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WORMS AT WORK: LONG-RUN IMPACTS OF A CHILDHEALTH INVESTMENT*
Sarah Baird
Joan Hamory Hicks
Michael Kremer
Edward Miguel
This study estimates long-run impacts of a child health investment, exploit-ing community-wide experimental variation in school-based deworming. Theprogram increased labor supply among men and education among women,with accompanying shifts in labor market specialization. Ten years after de-worming treatment, men who were eligible as boys stay enrolled for more yearsof primary school, work 17% more hours each week, spend more time in non-agricultural self-employment, are more likely to hold manufacturing jobs, andmiss one fewer meal per week. Women who were in treatment schools as girlsare approximately one quarter more likely to have attended secondary school,
*We thank Kevin Audi, Pierre Bachas, Chris Blattman, Seth Blumberg, HanaBrown, Lorenzo Casaburi, Lisa Chen, Garret Christensen, Evan DeFilippis,Lauren Falcao, Francois Gerard, Eva Arceo Gomez, Felipe Gonzalez, JonasHjort, Gerald Ipapa, Maryam Janani, Anne Karing, Jen Kwok, Andrew FischerLees, Leah Luben, Jamie McCasland, Owen Ozier, Kristianna Post, Adina Rom,Martin Rotemberg, Jon Schellenberg, Changcheng Song, Sebastian Stumpner,Paula Vinchery, Michael Walker, Paul Wang, Zhaoning Wang and Ethan Yeh forproviding excellent research assistance on the KLPS project. We thank MichaelAnderson, Kathleen Beegle, Jere Behrman, David Card, Alain de Janvry, EricaField, Fred Finan, Paul Glewwe, Michael Greenstone, Jim Heckman, AdrianaLleras-Muney, Steve Luby, Isaac Mbiti, Mark Rosenzweig, T. Paul Schultz, JimSmith, John Strauss, Glen Weyl, Alix Zwane; seminar participants at UC Berkeley,USC, Harvard, the J-PAL Africa Conference, the Pacific Conference onDevelopment Economics, UCSF, the Gates Foundation WASH Convening inBerkeley, Yale, University of Oklahoma, Hamilton College, RAND, CGD, theWorld Bank, Maseno University, the NBER Labor Studies group, BREAD/CEPRMeeting in Paris, American University, University of Chicago, ColumbiaUniversity, Stanford GSB, Makerere University, the AEA meetings (in SanDiego), Notre Dame, University of Washington, Mathematica, the Institute forFiscal Studies, Hong Kong University of Science and Technology, theInternational Health Economics Association conference; and the editor and fouranonymous referees for helpful suggestions. We gratefully acknowledge our collab-orators (International Child Support and Innovations for Poverty Action), andfunding from NIH grants R01-TW05612 and R01-HD044475, NSF grants SES-0418110 and SES-0962614, the World Bank, the Social Science ResearchCouncil, and the Berkeley Population Center. Michael Kremer declares that heworks with USAID, which supports deworming, and was formerly a boardmember of Deworm the World, a 501(c)3 organization. The content is solely theresponsibility of the authors and does not necessarily reflect the views of any of ourfunders.
! The Author(s) 2016. Published by Oxford University Press, on behalf of Presidentand Fellows of Harvard College. All rights reserved. For Permissions, please email:[email protected] Quarterly Journal of Economics (2016), 1637–1680. doi:10.1093/qje/qjw022.Advance Access publication on July 19, 2016.
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halving the gender gap. They reallocate time from traditional agriculture intocash crops and nonagricultural self-employment. We estimate a conservativeannualized financial internal rate of return to deworming of 32%, and showthat mass deworming may generate more in future government revenue than itcosts in subsidies. JEL Codes: I10, I20, J24, O15.
I. Introduction
The question of whether—and how much—child health gainsaffect adult outcomes is of major research interest across disci-plines and is of great public policy importance. The belief thatchildhood health investments may improve adult living stan-dards currently underlies many school health and nutrition pro-grams in low-income countries.
Existing research suggests several channels through whichincreasing child health investments could affect long-run earn-ings. Grossman’s (1972) seminal health human capital model in-terprets health care as an investment that increases futureendowments of healthy time. Bleakley (2010) further developsthis theory, arguing that how the additional time is allocatedwill depend on how health improvements affect relative produc-tivity in education and labor. Pitt, Rosenzweig, and Hassan(2012) (PRH) further note that time allocation will also dependon how the labor market values increased human capital andimproved raw labor capacity, and this in turn may vary withgender. They present a model in which exogenous health gainsin low-income economies tend to reinforce men’s comparative ad-vantage in occupations requiring raw labor while leading womento obtain more education and move into more skill-intensive oc-cupations, and they provide evidence consistent with this model.
We examine the case of intestinal worms, which globallyaffect approximately 2 billion people according to the WorldHealth Organization (WHO 2014). Worms (helminths) arespread when fecal matter containing eggs from infected individ-uals is deposited in the local environment. Intense infections leadto lethargy, anemia, and growth stunting (Stephenson et al. 1993;Stoltzfus et al. 1997; Guyatt et al. 2001; Silva et al. 2003) and mayweaken the immunological response to other infections (Kjetlandet al. 2006; Kirwan et al. 2010). Chronic parasitic infections inchildhood may lead to inflammation and elevated cortisol thatproduce adverse health consequences later in life (Crimmins
QUARTERLY JOURNAL OF ECONOMICS1638
and Finch 2005), as well as increased maternal morbidity, lowbirth weight, and miscarriage (Larocque et al. 2006; Hotez 2009).
There is ongoing debate about whether it is appropriate tocarry out mass deworming treatment programs in endemic re-gions. Because treatment is safe and cheap but diagnosis is expen-sive, WHO recommends periodic mass school-based deworming inhigh-prevalence areas (WHO 1992). Several other groups alsohighlight deworming as a cost-effective investment (DiseaseControl Priorities Project 2008; Hall and Horton 2008; JameelPoverty Action Lab 2012; Givewell 2013). In contrast, a recentCochrane Review argues that although treatment of thoseknown to be infected may be warranted, there is ‘‘quite substan-tial’’ evidence that mass deworming program does not improveaverage nutrition, health, or school performance outcomes(Taylor-Robinson et al. 2015).1
Because of its selection criteria focusing on medical-stylerandomized control trials, the Cochrane Review includes numer-ous studies subject to well-known methodological limitations(Bundy et al. 2009) and excludes rigorous social science evidence.For instance, the review excludes Bleakley (2007), which esti-mates the community-wide impact of deworming in the earlytwentieth-century U.S. South using quasi-experimental differ-ence-in-difference methods. That study finds that mass deworm-ing improved literacy and raised long-run adult income by 17%;extrapolating to the higher infection rates in tropical Africa,Bleakley (2010) estimates deworming could boost income thereby 24%.2
The present article exploits community-wide experimentalvariation in a deworming program for children in Kenyan pri-mary schools, combined with a longitudinal data set trackingthese children into adulthood, to causally identify the effect of
1. The Cochrane Reviews are systematic reviews of primary research inhuman health care and health policy. They are influential among health policymakers.
2. A small body of social science research studies the impact of deworming onlabor outcomes. In addition to Bleakley (2007, 2010), early work by Schapiro (1919)using a first-difference research design found wage gains of 15–27% on Costa Ricanplantations after deworming, whereas Weisbrod et al. (1973) observe little contem-poraneous correlation in the cross section between worm infections and labor pro-ductivity in St. Lucia. We discuss the related literature estimating dewormingimpacts on educational outcomes later.
WORMS AT WORK 1639
improved child health on later life outcomes. At the time of treat-ment, program participants had already passed the age windowconsidered most critical for early childhood development, sug-gesting that the time endowment and time allocation effects em-phasized in Bleakley (2010), Grossman (1972), and PRH (2012)may be the most relevant channels of impact. Indeed a surveyconducted one to two years after treatment found no cognitivegains. However, consistent with Grossman (1972), treatmentled to large gains in school participation, reducing absenteeismby one quarter (Miguel and Kremer 2004). There was also evi-dence for epidemiological externalities within this primaryschool-age population: untreated children in treatment schoolsas well as children living near treatment schools had lowerworm infection rates and higher school participation (Migueland Kremer 2004, 2014), and children less than one year old(who were not eligible for treatment) in treated communitiesshowed cognitive gains in later tests (Ozier 2014).
As discussed in Miguel and Kremer (2014), the originalMiguel and Kremer (2004) paper contained several rounding er-rors and a coding error in the estimation of cross-school external-ities. Correcting this coding error indicates that short-run, one-year epidemiological externalities extend out to 3 km or 4 km,rather than 6 km (Aiken et al. 2015; Clemens and Sandefur2015; Miguel, Kremer, and Hicks 2015). This coding error hasbeen resolved in the current article. Davey et al. (2015) expressconcerns that there were differences across schools in the numberof visits to measure school attendance in Miguel and Kremer(2004). As noted in Hicks, Kremer, and Miguel (2015), there isno statistical evidence for any imbalance in data collection pat-terns across treatment and control schools, and the Miguel andKremer (2004) results are robust to weighting each individualequally in the analysis. This issue is not relevant to the currentarticle, which employs a different data set than Miguel andKremer (2004).
In the current analysis, we examine health, education, andlabor market outcomes a decade later, at which point most sub-jects were young adults 19–26 years of age. We find improve-ments in self-reported health, but not in height. Consistentwith PRH, we find important gender distinctions in long-termdeworming impacts. Men who were in treatment schools asboys work 3.5 more hours each week (on a base of 20.3 hours),spend more time in nonagricultural self-employment, and are
QUARTERLY JOURNAL OF ECONOMICS1640
more likely to hold manufacturing jobs with higher wage earn-ings. Their living standards improve as well, with men in treat-ment schools eating one more meal per week on average. Womenwho were in treatment schools spend more time in school as girlsand are approximately one quarter more likely to have passed thesecondary school entrance exam and to have attended secondaryschool. They reallocate time from traditional agriculture to non-agricultural self-employment and are also more likely to growcash crops. Estimated effects on labor hours and living standardsare larger for those who were older than 12 years of age (themedian age) at baseline, who are much more likely to be out ofschool by the follow-up survey.
In line with Miguel and Kremer (2004), we also find evidenceof positive epidemiological externalities on long-run outcomesacross a range of outcomes using a seemingly unrelated regres-sion framework. We report point estimates using the linear ap-proach to estimating externalities employed in that paper, and wedevelop a procedure for bounding the impacts of deworming validunder the more general monotonicity assumption that the directand epidemiological externality effects on labor market outcomeshave the same sign.
Last, the estimated impacts of deworming on labor marketoutcomes, combined with other data, allow us to estimate fiscalimpacts. We find that the additional net government revenuesgenerated by increased work hours caused by deworming subsi-dies may be greater than the direct subsidy cost, suggesting thatin the case of deworming, health human capital subsidies arepotentially Pareto-improving. At a minimum, this suggests thatthe expected costs to taxpayers are less than would be suggestedby multiplying program costs by 1.2 or 1.4 or some other standardmultiplier for the deadweight loss of taxation. We also estimatean annualized financial internal rate of return to deworming sub-sidies of 32%, a high return.
The rest of the article is organized as follows. Section II dis-cusses the Kenyan context, the deworming project, and the data.Section III presents the estimation strategy. Section IV discussesthe main results. Section V combines the results on the price re-sponsiveness of take-up and long-run impacts to assess the fiscalimpacts of deworming subsidies, and computes the internal rateof return. The final section concludes. All supplementary mate-rial is in the Online Appendix.
WORMS AT WORK 1641
II. Data
This section describes the study area, the deworming pro-gram, and the survey, including our respondent tracking ap-proach and sample summary statistics.
