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Most earnings and older and hours. areas, consumption and work urban males nonagricultural in among in in live concentrated shifts increase to likely 9% also individ- more group a are 9% treatment There are employment: earnings. and uals residence hourly childhood of in of sectors increase years 13% additional expenditures consumption and three in Individ- gain to 14% y 84%. a 20 experienced two was deworming to received rate up who tracking outcomes respondent uals economic longitudi- effective on uses The impacts and later. estimate to children, data provided Kenyan nal study that to intervention This treatment health settings. deworming school living low-income randomized measuring a in accurately inability exploits productivity and an time, status, and over health standards individuals in variation track experimental to including of challenges, lack living Urquiola) methodological Miguel the adult and on multiple Lo investments C. entails Nathan health by standards child reviewed of 2020; impact 20, November the review Estimating for (sent 2021 25, January Kremer, Michael by Contributed and 60637; IL Chicago, Chicago, 20052 of 94720; DC University CA Economics, Berkeley, of California, Department of Griffin University Action, Global Effective for a Hamory deworming Joan of impacts economic Twenty-year PNAS eateto cnmc,Uiest fOlhm,Nra,O 73072; OK Norman, Oklahoma, of University Economics, of Department hssuycnrbtseiec htadessleading addresses that evidence contributes study This eble htivsigi hl elhadntiincan nutrition life and of health quality future child individuals’ in in improvements investing generate that belief he 01Vl 1 o 4e2023185118 14 No. 118 Vol. 2021 | ∗ hl health child a dadMiguel Edward , | ogrnimpacts long-run b,c,d | Kenya ihe Walker Michael , c d b h ainlBra fEooi eerh abig,M 02138; MA Cambridge, Research, Economic of Bureau National The ihe Kremer Michael , eateto cnmc,Uiest fClfri,Bree,C 94720; CA Berkeley, California, of University Economics, of Department f icmeec 2) hr sltl vdnergriglong-run regarding arm evidence upper little mid is and There height, (21). weight, recent circumference child a on However, and impacts positive (20). significant larger finds treatment studies more mass population- incorporating from few metaanalysis claimed gains that child actively article wide been survey a has (19). following recommendation expensive debated this (through more of far infections and appropriateness diagnosing imprecise The than while is less analysis) child, cost sample per stool and year safe per are attractive US$1 drugs infec- is deworming with treatment Mass common regions 18). because (17, in 20% treatment above prevalence school-based tion World mass the recommendation provide for long-standing to basis (WHO) the form Organization’s as effects Health health such adverse weight worm birth These child infections 15); reduce (16). (14, also may microbiome other mothers gut pregnant the in to infections altering by and prone instance, 13) (12, for more malaria infections effects, individuals immunological The including broader (7–11). making children, have adverse anemia may have for and also and weakness, consequences (6), growth, nutritional worldwide stunted people and five health in one infecting Africa, East Kenya, of parts other to beyond. and region, study original the doi:10.1073/pnas.2023185118/-/DCSupplemental at online information supporting contains article This 1 BY-NC-ND) (CC 4.0 the NoDerivatives License distributed under Deworm is article of access open member This board a Develop- formerly International organization.y was 501(c)3 for a and Agency World, deworming, US supports with which works ment, M.K. University. statement: Columbia interest M.U., and Competing Francisco; San California, of University N.C.L., Reviewers: ulse ac 1 2021. 31, March Published J.H., and data; analyzed S.B. paper. and the M.W., wrote E.M., S.B. and J.H., M.K., M.W., research; M.W., E.M., E.M., performed J.H., S.B. research; designed and S.B. and M.K., M.K., M.W., E.M., J.H., contributions: Author * eateto lblHat,Gog ahntnUiest,Washington, University, Washington George Health, Global of Department owo orsodnemyb drse.Eal [email protected] Email: addressed. be may correspondence whom To oe n on hlrn(3 epnettakn ae.Rf nsfwsuisof studies follow-up. few 7-y finds a 4 than Ref. more rate). with tracking interventions respondent development (63% children young and women Nutrici de tuto oal xeto ste3- olwu 3 ftefu ilgsi h utml Insti- Guatemala the in villages four the of (3) follow-up 35-y the is exception notable A an nautlvn tnad n anns n hfsin shifts and earnings, and employment. and standards residence meaningful living of experience sectors adult group in treatment later gains deworming y those 20 the find to and in participants, up of sample standards representative living a among esti- individual We on children. impacts that Kenyan mate intervention to health treatment randomized deworming varia- a provided via using health that by child in evidence concerns, tion in methodological contributes especially impacts leading standards, study causal addresses living This the adult countries. on on developing gains evidence health world. child limited the of around remains can initiatives life policy there nutrition many of Yet and for quality rationale health future the child individuals’ is in in improvements investing generate that belief The Significance sbcgon,itsia emnhifcin r widespread, are infections helminth intestinal background, As nd etoAm Centro de on ´ d,e,1 n aa Baird Sarah and , https://doi.org/10.1073/pnas.2023185118 rc Panam y erica ´ . y raieCmosAttribution-NonCommercial- Commons Creative y . urtoa nevninfrpregnant for intervention nutritional a ´ y f https://www.pnas.org/lookup/suppl/ e ent C. 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SUSTAINABILITY ECONOMIC SCIENCES SCIENCE economic impacts, with the exception of ref. 22, which finds that gender, for instance, as hypothesized in ref. 33, who argue that deworming in the US South in the early 20th century led to child health gains in low-income, “brawn-based” economies may higher adult educational attainment and income. (See ref. 23 for translate into greater labor market gains for males. a critique of ref. 22 that reaches different conclusions.) In 1998, a nongovernmental organization (NGO) launched Several studies analyze the PSDP experiment. Ref. 24 finds the PSDP in two geographic divisions of Busia, in 75 schools improvements in child school participation in treatment schools enrolling over 32,000 pupils. Baseline parasitological surveys over the first 2 y of the program, with absenteeism falling indicated that helminth infection rates were over 90%, and over by one-quarter. They also estimate sizable treatment external- a third had a moderate–heavy infection according to a modi- ities, presumably as treatment kills off worms already in the fied WHO infection criteria (24). (Rates this high are also found body, reducing transmission to others in the community; in in some other African settings.) The 75 schools were experi- particular, they document reductions in worm infection rates mentally divided into three groups (groups 1, 2, and 3) of 25 among both untreated children attending treatment schools schools each: The schools were first stratified by administrative and children attending other schools located within 4 km of subunit (zone), zones were listed alphabetically within each geo- the treatment schools.