survey)

0�973 0�963 0�969 0�003 −0�006(0�004) (0�004)

Access to latrine at home 0�82 0�81 0�82 0�00 −0�01(0�03) (0�03)

Have livestock (cows, goats, pigs,sheep) at home

0�66 0�67 0�66 −0�00 0�01(0�03) (0�03)

Weight-for-age Z-score (lowscores denote undernutrition)

−1�39 −1�40 −1�44 0�05 0�04(0�05) (0�05)

Blood in stool (self-reported) 0�26 0�22 0�19 0�07** 0�03(0�03) (0�03)

Sick often (self-reported) 0�10 0�10 0�08 0�02** 0�02**

(0�01) (0�01)Malaria/fever in past week

(self-reported)0�37 0�38 0�40 −0�03 −0�02

(0�03) (0�03)Clean (observed by field workers) 0�60 0�66 0�67 −0�07** −0�01

(0�03) (0�03)Panel C: School characteristicsDistrict exam score 1996,

grades 5–8b−0�10 0�09 0�01 −0�11 0�08

(0�12) (0�12)Distance to Lake Victoria 10�0 9�9 9�5 0�6 0�5

(1�9) (1�9)Pupil population 392�7 403�8 375�9 16�8 27�9

(57�6) (57�6)School latrines per pupil 0�007 0�006 0�007 0�001 −0�000

(0�001) (0�001)Proportion moderate-heavy

infections in zone0�37 0�37 0�36 0�01 0�01

(0�03) (0�03)Group 1 pupils within 3 kmc 461�1 408�3 344�5 116�6 63�8

(120�3) (120�3)Group 1 pupils within 3–6 km 844�5 652�0 869�7 −25�1 −217�6

(140�9) (140�9)

WORMS: IDENTIFYING IMPACTS 167

TABLE I

(CONTINUED)

Group 1 Group 2 Group 3 Group 1 − Group 2 −(25 schools) (25 schools) (25 schools) Group 3 Group 3

Total primary school pupilswithin 3 km

1229�1 1364�3 1151�9 77�2 212�4(205�5) (205�5)

Total primary school pupilswithin 3–6 km

2370�7 2324�2 2401�7 −31�1 −77�6(209�5) (209�5)

aSchool averages weighted by pupil population. Standard errors in parentheses. Significantly different than zeroat 99 (***), 95 (**), and 90 (*) percent confidence. Data from the 1998 ICS Pupil Namelist, 1998 Pupil Questionnaireand 1998 School Questionnaire.

b1996 District exam scores have been normalized to be in units of individual level standard deviations, and so arecomparable in units to the 1998 and 1999 ICS test scores (under the assumption that the decomposition of test scorevariance within and between schools was the same in 1996, 1998, and 1999).

cThis includes girls less than 13 years old, and all boys (those eligible for deworming in treatment schools).

information comes from school registers, which are not considered reliable inKenya.

To the extent that there were significant differences between treatmentand comparison schools, treatment schools were initially somewhat worse off.Group 1 pupils had significantly more self-reported blood in stool (a symp-tom of schistosomiasis infection), reported being sick more often than Group 3pupils, and were not as clean as Group 2 and Group 3 pupils (as observed byNGO field workers). They also had substantially lower average scores on 1996Kenyan primary school examinations than Group 2 and 3 schools, although thedifference is not significant at traditional confidence levels.

In January and February 1998, prior to treatment, a random sample of ninetygrade three to eight pupils (fifteen per grade) in each of the 25 Group 1 schoolswere selected to participate in a parasitological survey conducted by the KenyaMinistry of Health, Division of Vector Borne Diseases.11 Ninety-two percentof surveyed pupils had at least one helminth infection and thirty-seven per-cent had at least one moderate-to-heavy helminth infection (Table II),12 al-though these figures understate actual infection prevalence to the extent thatthe most heavily infected children were more likely to be absent from schoolon the day of the survey. Worm infection rates are relatively high in this re-gion by international standards, but many other African settings have similar

11Following the previous literature, infection intensity is proxied for worm eggs per gram (epg)in stool (Medley and Anderson (1985)). Each child in the parasitological sample was given aplastic container and asked to provide a stool sample; samples were examined in duplicate withintwenty-four hours using the Kato-Katz method. Group 2 and Group 3 schools were not includedin the 1998 parasitological survey since it was not considered ethical to collect detailed healthinformation from pupils who were not scheduled to receive medical treatment in that year.

12Following Brooker, Miguel, et al. (2000), thresholds for moderate infection are 250 epg forSchistosomiasis. mansoni and 5,000 epg for Roundworm, the WHO standards, and 750 epg forHookworm and 400 epg for Whipworm, both somewhat lower than the WHO standard.

12

Groups 1, 2, 3: similar enrolment and attendance, location, sanitation facilities, weight-for-age indicators, asset ownership, malaria, local density, worm infection rates in surrounding area Group 1: more blood in stool (symptom of worm infection), sick more often, not as clean, lower average test scores


TABLE II

JANUARY 1998 HELMINTH INFECTIONS, PRE-TREATMENT, GROUP 1 SCHOOLSa

Prevalence of Prevalence of Average infectioninfection moderate-heavy intensity, in

infection eggs per gram (s.e.)

Hookworm 0.77 0.15 426(1055)

Roundworm 0.42 0.16 2337(5156)

Schistosomiasis, all schools 0.22 0.07 91(413)

Schistosomiasis, 0.80 0.39 487schools <5 km from Lake Victoria (879)

Whipworm 0.55 0.10 161(470)

At least one infection 0.92 0.37 –Born since 1985 0.92 0.40 –Born before 1985 0.91 0.34 –Female 0.91 0.34 –Male 0.93 0.38 –

At least two infections 0.31 0.10 –At least three infections 0.28 0.01 –

aThese are averages of individual-level data, as presented in Brooker, Miguel, et al. (2000); correcting for theoversampling of the (numerically smaller) upper grades does not substantially change the results. Standard errors inparentheses. Sample size: 1894 pupils. Fifteen pupils per standard in grades 3 to 8 for Group 1 schools were randomlysampled. The bottom two rows of the column “Prevalence of moderate-heavy infection” should be interpreted as theproportion with at least two or at least three moderate-to-heavy helminth infections, respectively.

The data were collected in January to March 1998 by the Kenya Ministry of Health, Division of Vector BorneDiseases (DVBD). The moderate infection thresholds for the various intestinal helminths are: 250 epg for S. mansoni,and 5,000 epg for Roundworm, both the WHO standard, and 750 epg for Hookworm and 400 epg for Whipworm, bothsomewhat lower than the WHO standard. Refer to Brooker, Miguel, et al. (2000) for a discussion of this parasitologicalsurvey and the infection cut-offs. All cases of schistosomiasis are S. mansoni.

infection profiles (Brooker, Rowlands, et al. (2000)). Moderate-to-heavy worminfections are more likely among younger pupils and among boys. Pupils whoattend schools near Lake Victoria also have substantially higher rates of schis-tosomiasis. Latrine ownership is negatively correlated with moderate-to-heavyinfection (results not shown).