II.A. Study Area and Local Labor Markets
The primary study area is Busia District, a densely settledfarming region in western Kenya adjacent to Lake Victoria that issomewhat poorer than the national average. Outside labormarket opportunities for children are meager, and boys andgirls typically attend primary school, with dropout rates risingin grades 7 and 8 (the final two years of primary school). Primaryschool completion, when children in the study area are typicallybetween 15–18 years of age, is a key time of labor market transi-tion. Secondary education in Kenya, like tertiary education in theUnited States, depends on exam performance, requires a sub-stantial financial outlay, and often involves moving away fromhome. In our data, just over half of control group males and justunder one third of females continue to secondary school.Occupational and family roles differ markedly by sex, with cer-tain occupations, such as fishing, driving bicycle taxis, andmanufacturing, overwhelmingly male and others, such assmall-scale market trading and domestic service, largelyfemale. The model in PRH (2012) suggests that labor market op-portunities will affect gender-specific educational and labor re-sponses to health investments.
II.B. The Primary School Deworming Project (PSDP)
In 1998 the nongovernmental organization (NGO)InternationalChild Support (ICS) launched the Primary School DewormingProgram (PSDP) in two divisions of the district, in 75 primary schoolswith a total of 32,565 pupils. Parasitological surveys indicated thatbaseline helminth infection rates were over 90% in these areas.Using modified WHO infection thresholds, over a third of thesample had moderate to heavy infections with at least one helminth(Miguel et al. 2014), a high but not atypical rate in African settings(Brooker et al. 2000; Pullan et al. 2011).
The schools were experimentally divided into three groups(Groups 1, 2, and 3) of 25 schools each: the schools were first strat-ified by administrative subunit (zone), zones were listed alphabet-ically within each geographic division, and schools were then listed
QUARTERLY JOURNAL OF ECONOMICS1642
in order of pupil enrollment within each zone, with every thirdschool assigned to a given program group. Figure I presents theproject research design and describes the timing of data collection.Online Appendix section A contains a detailed description of theexperimental design, provides further information on the sample,and shows that the three groups were well balanced along baselinecharacteristics (Online Appendix Table S1).
Due to the NGO’s administrative and financial constraints, theschools were phased into deworming treatment during 1998–2001:
2007-09: Kenya Life Panel Survey (KLPS) Round 2 data collection (Wave 1 2007-08,
Wave 2 2008-09), N=5,084.
2003-05: Kenya Life Panel Survey (KLPS) Round 1 data collection (Wave 1 2003-04,
Wave 2 2004-05), N=5,211.
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.
2002-2003: Group 3
receives free
deworming
2002-2003: Group 2
receives free
deworming
2002-2003: Group 1
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
1999-2000: Group 2
receives free
deworming
1999-2000: Group 1
receives free
deworming
1998: Group 3 does not
receive deworming
1998: Group 2 does not
receive deworming1998: Group 1 receives
free deworming
1998-2001: Ongoing unannounced school participation data collection visits
FIGURE I
Project Timeline of the Primary School Deworming Program (PSDP) and theKenya Life Panel Survey (KLPS)
WORMS AT WORK 1643
Group 1 schools began receiving free deworming and health educa-tion in 1998, Group 2 schools in 1999, and Group 3 in 2001.Children in Group 1 and 2 schools were thus assigned 2.41 moreyears of deworming than Group 3 children on average (OnlineAppendix Table S2), and these early beneficiaries are the treatmentgroup in the analysis. Take-up rates were approximately 75% in thetreatment group and 5% in the control group (Miguel and Kremer2004). In 2001, the NGO required cost-sharing contributions fromparents in a randomly selected half of the Group 1 and Group 2schools, substantially reducing take-up, and in 2002–2003 it pro-vided free deworming in all schools (Kremer and Miguel 2007).
II.C. Kenya Life Panel Survey (KLPS) Data
The Kenya Life Panel Survey Round 2 (KLPS-2) was collectedduring 2007–2009 and tracked a representative sample of approx-imately 7,500 respondents who were enrolled in grades 2–7 in thePSDP schools at baseline. Survey enumerators traveled through-out Kenya and Uganda to interview those who had moved out oflocal areas. The effective survey tracking rate in KLPS-2 is 82.5%,and 83.9% among those still alive (see Online Appendix sections Aand C for further details on survey methodology, tracking rates,and attrition). The effective tracking rate is calculated as a fractionof those found or not found but searched for during intensive track-ing, with weights adjusted appropriately, in a manner analogousto the approach in the U.S. Moving to Opportunity study (Orr et al.2003; Kling, Liebman, and Katz 2007).
These are high tracking rates for any age group over a decade,especially for a mobile group of adolescents and young adults.Tracking rates are nearly identical and not significantly differentin the treatment and control groups (Online Appendix Table S2).
III. Estimation Strategy
In this section, we define the quantities of interest, describehow to bound them in the presence of potential epidemiologicalexternalities, and present our econometric strategy.
III.A. Bounding Deworming Treatment Effects in the Presence ofExternalities
We need to account for the possibility of externalities in em-pirically estimating the impact of deworming subsidies. Recall
QUARTERLY JOURNAL OF ECONOMICS1644
that deworming subsidies were assigned at the school level ratherthan the individual level. It is therefore worth distinguishingwithin-school and cross-school externalities. In the potentialpresence of within-school epidemiological externalities, wecannot separately identify the labor market impact of individualdeworming status and of deworming status of others within theschool. We can, however, identify the aggregate school-level labormarket effect of the deworming subsidy. Therefore we classify allindividuals in schools with a deworming subsidy as ‘‘treated’’ inthe empirical analysis.
The remaining issue is cross-school epidemiological external-ities. In the remainder of this subsection, we first show that underthe relatively weak assumption that the sign of cross-school epi-demiological effects on labor market outcomes is not opposite to thesign of direct effects, the difference in outcomes between treatmentand control communities is a lower bound on the true total impactof a mass deworming program. For expositional clarity, and toparallel Miguel and Kremer (2004), we start with a discussion ofexternality effects after one period but generalize them below tolonger timeframes. We consider a simple epidemiological model inwhich worm infection can spread only � km in a single year, forinstance, due to the natural movement of and interaction amongthe local population. Miguel and Kremer (2004, 2014) and Hicks,Kremer, and Miguel (2015) estimate substantial and significantshort-run (after one year) cross-school externalities on worm infec-tions within 3 km of treatment schools.
Consider an outcome Yijt for individual i in school j at time t,for example, a labor market outcome. Yijt is a function of laggedschool-level deworming subsidy treatment assignment,Tj;t�1 2 f0;1g, and the proportion of other individuals in commu-nities within � km of that school also received deworming,Pj;t�1;� 2 0;1½ �. This proportion captures the local ‘‘saturation’’ ofthe program. This local treatment rate is a function of both theprogram’s ‘‘coverage’’, Rj;t�1;�—that is, the fraction of pupils innearby schools assigned to the deworming subsidy treatment,as determined by the research design—and the dewormingtake-up rate, which is a function of the deworming subsidylevel, Q Sð Þ. Local treatment saturation is the product of coverageand take-up, Pj;t�1;� ¼ Rj;t�1;�Q Sð Þ þ ð1� Rj;t�1;�ÞQ 0ð Þ, where take-up in the zero subsidy control group is Q 0ð Þ. Kremer and Miguel(2007) found empirically that control group take-up was veryclose to zero, implying that Pj;t�1;� ¼ Rj;t�1;�Q Sð Þ is a reasonable
WORMS AT WORK 1645
approximation.3 For now we focus on saturation, which is theepidemiologically relevant quantity, and we return to the distinc-tion between saturation and coverage in the empirical implemen-tation later.
The first quantity of interest, �t 1ð Þ, is the expected overallimpact of a mass deworming program, namely, the difference inexpected outcomes between individuals in treated communitiesfully exposed to other treatment communities (Pj;t�1;� ¼ 1) versusindividuals in untreated communities surrounded by untreatedcommunities:
�t 1ð Þ�E YijtjTj;t�1¼1;Pj;t�1;�¼1� �
�E YijtjTj;t�1¼0;Pj;t�1;�¼0� �
:ð1Þ
The second quantity of interest, �t pð Þ, is the impact of a pro-gram, such as the one we study, in which the share of nearbypopulation receiving deworming is Pj;t�1;�¼p;p2 0;1ð Þ. For eachquantity of interest we may also be interested in scaling impactby cost, that is, �t 1ð Þ=ðCost of Pj;t�1;�¼1Þ and�t pð Þ=ðCost of Pj;t�1;�¼pÞ.
Define the expected outcome in untreated communities sur-rounded by other untreated communities (i.e., ‘‘pure control’’ com-munities uncontaminated by exposure to nearby treatmentschools) as y0;t � E YijtjTj;t�1 ¼ 0;Pj;t�1;� ¼ 0
� �and define the dif-
ference in expected outcomes between treated and untreatedcommunities at a given local treatment saturation proportion pas:
l1t pð Þ�E YijtjTj;t�1¼1;Pj;t�1;�¼p� �
�E YijtjTj;t�1¼0;Pj;t�1;�¼p� �
:ð2Þ
Define the difference in average outcomes between untreatedcommunities at a local treatment proportion p versus pure con-trol communities as:
l2t pð Þ � E YijtjTj;t�1 ¼ 0;Pj;t�1;� ¼ p� �
� y0;t:ð3Þ
The sum of these two effects is �t pð Þ � l1t pð Þ þ l2t pð Þ.The biological mechanism underlying the spread of worm
infections implies that worm load in a particular location attime t is nondecreasing in worm load in that location and neigh-boring areas within distance ~� at lagged time t� ~t. Both own and
3. To the extent there was some take-up in control schools, estimates are alower bound on the impact of deworming.
QUARTERLY JOURNAL OF ECONOMICS1646
neighbors’ treatment at time t� ~t should thus reduce own wormload at t. This is captured in our first assumption (where to makethe notion of monotonicity concrete, the first inequality estab-lishes that the direct effect of treatment on Y is positive, withoutloss of generality):
ASSUMPTION 1. (Monotonic externality effects). Suppose for all p,
E YijtjTj;t�1 ¼ 1;Pj;t� ~t; ~� ¼ ph i
� E YijtjTj;t�1 ¼ 0;Pj;t� ~t; ~� ¼ ph i
,
then for any two levels of local treatment saturation p00 > p0,
E YijtjTj;t�1 ¼ �;Pj;t� ~t; ~� ¼ p00h i
� E YijtjTj;t�1 ¼ �;Pj;t� ~t; ~� ¼ p0h i
for all � 2 0; 1f g.
In a setting with real-world saturation level p, analysis thatdoes not account for cross-community spillover effects focuses onestimating l1t pð Þ. Assumption 1 implies that l1t pð Þ is a lowerbound on both quantities of interest, �t 1ð Þ and �t pð Þ.
PROPOSITION 1. (Bounding the treatment effect) Suppose forall p, E YijtjTj;t�1 ¼ 1;Pj;t�1;� ¼ p
� �� E YijtjTj;t�1 ¼ 0;
�Pj;t�1;� ¼ p�, then �t 1ð Þ � �t pð Þ � l1t pð Þ for all p 2 0; 1ð Þ:
Proof. We proceed in two steps. We first show that �t p00ð Þ � �t p0ð Þ for all p00 > p0. Note that �t p00ð Þ ��t p0ð Þ ¼ E YijtjTj;t�1 ¼ 1;
��Pj;t�1;� ¼ p00� � y0;tÞ � E YijtjTj;t�1 ¼ 1;Pj;t�1;� ¼ p0
� �� y0;t
� �¼
E YijtjTj;t�1 ¼ 1;Pj;t�1;� ¼ p00� �
� E YijtjTj;t�1 ¼ 1;Pj;t�1;� ¼ p0� �
.This is greater than or equal to 0 by the monotonicityassumption, implying that �t 1ð Þ � �t pð Þ for all p < 1. We nextshow that �t pð Þ � l1t pð Þ þ l2t pð Þ � �1t pð Þ. For all p > 0,Assumption 1 implies that l2t pð Þ � E YijtjTj;t�1 ¼ 0;Pj;t�1;� ¼ p
� �� E YijtjTj;t�1 ¼ 0;Pj;t�1;� ¼ 0
� �� 0. The result follows.