† Ref. 30 provides further evidence on graphic division, and schools were then listed in order of pupil externalities, showing that young children living in the treat- enrollment within each zone, with every third school assigned to ment communities—who were not yet school aged and thus a given program group. The three treatment groups were well did not themselves receive deworming—experienced gains in balanced along baseline characteristics (see refs. 24 and 31 and learning outcomes up to 10 y later, equivalent to 0.5 y of school- SI Appendix, Fig. S1 for project details). ing on average. The current study most directly builds on ref. Due to the NGO’s administrative and financial constraints, 31, which documented deworming impacts 10 y later, includ- the schools were phased into deworming treatment during 1998– ing improved self-reported health, educational attainment (by 2001: Group 1 schools began receiving free deworming and 0.3 y on average), test scores, and secondary schooling attain- health education in 1998, group 2 schools in 1999, and group 3 in ment (concentrated among females), as well as higher incomes 2001. Children in group 1 and 2 schools were thus, on average, among wage earners (20% gains), and more meals eaten, assigned 2.41 more years of deworming than group 3 children; hours worked, and manufacturing employment (concentrated these two early beneficiary groups are denoted the treatment among males). group here, following ref. 31. Drug take-up rates were high, at Ref. 31 was subject to several limitations that the current study approximately 75% in the treatment group, and under 5% in the was designed to address. First, because many respondents were control group (24). still in school at the 10-y follow-up, estimation of some labor The KLPS was launched in 2003 to track a representative sam- market effects was necessarily conducted on selected samples. ple of approximately 7,500 respondents enrolled in grades 2 to Second, only partial information was collected on subsistence 7 in the PSDP schools at baseline, where the KLPS subsample agricultural production. Third, consumption data were not avail- was selected using a computer random number generator. Dur- able for that round, leading to a reliance on a proxy (meals ing round 1 (2003–2005), sample respondents were still mainly eaten). The current paper makes several contributions. The anal- teenagers, and few were active in the labor market; the subse- ysis uses two additional survey rounds to estimate impacts at quent survey rounds collected between 2007 and 2019 are the 15 y and 20 y after deworming treatment—an unusually long focus of this study. From the start, KLPS enumerators have trav- timeframe for experimental studies (4)—when most respon- eled throughout Kenya and beyond to interview respondents (SI dents were between 29 y and 35 y old, allowing us to estimate Appendix, Fig. S2). The spread of mobile phones in Kenya during impacts during individuals’ prime working years. The measure- the study period has greatly facilitated tracking, and, as a result, the effective tracking rate has remained high across KLPS rounds ment of economic outcomes was also improved: KLPS round ‡ 4 (KLPS-4) incorporates a detailed consumption expenditure (SI Appendix, Table S1). In KLPS-4, 87% were found and 83.9% questionnaire (modeled on the World Bank Living Standards surveyed among those still alive (SI Appendix, Table S1, KLPS- Measurement Survey; see ref. 32) for all respondents, and round 4 E+ module, column 1). Rates are similar and not statistically 3 collected this for a representative subsample. Both KLPS-3 significantly different across the treatment and control groups, and KLPS-4 also contain improved measures of agricultural pro- and the same holds by gender (SI Appendix, Table S1, columns ductivity, including in subsistence agriculture, which, combined 4 to 6) and among those above and below median age (specif- with other measures, provides a measure of total household ically, baseline age 12 y; SI Appendix, Table S2). Notably, rates earnings. Finally, while earlier PSDP deworming cost–benefit are similarly high and balanced in earlier rounds.§ In all, 86% of analyses were necessarily speculative, our use of long-run follow- the KLPS sample was surveyed at least once during the 10-, 15-, up data means the calculations here are based almost entirely on or 20-y rounds. observed outcomes. Two other cross-cutting experiments are relevant for the anal- ysis. First, in 2001, the NGO required cost-sharing contributions Data and Estimation Strategy from parents in a randomly selected half of the group 1 and Program Background and Data Collection. The PSDP study area group 2 schools, reducing deworming drug take-up from 75% is Busia District (since renamed Busia County), a largely agrar- to 18% (SI Appendix, Fig. S1); group 3 schools received free ian region in western Kenya that is fairly representative of rural deworming treatment in 2001. In 2002–2003, the NGO again Kenya in terms of living standards. At the start of the program provided free deworming in all 75 schools (36). We estimate in 1998, the vast majority of children attended primary school, the effect of this temporary reduction in deworming on later but dropout rates were high in grades 6, 7, and 8 (the final 3 y), and fewer than half went on to secondary school. Secondary

schooling rates increased dramatically in the region over the next ‡The effective tracking rate is calculated as a fraction of those found, or not found decade. Among adults, occupational and family roles continue but searched for during intensive tracking, with weights adjusted appropriately, in a to differ markedly by gender. This segmentation makes it plau- manner analogous to the approach in the US Moving To Opportunity study (34, 35), sible that the impacts of a health intervention could differ by and ref. 31. §A representative subsample of respondents were visited again in KLPS-3 for the con- sumption expenditures module; the effective tracking rate is lower in this subsample (76.0%, SI Appendix, Table S1, KLPS-3 E Module), although rates are balanced across †For discussions of the original school participation cross-school externalities estimates, treatment arms. The survey rate among those still alive in KLPS-2 is 83.9% (SI Appendix, see refs. 25–29; the current analysis employs a new dataset. Table S1, KLPS-2).