3.2. The Intervention

Following World Health Organization recommendations (WHO (1992)),schools with geohelminth prevalence over 50 percent were mass treated withalbendazole every six months, and schools with schistosomiasis prevalenceover 30 percent were mass treated with praziquantel annually.13 All treatment

13The medical protocol was designed in collaboration with the Partnership for Child Devel-opment, and was approved by the Ethics Committee of the Kenya Ministry of Health and Busia

14

The Intervention Following WHO recommendations – schools with geohelminth prevalence >50% mass treated with de-worming drug every 6 months All treatment schools met this cutoff in both 1998 and 1990 Following medical protocol girls >13 years did not receive the drugs Treatment schools also received worm prevention education Needed consent from parents for children to participate


3.3. Assigned and Actual Deworming Treatment

Seventy-eight percent of those pupils assigned to receive treatment (i.e., girlsunder thirteen years old and all boys in the treatment schools) received at leastsome medical treatment through the program in 1998 (Table III).16 Since ap-proximately 80 percent of the students enrolled prior to the start of the pro-

TABLE III

PROPORTION OF PUPILS RECEIVING DEWORMING TREATMENT IN PSDPa

Group 1 Group 2 Group 3

Girls <13 Girls ≥ Girls <13 Girls ≥ Girls <13 Girls ≥years, and 13 years years, and 13 years years, and 13 years

all boys all boys all boys

Treatment Comparison ComparisonAny medical treatment in 1998 0.78 0.19 0 0 0 0(For grades 1–8 in early 1998)

Round 1 (March–April 1998),Albendazole

0.69 0.11 0 0 0 0

Round 1 (March–April 1998),Praziquantelb

0.64 0.34 0 0 0 0

Round 2 (Oct.–Nov. 1998),Albendazole

0.56 0.07 0 0 0 0

Treatment Treatment ComparisonAny medical treatment in 1999 0.59 0.07 0.55 0.10 0.01 0(For grades 1–7 in early 1998)

Round 1 (March–June 1999),Albendazole

0.44 0.06 0.35 0.06 0.01 0

Round 1 (March–June 1999),Praziquantelb

0.47 0.06 0.38 0.06 0.01 0


0.53 0.06 0.51 0.08 0.01 0

Any medical treatment in 1999 0.73 0.10 0.71 0.13 0.02 0(For grades 1–7 in early 1998),among pupils enrolled in 1999

Round 1 (March–June 1999),Albendazole

0.55 0.08 0.46 0.08 0.01 0

Round 1 (March–June 1999),Praziquantelb

0.53 0.07 0.45 0.07 0.01 0


0.65 0.09 0.66 0.11 0.01 0

aData for grades 1–8. Since month of birth information is missing for most pupils, precise assignment of treatmenteligibility status for girls born during the “threshold” year is often impossible; all girls who turn 13 during a given yearare counted as 12 year olds (eligible for deworming treatment) throughout for consistency.

bPraziquantel figures in Table III refer only to children in schools meeting the schistosomiasis treament threshold(30 percent prevalence) in that year.

16In what follows, “treatment” schools refer to all twenty-five Group 1 schools in 1998, and allfifty Group 1 and Group 2 schools in 1999.

16

Actual deworming treatment: • 78% pupils in 1998 assigned to receive treatment did so -

mainly due to absenteeism from school • 72% in 1999

5% sample received deworming treatment outside of school program


TABLE IV

PROPORTION OF PUPIL TRANSFERS ACROSS SCHOOLS

1998 transfer to a 1999 transfer to a

School in early 1998 Group 1 Group 2 Group 3 Group 1 Group 2 Group 3(pre-treatment) school school school school school school

Group 1 0.005 0.007 0.007 0.032 0.026 0.027Group 2 0.006 0.007 0.008 0.026 0.033 0.027Group 3 0.010 0.010 0.006 0.022 0.036 0.022Total transfers 0.021 0.024 0.021 0.080 0.095 0.076

drugs. To the extent that children in Busia also self-treat helminth symptomswith herbs, in this study we measure the net benefit of deworming drugs aboveand beyond the impact of herbs and of any individually purchased medicines.

Although pupils assigned to comparison schools could also potentially havetransferred to treatment schools to receive deworming medical treatmentthrough the program, there is no evidence of large asymmetric flows of pupilsinto treatment schools, which could bias the results (Table IV). Among samplepupils, approximately two percent transferred into a different school in 1998,with nearly equal proportions transferring into Groups 1, 2, and 3 schools, andapproximately eight percent of pupils had transferred into a different schoolby the end of 1999, again with similar proportions transferring to all threegroups (the transfer rates from early 1998 through the end of 1999 are sub-stantially higher than rates through the end of 1998 because most transfersoccur between school years). As we discuss in Section 4, we also use a stan-dard intention-to-treat (ITT) estimation strategy, in which pupils are assignedthe treatment status of the school in which they were initially enrolled in early1998 even if they later switched schools, to address potential transfer bias.

3.4. Health Outcome Differences Between Group 1 and Group 2 Schools

Before proceeding to formal estimation in Section 4, we present simple dif-ferences in health outcomes between treatment and comparison schools, al-though as we discuss below, these differences understate overall treatmenteffects if there are deworming treatment externalities across schools. TheKenyan Ministry of Health conducted a parasitological survey of grade threeto eight pupils in Group 1 and Group 2 schools in January and February 1999,one year after the first round of treatment but before Group 2 schools hadbeen treated. Overall, 27 percent of pupils in Group 1 (1998 treatment) schoolshad a moderate-to-heavy helminth infection in early 1999 compared to 52percent in Group 2 (1998 comparison) schools, and this difference is signifi-cantly different than zero at 99 percent confidence (Table V). The prevalencesof moderate-to-heavy hookworm, roundworm, schistosomiasis, and whipworminfections were all lower in Group 1 (1998 treatment) schools than in Group 2


TABLE V

JANUARY TO MARCH 1999, HEALTH AND HEALTH BEHAVIOR DIFFERENCES BETWEEN GROUP 1(1998 TREATMENT) AND GROUP 2 (1998 COMPARISON) SCHOOLSa

Group 1 Group 2 Group 1 – Group 2

Panel A: Helminth Infection RatesAny moderate-heavy infection, January–March 1998 0�38 – –Any moderate-heavy infection, 1999 0�27 0�52 −0�25***

(0�06)Hookworm moderate-heavy infection, 1999 0�06 0�22 −0�16***

(0�03)Roundworm moderate-heavy infection, 1999 0�09 0�24 −0�15***

(0�04)Schistosomiasis moderate-heavy infection, 1999 0�08 0�18 −0�10*

(0�06)Whipworm moderate-heavy infection, 1999 0�13 0�17 −0�04

(0�05)

Panel B: Other Nutritional and Health OutcomesSick in past week (self-reported), 1999 0�41 0�45 −0�04**

(0�02)Sick often (self-reported), 1999 0�12 0�15 −0�03**

(0�01)Height-for-age Z-score, 1999 −1�13 −1�22 0�09*

(low scores denote undernutrition) (0�05)Weight-for-age Z-score, 1999 −1�25 −1�25 −0�00

(low scores denote undernutrition) (0�04)Hemoglobin concentration (g/L), 1999 124�8 123�2 1�6

(1�4)Proportion anemic (Hb < 100g/L), 1999 0�02 0�04 −0�02**

(0�01)

Panel C: Worm Prevention BehaviorsClean (observed by field worker), 1999 0�59 0�60 −0�01

(0�02)Wears shoes (observed by field worker), 1999 0�24 0�26 −0�02

(0�03)Days contact with fresh water in past week 2�4 2�2 0�2

(self-reported), 1999 (0�3)

aThese are averages of individual-level data for grade 3–8 pupils; disturbance terms are clustered within schools.Robust standard errors in parentheses. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confi-dence.

Obs. for parasitological results: 2328 (862 Group 1, 1467 Group 2); Obs. for hemoglobin results: 778 (292 Group 1,486 Group 2); Obs. for 1999 Pupil Questionnaire health outcomes: 9,102 (3562 Group 1, 5540 Group 2 and Group 3).