It is possible to tie this result more closely to the empiricalanalysis by taking into account the fact that local saturation ratesactually differ across communities. Allow Pj;t�1;� to be distributedacross communities as Pj;t�1;� � F, with density f . Then in prac-tice the average difference in outcomes across treated anduntreated communities is:Z P¼1
P¼0l1t Pð Þf Pð ÞdP:
WORMS AT WORK 1647
Since the result in Proposition 1 holds for all p 2 ð0;1Þ, it holdsfor this expression, which is effectively a weighted averageacross different saturation proportions p in this set.
The foregoing discussion abstracts away from other covari-ates. As we discuss later, their inclusion in a regression analysisis important given the nature of the experimental design andstratified sampling and potentially improves statistical preci-sion. One covariate that we include in the empirical analysis isthe local density of all primary school pupils (in all schools,treatment and control). We show in Table S2 of the OnlineAppendix and in Miguel and Kremer (2004) that the local num-bers of all primary school pupils and treatment school pupils areunrelated to treatment school assignment, although there is astatistically significant but small difference in the treatmentsaturation proportion; the fact that this proportion is slightlylower in treatment schools implies that the treatment schoolversus control school difference is once again likely to be alower bound on true impacts. Drug take-up rates in treatmentschools are also not significantly correlated with the local den-sity of either treatment schools or of all schools (Miguel andKremer 2004, Appendix Table A.II). Taken together, these pat-terns imply that any potential bias in the coefficient estimate onthe treatment school indicator would again lead us to understatedeworming impacts.
Note that the bound above will still be valid, albeit looser, ifthe geographic spread of epidemiological externalities over timemeans that even ‘‘pure control’’ (i.e., T ¼ 0 and P ¼ 0) schoolsare subject to some spillover from the program. Those whoseinfection intensity falls due to cross-school spillovers couldthemselves generate positive spillovers for other nearby schools,which would then lead to less local reinfection with worms, andso on.
Denote worm prevalence at location j at time t by !jt. Giventhe geographic spread of worm infections by � km per year, !jt willbe a nondecreasing function of worm prevalence at time t� ~t atall locations within radius � ~t. Thus, given the results in Migueland Kremer (2004), worm infection prevalence after the decade-long gap between treatment and the follow-up survey in our studywill potentially be reduced by worm treatment within a distanceof at least 30 km (= 10 years � 3 km a year) and perhaps beyond.Of course, these effects may fade over time, but no school in our
QUARTERLY JOURNAL OF ECONOMICS1648
study area of roughly 15 km � 40 km can be considered a ‘‘purecontrol’’ in the presence of these externalities.
It is straightforward to generalize the bounding result aboveto the empirically relevant case of an extended follow-up period.Denote the time period of the original deworming program ast ¼ 0, and subsequent years take on values of t ¼ 1; 2; 3; . . . t�,where t� is the period of the follow-up survey. While in theshort run (as in Miguel and Kremer 2004) the cross-school localtreatment saturation measure due to the deworming program(Pj;0;�) is likely to fairly accurately capture the magnitude of theexternality impacts, over time the infection ‘‘feedback’’ effectsgenerated in all directions among nearby schools would lead usto understate the magnitude of the true cross-school externali-ties. Determining the magnitude of all these externality effects isbeyond the scope of this article, as the spatial and temporal var-iation in our data do not allow us to precisely estimate the widerange of potentially relevant parameters, but in Online AppendixB we prove that the bounding result still holds in this case.
As noted, Miguel and Kremer (2004) report cross-school ex-ternalities up to 3 km from the school and at 3–6 km. There was astatistical program coding error in the construction of the cross-school externality term in Miguel and Kremer (2004) limiting theanalysis to the 12 closest schools. Correcting the coding error doesnot substantively alter the estimated effects of externalities be-tween 0–3 or 0–4 km, because there were never more than 12schools within 4 km, but does lead to less precisely estimatedoverall effects between 3 and 6 km from a school; Miguel andKremer (2014) and Ahuja et al. (2015) contain a complete discus-sion of the updated empirical results. We consider cross-schoolexternalities up to 6 km in the analysis in this article for tworeasons. First, spillover effects are likely to diffuse spatiallyover time, as discussed already. Second, we consider externalityeffects out to 6 km because an F-test in a seemingly unrelatedregression (SUR) framework rejects the hypothesis that the ex-ternality effects are 0 in the 3–6 km range for the outcomes weconsider (p-value < .001), indicating that their inclusion is appro-priate (see Online Appendix B2 for details). The main results arelargely unchanged using alternative specifications for the cross-school externality effect, including dropping these terms from theanalysis entirely, as we discuss later.
WORMS AT WORK 1649
III.B. Estimation
The econometric approach relies on the PSDP’s prospectiveexperimental design, namely, that the program exogenously pro-vided individuals in treatment (Groups 1 and 2) schools two to threeadditional years of deworming. We focus on intention-to-treat esti-mates, since compliance rates are high, and previous researchshowed that untreated individuals within treatment communitiesexperienced gains (Miguel and Kremer 2004), complicating estima-tion of treatment effects on the treated within schools. Since PRHsuggest potentially different labor market effects of health invest-ments on men and women in low-income ‘‘brawn-based economies,’’occupations are sharply differentiated by gender in our data, androughly twice as many women in our sample have children com-pared to the men, we follow the tradition in the labor market liter-ature of examining prime-age women and men separately (Altonjiand Blank 1999; Bertrand 2011).4
The dependent variable is outcome Yij, for individual i inschool j, in the KLPS-2 survey:
Yij ¼ �þ l1Tj þ l2Pj þ X 0ij;0�þ "ijð4Þ
The outcome is a function of the assigned deworming programtreatment status of the individual’s primary school (Tj), thetreatment saturation proportion among neighboring schoolswithin 6 km during the original treatment phase of the PSDP(Pj), a vector Xij;0 of baseline individual and school controls, anda disturbance term "ij, which is clustered at the school level.The Xij;0 controls include school geographic and demographiccharacteristics used in the PSDP ‘‘list randomization,’’ the stu-dent gender and grade characteristics used for stratification indrawing the KLPS sample (Bruhn and McKenzie 2009), apreprogram average school test score to capture academic qual-ity, the 2001 cost-sharing school indicator (described below), thetotal number of primary school pupils within 6 km of the school,and survey month and wave controls. Estimates are weightedto make the results representative of the full PSDP sampleoriginally in grades 2–7, taking into account the sampling forKLPS and the tracking strategy.
4. This study is registered on the American Economic Association RCT regis-try (#AEARCTR-0001191). We did not register a preanalysis plan, as they wereuncommon in economics when data collection for this study was completed in 2009.
QUARTERLY JOURNAL OF ECONOMICS1650
One issue with employing local saturation rates as an ex-planatory variable in practice is that they are a function of thelocal treatment decisions of households in the relevant area, lead-ing to possible endogeneity concerns, for instance, if take-up ishigher in areas where people have unobservably better labormarket prospects. To address these concerns, we construct thelocal saturation measure Pj as a function of the local coveragerate Rj of treatment school pupils within 6 km of school j, whichis exogenously determined by the experimental design, times theaverage take-up rate of deworming drugs in the entire sample atthe full subsidy level. This implies that variation in the local sat-uration variable is driven entirely by the experimental design,with the average take-up rate serving as a useful ‘‘rescaling’’ toallow for a more meaningful interpretation of the magnitude ofestimated effects.
The main coefficient of interest is l1, which captures gainsaccruing to individuals in treatment schools relative to the con-trol; because deworming was assigned by school rather than atthe individual level, some of the gains in treatment schools arelikely due to within-school externalities. This is an attractive co-efficient to focus on because it is a lower bound on the overalleffect of deworming (Proposition 1). Another coefficient of someinterest is l2, which captures the spillover effects for nearbyschools, following the approach in Miguel and Kremer (2004), inwhich cross-school externalities are estimated by taking advan-tage of variation in the local density of treatment schools inducedby the randomization. As explained further in that paper, sincereinfection rates are high in the area, the magnitude of external-ity effects may be either larger or smaller than the effect of own-school treatment. We have analyzed other specifications, includ-ing interactions between treatment and local saturation, andnonlinearities in saturation (Online Appendix B), but we cannotreject that Tj and Pj are additively separable and enter inlinearly.
The direct treatment effect estimates and externality effectsare locally relevant to the infection rates and treatment satura-tion rates in the setting we study, and although we do not findevidence of interaction effects or nonlinear externalities, it re-mains possible that such effects would emerge at treatmentlevels outside the support of values we observe. One case of po-tential interest is one where treatment coverage rates are evenhigher than those observed in our setting, for instance, if all local
WORMS AT WORK 1651
schools were assigned to treatment (rather than approximatelytwo thirds, as in our case). In this case, it is possible to placebounds on the cost-effectiveness of deworming using our dataunder the highly conservative assumption that there are no ad-ditional benefits from boosting deworming treatment saturation,that is, in the notation above that � pð Þ ¼ � p0ð Þ and l2 pð Þ ¼ l2 p0ð Þfor all p0 > p.
For concreteness, consider the case in which all estimates arebased on local treatment saturation rates in the neighborhood ofp < 1 and program coverage R < 1. Due to externalities, programbenefits are experienced in the schools assigned to treatment andthe control schools and can be represented asR� pð Þ þ 1� Rð Þl2 pð Þ ¼ Rl1 pð Þ þ l2 pð Þ. Then under an assumptionof constant marginal per capita treatment costs (which again islikely to be conservative given the fixed costs of setting up a treat-ment program), the cost of expanding local program coverage toall schools in the area (R ¼ 1) is 1
R times the cost of covering pro-portion R of the population. In our case, this is implemented bymultiplying the baseline costs of deworming treatment by
1ð2=3Þ ¼ 1:5, whereas the total benefits are assumed to remainunchanged. We present bounds using this approach in Section V.5
IV. Results
After briefly discussing long-run health effects, we presentimpacts on education, labor outcomes, and living standards, bygender. Results are broadly consistent with the PRH model.
IV.A. Long-Run Health Impacts
Although treatment dramatically reduced moderate-heavyinfections in the short run (Table I), adult helminth life spansare typically between one and four years (Hotez et al. 2006), sothe direct effects of treatment will no longer be present a decadelater in the data used in this analysis. Any long-run effects wouldlikely instead be due to effects on other diseases through an im-munological channel or to the effects of changes in schooling orlabor outcomes.
5. Of course, if � pð Þ ¼ � p0ð Þ and �2 pð Þ ¼ �2 p0ð Þ for all p0 > p, policy makers havethe option of replicating a program like that implemented in this study, in whichcase the relevant cost-effectiveness calculations would be based on the costs andbenefits at coverage and saturation levels found in our data.