2 of 9 | PNAS Hamory et al. https://doi.org/10.1073/pnas.2023185118 Twenty-year economic impacts of deworming Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 ee ihteKP- nyrslspeetdin focus presented our results is only and KLPS-4 above), the noted with as here, are rounds, (who later informa- recipients the grant including from cash data, dropped and training possible vocational all the on using approach tion first of The advantage data. the KLPS-4 to has the prior KLPS- 37 on as ref. analyses only in any well using prespecified conducting as were regressions approaches, measures, outcome 2) two main These and the follow-up. treatment 10 longest-term and after effects the y deworming 4, KLPS-3, 20 long-run KLPS-2, to overall from the y data estimate to use KLPS-4 that within regressions treated pooled the on effects treatment compli- schools. of (24), estimation gains experienced sec- cating communities and individuals untreated treatment high, that within shows relatively research deworming. was previous reasons: of because compliance main ond, years two treatment additional for since estimates three schools first, intention-to-treat to on treatment two pro- focus 2) in We the the and individuals that 1 exploit provided (groups namely, We exogenously (37). design, (PAP) gram research plan experimental preanalysis PSDP’s our follows and Strategy. Estimation the the in of weight representativeness sample. greater PSDP the original maintain given to and analysis, throughout, econometric retained individ- (nonrecipient) are group uals control treatments. cash other and the voucher assigned to assignment programs, rounds survey their other for after analysis with the deworming from interactions dropped on are individuals possible analysis these present avoid the a and focus in impacts, To voucher part grant. training took a cash these receive also and/or of to these 1,070 selected KLPS-4; randomly of to were subset the individuals prior program a to vocational grant and prior a cash KLPS-3, randomized (RCT) in the trial part of took control start additionally randomized sample voucher individu- KLPS training 1,500 the approximately in 2009, als early in Second, outcomes. wnyya cnmcipcso deworming of impacts economic Twenty-year al. et Hamory grant cash and representa- maintain training to vocational weighted are the Estimates for group. control indicator round, and an wave month, grade), calendar and indi- survey and KLPS zone (gender the administrative for characteristics indicators and individual km, baseline 6 population, cators), within score, students test of (average number of characteristics presence school lower the baseline a in 31. is ref. deworming it in shown of since as externalities, effect on treatment focus overall cross-school to the in externalities. coefficient on gains within-school attractive bound the to an of due is some likely This level, are by individual assigned schools the rela- was treatment at schools deworming than Since treatment rather schools. 50 control school 25 the the The in to school. individuals tive is to individual’s interest accruing the of gains coefficient of main status prespecified treatment program of function ing a is outcome The school PSDP original # ¶ ntetetshosa tetd nteanalysis. the in individuals “treated” all as classify thus schools We and treatment status. effect, in school-level deworming aggregate schoolmates’ the versus identify however, treatment can, individual of effects the identify r tde nsprt ok h eut eo r outt nldn hs voucher these including to see robust analysis; are the below interventions in results those winners The grant KLPS-4; and work. from separate dropped in are studied winners are grant cash and analysis, KLPS-4 ntepeec fwti-coleieilgcletraiis ecno separately cannot we externalities, epidemiological within-school of presence the In pcfial,vctoa riigvuhrwnesaedopdfo ohKP- and KLPS-3 both from dropped are winners voucher training vocational Specifically, h eedn variable dependent The 1) namely, approaches, main two on focuses analysis The h vector The Y ijt = X α ij + ,0 λ fidvda n colcvrae includes covariates school and individual of h nltclapoc ulso e.31 ref. on builds approach analytical The 1 T j smaue nsre round survey in measured as j + λ Y 2 IAppendix SI ijt C T sa ucm o individual for outcome an is j j + ,1 {0, ∈ λ 3 alsS n S6. and S5 Tables , P j h sinddeworm- assigned the }, + X ij λ 0 ,0 1 hc captures which , β ¶ IAppendix. SI + h randomly The ε t ijt , . # i [1] in htacutfrubnrrlpiedfeecs ae nregular on based differences, below price values real urban–rural present for We account outliers. that prespecified) of across (as influence observations the differences of reduce price 1% to top for the trim accounts and which power countries), purchasing (PPP, USD 2017 terms constant is parity in other both We for the subsection). results next outcomes; present the main in (presented two earnings of respondent consumption total one household be capita per would Kenya. that expenditures rural specified like we settings PAP, the in In measures income may direct stan- consumption living than (and of dards) income measure household total It resulting capture items. accurately the more distinct that 150 argued over often on is expen- questions consumption featuring (15-y) detailed module KLPS-3 diture a of administered one-sixth of were subset respondents representative a and dents Standards. Living on living residential Impacts adult and on outcomes, estimates market choice. effect labor treatment earnings, present standards, we Here Results Main effects. (cohort-specific) gender-specific Eq. PAP. in of the variables age in (baseline than females mentioned greater for age as baseline y), (namely, 12 age respondent and gender similarly a use thus we here. specification