Following Brooker, Miguel, et al. (2000), moderate-to-heavy infection thresholds for the various intestinalhelminths are: 250 epg for S. mansoni, and 5,000 epg for Roundworm, both the WHO standard, and 750 epg forHookworm and 400 epg for Whipworm, both somewhat lower than the WHO standard. Kenya Ministry of Health of-ficials collected the parasitological data from January to March 1998 in Group 1 schools, and from January to March1999 in Group 1 and Group 2 schools. A random subset of the original 1998 Group 1 parasitological sample was resur-veyed in 1999. Hb data were collected by Kenya Ministry of Health officials and ICS field officers using the portableHemocue machine. The self-reported health outcomes were collected for all three groups of schools as part of PupilQuestionnaire administration.

19

Estimation strategy Randomnization of deworming treatment across schools allows: • estimation of overall effect of program by comparing

treatment and comparison schools Even in presence of within-school externalities

20

Externalities may take place across schools • most people live on farms rather than concentrated in

villages • neighbours (or siblings) attend different schools • usually more than one primary school within walking

distance Estimate cross-school externalities: impact of deworming for pupils in schools located near treatment schools • take advantage of variation in local density of treatment

schools induced by randomnization

21

Randomnization across schools -- identify both overall program effect and cross-school externalities Must rely on non-experimental methods to • decompose effect on treated schools • direct effect vs. within-school externality effect

22

First estimate program impacts in treatment schools and across-school treatment externalities: (1)

ijteiuditNddTditNddijtXitTitTijtY +∑ +∑ +++++= )()('2211 φγδββα

ijtY = individual health or education outcome

i refers to school j refers to student t = 1 or 2, year of program

23


Direct Treatment Effect:

itT1 = indicator variable for school assignment to first

deworming treatment

itT2= indicator variable for school assignment to second

deworming treatment

ijtX = school and pupil characteristics

24


Across-school externality

ditN = number pupils in primary schools at distance d from school i in year t

TditN = number of these pupils randomly assigned to

deworming treatment

Population density can affect spread of disease

25


∑+ )(1

TditNdd γβ

is average effect of first year of deworming treatment

∑+ )(2

TditNdd γβ average effect of second year

1β and 2β capture direct effects of deworming treatment on treated and externalities on untreated pupils within treatment schools

want to decompose this

26

Randomization conducted at level of schools rather than individuals within schools Not possible to experimentally decompose direct effect and within school externality effect

27

Estimating within school externalities: Decompose effect for treatment schools into: • direct effect on treated pupils • externality effect on untreated pupils within treatment

schools Sicker pupils not more likely to obtain deworming treatment because main reason for not obtaining treatment was school absenteeism

28

Children obtain treatment if net gain from treatment is more than costs:

)),((1 tCijtijteijtXSIijD >+= ε

ijD1 = 1 if individual j in school i received treatment in first

year school was eligible for treatment I() is indicator function

tC = total cost to household of obtaining treatment in year t (parental consent forms/cost of sending sick child to school)

29

Given no randomnization of treatment within schools Two approaches to estimate within school externality effect Approach 1: Compare Group 1 pupils who did not receive treatment in 1998 to Group 2 pupils who did not receive treatment in 1999

)01,1,011|1()01,1,111|1([

)01,011|1()01,111|1([

)01,011|1()01,111|1([1

)01,1,011|1()01,1,111|1(

==−==+

==−==Σ+

==−==Σ+=

==−==

ijDijXiTijeEijDijXiTijeEijDiTdiNEijDiTdiNEdd

ijDiTTdiNEijDiTT

diNEdd

ijDijXiTijYEijDijXiTijYE

γ

γβ (2)

30

first-term on right hand side: 1β within school externality effect second and third terms: effects due to differing local densities of primary schools between treatment and comparison schools (approx. 0) final term: unobserved differences between untreated pupils in Group 1 and Group 2 schools

31

Approach 2: Estimate both within-school and cross-school treatment externalities in 1998: (3)

ijteiuditNddTditNddijtXijDitTbijDbitTijtY +∑ +∑ ++++++= )()(')11(21111 φγδβα

1β within school externality effect on untreated

1β + 2b is within school externality effect + direct effect on treated

32

Estimate on untreated in 1998 and 1999 Estimation works only if untreated pupils in Group 1 (1998) and Group 2 (1999) are similar otherwise bias could under or over estimate within school externality effect


TABLE VI

DEWORMING HEALTH EXTERNALITIES WITHIN SCHOOLS, JANUARY TO MARCH 1999a

Group 1, Group 1, Group 2, Group 2, (Group 1, (Group 1,Treated Untreated Treated Untreated Treated Untreatedin 1998 in 1998 in 1999 in 1999 1998) − 1998) −

(Group 2, (Group 2,Treated Untreated1999) 1999)

Panel A: Selection into TreatmentAny moderate-heavy infection, 1998 0�39 0�44 – – – –Proportion of 1998 parasitological

sample tracked to 1999 sampleb0�36 0�36 – – – –

Access to latrine at home, 1998 0�84 0�80 0�81 0�86 0�03 −0�06(0�04) (0�05)

Grade progression(= Grade − (Age − 6)), 1998

−2�0 −1�8 −1�8 −1�8 −0�2** −0�0(0�1) (0�2)

Weight-for-age (Z-score), 1998 −1�58 −1�52 −1�57 −1�46 −0�01 −0�06(low scores denote undernutrition) (0�06) (0�11)

Malaria/fever in past week(self-reported), 1998

0�37 0�41 0�40 0�39 −0�03 −0�01(0�04) (0�06)

Clean (observed by field worker), 1998 0�53 0�59 0�60 0�66 −0�07 −0�07(0�05) (0�10)

Panel B: Health OutcomesGirls <13 years, and all boysAny moderate-heavy infection, 1999 0�24 0�34 0�51 0�55 −0�27*** −0�21**

(0�06) (0�10)Hookworm moderate-heavy infection,

19990�04 0�11 0�22 0�20 −0�19*** −0�09*

(0�03) (0�05)Roundworm moderate-heavy infection,

19990�08 0�12 0�22 0�30 −0�14*** −0�18**

(0�04) (0�07)Schistosomiasis moderate-heavy

infection, 19990�09 0�08 0�20 0�13 −0�11* −0�05

(0�06) (0�06)Whipworm moderate-heavy infection,

19990�12 0�16 0�16 0�20 −0�04 −0�05

(0�16) (0�09)Girls ≥13 yearsAny moderate-heavy infection, 1998 0�31 0�28 – – – –Any moderate-heavy infection, 1999 0�27 0�43 0�32 0�54 −0�05 −0�10

(0�17) (0�09)Panel C: School ParticipationSchool participation rate, 0�872 0�764 0�808 0�684 0�064** 0�080**

May 1998 to March 1999c (0�032) (0�039)aThese are averages of individual-level data for grade 3–8 pupils in the parasitological survey subsample; distur-

bance terms are clustered within schools. Robust standard errors in parentheses. Significantly different than zero at99 (***), 95 (**), and 90 (*) percent confidence. The data are described in the footnote to Table V. Obs. for the 1999parasitological survey: 670 Group 1 treated 1998, 77 Group 1 untreated 1998, 873 Group 2 treated 1999, 352 Group 2untreated 1999.

bWe attempted to track a random sample of half of the original 1998 parasitological sample. Because some pupilswere absent, had dropped out, or had graduated, we were only able to resurvey 72 percent of this subsample.

cSchool averages weighted by pupil population. The participation rate is computed among pupils enrolled in theschool at the start of 1998. Pupils present in school during an unannounced NGO visit are considered participants.Pupils had 3.8 participation observations per year on average. Participation rates are for grades 1 to 7; grade 8 pupilsare excluded since many graduated after the 1998 school year, in which case their 1999 treatment status is irrelevant.Pre-school pupils are excluded since they typically have missing compliance data. All 1998 pupil characteristics inPanel A are for grades 3 to 7, since younger pupils were not administered the Pupil Questionnaire.