QUARTERLY JOURNAL OF ECONOMICS1652
TA
BL
EI
DE
WO
RM
ING
IMP
AC
TS
ON
HE
AL
TH
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rmC
ontr
olgro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Mod
erate
-hea
vy
wor
min
fect
ion
sin
2001
�0.1
66**
*�
0.1
91**
*�
0.1
44**
*�
0.0
74
0.3
27
0.3
19
0.3
37
(0.0
26)
(0.0
28)
(0.0
32)
(0.2
23)
(0.4
69)
(0.4
66)
(0.4
73)
2,2
97
1,2
16
1,0
81
Sel
f-re
por
ted
hea
lth
‘‘ver
ygoo
d’’
ind
icato
rat
KL
PS
-20.0
40**
0.0
23
0.0
51**
0.1
28
0.6
73
0.7
13
0.6
29
(0.0
18)
(0.0
25)
(0.0
25)
(0.1
15)
(0.4
69)
(0.4
52)
(0.4
83)
5,0
70
2,5
85
2,4
85
Hei
gh
tat
KL
PS
-2�
0.1
52
0.0
41
�0.3
67
�2.1
36
167.3
171.7
162.4
(0.2
72)
(0.3
76)
(0.3
96)
(1.6
32)
(7.9
)(6
.4)
(6.4
)5,0
57
2,5
79
2,4
78
Bod
ym
ass
ind
exat
KL
PS
-20.1
21
�0.1
31
0.3
58**
0.1
38
21.5
021.3
121.7
1(0
.104)
(0.1
12)
(0.1
67)
(0.5
39)
(2.3
6)
(2.1
0)
(2.6
2)
5,0
48
2,5
76
2,4
72
Mis
carr
iage
ind
icato
r(o
bs.
at
pre
gn
an
cyle
vel
)at
KL
PS
-2(f
orfe
male
s—th
em-
selv
es;
for
male
s—th
eir
part
ner
s)
�0.0
15*
0.0
00
�0.0
28**
�0.0
78**
0.0
30
0.0
15
0.0
39
(0.0
08)
(0.0
04)
(0.0
13)
(0.0
37)
(0.1
71)
(0.1
23)
(0.1
94)
5,0
22
1,6
22
3,2
38
Not
es.
Th
esa
mp
lein
clu
des
all
ind
ivid
uals
surv
eyed
inK
LP
S-2
(2007–2009),
exce
pt
for
the
mod
erate
-hea
vy
wor
min
fect
ion
data
,w
hic
his
from
the
2001
PS
DP
para
sito
logic
al
surv
ey.
Each
entr
yin
colu
mn
s(1
)–(3
)is
from
ase
para
teO
LS
regre
ssio
n,
exce
pt
the
mis
carr
iage
outc
omes
,w
hic
hare
marg
inal
pro
bit
spec
ifica
tion
sin
wh
ich
each
obse
rvati
onis
ap
regn
an
cy.
All
obse
rvati
ons
are
wei
gh
ted
tom
ain
tain
init
ial
pop
ula
tion
pro
por
tion
s,ex
cep
tfo
rth
e2001
mod
erate
-hea
vy
wor
min
fect
ion
resu
lts.
Sta
nd
ard
erro
rsare
clu
ster
edby
sch
ool.
Sig
nifi
can
tat
90%
(*),
95%
(**)
,99%
(***
)co
nfi
den
ce.
Th
eco
effi
cien
ton
the
dew
orm
ing
trea
tmen
tin
dic
ato
rte
rmis
l 1in
equ
ati
on(1
).T
he
cros
s-sc
hoo
lex
tern
ali
tyte
rmis
the
‘‘satu
rati
onra
te’’—
the
nu
mber
oftr
eatm
ent
gro
up
(Gro
up
1,
2)
pu
pil
sw
ith
in6
km
div
ided
by
the
tota
ln
um
ber
ofp
rim
ary
sch
ool
pu
pil
sw
ith
in6
km
,m
ult
ipli
edby
the
aver
age
dew
orm
ing
tak
e-u
pra
tein
the
sam
ple
—d
emea
ned
,an
dth
eco
effi
cien
ton
the
exte
rnali
tyte
rmis
l 2in
equ
ati
on(1
).A
llre
gre
ssio
ns
exce
pt
for
the
firs
tin
clu
de
con
trol
sfo
rbase
lin
e1998
pri
mary
sch
ool
pop
ula
tion
,geo
gra
ph
iczo
ne
ofth
esc
hoo
l,su
rvey
wave
an
dm
onth
ofin
terv
iew
,a
fem
ale
ind
icato
rvari
able
,base
lin
e1998
sch
ool
gra
de
fixed
effe
cts,
the
aver
age
sch
ool
test
scor
eon
the
1996
Bu
sia
Dis
tric
tm
ock
exam
s,to
tal
pri
mary
sch
ool
pu
pil
sw
ith
in6
km
,an
dth
eco
st-s
hari
ng
sch
ool
ind
icato
r.T
he
firs
tro
win
clu
des
con
trol
sfo
rbase
lin
e1998
pri
mary
sch
ool
pop
ula
tion
,geo
gra
ph
iczo
ne
ofth
esc
hoo
l,a
fem
ale
ind
icato
rvari
able
,base
lin
e1998
sch
ool
gra
de
fixed
effe
cts,
the
aver
age
sch
ool
test
scor
eon
the
1996
Bu
sia
Dis
tric
tm
ock
exam
s,an
dto
tal
pri
mary
sch
ool
pu
pil
sw
ith
in6
km
.S
elf-
rep
orte
dh
ealt
h‘‘v
ery
goo
d’’
tak
eson
avalu
eof
1if
the
an
swer
toth
equ
esti
on‘‘W
ould
you
des
crib
eyou
rgen
eral
hea
lth
as
som
ewh
at
goo
d,
ver
ygoo
d,
orn
otgoo
d?’
’is
‘‘ver
ygoo
d,’’
an
d0
oth
erw
ise.
Six
teen
un
reaso
nably
low
valu
esfo
rh
eigh
tare
dro
pp
ed,
bu
tth
eh
eigh
tre
sult
sare
subst
an
tivel
yth
esa
me
ifth
ese
are
not
dro
pp
ed.
Six
teen
un
reaso
nably
hig
hvalu
es(a
t98
an
dabov
e)fo
rB
MI
are
dro
pp
ed(t
hes
eare
the
sam
e16
obse
rvati
ons
that
wer
ed
rop
ped
for
hei
gh
t);
not
eth
at
the
fem
ale
BM
Ief
fect
esti
mate
isn
otst
ati
stic
all
ysi
gn
ifica
nt
at
trad
itio
nal
con
fid
ence
level
sif
thes
eob
serv
ati
ons
are
reta
ined
(not
show
n).
WORMS AT WORK 1653
Although we find no long-term effects on height or body massindex in the full sample, there is some evidence of persistenthealth gains in terms of self-reported health and reduced miscar-riage. Respondent reports that their health was ‘‘very good’’ roseby 4.0 percentage points (std. err. 1.8, p < .05), on a base of 67.3%in the control group. We cannot reject equal effects for both gen-ders, but gains are slightly larger for women. We detect gains inbody mass index among treated women (p<.05). Furthermore,deworming reduced miscarriage rates among treatment groupwomen by 2.8 percentage points (std. err. 1.3, p < .05) on a baseof 3.9% in a probit analysis (where each pregnancy is the unit ofobservation). The lack of miscarriage impact among the partnersof men in the treatment group suggests a health (rather than aliving standards) channel for the impacts estimated amongsample women.
IV.B. Education Impacts
The medium-run follow-up (Miguel and Kremer 2004) foundincreased primary school participation among both boys andgirls, consistent with the idea that health investment increasedthe endowment of healthy time (Grossman 1972), and that forchildren, this increased time went into schooling rather thanworking. The long-run follow-up data show that treatment con-tinued to boost boys’ primary school enrollment, but average ac-ademic performance did not improve, with higher enrollmenttranslating into higher rates of grade repetition but no increasein educational attainment and no significant differences betweenthe treatment and control groups in rates of passing the second-ary school exam or enrolling in secondary school (Table II). We donot have data on whether increased primary school enrollmentimproved noncognitive skills, a possible channel for later labormarket impacts (Heckman, Stixrud, and Urzua 2006). Recall thatin the models in Bleakley (2010) and Pitt, Rosenzweig, andHassan (2012), deworming would not increase secondary school-ing if attractive work opportunities emerged around the time ofprimary school completion (roughly ages 15–18) and if health in-vestments raised the marginal return to work as much as thediscounted return to secondary schooling.
In contrast, our primary specification suggests that deworm-ing leads to marked academic gains for girls, increasing the rateat which girls passed the secondary school entrance exam by 9.6
QUARTERLY JOURNAL OF ECONOMICS1654
TA
BL
EII
DE
WO
RM
ING
IMP
AC
TS
ON
ED
UC
AT
ION
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Tot
al
yea
rsen
roll
edin
sch
ool,
1998–2007
0.2
94**
0.1
50
0.3
54*
1.0
15
6.6
97.0
56.2
9(0
.145)
(0.1
66)
(0.1
79)
(0.8
39)
(2.9
7)
(2.9
3)
(2.9
6)
5,0
37
2,5
67
2,4
70
Tot
al
yea
rsen
roll
edin
pri
mary
sch
ool,
1998–2007
0.1
55**
0.2
38**
0.0
26
0.7
84
4.3
84.4
34.3
2(0
.075)
(0.1
02)
(0.0
98)
(0.4
85)
(2.4
8)
(2.4
2)
(2.5
5)
5,0
38
2,5
68
2,4
70
Rep
etit
ion
ofat
least
one
gra
de
(1998–
2007)
ind
icato
r0.0
63**
*0.0
72**
*0.0
53*
0.0
99
0.6
72
0.6
69
0.6
76
(0.0
18)
(0.0
25)
(0.0
30)
(0.1
23)
(0.4
70)
(0.4
71)
(0.4
68)
5,0
84
2,5
95
2,4
89
Gra
des
ofsc
hoo
lin
gatt
ain
edby
2007
0.1
50
�0.0
30
0.2
61
0.3
23
8.7
29.0
68.3
4(0
.143)
(0.1
48)
(0.1
71)
(0.8
42)
(2.2
1)
(2.2
8)
(2.0
7)
5,0
84
2,5
95
2,4
89
Att
end
edse
con
dary
sch
ool
ind
icato
r0.0
30
�0.0
35
0.0
90**
�0.0
32
0.4
21
0.5
04
0.3
29
(0.0
35)
(0.0
38)
(0.0
38)
(0.2
17)
(0.4
94)
(0.5
00)
(0.4
70)
5,0
84
2,5
95
2,4
89
Pass
edse
con
dary
sch
ool
entr
an
ceex
am
du
rin
g1998–2007
ind
icato
r0.0
50
0.0
04
0.0
96**
0.2
20
0.5
05
0.5
90
0.4
09
(0.0
31)
(0.0
30)
(0.0
40)
(0.1
61)
(0.5
00)
(0.4
92)
(0.4
92)
4,9
74
2,5
41
2,4
33
Ou
t-of
-sch
ool
(at
2007–2009
surv
ey)
ind
icato
r�
0.0
06
0.0
22
�0.0
29
0.1
85
0.7
50.7
00.8
0(0
.022)
(0.0
30)
(0.0
26)
(0.1
42)
(0.4
3)
(0.4
6)
(0.4
0)
5,0
58
2,5
82
2,4
76
Not
es.
For
det
ail
son
the
regre
ssio
ns,
see
the
not
esfo
rT
able
I.E
ach
entr
yin
colu
mn
s(1
)–(3
)is
from
ase
para
teO
LS
regre
ssio
n.
Sig
nifi
can
tat
90%
(*),
95%
(**)
,99%
(***
)co
nfi
den
ce.
WORMS AT WORK 1655
percentage points (p < .05) on a base of 41%. This increase ofroughly one quarter reduces the existing gender gap in examperformance by half. Consistent with the model in PRH (2012),in which positive health shocks disproportionately induce womento allocate more time to human capital acquisition, treatment alsohalved the gender gap in secondary school entry, increasing girls’secondary enrollment by 0.325 years, or a third (Online AppendixTable S3), and increasing overall years of school enrollment forwomen by 0.354 years (std. err. 0.179, p < .10) (Table II). Theestimated increase in girls’ educational attainment is 0.261 years(std. err. 0.171, p = .13), as some of the increased enrollmenttranslated into increase grade repetition, as was the case formales.