linearly; parsimonious enter and that separable and reject additively saturation, cannot ana- but local saturation, 31 and in Ref. nonlinearities treatment local effect. the between deworming of interactions direct sign lyzed the the and between relationship effect no saturation is there that reject eue ramn) hl eexpect as we for While estimated treatment). that to reduced opposite sign find) a (and main have expect we the Conceptually, not outcomes. While on saturation. deworming local focus, deworming, call received we that km which 6 school within cost-sharing schools boring 2001 the namely, indicator, deworming, to exposure those in individuals rounds. across survey across both and outcomes schools in correlation for program. methodology, ing grant cash tracking and training two-stage Finally, vocational the the in KLPS, inclusion and account for into taking sampling population, the PSDP baseline the with tiveness k †† ** osho ntesuyae fruhy1 mb 0k a entvl ecniee a considered be definitively likely. can less km effects time, 40 spillover over by cross-school fade km long-run may 15 meaningful effects roughly making of such control,” area “pure While study on. the so in positive and school generate no schools, themselves nearby could other externalities for cross-school spillovers to due falls (i.e., intensity control” tion “pure even that means time T over externalities epidemiological of spread oeta h on rvni e.3 ssilvld letloe,i h geographic the if looser, albeit valid, still is 31 ref. in proven bound the that Note eto,freape elh arae n etlt,adwl etefcso future of focus the be will and fertility, and marriage, research. health, example, for lection, there that show We and KLPS-4, then. and school KLPS-4, KLPS-3 in by in enrolled below. effects impacts still explanations treatment in discuss was in differences differences sample age cohort the meaningful minimal of remain only 2% be than postulate, would to that less us there as hypothesis led that patterns The PAP, enrollment largely not. the school KLPS-2—had by in had by driven individuals were old effects y younger age 22 differential while least at schooling, attribute were they their individuals—who finding completed these a that earnings, fact nonagricultural the and to eaten, meals worked, hours of e.3 rseie te ucm oan htaetesbeto non aacol- data ongoing of subject the are that domains outcome other prespecifies 38 Ref. terms in gains larger experienced baseline at y 12 than older those that shows 31 Ref. j epeetrslsfrteetr apeadboe u by out broken and sample entire the for results present We ecnie w eodr ore feoeosvrainin variation exogenous of sources secondary two consider We = λ 1 0, npatc,fwetmtsaesgicn,adw cannot we and significant, are estimates few practice, in , P eto B section Appendix, SI †† j ε = ijt )shosaesbett oesilvr.I atclr hs hs infec- whose those particular, In spillovers. some to subject are schools 0) C steerrtr lsee ttesho ee,allow- level, school the at clustered term error the is j ,1 {0, ∈ n s h eutn siae oconstruct to estimates resulting the use and 1, n h rprino tdnsi neigh- in students of proportion the and }, l LS4(0yflo-p respon- follow-up) (20-y KLPS-4 All 2y ihtemi explanatory main the with y) >12 rsnseiec ntereffects their on evidence presents k https://doi.org/10.1073/pnas.2023185118 ∗∗ eitrc nindicator an interact We λ 3 ohv h aesign same the have to λ 1 snecs sharing cost (since λ T 2 PNAS j ogenerally to and P j ∈ | P 0 1] [0, j f9 of 3 are ,

SUSTAINABILITY ECONOMIC SCIENCES SCIENCE price surveys we collected in multiple Kenyan regions and cities estimate corresponds to a 6.5% increase in earnings. The esti- (including Nairobi and Mombasa). mated treatment effect is quite stable across KLPS-2 (USD Deworming treatment has a positive impact on total house- PPP 87), KLPS-3 (USD PPP 83), and KLPS-4 (USD PPP hold per capita consumption expenditures between 15 and 85; see SI Appendix, Fig. S3), although none are statistically 20 y after treatment: Pooling KLPS-3 and KLPS-4, the esti- significant. The effect falls as a percentage of the control mean mated effect is USD PPP 305 (SE 159, P value < 0.10), a 14% across rounds, as average earnings rise over time. The increase increase relative to the control mean of USD PPP 2156 (Table in the CI surrounding estimates from KLPS-2 through KLPS-4 1, A: annual per capita consumption, column 1). A shift to the also appears likely to be driven by the growth in both the mean right in the distribution of consumption is visually apparent (Fig. and variability of earnings as individuals move into their prime 1A). Estimated effects by round are presented in SI Appendix, working years. Fig. S3A. In the 20 y data, treatment group individuals report As with consumption, estimated effects are larger for males a 10% increase in consumption (USD PPP 199, SE 130; SI (USD PPP 118) than females (USD PPP 41; Table 1, B: annual Appendix, Table S3, column 1). We find positive point estimates individual earnings, columns 2 and 3), although this difference on subcategories, including both food and nonfood consumption is also not significant. Average individual earnings are nearly 3 (see ref. 39). times larger for males than females, again highlighting women’s Effects on consumption are larger in magnitude for male labor market disadvantages. Earnings gains are far larger for (USD PPP 513, P value < 0.10) than female respondents (USD older (USD PPP 258, P value < 0.05) than younger (USD PPP PPP 89) in both absolute and percentage terms (Table 1, A: − 75) individuals, and, once again, the effect for the older group annual per capita consumption, columns 2 and 3), although the remains significant when the FDR multiple testing adjustment is gender difference is not significant at traditional levels. Women applied. also have far lower average