34

Deworming treatment effects on health and nutrition Estimation of (1): Estimate program impacts in treatment schools and across-school treatment externalities Table 7 – Regression 1 Proportion of pupils infected lower in Group 1 schools in early 1999 – 25% lower

WO

RM

S:IDE

NT

IFY

ING

IMPA

CT

S185

TABLE VII

DEWORMING HEALTH EXTERNALITIES WITHIN AND ACROSS SCHOOLS, JANUARY TO MARCH 1999a

Any moderate-heavy Moderate-heavy Moderate-heavyhelminth infection, 1999 schistosomiasis infection, 1999 geohelminth infection, 1999

(1) (2) (3) (4) (5) (6) (7) (8) (9)Indicator for Group 1 (1998 Treatment) School −0�25*** −0�12* −0�09 −0�03 −0�02 −0�07 −0�20*** −0�11** −0�03

(0�05) (0�07) (0�11) (0�03) (0�04) (0�06) (0�04) (0�05) (0�09)Group 1 pupils within 3 km (per 1000 pupils) −0�26*** −0�26*** −0�11 −0�12*** −0�12*** −0�11** −0�12* −0�12* −0�01

(0�09) (0�09) (0�13) (0�04) (0�04) (0�05) (0�06) (0�07) (0�07)Group 1 pupils within 3–6 km (per 1000 pupils) −0�14** −0�13** −0�07 −0�18*** −0�18*** −0�27*** 0�04 0�04 0�16

(0�06) (0�06) (0�14) (0�03) (0�03) (0�06) (0�06) (0�06) (0�10)Total pupils within 3 km (per 1000 pupils) 0�11*** 0�11*** 0�10** 0�11*** 0�11*** 0�13*** 0�03 0�04 0�02

(0�04) (0�04) (0�04) (0�02) (0�02) (0�02) (0�03) (0�03) (0�03)Total pupils within 3–6 km (per 1000 pupils) 0�13** 0�13** 0�12* 0�12*** 0�12*** 0�16*** 0�04 0�04 0�01

(0�06) (0�06) (0�07) (0�03) (0�03) (0�03) (0�04) (0�04) (0�04)Received first year of deworming treatment, when

offered (1998 for Group 1, 1999 for Group 2)−0�06* 0�03** −0�04**

(0�03) (0�02) (0�02)(Group 1 Indicator) ∗ Received treatment, when offered −0�14* −0�02 −0�10***

(0�07) (0�04) (0�04)(Group 1 Indicator) ∗ Group 1 pupils within 3 km

(per 1000 pupils)−0�25* −0�04 −0�18**

(0�14) (0�07) (0�08)(Group 1 Indicator) ∗ Group 1 pupils within 3–6 km

(per 1000 pupils)−0�09 0�11 −0�15(0�13) (0�07) (0�10)

Grade indicators, school assistance controls, districtexam score control

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Number of observations 2328 2328 2328 2328 2328 2328 2328 2328 2328Mean of dependent variable 0.41 0.41 0.41 0.16 0.16 0.16 0.32 0.32 0.32

aGrade 3–8 pupils. Probit estimation, robust standard errors in parentheses. Disturbance terms are clustered within schools. Observations are weighted by total schoolpopulation. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. The 1999 parasitological survey data are for Group 1 and Group 2 schools. Thepupil population data is from the 1998 School Questionnaire. The geohelminths are hookworm, roundworm, and whipworm. We use the number of girls less than 13 years oldand all boys (the pupils eligible for deworming in the treatment schools) as the school population for all schools.

36

Estimation of (3) – considers untreated individuals Table 7 – Regression 2 Decomposes effect of program on treated schools into effect on treated pupils and within-school externality effect Within-school externality effect = coefficient estimate on Group 1 indicator variable --- 12% reduction in infection rates Direct effect: 14% reduction

37

Children who attend primary schools located near Group 1 schools have lower infection rates: • presence of each additional 1000 pupils attending Group 1

schools within 3km of a school is associated with 26% fewer infections

• within 6km --- 14% fewer Table 7 – Regression 3: • average cross-school externality reduction in infections

for Group 2 is 9% • for Group 1, 29%

38

Existence of cross-school health externalities: • difference in avg. outcomes between treatment and

comparison schools understates actual effects of mass deworming treatment on treated

If externalities disappear after 6km, true measure of reduction: 9% (avg. cross-school externality for comparison school pupils) + 25% (effect of being in treatment school, Table 7) = 35% Estimated number of infections eliminated by program = 5190 (underestimated since excludes cross-school effect of > 6km)

39

Deworming treatment effects on school participation School participation differences across treatment and comparison schools Table 8 – differences in school participation across project groups and through time These do not take into account cross-school externalities and potentially underestimate overall treatment effects


TABLE VIII

SCHOOL PARTICIPATION, SCHOOL-LEVEL DATAa

Group 1 Group 2 Group 3(25 schools) (25 schools) (25 schools)

Panel A:First year post-treatment 1st Year Group 1 − Group 2 −(May 1998 to March 1999) Treatment Comparison Comparison (Groups 2 & 3) Group 3Girls <13 years, and all boys 0.841 0.731 0.767 0�093*** −0�037

(0�031) (0�036)Girls ≥13 years 0.864 0.803 0.811 0�057** −0�008

(0�029) (0�034)Preschool, Grade 1, Grade 2 in

early 19980.795 0.688 0.703 0�100*** −0�018

(0�037) (0�043)Grade 3, Grade 4, Grade 5 in

early 19980.880 0.789 0.831 0�070*** −0�043


early 19980.934 0.858 0.892 0�059*** −0�034

(0�021) (0�026)Recorded as “dropped out” in

early 19980.064 0.050 0.030 0�022 0�020

(0�018) (0�017)Femalesb 0.855 0.771 0.789 0�076*** −0�018

(0�027) (0�032)Males 0.844 0.736 0.780 0�088*** −0�044

(0�031) (0�037)Panel B:Second year post-treatment 2nd Year 1st Year Group 1 − Group 2 −(March to November 1999) Treatment Treatment Comparison Group 3 Group 3Girls <13 years, and all boys 0.713 0.717 0.663 0�050* 0�055*

(0�028) (0�028)Girls ≥14 yearsc 0.627 0.649 0.588 0�039 0�061*

(0�035) (0�035)Preschool, Grade 1, Grade 2 in

early 19980.692 0.726 0.641 0�051 0�085**


early 19980.750 0.774 0.725 0�025 0�049**


early 19980.770 0.777 0.751 0�020 0�026

(0�027) (0�028)Recorded as “dropped out” in

early 19980.176 0.129 0.056 0�120* 0�073

(0�063) (0�053)Femalesb 0.716 0.746 0.648 0�067** 0�098***

(0�027) (0�027)Males 0.698 0.695 0.655 0�043 0�041

(0�028) (0�029)

aThe results are school averages weighted by pupil population. Standard errors in parentheses. Significantly dif-ferent than zero at 99 (***), 95 (**), and 90 (*) percent confidence. The participation rate is computed among allpupils enrolled in the school at the start of 1998. Pupils who are present in school on the day of an unannounced NGOvisit are considered participants. Pupils had 3.8 participation observations per year on average. The figures for the“Preschool–Grade 2”; “Grade 3–5”; “Grade 6–8”; and “Dropout” rows are for girls <13 years, and all boys.

b396 pupils in the sample are missing information on gender. For this reason, the average of the female and maleparticipation rates does not equal the overall average.

cExamining girls ≥14 years old eliminates the cohort of girls in Group 1 schools (12 year olds in 1998) who weresupposed to receive deworming treatment in 1998.