IV.C. Impact on Labor Hours and Occupation
Average weekly hours worked in the control group are quitelow, at 20.3 for men and 16.3 for women (although many womenin our sample are engaged in home production or child-rearingactivities, and time spent on these activities was not systemati-cally collected in KLPS-2). Among men, deworming increasedtime spent working by 17%, or 3.49 hours a week (std. err. 1.42,p < .05, Table III, Panel A). In contrast, estimated effects onnonhousehold work hours among women are small. It is worthnoting that one quarter of both the treatment and control groupswere still in school by the time of the survey (Table II), and labormarket outcomes are less meaningful for this group. We nextfocus on a subpopulation that is largely older than school age,which we operationalize as those who were older than 12 years(the median age) at baseline, and thus at least 22 or 23 years ofage at follow-up: only 5% of control individuals in this age groupwere still enrolled in any school at follow-up, compared with 39%among younger control individuals. In this older subpopulation,average hours worked per week in the control group is somewhathigher: 28.2 hours for men and 21.7 hours for women. For thissubgroup among both sexes, deworming increased time spentworking by 13.0%, or 3.29 hours a week (std. err. 1.80, p < .10),and treated men worked 3.74 more hours a week (p < .10).Treated women worked 2.01 more hours a week, and althoughwe cannot reject the hypothesis of no effect for women, we alsocannot reject the hypothesis of equal treatment effects by gender.
QUARTERLY JOURNAL OF ECONOMICS1656
TA
BL
EII
I
DE
WO
RM
ING
IMP
AC
TS
ON
LA
BO
RH
OU
RS
AN
DO
CC
UP
AT
ION
AL
CH
OIC
E
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Pan
elA
:H
ours
wor
ked
Hou
rsw
ork
edin
all
sect
ors
inla
stw
eek
,fu
llsa
mp
le1.5
83.4
9**
0.3
210.2
018.4
20.3
16.3
(1.0
4)
(1.4
2)
(1.3
6)
(7.8
0)
(23.1
)(2
4.6
)(2
1.1
)5,0
84
2,5
95
2,4
89
Hou
rsw
ork
edin
all
sect
ors
inla
stw
eek
,ol
der
than
sch
ool
age
subsa
mp
le(o
lder
than
12
yea
rsof
age
at
base
lin
e)
3.2
9*
3.7
4*
2.0
118.0
25.4
28.2
21.7
(1.8
0)
(2.2
1)
(2.4
5)
(11.8
)(2
6.1
)(2
7.2
)(2
4.1
)2,2
35
1,2
01
1,0
34
Pan
elB
:S
ecto
ral
tim
eall
ocati
on(f
ull
sam
ple
)H
ours
wor
ked
inn
onagri
cult
ura
lse
lf-e
mp
loym
ent
inla
stw
eek
1.5
1**
*1.3
5*
1.8
6**
6.0
0*
3.3
3.8
2.7
(0.5
5)
(0.7
3)
(0.8
1)
(3.2
3)
(12.8
)(1
3.7
)(1
1.7
)5,0
84
2,5
95
2,4
89
Hou
rsw
ork
edin
agri
cult
ure
inla
stw
eek
�0.0
71.0
3*
�1.2
7**
�0.5
58.3
7.8
8.8
(0.4
2)
(0.5
5)
(0.5
6)
(3.4
1)
(11.4
)(1
1.6
)(1
1.2
)5,0
84
2,5
95
2,4
89
Hou
rsw
ork
edin
wage
earn
ing
inla
stw
eek
0.1
41.1
1�
0.2
74.7
46.9
8.8
4.8
(0.8
4)
(1.3
2)
(1.0
8)
(5.0
7)
(18.5
)(2
0.0
)(1
6.5
)5,0
84
2,5
95
2,4
89
WORMS AT WORK 1657
TA
BL
EII
I
(CO
NT
INU
ED)
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Pan
elC
:O
ccu
pati
onal
choi
ce(f
ull
sam
ple
)M
an
ufa
ctu
rin
gjo
bin
dic
ato
r0.0
110**
*0.0
192**
0.0
050
0.0
531**
0.0
049
0.0
068
0.0
027
(0.0
040)
(0.0
077)
(0.0
035)
(0.0
250)
(0.0
698)
(0.0
824)
(0.0
522)
5,0
84
2,5
95
2,4
89
Con
stru
ctio
n/c
asu
al
labor
job
ind
icato
r�
0.0
053**
�0.0
031
�0.0
073
�0.0
196
0.0
048
0.0
040
0.0
057
(0.0
026)
(0.0
030)
(0.0
045)
(0.0
154)
(0.0
691)
(0.0
628)
(0.0
756)
5,0
84
2,5
95
2,4
89
Dom
esti
cse
rvic
ejo
bin
dic
ato
r�
0.0
050
0.0
016
�0.0
134
�0.0
097
0.0
192
0.0
067
0.0
331
(0.0
061)
(0.0
038)
(0.0
129)
(0.0
322)
(0.1
372)
(0.0
813)
(0.1
791)
5,0
84
2,5
95
2,4
89
Gro
ws
cash
crop
ind
icato
r0.0
136**
0.0
068
0.0
207**
0.0
111
0.0
073
0.0
080
0.0
065
(0.0
060)
(0.0
071)
(0.0
094)
(0.0
260)
(0.0
850)
(0.0
890)
(0.0
803)
5,0
68
2,5
88
2,4
80
Not
es.
For
det
ail
son
the
regre
ssio
ns,
see
the
not
esfo
rT
able
I.E
ach
entr
yin
colu
mn
s(1
)–(3
)is
from
ase
para
teO
LS
regre
ssio
n.
‘‘Old
erth
an
sch
ool
age’
’d
enot
esth
ose
old
erth
an
12
yea
rsof
age
(th
em
edia
nage)
at
base
lin
ein
1998.
Agri
cult
ura
lw
ork
inP
an
elB
incl
ud
esbot
hfa
rmin
gan
dp
ast
oral
act
ivit
ies.
Sig
nifi
can
tat
90%
(*),
95%
(**)
,99%
(***
)co
nfi
den
ce.
QUARTERLY JOURNAL OF ECONOMICS1658
Deworming changes how work hours are allocated acrosssectors and occupations, with important distinctions by gender(Table III, Panel B). Considering the genders together, hours innonagricultural self-employment increase by 45% (p < .01), andresults are shown by gender in Figure II (Panels A and B). Thereare no statistically significant changes in hours worked in agri-culture or wage employment.
Breaking results down by gender, point estimates suggestthat deworming leads men to increase total work hours, and wecannot reject the hypothesis of equal percentage increases acrosssectors (Table III, Panel B). In contrast, women increase time innonagricultural self-employment by 1.86 hours (std. err. 0.81, p<.05) on a base of 2.7 hours, nearly 70%, and reduce hours workedin agriculture by 1.27 hours (std. err. 0.56, p < .05). This shiftfrom agricultural work into nonagricultural self-employmentcould potentially be interpreted as consistent with PRH, althoughthe evidence is not dispositive. Seventy-seven percent of self-employed women work in retail, which seems less physically in-tensive than agriculture, and there is evidence that retail profitsare tied to math skills (Kremer et al. 2013). However, there is nosignificant difference in education levels between women workingin agriculture and those in nonagricultural self-employment.
Deworming treatment also leads to shifts in occupationalchoice (Table III, Panel C). Treatment respondents are threetimes more likely to work in manufacturing (coefficient 0.0110,p < .05) from a low base of 0.005. On the flip side, casual labor—which typically does not require regular work hours—falls signif-icantly (p < .05). Manufacturing jobs require more hours a weekthan other occupations: they average 53 hours a week, comparedwith 42 hours for all wage-earning jobs, 34 hours for self-employment, and 15 hours for agriculture. Workers inmanufacturing tend to miss relatively few work days due topoor health, at just 1.1 days in the past month (in the controlgroup), compared with 1.5 days among all wage earners.Manufacturing jobs are highly paid, with average earningsmore than double those in casual labor (Table S17). Dewormingalso leads to an increase in cash crop cultivation for the entiresample (Table III, Panel C), with a gain of 1.36 percentage points(p < .05) on a low base of 0.73%.
Estimates of occupational effects by sex are less precise, butthere are significant increases in manufacturing among men andin growing cash crops among women. The particularly large effect
WORMS AT WORK 1659
Trea
tmen
t
Cont
rol
A
.005.01.015.02Kernel density
1030
5070
Hou
rs w
orke
d in
self-
empl
oym
ent,
male
s
B
.005.01.015.02
1030
5070
Hou
rs w
orke
d in
self-
empl
oym
ent,
fem
ales
C
0.1.2.3.4Kernel density
45
67
89
1011
Log
earn
ings
in w
age
empl
oym
ent,
male
s
D
0.1.2.3.4
45
67
89
1011
Log
earn
ings
in w
age
empl
oym
ent,
fem
ales
FIG
UR
EII
Ker
nel
Den
siti
esof
Hou
rsW
ork
edin
Sel
f-E
mp
loym
ent
an
dL
ogE
arn
ings
inW
age
Em
plo
ym
ent,
Tre
atm
ent
ver
sus
Con
trol
Ker
nel
den
sity
inth
etr
eatm
ent
gro
up
issh
own
inbla
ck,
an
din
the
con
trol
gro
up
show
nin
gra
y.
Pan
elA
dis
pla
ys
hou
rsw
ork
edin
self
-em
plo
ym
ent
inth
ela
stw
eek
(am
ong
thos
ew
ork
ing
10–80
hou
rsin
the
sect
or)
for
male
s,an
dP
an
elB
dis
pla
ys
the
sam
efo
rfe
male
s.P
an
elC
dis
pla
ys
log
earn
ings
inw
age
emp
loym
ent
inth
ep
ast
mon
th(a
mon
gth
ose
wit
hp
osit
ive
earn
ings)
for
male
s,an
dP
an
elD
dis
pla
ys
the
sam
efo
rfe
male
s.
QUARTERLY JOURNAL OF ECONOMICS1660
of deworming on physically demanding and well-paidmanufacturing employment among men is consistent with thePRH model. There is suggestive evidence of a shift into highwork hour occupations for men but not women (see OnlineAppendix C).
The increase in secondary education, nonagricultural self-employment, and cash crop cultivation among women may reflecta desire to engage in higher productivity activities within existingfamily and social constraints, which may complicate moves intomanufacturing or other lucrative male-dominated jobs. Morespeculatively, these may pay off in the form of higher future earn-ings, even if not yet apparent in our data.
IV.D. Impact on Living Standards
Living standards can be assessed using data on either con-sumption or earnings. We do not have data on overall consump-tion, but we do have data on the number of meals consumed.Treatment respondents eat 0.095 more meals per day (std. err.0.029, p < .01, Table IV, Panel A). The increase in meals eaten islarger for men, at 0.125 meals/day (p< .01) than for women (0.051meals), implying that treatment males miss just under one fewermeal each week than control males. Treatment effects are parti-cularly large for the older than school age subsample (across bothgenders), at 0.119 more meals per day (p < .01).
Total earnings are the sum of earnings in wage labor, innonagricultural self-employment, and in agriculture, eachweighted by the proportions working in each sector. We beginby considering total nonagricultural earnings (the sum of wagelabor earnings and nonagricultural self-employment profits),which are likely to be more accurately captured than agriculturalproduction in this setting. Those with no nonagricultural earn-ings are included in the analysis (with zero earnings). In the fullsample, treatment respondents’ total nonagricultural earningsare 15.0% higher (112 shillings, std. err. 96, Table IV, Panel A),although the effect is not statistically significant. In the olderthan school age subsample, the effect is considerably larger at22.6% (278 shillings, std. err. 167, p = .101).