consumption, a pattern mirrored for Effects on the narrow measure of individual reported farm- all living standards and labor market measures, and likely indica- ing profits are close to zero, but, as noted above, these exclude tive of the limited economic opportunities open to many women most household agricultural activity. In contrast, there is a siz- in Kenya. Gender differences in reporting or household structure able deworming effect on total household earnings per capita could also potentially contribute to these gaps. Consumption (only collected in KLPS-4), at USD PPP 239 (P value < 0.10, effects are also far larger for older individuals (those older than Table 1, C: annual per capita household earnings, column 1), and 12 y at baseline, who were typically 32 y to 36 y old by KLPS-4), this effect is reassuringly similar in magnitude to the estimated at USD PPP 886 (Table 1, column 4, P value < 0.01), an effect impact on total household consumption per capita in KLPS-4 that remains significant at traditional levels accounting for the (USD PPP 199; SI Appendix, Table S3). Total household earn- false discovery rate (FDR) adjustment (40).‡‡ Note that average ings gains are again concentrated among males (USD PPP 439, living standards (in the control group) are considerably higher P value < 0.10, Table 1, column 3) and older individuals (USD for younger than older individuals (Table 1, column 5), which PPP 565, P value < 0.05, column 4).§§ likely at least partially reflects rapidly rising schooling levels in There are meaningful changes in other labor market out- western Kenya in the years following the launch of the PSDP comes. Log annual earnings increase by nine log points among (SI Appendix, Table S10, education and labor market outcomes those with nonzero earnings, and the likelihood that individuals summary statistics). have nonzero earnings rises by two percentage points (P value < 0.10; Table 2, A: earnings and wealth, column 1). Gains in both Impacts on Earnings and Other Labor Outcomes. The second pre- wage earnings and self-employed profits appear to be contribut- specified main outcome measure, total individual earnings, ing to the overall effect, and individual earnings per hour also includes the sum of earnings in the past year in wage employ- increase, by USD PPP 0.14 (P value < 0.10), or 13%. Patterns ment (across all jobs), nonagricultural self-employment profits are similar in the KLPS-4 data (SI Appendix, Table S4). Treat- (for all businesses), and farming profits, including in subsistence ment individuals live in households with roughly 13% greater agriculture. Note that those without any reported earnings in the wealth per capita (collected in KLPS-4), although this effect is last year are included in the analysis as zeros. To be sure we are not significant at traditional levels. For most measures, gains are focusing on individual labor productivity, we first only include meaningfully larger among males and older individuals (Table 2, farming profits in activities (e.g., growing a particular crop) for columns 2 and 3). which the respondent reported providing all household labor There are also shifts in the nature and sector of employment. hours. This measure thus misses agricultural profits derived from While total labor supply (hours worked) increases only slightly, activities to which the respondent contributed jointly with other if at all, in the treatment group (1.04 h, SE 0.66, Table 2, B: household members. The data indicate that 70% of agricultural labor supply, occupation, and sectoral choice, column 1), there is activities are, in fact, conducted jointly with others, making it a significant increase in hours worked in nonagricultural employ- challenging to confidently assess individual agricultural produc- ment (1.91 h, P value < 0.01), concentrated among males (2.77 tivity; this is a well-known concern in development economics. h, P value < 0.01, column 2) and older individuals (2.24 h, P We later present a measure of total household income per capita value < 0.05, column 3). Some of this shift is likely related to that includes all household agricultural profits as well as earn- the substantial increase in urban residence, which rises by four ings generated by the respondent and other adult household percentage points on a base of 45% (P value < 0.05), or 9%; members. note that roughly one-third of urban migrants live in Nairobi, Across the 10- to 20-y follow-up rounds, individual earnings and many others live in Mombasa or other large cities. (Urban are USD PPP 80 (SE 76) higher in the deworming treatment residence was included as an outcome in the later (38) PAP, as group (Table 1, B: annual individual earnings, column 1). This we collect a more detailed migration history as part of ongo- ing survey modules relative to the data used in this paper.) In

‡‡Following the PAP, the FDR adjustment in Table 1, column 1 is carried out across the two λ1 coefficient estimates from A: annual per capita consumption and B: annual individual earnings in column 1. The FDR adjustment in Table 1, columns 2 and 3 are §§The FDR adjustment is not presented in Table 1, C: annual per capita household earn- carried out across the four λ1 estimates from A: annual per capita consumption and B: ings since the total household earnings measure was not one of the two prespecified annual individual earnings in columns 2 and 3. Similarly, the FDR adjustment in Table primary outcomes. If the FDR adjustment is carried out across the six λ1 coefficient 1, columns 4 and 5 are carried out across the four λ1 estimates from A: annual per estimates in Table 1, columns 4 and 5 across the three outcomes, all three estimates for capita consumption and B: annual individual earnings of those columns. the older subgroup are significant with q value < 0.05.