41

Smaller impact of program on school participation in 1999 • lower proportion of pupils taking deworming drugs • El Nino? • Flooding in 1998, increased worm loads

42

Time pattern of school participation differences consistent with causal effect of deworming on school participation Figure 1 – School participation gains particularly large among youngest pupils • higher rates of infection among younger pupils • dropout rate is high • older kids remaining more serious academically


FIGURE 1.—School participation rate May 1998 to November 1999 for girls under 13 years oldand for all boys (difference between Group 1 and Group 3 (diamonds), and difference betweenGroup 2 and Group 3 (squares)).a

aThe shaded regions are periods in which medical treatment was being provided (in March–April and November1998 to Group 1 schools, and March–June and October–November 1999 to Group 1 and Group 2 schools).

participation gains in early 1999. Participation in Group 2 schools is substan-tially greater than in Group 3 schools by mid-1999 when the first round of 1999treatment was concluded. These gains resulted primarily from a greater pro-portion of pupils with participation above 80 percent, although there were alsosubstantially fewer dropouts (results not shown).

The school participation gains are particularly large among the youngestpupils: in 1998 the average difference in participation between treatment andcomparison groups for preschool through grade 2 was 10.0 percentage points(significantly different than zero at 99 percent confidence), while for pupils ingrades 6 to 8 it was 5.9 percentage points, and in 1999 the comparable gainsfor Group 2 pupils were 8.5 percentage points and 2.6 percentage points, re-spectively. The larger impact of treatment in lower grades may partially resultfrom higher rates of moderate-to-heavy infection among younger pupils (Ta-ble II). It is also possible that school participation is more elastic with respectto health for younger pupils; many Kenyan children drop out before reachingthe upper primary grades, so older children who remain in school may be themost academically serious and determined to attend school despite illness.

Untreated pupils in Group 1 (1998 treatment) had higher school partici-pation than their counterparts in Group 2 schools who were later untreatedduring 1999, consistent with deworming externalities on school participa-tion. Among girls under thirteen years old and all boys, May 1998 to March

44

Estimating overall school participation impacts Table 9: Dependent variable : individual school participation Regressions 1 and 2 ignore externality effects Regression 3: estimate equation (1) Regression 4 & 5: estimate equation (3)


TABLE IX

SCHOOL PARTICIPATION, DIRECT EFFECTS AND EXTERNALITIESa

DEPENDENT VARIABLE: AVERAGE INDIVIDUAL SCHOOL PARTICIPATION, BY YEAR

OLS OLS OLS OLS OLS OLS IV-2SLS

(1) (2) (3) (4) (5) (6) (7)

May 98– May 98– May 98– May 98–

March 99 March 99 March 99 March 99

Moderate-heavyinfection, early 1999

−0�028*** −0�203*

(0�010) (0�094)Treatment school (T) 0�051***

(0�022)First year as treatment

school (T1)0�062*** 0�060*** 0�062* 0�056***

(0�015) (0�015) (0�022) (0�020)Second year as treatment

school (T2)0�040* 0�034*

(0�021) (0�021)Treatment school pupils

within 3 km(per 1000 pupils)

0�044** 0�023(0�022) (0�036)

Treatment school pupilswithin 3–6 km(per 1000 pupils)

−0�014 −0�041(0�015) (0�027)

Total pupils within 3 km −0�033** −0�035* 0�018 0�021(per 1000 pupils) (0�013) (0�019) (0�021) (0�019)

Total pupils within 3–6 km −0�010 0�022 −0�010 −0�021(per 1000 pupils) (0�012) (0�027) (0�012) (0�015)

Indicator received firstyear of dewormingtreatment, whenoffered (1998 forGroup 1, 1999 forGroup 2)

0�100***

(0�014)

(First year as treatmentschool Indicator) ∗(Received treatment,when offered)

−0�012(0�020)

1996 district exam score,school average

0�063*** 0�071*** 0�063*** 0�058 0�091** 0�021 0�003(0�021) (0�020) (0�020) (0�032) (0�038) (0�026) (0�023)

is measured continuously beginning immediately following treatment, includ-ing the period when the ratio of externalities to direct effects is likely to below.44

44The cross-school externalities for school participation may also be weaker than worm infec-tion externalities because only schistosomiasis has robust health externalities across schools, andmoderate to heavy schistosomiasis infection is rarer than geohelminth infection (only seven per-cent of Group 1 pupils had moderate to heavy schistosomiasis infections prior to treatment, while

46

Deworming treatment effects on test scores Deworming could increase test scores: • increasing total amount of time in school – effect likely

weak given observed impact on deworming on school participation

• improve efficiency of learning per unit of time spent in school – though severe anemia is rare

Table 10 – Test score results Differences in test scores between pupils in treatment and comparison schools not significantly different from zero


TABLE X

ACADEMIC EXAMINATIONS, INDIVIDUAL-LEVEL DATAa

Dependent variable: ICS Exam Score(normalized by standard)

(1) (2) (3)Among those

who filled in the1998 pupil survey

Average school participation (during the yearof the exam)

0�63***

(0�07)First year as treatment school (T1) −0�032 −0�030

(0�046) (0�049)Second year as treatment school (T2) 0�001 0�009

(0�073) (0�081)1996 District exam score, school average 0�74*** 0�71*** 0�75***

(0�07) (0�07) (0�07)Grade indicators, school assistance controls,

and local pupil density controlsYes Yes Yes

R2 0.14 0.13 0.15Root MSE 0.919 0.923 0.916Number of observations 24958 24958 19072Mean of dependent variable 0.020 0.020 0.039

aEach data point is the individual-level exam result in a given year of the program (either 1998 or 1999); dis-turbance terms are clustered within schools. Linear regression, robust standard errors in parentheses. Significantlydifferent than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Regression 3 includes only pupils who com-pleted the 1998 Pupil Questionnaire. Additional explanatory variables include an indicator variable for girls <13 yearsand all boys, and the rate of moderate-to-heavy infections in geographic zone, by grade (zonal infection rates amonggrade 3 and 4 pupils are used for pupils in grades 4 and below and for pupils initially recorded as dropouts as there isno parasitological data for pupils below grade 3; zonal infection rates among grade 5 and 6 pupils are used for pupilsin grades 5 and 6, and similarly for grades 7 and 8). The local pupil density terms include treatment school pupilswithin 3 km (per 1000 pupils), total pupils within 3 km (per 1000 pupils), treatment school pupils within 3–6 km (per1000 pupils), and total pupils within 3–6 km (per 1000 pupils). We use the number of girls less than 13 years old andall boys (the pupils eligible for deworming in the treatment schools) as the school population for all schools.

The ICS tests for 1998 and 1999 were similar in content, but differed in two important respects. First, the 1998 examfeatured multiple-choice questions while the 1999 test featured short answers. Second, while each grade in 1998 wasadministered a different exam, in 1999 the same exam—featuring questions across a range of difficulty levels—wasadministered to all pupils in grades 3 to 8. Government district exams in English, Math, Science-Agriculture, Kiswahili,Geography-History, Home Science, and Arts-Crafts were also administered in both years. Treatment effect estimatesare similar for both sets of exams (results not shown).

unchanged if subjects are examined separately (regressions not shown). Forboth 1998 and 1999, test scores were normalized to be mean zero and standarddeviation one among comparison pupils initially enrolled in the same grade inearly 1998.