We next consider each source of income separately. In prin-ciple, the proportions working in different sectors could differ bytreatment group, but note that there are no significant differ-ences by treatment status (Online Appendix Table S5, odd
WORMS AT WORK 1661
TA
BL
EIV
DE
WO
RM
ING
IMP
AC
TS
ON
LIV
ING
ST
AN
DA
RD
SA
ND
LA
BO
RE
AR
NIN
GS
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Pan
elA
:C
onsu
mp
tion
an
dn
onagri
cult
ura
lea
rnin
gs
Nu
mber
ofm
eals
eate
nyes
terd
ay,
full
sam
ple
0.0
95**
*0.1
25**
*0.0
51
0.4
15**
*2.1
62.1
02.2
3(0
.029)
(0.0
41)
(0.0
43)
(0.1
24)
(0.6
4)
(0.6
5)
(0.6
2)
5,0
83
2,5
95
2,4
88
Nu
mber
ofm
eals
eate
nyes
terd
ay,
old
erth
an
sch
ool
age
subsa
mp
le(o
lder
than
12
yea
rsof
age
at
base
lin
e)
0.1
19**
*0.1
47**
*0.0
70
0.4
06*
2.1
12.0
42.2
0(0
.042)
(0.0
51)
(0.0
63)
(0.2
36)
(0.6
6)
(0.6
7)
(0.6
3)
2,2
34
1,2
01
1,0
33
Tot
al
non
agri
cult
ura
lea
rnin
gs
(wage
earn
ings
plu
sse
lf-e
mp
loyed
pro
fits
),p
ast
mon
th,
full
sam
ple
112
139
98
226
749
1,1
15
340
(96)
(171)
(68)
(694)
(2,1
32)
(2,7
03)
(1,0
75)
5,0
84
2,5
95
2,4
89
Tot
al
non
agri
cult
ura
lea
rnin
gs
(wage
earn
ings
plu
sse
lf-e
mp
loyed
pro
fits
),p
ast
mon
th,
old
erth
an
sch
ool
age
subsa
mp
le(o
lder
than
12
yea
rsof
age
at
base
lin
e)
278
312
188
1,1
52
1,2
31
1,7
74
527
(167)
(265)
(139)
(971)
(2,4
40)
(2,9
03)
(1,3
75)
2,2
35
1,2
01
1,0
34
QUARTERLY JOURNAL OF ECONOMICS1662
TA
BL
EIV
(CO
NT
INU
ED)
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Pan
elB
:W
age
earn
ings
(am
ong
wage
earn
ers)
Ln
(Tot
al
labor
earn
ings)
,p
ast
mon
th0.2
69**
*0.2
44**
0.1
65
1.1
41
7.7
97.9
27.4
6(0
.085)
(0.1
09)
(0.1
75)
(0.8
69)
(0.8
8)
(0.8
7)
(0.8
1)
710
542
168
Ln
(Wage
=T
otal
labor
earn
ings
/h
ours
),p
ast
mon
th,
if�
10
hou
rsp
erw
eek
ofw
ork
0.1
97*
0.1
81
0.2
25
0.3
78
2.6
82.8
82.2
1(0
.102)
(0.1
28)
(0.1
94)
(0.8
98)
(0.9
1)
(0.8
9)
(0.8
1)
601
448
153
Ln
(Tot
al
labor
earn
ings)
,m
ost
rece
nt
mon
thw
ork
edsi
nce
2007
0.2
25**
*0.2
21**
0.1
78*
0.9
41
7.8
37.9
77.5
4(0
.070)
(0.0
97)
(0.1
04)
(0.5
97)
(0.9
1)
(0.8
9)
(0.8
9)
1,1
75
819
356
WORMS AT WORK 1663
TA
BL
EIV
(CO
NT
INU
ED)
Coe
ffici
ent
esti
mate
(std
.er
r.)
ond
ewor
min
gtr
eatm
ent
ind
icato
r
Coe
ff.
est.
(std
.er
r.)
exte
rnali
tyte
rm
Con
trol
gro
up
mea
n(s
td.
dev
.);
nu
mber
ofob
serv
ati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All
Male
Fem
ale
All
All
Male
Fem
ale
Pan
elC
:N
onagri
cult
ura
lse
lf-e
mp
loym
ent
outc
omes
(am
ong
non
agri
cult
ura
lse
lf-e
mp
loyed
)T
otal
self
-em
plo
yed
pro
fits
(sel
f-re
por
ted
)p
ast
mon
th384
111
250
�77
1,7
66
2,1
35
1,2
65
(308)
(465)
(265)
(1,6
46)
(2,6
19)
(3,2
35)
(1,2
61)
585
313
272
Tot
al
self
-em
plo
yed
pro
fits
past
mon
th,
top
5%
trim
med
341*
259
80
440
1,2
21
1,1
84
1,2
65
(177)
(309)
(219)
(1,2
56)
(1,1
51)
(1,0
56)
(1,2
61)
553
284
269
Tot
al
emp
loyee
sh
ired
(excl
ud
ing
self
)0.4
16
0.2
45
0.6
03
�0.8
86
0.1
88
0.2
53
0.0
97
(0.3
61)
(0.4
03)
(1.2
75)
(2.5
47)
(0.6
24)
(0.6
14)
(0.6
30)
633
343
290
Not
es.
For
det
ail
son
the
regre
ssio
ns,
see
the
not
esfo
rT
able
I.E
ach
entr
yin
colu
mn
s(1
)–(3
)is
from
ase
para
teO
LS
regre
ssio
n,
exce
pt
for
‘‘tot
al
emp
loyee
sh
ired
’’in
Pan
elC
,w
hic
hu
ses
an
egati
ve
bin
omia
lre
gre
ssio
n.
‘‘Old
erth
an
sch
ool
age’
’d
enot
esth
ose
old
erth
an
12
yea
rsof
age
(th
em
edia
nage)
at
base
lin
ein
1998.
Rea
lea
rnin
gs
mea
sure
sacc
oun
tfo
rth
eh
igh
erp
rice
sfo
un
din
the
urb
an
are
as
ofN
air
obi
an
dM
ombasa
.W
eco
llec
ted
pri
cesu
rvey
sin
bot
hru
ral
wes
tern
Ken
ya
an
din
urb
an
Nair
obi
du
rin
gK
LP
S-2
,an
dbase
the
urb
an
pri
ced
eflato
ron
thes
ed
ata
;re
sult
sare
un
chan
ged
wit
hou
tth
isp
rice
ad
just
men
t.T
he
tota
ln
onagri
cult
ura
lea
rnin
gs
mea
sure
inP
an
elA
incl
ud
esth
ose
wit
hze
rore
por
ted
earn
ings
an
dp
rofi
ts.
Th
ew
age,
earn
ings,
an
dp
rofi
tsre
sult
sin
Pan
els
Ban
dC
are
am
ong
thos
ew
ho
rep
orte
dw
age
emp
loym
ent
orn
onagri
cult
ura
lse
lf-e
mp
loym
ent,
resp
ecti
vel
y.
Wh
enco
mp
uti
ng
wages
,w
eex
clu
de
thos
ew
ith
few
erth
an
10
hou
rsa
wee
kto
ad
dre
ssd
ivis
ion
bia
sfr
omn
oise
ines
tim
ati
onof
nu
mber
ofh
ours
wor
ked
.‘‘T
otal
emp
loyee
sh
ired
’’is
am
ong
thos
ew
ho
are
self
-em
plo
yed
.S
ign
ifica
nt
at
90%
(*),
95%
(**)
,99%
(***
)co
nfi
den
ce.
QUARTERLY JOURNAL OF ECONOMICS1664
TA
BL
EV
FIS
CA
LIM
PA
CT
SO
FD
EW
OR
MIN
GS
UB
SID
IES
No
subsi
dy
Part
ial
subsi
dy
Fu
llsu
bsi
dy
Not
es
Pan
elA
:C
ali
bra
tion
para
met
ers
Siz
eof
subsi
dy:
S$0.0
0$1.1
5$1.4
2F
rom
Dew
orm
the
Wor
ld;
Kre
mer
an
dM
igu
el(2
007)
Tak
e-u
pra
te:
Q(S
)5%
19%
75%
Fro
mK
rem
eran
dM
igu
el(2
007)
Aver
age
per
-per
son
cost
:S
Q(S
)$0.0
0$0.2
2$1.0
7S
ubsi
dy�
tak
e-u
pra
teM
ean
per
per
son
incr
ease
inw
ork
hou
rs/w
eek
:l 1
0.0
00.4
41.7
5M
en:
incr
ease
of3.4
9h
ours
/wee
k;
wom
en:
no
chan
ge
(Table
III)
.P
art
ial
subsi
dy
mu
ltip
lied
by
QðSÞ
Qðf
ullÞ
Mea
nin
crea
sein
wor
kh
ours
/wee
kfr
omex
tern
ali
ty:
pl 2
0.0
01.7
65.2
110.2
0(T
able
III)�
Cov
erage
oftr
eatm
ent
sch
ool
stu
den
tsw
ith
in6
km
(R,
68.1
%)�
[Q(S
)fo
rfu
llsu
bsi
dy,
QðSÞ
Qðf
ullÞ
for
part
ial
subsi
dy]
Mea
nin
crea
sein
sch
ooli
ng
cost
s0.0
02.7
110.7
1N
PV
of(a
dd
itio
nal
seco
nd
ary
sch
ooli
ng
cost
sp
erp
up
il-y
ear
($116.8
5)�
dir
ect
incr
ease
inse
con
dary
sch
ooli
ng).
Part
ial
subsi
dy
mu
lti-
pli
edby
QðSÞ
Qðf
ullÞ.
Mea
nin
crea
sein
sch
ooli
ng
cost
sfr
omex
tern
ali
ty0.0
03.4
013.4
2N
PV
of(a
dd
itio
nal
seco
nd
ary
sch
ooli
ng
cost
sp
erp
up
il-y
ear
($116.8
5)�
exte
rnali
tyin
-cr
ease
inse
con
dary
sch
ooli
ng).
Part
ial
sub-
sid
ym
ult
ipli
edby
QðSÞ
Qðf
ullÞ.
WORMS AT WORK 1665
TA
BL
EV
(CO
NT
INU
ED)
No
subsi
dy
Part
ial
subsi
dy
Fu
llsu
bsi
dy
Not
es
Pan
elB
:N
oh
ealt
hsp
illo
ver
sA
nn
ual
incr
ease
inp
erp
erso
nea
rnin
gs
$0.0
0$3.9
1$15.4
4l 1�
start
ing
wage�
52
NP
Vin
crea
sein
per
per
son
earn
ings
(rel
ati
ve
ton
osu
bsi
dy)
—$36.0
8$142.4
39.8
5%
an
nu
al
(rea
l)in
tere
stra
tein
Ken
ya
NP
Vin
crea
sein
per
per
son
gov
ern
men
tre
ven
ue
—$3.2
7$12.9
0N
PV
earn
ings�
16.5
75%
tax
rate�
Dir
ect
sch
ooli
ng
cost
sP
an
elC
:W
ith
hea
lth
spil
lover
sA
nn
ual
incr
ease
inp
erp
erso
nea
rnin
gs
$0.0
0$26.7
7$83.1
1(l
1+
(p R)l 2
)�
start
ing
wage
x52
NP
Vin
crea
sein
per
per
son
earn
ings
(rel
ati
ve
ton
osu
bsi
dy)
—$246.9
9$766.8
19.8
5%
an
nu
al
(rea
l)in
tere
stra
tein
Ken
ya
NP
Vin
crea
sein
per
per
son
gov
ern
men
tre
ven
ue
—$34.8
3$102.9
7N
PV
earn
ings�
16.5
75%
tax
rate�
(Dir
ect
+ex
tern
ali
tysc
hoo
lin
gco
sts)
Not
es.
Th
ed
ewor
min
gco
stis
US
$0.5
9p
eryea
r,an
dth
eaver
age
nu
mber
ofyea
rstr
eate
dw
as
2.4
1yea
rs.