4 of 9 | PNAS Hamory et al. https://doi.org/10.1073/pnas.2023185118 Twenty-year economic impacts of deworming Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 wnyya cnmcipcso deworming of impacts economic Twenty-year al. et Hamory C n oE oto ru egt,adKP nesv rcigwihs tnaderr r clustered are 10%, errors at Standard weights, significance weights. population denotes tracking KLPS * intensive include Observations level. 1%. KLPS and sample. at school population, and KLPS-4 significance (Start- 1998 PSDP weights, the intervention original from the group grant dropped the control at small are of VocEd separate KLPS-3 representative and a after be SCY in vocational occurred to treated separate pri- which weighted Those a SCY) total are are samples. in Youth, KLPS-3 examinations, KLPS-4 treated for to mock Those prior and Capital District indicator. occurred school up KLPS-3 which school Busia 1998 the VocEd) cost-sharing 1996 baseline Program, from a Vouchers the variable, dropped Vocational and indicator on and km, four (Technical female score intervention 6 the a test training within interview, across school pupils geographic of out population, average school month carried school the mary and primary are effects, 1998 wave 5 baseline fixed survey and for grade school, controls 4 include the columns and of in 31 zone ref. adjustment follow FDR Covariates the 5. and Similarly, 3. and λ 2 FDR columns annual the PAP, of specified PAP ings the two The the Following 3. across outcomes. and out those primary 2 for carried as are columns indicator is earnings male to an 1 individual terms and using column interaction regression, female annual in single analogous for and adjustment a and estimates consumption from baseline report capita results at 3 report per y and also age 12 2 5 and than Columns gender and saturation–female older by younger). 4 and cost-sharing–female, separately Columns or treatment–female, deworm- estimates terms. y including of an report interaction regression 12 y is 5 single 2.4 y, Treatment through a additional 12 trimmed. from 2 an constructed are are than received Columns which observations 3. outcomes (older 2, of group All baseline and 1% to 1 at KLPS-4. top compared groups in wage worm the average, PSDP available of and on for ing, rates, only sum one PPP to are the equal at earnings as variable USD indicator Household calculated 2017 members. constant are divided household to members, earnings converted of household household Wage all number were across mo. capita the profits profits 12 per agricultural agricultural by last and KLPS-4; Annual respondent the profits, and self-employment KLPS-4. within the KLPS-3, earnings, maker which and employment KLPS-2, decision for KLPS-3 in main activities collected in the farming were collected was profits crop wage and self-employment and hours of and noncrop labor sum earnings from household farming the individual generated reported as and for all profit calculated business; differences provided net all are price across as earnings urban–rural profit defined individual self-employment for of profit, nonagricultural adjusted Annual number jobs; is Mombasa. dur- the all Consumption sample and across by KLPS-4. employment the Nairobi divided during of in mo, subset sample living 12 a full respondents last to the the administered and in was KLPS-3 module production ing consumption/expenditure home The or members. barter, household gift, purchase, through household consumption capita per Annual A: KLPS-2, earnings, and consumption on effects treatment deworming 20-y KLPS-4 to and 10- KLPS-3, The 1. Table upinadana niiulerig fclm .TeFRajsmn nclms2ad3aecarried are 3 and 2 columns in adjustment FDR The 1. column four of the earnings across out individual annual and sumption :Ana e aiahousehold capita per Annual C: earnings individual Annual B: 1 D value q FDR value q FDR (%) effect Treatment mean Control (λ Treatment oto mean Control Treatment Treatment ubro observations of Number (%) effect Treatment mean Control observations Number ramn (λ Treatment (λ Treatment ubro observations of Number Treatment (%) effect Treatment nulprcpt osmto scluae stesmo h oeayvleo od osmdb the by consumed goods of value monetary the of sum the as calculated is consumption capita per Annual ofcetetmtsfo nulprcpt osmto n nulidvda annso oun 4 columns of earnings individual annual and consumption capita per annual from estimates coefficient KP- n KLPS-4) and (KLPS-3 anns(KLPS-4) earnings KLPS-4) and KLPS-3, (KLPS-2, P P P value value value 1 1 1 0*8 1*886*** 513* 89 305* ) ) ) λ 1 ofcetetmtsfo nulprcpt osmto n nulidvda earn- individual annual and consumption capita per annual from estimates coefficient ulsml eaeMl le Younger Older Male Female sample Full 3646866786716,780 6,791 6,798 6,826 13,624 .9 .1 .7 .1 0.451 0.019 0.376 0.515 0.337 0.297 0.000 0.096 0.505 0.058 .6 .3 .8 .1 0.897 0.017 0.086 0.738 0.069 .7 .3 .3 .3 0.292 0.030 0.630 0.630 0.290 0.175 0.001 2,381 46.44 0.623 1,908 19.76 0.630 2,594 5.21 1,715 0.132 14.15 2,156 ,9 7 ,2 ,8 1,501 1,082 1,623 973 1,296 ,1 7 ,2 ,7 1,242 1,177 1,728 2,341 674 2,402 1,218 2,321 2,473 4,794 ,7 ,9 ,7 ,3 1,982 2,039 52.17 1,975 27.06 2,099 3.68 4,074 18.44 19 14 34 23 (185) (223) (304) (134) (159) 19 17 22 22 (171) (232) (252) (107) (129) 239* 6.53 (76) 1 2 3 4 (5) (4) (3) (2) (1) 80 λ 1 ofcetetmtsfo nulprcpt con- capita per annual from estimates coefficient ∗∗ .268 21.93 6.84 6.02 6)(3)(0)(100) (108) (133) (62) eoe infiac t5,ad**denotes *** and 5%, at significance denotes 649 565** 439* 36 118258** 118 41 https://doi.org/10.1073/pnas.2023185118 −6.07 −7.52 −1.48 −179 −22 −75 PNAS | f9 of 5