A one percentage point increase in measured school participation is asso-ciated with a 0.63 standard deviation increase in test scores (Table X, regres-sion 1). The coefficient estimate suffers from attenuation bias due to samplingerror since the school participation measure for each individual is the aver-age of only 3.8 participation observations per year, but it is straightforward to

48

Cost effectiveness and welfare analysis Explore controversy over whether mass school-based deworming treatment should be public policy priority for poorest countries Health cost effectiveness approach Annual government expenditure on health in Kenya approximately 5$/capita Mass deworming is only one of many health interventions competing for scarce public resources

49

Using costs of program in Tanzania = 49 cents/ pupil Total number of DALYs (dollars per disability-adjusted life year) averted as result of program = 649 = $5 per DALY averted (using costs from Tanzania) Assuming drug delivery costs remain the same: • cost per DALY averted would be $280 • not meet strict cost effectiveness criteria in poorest

countries based solely on health impacts for geohelminth but would for schistosomiasis

50

Educational cost effectiveness Deworming was most cost-effective method of improving school participation Deworming program increased school participation by 0.14 years per treated child Using Tanzania costs: $3.50 per additional school participation

51

Human capital investment approach Did not increase test scores but increased school participation Requires additional 0.0047 teachers ($1942) : $9.06 per treated pupil Additional wage benefits of $30 at cost of $9.55

52

Conclusion School-based deworming program in Kenya led to significant gain in primary school attendance Treatment created positive health and school participation externalities for untreated students These spillovers alone are sufficient to justify not only fully subsidizing treatment but even paying people to receive treatment

53

Results have methodological implications Previous studies have ignored externality benefits Need to randomize at larger units (schools) rather than at individual level

Notes from: “Worms at Work: Long-run impacts of Child Health Gains” (Baird, Hicks, Kremer, Miguel 2011)

Impact of deworming drugs in primary schools in Kenya on outcomes a decade later

Effective tracking rate – 83%

Due to experimental design – deworming treatment group received 2-3 more years of deworming than control group

Group 1 and 2 schools received 2.41 years more deworming that Group 3

Group 1 and 2 are treatment group

Group 3 eventually received deworming – will dampen treatment effects (compared to control group which never received any).

Tracking these individuals was an onerous task

86% located – only 3% deceased, refused or unable to be surveyed

Tracking rates are almost identical in treatment and control groups

Migration rates are similar between treatment and control so are occupation patterns

54

Figure 1: Project Timeline of the Primary School Deworming Program (PSDP) and the Kenya Life Panel Survey (KLPS)

January 1998: 75 primary schools chosen for Primary School Deworming Program (PSDP), and assigned to three groups of 25 schools (Group 1, Group 2, Group 3). Baseline pupil and school survey data collection.

2007-09: Kenya Life Panel Survey (KLPS) Round 2 data collection (Wave 1 2007-08, Wave 2 2008-09). N=5,084 (82.5% effective tracking rate)

2003-05: Kenya Life Panel Survey (KLPS) Round 1 data collection (Wave 1 2003-04, Wave 2 2004-05). N=5,211 (82.7% effective tracking rate)

2002-2003: Group 3 receives free deworming



2001: Group 3 receives free deworming

2001: A random half of Group 2 receives free deworming, half participate in cost-sharing

2001: A random half of Group 1 receives free deworming, half participate in cost-sharing

1999-2000: Group 3 does not receive deworming



1998: Group 3 does not receive deworming

1998: Group 2 does not receive deworming

1998: Group 1 receives free deworming

1998-2001: Ongoing unannounced school participation data collection visits

43

Table 1: Baseline (1998) summary statistics and PSDP randomization checks

All mean (s.d.)

Treatment mean (s.d.)

Control mean (s.d.)

Treatment – Control

(s.e.)

Kolmogorov-Smirnov p-value

Age (1998) 11.9 (2.6)

11.9 (2.6)

12.0 (2.6)

-0.04 (0.11)

0.258

Grade (1998) 4.23 (1.68)

4.22 (1.70)

4.25 (1.66)

-0.03 (0.05)

0.450

Female 0.470 0.469 0.473 -0.004 (0.019)

--

Assignment to the deworming treatment group 0.678 1 0 --

--

Years of assigned deworming treatment, 1998-2003 3.31 (1.82)

4.09 (1.52)

1.68 (1.23)

2.41***

(0.08) --

Primary school located in Budalangi division 0.370 0.364 0.381 -0.017

(0.137) --

Population of primary school 476 (214)

494 (237)

436 (146)

58 (54)

0.307

School average test score (1996) 0.029 (0.427)

0.024 (0.436)

0.038 (0.406)

-0.013 (0.109)

0.310

Total treatment (Group 1, 2) primary school students within 6 km 3,180 (917)

3,085 (845)

3,381 (1,022)

-296 (260)

0.206

Total primary school students within 6 km 4,709 (1,337)

4,698 (1,220)

4,732 (1,555)

-34 (389)

0.119

Notes: The data are from the PSDP, and includes all individuals surveyed in the KLPS2. There are 5,084 observations for all variables, except for Age (1998) where there are 5,072 observations due to missing survey data. All observations are weighted to maintain initial population proportions. All variables are 1998 values unless otherwise noted. Years of assigned deworming treatment is calculated using the treatment group of the respondent’s school and their grade, but is not adjusted for the treatment ineligibility of females over age 13 or assignment to cost-sharing in 2001. Those individuals who “age out” of primary school are no longer considered assigned to deworming treatment. The average school test score is from the 1996 Busia District mock exam, and has been converted to units of normalized individual standard deviations. The “Treatment – Control” differences are derived from a linear regression of the outcome on a constant and the treatment indicator, but results are similar if we include further controls (for survey wave, 1998 administrative zone of residence, cost sharing school indicator, and baseline 1998 population of the individual’s primary school). Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. The Kolmogorov-Smirnov p-values are only presented for the non-binary variables, where it is informative.

Estimations

17

The econometric approach relies on the PSDP’s prospective experimental design, namely, the fact

that the program exogenously provided individuals in treatment (Group 1 and 2) schools two to three

additional years of deworming treatment. We also adopt the approach in Miguel and Kremer (2004)

and estimate the cross-school externality effects of deworming. Exposure to spillovers is captured by

the number of pupils attending deworming treatment schools within 6 kilometers; conditional on the

total number of primary school pupils within 6 kilometers, the number of treatment pupils is also

determined by the experimental design, generating credible estimates of local spillover impacts.

In the analysis below, the dependent variable is a labor market outcome (such as wage

earnings), Yij,2007-09, for individual i from school j, as measured in the 2007-09 KLPS-2 survey:

(eqn. 4) Yij,2007-09 = a + bTj + Xij,0c + d1NjT + d2Nj + eij,2007-09

The labor market outcome is a function of the assigned deworming program treatment status of the

individual’s primary school (Tj), and thus this is an intention to treat (ITT) estimator; a vector Xij,0 of

baseline individual and school controls; the number of treatment school pupils (NjT) and the total

number of primary school pupils within 6 km of the school (Nj); and a disturbance term eij,2007-09,

which is clustered at the school level.9 The Xij,0 controls include school geographic and demographic

characteristics used in the “list randomization”, the student gender and grade characteristics used for

stratification in drawing the KLPS sample, the pre-program average school test score to capture

school academic quality, the 2001 cost-sharing school indicator, as well as controls for the month and

wave of the interview.