Fig
ure
sin
Pan
els
Ban
dC
are
rela
tive
toth
e‘‘n
osu
bsi
dy’’
case
.W
eu
sea
start
ing
hou
rly
wage
rate
(w)
of$0.1
7,
aw
eigh
ted
aver
age
ofw
ages
by
sect
orw
ith
wei
gh
tsco
rres
pon
din
gto
con
trol
gro
up
mea
nh
ours
per
sect
or(T
able
IV).
We
use
Su
ri’s
(2011)
mea
nw
age
of$0.1
6as
the
agri
cult
ura
lw
age,
an
dth
eco
ntr
olgro
up
mea
nof
$0.2
3(T
able
IV,
Pan
elA
)fo
rth
ose
wor
kin
gfo
rw
ages
.S
elf-
emp
loyed
wages
are
calc
ula
ted
by
div
idin
gco
ntr
olgro
up
mon
thly
pro
fits
(Table
IV,
Pan
elB
)by
4.5
tim
esth
eh
ours
wor
ked
aw
eek
am
ong
thos
ew
ork
ing
inse
lf-e
mp
loym
ent,
for
aw
age
of$0.1
4.
Th
ep
ubli
cfi
nan
ced
ata
isfr
omth
eK
enyan
Cen
tral
Ban
kw
ebsi
tean
dth
eW
orld
Ban
kD
evel
opm
ent
Ind
icato
rs.
Th
eN
PV
ofp
erp
erso
nli
feti
me
earn
ings
inth
en
osu
bsi
dy
an
dn
oh
ealt
hsp
illo
ver
sca
seis
$1,5
09.9
6.
We
ass
um
eth
at
earn
ings
start
10
yea
rsaft
erd
ewor
min
gtr
eatm
ent
an
dco
nti
nu
efo
r40
yea
rs.
Lif
ecy
cle
earn
ings
pro
file
sfo
rK
enya
are
crea
ted
usi
ng
data
from
the
1998/1
999
Ken
ya
Inte
gra
ted
Labou
rF
orce
Su
rvey
,by
regre
ssin
gin
div
idu
al
earn
ings
onage,
age
squ
are
d,
an
din
dic
ato
rvari
able
sfo
rfe
male
,att
ain
eda
sch
ooli
ng
level
ofp
rim
ary
/se
con
dary
/bey
ond
,an
dp
rovin
ceof
resi
den
ce.
Fu
ture
earn
ings
are
als
oass
um
edto
incr
ease
by
the
aver
age
per
cap
ita
GD
Pgro
wth
rate
inK
enya
du
rin
gth
e2001–2011
per
iod
,n
am
ely,
1.5
2%
per
an
nu
m(W
orld
Ban
kD
evel
opm
ent
Ind
icato
rs).
QUARTERLY JOURNAL OF ECONOMICS1666
columns). Although weighted earnings by sector can always besummed to generate total earnings, the treatment versus controldifferences within particular sectors presented above reflect acombination of treatment and selection effects. Treatment andcontrol individuals work as wage laborers at similar rates andhave similar selection patterns along observable dimensions(Tables S5, S14–S15), but there are significantly different pat-terns of selection into wage employment and nonagriculturalself-employment by treatment status (Table S5). This suggeststhat selection concerns are potentially important and that itmay not be appropriate to interpret the differences between treat-ment and control individuals within employment sectors ascausal impacts. Recall that the consumption and total nonagri-cultural earnings results above (in Table IV, Panel A) are basedon the full sample and the issue of sorting across employmentsectors does not apply.
Those working in wage employment likely have the best mea-sured data. The distribution of log wage earnings is shifted toright for both men (Figure II, Panel C) and women (Panel D) inthe treatment group relative to control. Log earnings (Table IV,Panel B) are 26.9 log points (std. err. 8.5, p < .01) greater. Theestimated differences in earnings are larger than those of hours,consistent with the hypotheses that treatment leads men to shiftinto jobs that require more work hours and pay better. Log wagescomputed as earnings per hour worked (among those who work atleast 10 hours a week) are 19.7 log points (std. err. 10.2, p < .10)greater in the treatment group. Wage earnings differences be-tween treatment and control are also positive among the largernumber of respondents who had ever earned wages since 2007,with an average difference of 22.5 log points (p < .01) during themost recent earnings period.
The data on self-employment profits are likely measuredwith somewhat more noise. Monthly profits are 22% larger inthe treatment group, but the difference is not significant(Table IV, Panel C), in part due to large standard errors createdby a few male outliers reporting extremely high profits. In a ver-sion of the profit data that trims the top 5% of observations, thedifference is 28% (p < .10).
With no changes in the proportion of respondents in differentsectors, and estimated increases in earnings of more than 20%among wage earners—and similar (if less precisely estimated)profit increases among the self-employed—treatment will have
WORMS AT WORK 1667
increased overall earnings unless agricultural earnings declined.Unfortunately, we lack sufficient data on agricultural earnings toperform a direct test. However, several patterns suggest that it isunlikely agricultural earnings declined, and highly unlikely thatthey declined sufficiently to outweigh the gains in other sectors.Recall that cash crop cultivation increased and hours worked in ag-riculture did not change. Most important, if agricultural productivityhad declined, one might expect that food consumption among thoseworking in agriculture would decline, but there is in fact an increaseof 0.065 meals (std. err. 0.033) in this group (Online Appendix C).
IV.E. Heterogeneous Treatment Effects and AlternativeSpecifications
Although statistical power is limited, we do not find strongevidence of heterogeneous treatment effects on education, labormarket, or living standards outcomes by baseline school grade,local treatment saturation, or the presence of schistosomiasis (asproxied for by distance to Lake Victoria, see Online AppendixSection C.4 and Tables S6–S13).
Estimated deworming impacts are largely robust to whetherwe account for the cross-school spillovers at all and to accountingfor cross-school externalities at different distances (OnlineAppendix Tables S6–S9, column (5)). Online Appendix FigureS4 shows that effects typically remain statistically significantacross alternative specifications of the externality effects forkey outcome measures (although for the ‘‘passed primary exam’’outcome for women, p-values range from .02 to .26). The exter-nality results are similar if we focus on the number of local pupils,rather than the proportion, in treatment schools (OnlineAppendix Tables S6–S9, column (2)).
IV.F. Accounting for Multiple Inference
To further assess robustness, we next account for multipleinference, and then examine two additional sources of variationin exposure to deworming.
Online Appendix Tables S18–S21 present the false discoveryrate adjusted q-values (analogues to the standard p-value) thatlimit the expected proportion of rejections within a set of hypothesesthat are Type I errors (Benjamini, Krieger, and Yekutieli 2006;Anderson 2008). Key results are robust to this adjustment: takingboth genders, the deworming impact on meals eaten and labor
QUARTERLY JOURNAL OF ECONOMICS1668
earnings is statistically significant at the 1% level (q-value < 0.01),on total hours worked in nonagricultural self-employment andmanufacturing employment is significant at the 5% level, and thereduction in casual labor jobs, the increase in cash crops, andtrimmed self-employed profits are significant at the 10% level.There is less power with the gender subsamples, but most key resultscontinue to hold at the 10% level (Online Appendix Section C.5).
IV.G. Variation in Cost Sharing
Because the temporary 2001 deworming treatment cost-shar-ing program substantially reduced take-up, it provides an addi-tional orthogonal source of variation in treatment, albeit withless statistical power. Reassuringly, the estimated effect of costsharing has the opposite sign of the main deworming treatmenteffect for 26 of the 30 outcomes presented in Tables I–IV (excludingthe first outcome in Table I, which was measured before cost shar-ing was introduced), and this pattern seems extremely unlikely tooccur by chance. In addition, stacking the data and using seem-ingly unrelated regression (SUR) estimation across outcomes, wereject the hypothesis that the cost-sharing coefficients are 0 (p <.001); see Online Appendix Section B for further details.
IV.H. Cross-School Treatment Externalities
Cross-school externalities provide a third source of exoge-nous variation in exposure to deworming. Several of the external-ity effect estimates in Tables I–IV are significant and large inmagnitude, including for miscarriage, manufacturing employ-ment, and meals eaten (p < .05). Under the null hypothesis ofno epidemiological externalities, there should be no correlationwith the direct treatment effect. In 26 of the 30 post-2001 speci-fications in Tables I–IV, the sign of the treatment effect and thecross-school externality effect are the same, which is extremelyunlikely to occur by chance; an alternative test estimates a cor-relation of 0.750 between the t-statistics for the direct effect andthe externality effect across outcomes (p < .001); and using SUR,we reject the hypothesis that the 0–6 km cross-school externalityeffects are 0 (p < .001); see Online Appendix B. The existence ofcross-school externalities provides additional evidence on the ro-bustness of the deworming impacts and reassurance that esti-mated effects are not simply due to some form of reporting biasin the treatment schools.
WORMS AT WORK 1669
V. The Rate of Return and Fiscal Impacts of Deworming
Subsidies
The estimated impacts of deworming on labor market out-comes, combined with other data, allow us to estimate the internalfinancial rate of return and fiscal impacts of deworming subsidies.
We observe only a snapshot of labor market outcomes at thetime of the follow-up survey, rather than the whole path of futurehours and earnings, and thus the calculations in this section aresomewhat speculative by necessity. We adopt what we consider tobe a reasonably conservative approach in bounding the effect oflifetime income. In particular, we base our calculations on differ-ences in hours worked between the treatment and control groups.This is likely to be conservative for a number of reasons: (i) esti-mated differences in earnings among wage workers are largerthan differences in hours (Table IV, Panel B); (ii) amongwomen, treatment is associated with greater educational attain-ment and higher test scores, and it seems plausible that this couldlead to higher future earnings, particularly if education and ex-perience are complements (Card 1999); (iii) there is increasednonagricultural self-employment, particularly among women,and it seems plausible that some of this consists of investmentsthat could pay off in increased earnings later; and (iv) estimatedeffects on hours worked and nonagricultural earnings are largeramong those who are older and more likely to be out of school.
For projections about the future path of earnings and thusgovernment revenues, we examine the following expression:
S2Q S2ð Þ�S1Q S1ð Þ<X
�N�
" S1ð Þ
Xt¼50
t¼0
1
1þ r
� �t
wt ðl1;� þpl2;�
R
!
�KXt¼50
t¼0
1
1þ r
� �t
�E�t S1;S2ð Þ
#:ð5Þ
The left-hand side is the fiscal cost to the government of in-creasing a deworming subsidy from S1 to S2, which in turn mayaffect deworming take-up Q; take-up is nondecreasing in thesubsidy. To compute this, we use information on take-up atdifferent price levels from Kremer and Miguel (2007), and cur-rent estimates of per pupil mass deworming treatment costs(provided by the NGO Deworm the World) of $0.59 per year.The total direct deworming cost then is the 2.41 years ofaverage deworming in the treatment group times this figure,
QUARTERLY JOURNAL OF ECONOMICS1670
or M = $1.42 per person treated and $1.07 per pupil in a de-worming treatment school, given average take-up of 75%.Under partial deworming subsidies, as implemented in the2001 cost-sharing program, individuals paid an average of$0.27 for the medicines, so the direct cost to the governmentwould be $1.15 for each fully dewormed individual over 2.41years. In Table V, Panel A, we compare these subsidy levelswith the default case of no subsidies, S1 ¼ 0.
The right-hand side captures the implications for govern-ment revenue of increasing the subsidy from S1 to S2. N� is thefraction of individuals in the sample of type � 2 �, which we oper-ationalize as gender, following the empirical analysis. The firstterm in the square brackets captures the increase in tax revenuegenerated by any increase in work hours: S1ð Þ is the prevailingtax rate; r is the per period interest rate; wt is the wage rate inyear t; l1;� is the estimated deworming impact on work hours intreatment schools for gender �; l2;� is the estimated externalityeffect; and p and R denote the program’s saturation and coverage,as above. These gains are captured over an individual’s workinglife, which we take to be 50 years.