SUSTAINABILITY ECONOMIC SCIENCES SCIENCE ing gains alone are not sufficient to guarantee later labor market A .5 Annual Per−Capita Consumption (KLPS 3 & 4) .4 benefits—as demonstrated by the experience of females—they Control .3 Treatment are plausibly driving some of the long-run gains in the older .2 group. .1

Kernel Density 0 Since deworming was assigned at the school level, changes in −2 0 2 4 6 8 10 social networks could also be a channel. We find that older indi- .4 B Annual Individual Earnings (KLPS 2, 3 & 4) viduals in the treatment group are indeed more likely to learn .3 of a job through a primary school classmate (+6 percentage .2 points on a base of 13%, P value < 0.05; SI Appendix, Table S11), .1 suggesting this could also be a partial explanation. 4Kernel Density 0 −2 0 2 4 6 8 10 A more speculative explanation is that the level of deworm- .3 C Annual Per−Capita Household Earnings (KLPS−4) ing treatment is playing a role. While the average difference .2 in assigned years of deworming between treatment and con-

.1 trol schools is the same for younger and older cohorts (SI Appendix, Table S10), the distributions are different, and, in par- Kernel Density 0 −2 0 2 4 6 8 10 ticular, the average years of assigned treatment in the control group is far higher among younger individuals (SI Appendix, Fig. 1. Kernel densities of (log) consumption and earnings, KLPS-2, KLPS-3, Table S11), as many older control group students graduate and KLPS-4. This figure plots the smoothed (Epanechnikov) kernel densities from (or leave) primary school before receiving any deworm- of (A) log per capita annual per capita consumption, (B) log annual individ- ing (SI Appendix, Fig. S4 A and B). If the marginal benefit of ual earnings, and (C) log annual per capita household earnings of the full deworming is declining with each additional year of treatment sample (2017 USD PPP, top 1% trimmed). See Table 1 for additional details on outcome construction. Household earnings are only available in KLPS-4. (leading to a concave functional form), this could lead treat- The gray line represents the control group, and the black line represents ment effects to be larger among the older subgroup. For the the treatment group. Observations are weighted to be representative of primary consumption per capita outcome, treatment effects are the original PSDP sample, and account for KLPS population weights, SCY (reassuringly) monotonically increasing with additional years of and VocEd control group weights, and KLPS intensive tracking weights. deworming treatment assignment, and there is some evidence of concavity, especially at greater than 4 y (SI Appendix, Fig. S4A). contrast to ref. 31, there is no significant change in employment While promising, this explanation remains tentative given lim- in manufacturing or other broad job categories (among wage ited epidemiological evidence on the deworming dose response workers) overall or for males or older individuals when pool- function. ing KLPS-2, KLPS-3, and KLPS-4 (Table 2, B: labor supply, We are also able to rule out several alternative explanations occupation, and sectoral choice) or KLPS-4 alone (SI Appendix, for differential treatment effects, see SI Appendix, section C. Table S4). The most obvious explanation for heterogeneous effects would be differential baseline worm infection levels across subgroups, Heterogeneous Effects and Mechanisms. The concentration of or varying degrees of infection reduction, but we do not find deworming effects among males and those older than 12 y at meaningful differences along these lines by gender or age (SI baseline is notable. Here we briefly discuss potential explana- Appendix, Tables S10 and S11). Nor did ref. 31 estimate signif- tions for this heterogeneity, and what it suggests about the icant differences in impacts as a function of baseline local area mechanisms underlying long-run impacts. infection levels, although this latter analysis is somewhat statisti- It is puzzling that females show fewer economic benefits than cally underpowered. The differential gains by age do not appear males, since they experience larger gains in schooling attainment, to be due to life cycle or age-at-survey explanations, but instead test scores, and self-reported health than males (ref. 31 and SI are driven by cohort effects (SI Appendix, Table S9). There Appendix, Table S11, education and labor market outcomes). are differences in average levels of parental education across A possible explanation is that these human capital gains alone older and younger cohorts, but little evidence of heterogeneous may be insufficient in a context where many women face impor- treatment effects by level of parental education (SI Appendix, tant constraints and fewer economic opportunities than men Table S8). (41). For instance, KLPS sample women spend roughly three times more hours than men doing household chores and more Rate of Return and Fiscal Impacts of Deworming than twice as much time providing childcare, and their participa- Here we present deworming cost-effectiveness estimates tion in the nonagricultural labor force is far lower (SI Appendix, (see SI Appendix, section D for details). Table S10, education and labor market outcomes summary The social net present value (NPV) of providing free deworm- statistics). ing treatment takes into account the cost of deworming medica- The larger estimated gains among older participants may also tion, the cost of additional schooling resulting from deworming be surprising at first given an intuitive sense that younger chil- (31), and economic gains measured via consumption or earnings. dren might gain more from human capital investments, but Fig. 2 displays these components graphically, where the direct note that all sample individuals are already outside of hypoth- costs are illustrated in the darkest gray in the