The main coefficients of interest are b, which captures gains accruing to deworming

treatment schools, and d1, which captures any spillover effects of treatment for nearby schools. Bruhn

and McKenzie (2009) argue for including variables used in the randomization procedure as controls

9 Miguel and Kremer (2004) separately estimate effects of the number of pupils between 0-3 km and 3-6 km. Since the analysis in the current paper does not generally find significant differences in externality impacts across these two ranges, we focus on 0-6 km for simplicity. The externality results are unchanged if we focus on the proportion of local pupils who were in treatment schools as the key spillover measure (i.e., Nj

T / Nj, results not shown). Several additional econometric issues related to estimating externalities are discussed in Miguel and Kremer (2004).

Impacts on health and nutrition

46

Table 3: Impacts on health, wellbeing and nutritional outcomes

Dependent variable Panel A: Health outcomes during 1999-2001

Comparison group variable mean

(s.d.)

Coefficient estimate (s.e.) on deworming treatment indicator

Coefficient estimate (s.e.) on deworming Treatment school pupils within 6 km (in ‘000s),

demeaned Moderate-heavy worm infection (1999, 2001 parasitological surveys) 0.321 -0.245*** -0.075*** (0.467) (0.030) (0.026) Hemoglobin (Hb) level (1999, 2001 parasitological survey samples) 126.1 1.03 0.91 (14.7) (0.81) (0.96) Falls sick often (self-reported), 1999 0.154 -0.037** 0.001 (0.361) (0.015) (0.014) Malaria in the last week (self-reported), 1999 0.218 -0.019 -0.018 (0.413) (0.017) (0.018) Panel B: Health, wellbeing and nutritional outcomes, KLPS-2 (2007-09) Self-reported health “very good” 0.673 0.041** 0.028 (0.469) (0.018) (0.022) Self-reported currently “very happy” 0.673 0.020 0.028 (0.469) (0.018) (0.023) Index of wellbeing (0 to 1) 0.831 0.018 -0.013 (0.290) (0.012) (0.012) Body mass index (BMI = Weight in kg / (height in m)2) 27.2 0.024 0.064 (1.3) (0.044) (0.053) Height (cm) 167.3 -0.12 -0.39 (8.0) (0.26) (0.33)

Notes: The sample size in Panel A is 2,720 for worm infection, 1,765 for Hb, and 3,861 for health self-reports. Representative subsets of pupils in all schools were surveyed for these 1999 and 2001 pupil surveys. The sample in Panel B includes all individuals surveyed in KLPS-2. Each row is from a separate OLS regression analogous to equation 4. All observations are weighted to maintain initial population proportions. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator. Self-perceived health “very good” takes on a value of one if the answer to the question “Would you describe your general health as somewhat good, very good, or not good?” is “very good”, and zero otherwise. Self-reported currently “very happy” takes on a value of one if the answer to the question “Taking everything together, would you say you are somewhat happy, very happy or not happy?” is “very happy”, and is zero otherwise. The underlying index of well being takes on a value of 0 to 4 where 4 implies answering no to all of the following four questions: “In the past week, have you felt tense, nervous or worried?” “In the past week have you generally not enjoyed your daily activities?” “In the past week have you felt more unhappy than usual?” “In the past week have you found it difficult to make decisions?” Indicator of no major health problem since 1998 takes on a value of one if they answer no to the following question “Have you experienced any major health problems that seriously affected your life or work, since 1998?”.

Impacts on Education

47

Table 4: Impacts on schooling and test score outcomes

Dependent variable

Comparison group variable

mean (s.d.)

Coefficient estimate (s.e.) on

deworming treatment indicator

Coefficient estimate (s.e.) on deworming Treatment

school pupils within 6 km (in ‘000s), demeaned

Panel A: School participation, enrollment and attainment Total primary school participation, 1998-2001 2.51 0.129*** 0.056 (1.12) (0.064) (0.048) Total years enrolled in school, 1998-2007 6.69 0.279* 0.138 (2.97) (0.147) (0.149) Grades of schooling attained 8.72 0.153 0.070 (2.21) (0.143) (0.146) Indicator for repetition of at least one grade (1998-2007) 0.672 0.060*** 0.010 (0.470) (0.017) (0.023) Attended some secondary school 0.421 0.032 0.000 (0.494) (0.035) (0.039) Panel B: Test scores Mean effect size (1999 test, passed primary school exam, 2007-09 English test) 0.000 0.112 0.068 (1.000) (0.067)* (0.058) Academic test score (normalized across all subjects), 1999 0.026 0.059 0.158 (1.000) (0.090) (0.101) Passed primary school leaving exam during 1998-2007 0.509 0.046 0.032 (0.500) (0.031) (0.030) English vocabulary test score (normalized), 2007-09 0.000 0.076 0.067 (1.000) (0.055) (0.053) Raven’s Matrices cognitive test score (normalized), 2007-09 0.000 -0.011 0.055 (1.000) (0.048) (0.042)

Notes: Each row is from a separate OLS regression analogous to equation 4 using the full KLPS-2 sample. All observations are weighted to maintain initial population proportions. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator.

Impacts on living standards and labor market activity

48

Table 5: Deworming impacts on living standards and labor supply

Dependent variable

Control group variable mean

(s.d.)


deworming Treatment indicator

Coefficient estimate (s.e.) on deworming

Treatment pupils within 6 km (in

‘000s), demeaned

Obs.

Panel A: Living standards Number of meals eaten yesterday 2.16

(0.64) 0.096*** (0.028)

0.080*** (0.023)

5,083

Respondent health expenditures (medicine, in/out-patient) in past month (KSh) 119.2 (389.9)

91.1***

(30.0) 40.7

(55.9) 5.083

Panel B: Labor supply Hours worked (for wages, self-employed, agriculture) in last week 15.2

(21.9) 1.76* (0.97)

1.54 (1.16)

5,084

Hours worked (for wages, self-employed, agriculture) in last week – median regression 15.2 (21.9)

1.10** (0.54)

0.66 (0.63)

5,084

Indicator for hours worked > 0 (for wages, self-employed, agriculture) in last week

0.662 (0.473)

0.010 (0.022)

-0.007 (0.025)

5,084

Hours worked (for wages, self-employed, agriculture) in last week, among those with hours worked > 0

23.0 (23.4)

2.40** (1.16)

2.75** (1.36)

3,514

Hours worked (for wages) in the last week, among those with hours worked > 0 42.2 (24.7)

5.19* (2.74)

6.60** (2.93)

693

Days of work missed due to poor health (among those working for wages), past month (negative binomial)

1.46 (2.99)

-0.499** (0.235)

-0.337 (0.305)

718

Hours worked (as self-employed) in last week, among those with hours worked > 0 33.9 (25.7)

8.9*** (3.0)

8.0*** (3.0)

583

Hours worked (in agriculture) in last week, among those with hours worked > 0 9.5 (9.1)

0.48 (0.53)

-0.75 (0.48)

2,829

Notes: Each row is from a separate OLS regression analogous to equation 4, except the median regression specification and the negative binominal specification. All observations are weighted to maintain initial population proportions. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator.