The second term in the square brackets accounts for the factthat improved child health may lead the government to accrue ad-ditional educational expenditures, for instance, if secondary school-ing rates increase for type �, which we find for females. Let Kcapture the cost of an additional unit of schooling, and �E�t
S1;S2ð Þ denote the average increase in schooling for type �when the deworming subsidy increases from S1 to S2. To computethe right-hand side of equation (5), we use a combination of esti-mates from this article and other Kenyan data. The hours workedestimates (Table III) indicate that treatment group males work3.49 more hours per week (l1;male ¼ 3:49), whereas the treatmenteffect estimate for women is near zero (l1;female ¼ 0:32). The pointestimate of the increase in work hours due to epidemiologicalexternalities is 10.20 hours/week for an increase in treatment sat-uration from 0% to 100%, and we combine this information witheach school’s local density of treated pupils to determine pl2;� .
6
Because this externality estimate is not significant at conven-tional levels, we focus on the case of no epidemiological external-ity (l2;� ¼ 0) in Panel B, and present results in Panel C assuming
6. Results are similar when externalities are disaggregated by gender (notshown).
WORMS AT WORK 1671
the externality has the estimated magnitude for completeness.We examine the impact of a program that treated two thirds oflocal schools, as in the PSDP, and scale up externality gains bythe inverse of the coverage rate (1
R) since the control group alsobenefits from externalities.
At the time of writing, the government of Kenya pays 11.85%interest on its sovereign debt and inflation is approximately 2%, sowe set the real cost of capital r to 9.85%.7 We assume that thesample population begins working 10 years after they first beganreceiving deworming and retires after 40 years of work.8 Fromyear 10 post-treatment onward, we combine estimated l1;� andl2;� values from Tables III–IV with the pattern of life cycle earn-ings reported in the most recent publicly available data, the 1998/1999 Kenya Integrated Labour Force Survey, and assume recentKenyan economic growth trends continue. This forward projectionof earnings is necessary given the limitations of existing data andimplies that the calculations that follow are somewhat speculative.We also assume the initial starting wage w is $0.17 an hour, whichis a weighted average of wages by sector in our data and the meanKenyan agricultural wage in Suri (2011), with weights correspond-ing to control group mean hours per sector (Table IV).9 Kenyantaxes (mainly on consumption) absorb roughly 16.6% of GDP, sowe set the tax rate under no subsidy to 16.6%.10
7. See http://www.centralbank.go.ke/securities/bonds/manualresults.aspxand World Bank Development Indicators. This is a conservative assumption be-cause other potential funders of deworming subsidies (e.g., international organi-zations, private donors) are likely to face lower interest rates; use of a lower interestrate greatly increases the returns to deworming in the calculations described later.
8. This 10-year gap roughly corresponds to the time elapsed from the start ofPSDP until the KLPS-2 survey (2007–2009). By ignoring the time before KLPS-2data were collected, it underestimates gains due to greater work hours prior to thesurvey. Yet it misses any reduction in work hours due to substitution of school forwork. However, existing estimates of child labor productivity suggest these forgoneearnings are likely to be small (Udry 1996).
9. In Suri (2011), the mean agricultural wage is $0.16, and the control groupmean is $0.23 (Table IV, Panel B) for those working for wages. Self-employed wagesare calculated by dividing control group monthly profits (Table IV, Panel C) by 4.5times the hours worked per week among those working in self-employment, for awage of $0.14.
10. From World Development Indicators, government expenditures areroughly 19.5% of GDP, and from http://blogs.worldbank.org/africacan/three-myths-about-aid-to-kenya about 15% of government expenditure is financed fromdonors, thus 0.195 * 0.85 = 0.166.
QUARTERLY JOURNAL OF ECONOMICS1672
We estimated deworming impacts on school enrollment bygender and year (Online Appendix Table S3) and gathered de-tailed information on current teacher salaries and class sizesfrom the Ministry of Education, allowing us to estimate percapita schooling costs K for both primary and secondary school-ing. Because the PSDP program did not increase the number ofteachers or classrooms in primary schools, and there is no reasonto believe the Kenyan government adjusted these factors in re-sponse to the program (based on our observations as well as ondiscussions with local officials), any costs of increased classroomcongestion at the primary level due to deworming would havebeen incurred by students in these schools and thus is alreadycaptured in the labor market outcomes in our data. We thereforefocus on measuring the fiscal costs to the government of increasedsecondary school enrollment, since these costs would be incurredeither by the government (by paying for additional teachers) or bysecondary school students. Teacher salaries constitute the bulk ofrecurrent government education spending, at over 90% of second-ary school spending (Otieno and Colclough 2009), and most otherexpenses are traditionally covered by tuition and local parentfees. We factor in the costs the government would need to incurto maintain the secondary school pupil-teacher ratio using ourestimated per student secondary school teacher cost of $116.85per year (Table V, Panel A).
Assuming no externality gains,P
�N�
Pt¼50t¼0
1ð1þrÞ
� twtl1;� =
$142.43, implying that individuals gain an average of $119 intake-home pay and the net present value (NPV) of governmentrevenue increases by $23 per person (Table V, Panel B). The ad-ditional public educational costs incurred are estimated to be ap-proximately $10.71, so the net increase in government revenue is$12.90, far greater than the $1.07 subsidy. If deworming alsogenerates positive externalities, the earnings gains are muchlarger, with a per capita net increase in government revenue of$102.97 (Panel C).
A policy-relevant case is one in which the coverage (R) of thepopulation assigned to deworming increased from the roughlytwo thirds in our study sample up to all local primary schools,as in a national mass treatment program. In that case, the rela-tive cost-effectiveness of the program could depend on the degreeto which total program treatment effects depend on local treat-ment saturation, that is, on the shapes of both � pð Þ and l2 pð Þ,something we cannot directly estimate (the 10–90 range for
WORMS AT WORK 1673
saturation rate Pj in our data is 0.427 to 0.599). However, we canbound the cost-effectiveness of a program that covered the entirepopulation under the conservative assumption that there are noadditional net benefits from boosting the treatment rate. The costper treatment school student (under full subsidies) would rise by50% from $1.07 to $1.60 while the NPV net increase in govern-ment revenue would remain unchanged at $12.90, implying thata program treating all schools would also be cost-effective.
In terms of other extensions, our model assumes a linearincome/consumption tax, but the result is robust to a range ofalternative assumptions on taxation, including the possibility ofa lower tax rate in our predominantly rural sample; see OnlineAppendix Section C for further discussion.
A standard approach to assessing the desirability of a programis to calculate the social internal rate of return (IRR), which solvesfor the interest rate that equates the NPV of the full social cost andall earning gains, taxed or untaxed: in the above notation, MQ Sð Þ ¼X
�N�
Xt¼50
t¼0
11þr
� twt �1;� þ
p�2;�
R
� �K
Xt¼50
t¼0
11þr
� t�E�t 0;Sð Þ
�:
The annualized social IRR with no health spillovers (l2;� ¼ 0) isvery high at 31.8%, and with health spillovers is a massive 51.0%.
These fiscal and IRR calculations are speculative for severalreasons, including the projection of future earnings, as noted.This exercise also ignores broader general equilibrium effects ofa mass national deworming program on wage levels and the cap-ital stock; these macroeconomic effects could theoretically eitherincrease or decrease the effects we present in this section, al-though they seem unlikely to overturn the main patterns(Online Appendix C contains a discussion). They are also rela-tively imprecisely estimated: we bootstrapped standard errors(with 1,000 runs), and find that net revenue gains are less thanzero 29% of the time for the case of no health spillovers. So al-though estimates indicate that the expected net revenue effects ofdeworming are large, there remains considerable uncertaintyaround these estimates.
Yet these calculations are conservative in several dimensions.For one thing, note that even in cases where the net revenue effectsare not positive, the gains in the labor market due to deworminghelp partially offset the original expenditure outlay on dewormingsubsidies, substantially reducing their net fiscal cost. The fiscaland IRR exercises also only rely on income and ignore any welfare
QUARTERLY JOURNAL OF ECONOMICS1674
gains through other channels. It is plausible that those who hadbetter health and nutrition as a result of deworming benefited froman increased endowment of healthy hours, and experienced directutility gains from simply feeling better; the same could be said forany inherent welfare benefits of increased schooling. Finally, we donot incorporate recent evidence that positive deworming external-ities extend beyond those in our sample to other age groups: Ozier(2014) finds that living in a deworming treatment community earlyin life (birth to age 2) leads to improved cognitive and academicperformance 10 years later. Older individuals in the area also plau-sibly benefited from the health spillovers of treatment, but we lackdata to quantify any such gains.
VI. Conclusion
Previous work (Miguel and Kremer 2004) found that a pri-mary-school deworming program increased school participation.This article shows that some education and labor market out-comes improve a decade after deworming. These gains couldhave substantial positive welfare impacts for households livingnear subsistence, like many in our Kenyan sample. A conserva-tive estimate of the annualized financial IRR to deworming ishigh at 31.8%. Our best estimate is that deworming subsidieswill generate more in future government revenue than theycost in up-front expenditures.11
The high rate of return to deworming in our Kenyan context isconsistent with the finding of sizable deworming impacts on edu-cation and incomes in the twentieth-century U.S. South (Bleakley2007, 2010) and recent evidence on positive long-run educationalimpacts in East Africa in Ozier (2014) and Croke (2014). Of course,there is uncertainty around our estimates, and returns could differin other environments, but even given some uncertainty, or sub-stantial weight on priors that the returns to deworming are smal-ler, this growing body of evidence suggests that the expectedfinancial rate of return would likely exceed conventional hurdlesfor public health investment (Ahuja et al. 2015).
The results also have implications for several related litera-tures. Many studies argue that early childhood health gains in
11. Some have argued that certain other public health investments could alsohave this property, including tobacco cessation (Lightwood and Glantz 2013) andreduced drunk driving (Ditsuwan et al. 2013).
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utero or before age three have the largest impacts (Almond andCurrie 2010), and some have argued that interventions outside anarrow window of child development will not have major effects.Our evidence suggests that health interventions among school-aged children, which are too late in life to affect cognition orheight, can have long-run impacts on labor outcomes by affectingthe amount of time people spend in school or work.
Although there is a literature on differences in work hoursacross wealthy countries (Prescott 2004), the determinants oflabor hours in poor countries are less studied. Work hours arequite low in some low-income settings (Fafchamps 1993), includ-ing among our control group. The findings here suggest that poorchild health may be one factor behind this low adult labor supply.
Finally, our analysis does not account for potential negativeexternalities from deworming through drug resistance. Geertsand Gryseels (2000, 2001) highlight mass deworming policyapproaches that could minimize the development of resistance,and although there is limited current evidence on drug resistancerelated to human deworming, it has been documented in livestock(Albonico, Engels, and Savioli 2004). Despite their concerns,Geerts and Gryseels (2001) still conclude that community-basedmass deworming treatment makes sense in high-morbidity set-tings, such as our Kenyan study area, and we agree it is unlikelythat resistance would be large enough to overturn the case forsubsidies. Worm prevalence is likely to decline over time witheconomic development, as more people have sanitation facilities,wear shoes, and take other actions to avoid infection, and it istherefore unlikely to be optimal to hold back on treating the sicktoday to ‘‘save’’ the drug for later. Moreover, if there is a need tocut back on drug administration to reduce the risk that resistancewill develop, cutting back on veterinary use in high-income coun-tries may be a more appropriate initial response.
George Washington University
University of California, Berkeley
Harvard University and National Bureau of Economic
Research
University of California, Berkeley and National Bureau
of Economic Research
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Supplementary Material
An Online Appendix for this article can be found at QJEonline (qje.oxfordjournals.org).
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