49

Table 6: Deworming impacts on labor earnings (2007-2009) Dependent variable: Ln(Total labor earnings, past month) Total labor earnings, past month

(in Kenya Shillings) (1) (2) (3) (4) (5) (6) (7) (8) Deworming Treatment indicator 0.193**

(0.077) 0.187** (0.076)

0.253*** (0.093)

0.277***

(0.104) 611**

(285) 627** (306)

780* (417)

906*

(504) Deworming Treatment pupils within 6 km (in ‘000s), demeaned 0.199

(0.168) 0.194

(0.170) 451

(740) 424

(740) Total pupils within 6 km (in ‘000s), demeaned -0.098

(0.127) -0.094 (0.129)

-201 (575)

-179 (571)

Group 2 school indicator -0.060 (0.099)

-327 (449)

Cost sharing school (in 2001) indicator -0.104 (0.085)

-0.139 (0.094)

-0.159* (0.088)

-0.154* (0.090)

-390 (370)

-540 (425)

-584 (410)

-557 (409)

Additional controls No Yes Yes Yes No Yes Yes Yes

R2 0.064 0.176 0.182 0.183 0.060 0.125 0.126 0.127 Observations 710 710 710 710 710 710 710 710 Mean (s.d.) in the control group 7.86

(0.88) 7.86

(0.88) 7.86

(0.88) 7.86

(0.88) 3,739

(3,744) 3,739

(3,744) 3,739

(3,744) 3,739

(3,744) Notes: The sample used here includes all individuals surveyed in the KLPS2 who report positive labor earnings at the time of survey. Labor earnings include cash and in-kind, and are deflated to reflect price differences between rural and urban areas. All observations are weighted to maintain initial population proportions. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, and survey wave and month of interview. Additional controls include a female indicator variable, baseline 1998 school grade fixed effects, and the average school test score on the 1996 Busia District mock exams. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence.

50

Table 7: Deworming impacts on labor earnings and wages

Dependent variable Panel A: Wage earner subsample

Control group variable mean (s.d.)




Treatment pupils within 6 km (in

‘000s), demeaned

Obs.

Ln(Total labor earnings, past month) 7.86 (0.88)

0.253*** (0.093)

0.199 (0.168)

710

Ln(Total labor earnings, past month) – top 1% trimmed

7.83 (0.85)

0.269*** (0.092)

0.237 (0.161)

698

Ln(Total labor earnings, past month) – with all gender-age fixed effects 7.86 (0.88)

0.270*** (0.093)

0.197 (0.159)

710

Ln(Wage = Total labor earnings / hours, past month) 2.82 (0.96)

0.165 (0.117)

0.012 (0.160)

625

Indicator for worked for wages (or in-kind) in last month 0.166 (0.372)

-0.015 (0.018)

-0.002 (0.020)

5,081

Panel B: Wage earner since 2007 subsample Ln(Total labor earnings, most recent month worked)

7.88 (0.91)

0.211*** (0.072)

0.170 (0.116)

1,175

Indicator for worked for wages (or in-kind) since 2007

0.244 (0.430)

0.000 (0.021)

0.040 (0.024)

5,081

Panel C: Full sample Ln(Total labor earnings, past month) – Heckman selection correction

7.86 (0.88)

0.285*** (0.108)

0.148 (0.170)

5,082

Total labor earnings, past month, earnings=0 for non- earners

619 (2,060)

27 (81)

-17 (97)

5,084

Total labor earnings, past month – 95th percentile (quantile regression), earnings=0 for non-earners

619 (2,060)

290** (117)

123 (140)

5,084

Notes: Each row is from a separate OLS regression analogous to equation 4, except the quantile regression in Panel C. Ln(Wage) adjusts for the different reporting periods for earnings (month) and hours (week), and is missing for those with zero earnings. All observations are weighted to maintain initial population proportions. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator.

51

Table 8: Deworming impacts on employment sector and occupation

Employment sector: Control group

proportion


deworming treatment indicator


treatment pupils within 6 km (in ‘000s), demeaned

Mean (s.d.) earnings in sector, past month (Kenya Shillings), control

Mean (s.d.) hours per week

worked in sector, control group

Agriculture and fishing 0.210 -0.038 (0.059)

-0.152*

(0.080) 2,872

(1,804) 35

(25) Retail 0.153 -0.018

(0.038) 0.025

(0.043) 2,049

(1,713) 39

(29) Trade contractors 0.092 -0.005

(0.028) 0.060

(0.004) 3,172

(2,170) 27

(22) Manufacturing 0.029 0.072***

(0.024) 0.041

(0.031) 5,311

(3,373) 53

(24) Manufacturing – males only 0.057 0.090***

(0.033) 0.031

(0.033) 6,277

(3,469) 49

(20) Wholesale trade 0.027 0.023

(0.029) 0.022

(0.035) 4,727

(3,963) 44

(14) Services (all) 0.417

0.032

(0.054) 0.037

(0.075) 4,694

(5,013) 47

(24) Domestic 0.115 -0.012

(0.032) -0.026 (0.038)

3,047 (1,754)

61 (18)

Domestic – females only 0.335 -0.174 (0.110)

-0.435*** (0.180)

2,795 (888)

65 (17)

Restaurants, cafes, etc. 0.060 -0.029 (0.023)

0.024 (0.034)

4,194 (3,567)

53 (21)

Casual/Construction laborer 0.029 -0.038**

(0.018) -0.020 (0.017)

2,246 (1,576)

51 (31)

Other 0.030 -0.028* (0.015)

-0.013 (0.014)

4,600 (1,740)

47 (13)

Notes: The sample used here includes all individuals surveyed in the KLPS2 who report working for pay (with earnings greater than zero) at the time of the survey. Each row is from a separate OLS regression analogous to equation 4. All observations are weighted to maintain initial population proportions. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator.

52

Table 9: Deworming impacts on other economic outcomes

Dependent variable

Control group variable mean (s.d.)



Coefficient estimate (s.e.) on deworming Treatment pupils

within 6 km (in ‘000s), demeaned

Obs.

Panel A: Self-employed profits, hours and employees Mean effect size (three profits measures, and total employees hired) 0.000 0.175** 0.014 555 (1.000) (0.089) (0.097) Total self-employed profits (self-reported) past month (among those >0) 1,766 343 -151 585 (2,619) (306) (320) Total self-employed profits (constructed) past month (among those >0) 1,535 1,211 2,088 595 (6,524) (1,091) (1,886) Total self-employed profits (self-reported) past year (among those >0) 12,193 1,952 -1,753 566 (17,346) (2,286) (2,590) Total employees hired (excluding self), among the self-employed 0.188 0.446 0.044 633 (0.624) (0.361) (0.492) Indicator for self-employed earnings in last month 0.100 0.015 0.004 5,083 (0.300) (0.012) (0.011) Panel B: Agricultural work, sales, hours and practices Total value (KSh) of crop sales past year (if farm household) 576 -81 -460** 3,758 (2458) (148) (206) Uses “improved” agricultural practice (if farming household) 0.310 0.032 0.005 3,766 (0.462) (0.026) (0.024) Indicator for respondent did agricultural work in last week 0.531

(0.499) -0.010 (0.025)

0.005 (0.031)

5,080

Notes: Each row is from a separate OLS regression analogous to equation 4. “Agricultural work” includes both farming and pastoral activities. The average of “typical monthly” and last week recall is used for household consumption. Standard errors are clustered by school. Significant at 90% (*), 95% (**), 99% (***) confidence. All regressions include controls for baseline 1998 primary school population, geographic zone of the school, survey wave and month of interview, a female indicator variable, baseline 1998 school grade fixed effects, the average school test score on the 1996 Busia District mock exams, total primary school pupils within 6 km, and the cost-sharing school indicator.

Main Findings

12% increase in hours worked (also reduction in work days lost to sickness)

21-29% higher wages, accompanied by shifts towards high paying manufacturing jobs away from more casual labour

Childhood health investments substantially increase adult earnings

randomized experimentsfaculty.arts.ubc.ca/asiwan/documents/econ542-week2-methodology.… · dambisa...

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