first years. We esized “critical” windows of early childhood development (SI use 2018 deworming drug costs, while schooling costs come from Appendix, Table S10, baseline summary statistics). One piece of multiplying secondary schooling rate increases (31) by recent evidence that could help explain the age pattern is the finding Kenyan teacher salary figures (42, 43). On the benefit side, we that deworming led to larger human capital gains among older use λ1t estimates for consumption and earnings generated from individuals. Older individuals in the control group have lower our pooled specification across KLPS-2, KLPS-3, and KLPS-4. levels of schooling than younger individuals reflecting the rapid For earnings, we assume these gains start 10 y after deworm- increase in schooling over the decade following the start of PSDP ing treatment, roughly coinciding with entry into adulthood and (SI Appendix, Table S10, education and labor market outcomes KLPS-2. Since we do not have consumption data until KLPS-3, summary statistics), but the deworming effect for the older group we conservatively assume that the average estimated effect from is +0.45 y of schooling (SE 0.18, P value < 0.05; SI Appendix, KLPS-3 and KLPS-4 only pertains during the period from 15 y to Table S11, education and labor market outcomes), while, for 25 y after treatment. We also make the conservative assumption, younger individuals, it is closer to zero (+0.04 y). While school- presented graphically in Fig. 2, that effects last for 5 y, roughly

6 of 9 | PNAS Hamory et al. https://doi.org/10.1073/pnas.2023185118 Twenty-year economic impacts of deworming Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 h iebtensre ons n alt eo5yatrKLPS-4 after y 5 zero to fall (at and rounds, survey between time the wnyya cnmcipcso deworming of impacts economic Twenty-year al. et Hamory ¶¶ n essethat an,adinrn rs-coletraiisaogsample among externalities cross-school (30). ignoring members community and other and gains, individuals health persistent any hscluaini locnevtv ynticuigdrc hl elhbnfisor benefits health child direct including not by conservative also is calculation This t 25). = ¶¶ :Lbrspl,ocpto,and occupation, supply, Labor B: ee.*dntssgicnea 0,* eoe infiac t5,ad**dntssgicnea % obs., 1%. at significance denotes *** school and 1998 the 5%, at at clustered significance are denotes variables construction, errors ** Standard control variable 1. 10%, and Table on observations. at for Weights details significance notes levels. denotes additional the significance * in for defined level. statistical (39) are and regression report with respondents, the those PAP in for younger the “1” included and as See coded female sector. variables for indicator given are results a variables across d; Employed in and 7 week. farming), last per employment and the h wage of self-employment, within 100 which wage-earning, worked at number area, (i.e., top-coded hours nonrural categories are the total job jobs a the by all within on in job, based divided living each are for ownership, in variables “1” calculated worked worked livestock as hours Hours is coded and cities. wealth variable and ownership household towns indicator capita both asset an includes Per is durable activities. residence last household all Urban the total across members. during household hours activities of Wage work all sum 1. across 10 Table the worked least hours from by as at total calculated earnings with the are those individual by earnings divided among annual Hourly trimmed 52, week, at on amounts. by and annual earnings based y PPP for individual are are at 12 annual profits observations USD earnings dividing than farming of 2017 individual and older number to profits, annual and those converted self-employment Log mean earnings, are and 1%. control wealth males top and sample for the earnings full at effects in the Variables treatment report respectively. 5 estimated outcome, and ( each report 4 effect 3 Columns treatment and respectively. overall baseline, 2 the columns reports 1 while Column sample, indicated. otherwise unless KLPS-4 wealth and Earnings A: occupation, supply, labor earnings, on KLPS-4 effects and treatment KLPS-3, deworming KLPS-2, 20-y choice, to sectoral 10- and The 2. Table ra residence Urban mlydcntuto/rd otatr0040.011 13,807 0.004 Employed—manufacturing 20.20 contractor 2.24** Employed—construction/trade 2.77*** Employed—services/wholesale/retail 1.91*** Employed—agriculture/fishing d) 7 (last worked—nonagriculture Hours d) 7 (last worked—agriculture Hours d) 7 (last worked hours Total hstberprstetetefcsfrnmru ucms sn aapoe cosKP-,KP-,and KLPS-3, KLPS-2, across pooled data using outcomes, numerous for effects treatment reports table This aeerig (annual) earnings Wage earnings individual annual Log e aiahueodwat (KLPS-4) wealth household capita Per earnings Hourly earnings Nonzero (annual) profit farming Individiual (annual) profit Self-employment etrlchoice sectoral ulsml aeOdrma fobs. of mean Older Male sample Full −0.87** .0 .0 .0 .2 13,760 0.026 0.002 0.002 −0.001 004 007 (0.006) (0.007) (0.009) (0.014) (0.004) (0.019) (0.020) (0.007) (0.012) (0.013) (0.014) (0.008) .4*00* .304 13,793 0.45 0.03 0.06** 0.04** -0.003 00)(.7 (0.08) (0.07) (0.06) 06)(.4 (1.08) (0.94) (0.56) (0.62) (0.91) (0.65) (0.92) (0.03) (0.43) (0.03) (0.66) (0.02) (0.16) (0.15) (0.02) (0.02) (0.08) (0.01) .0 0.012 0.002 .4 .203*10 6,096 1.07 13,794 0.32* 0.59 0.22 0.02 0.04** 0.14* 0.02* .422* .9*2.913,807 24.19 1.79** 2.20** 1.04 .900 .9*67 7,698 6.73 0.19** 0.06 0.09 5)(7 (89) (97) (50) 2)(8 (39) (89) (48) (110) (24) (68) 1 17*2213,638 212 70* 51 41* −0 1 2 3 4 (5) (4) (3) (2) (1) 2 3 (3) (3) (2) 91223* 2 4,085 522 253*** 102 13,628 69 887 162* 138 81 agrta h elitrs aeo 0 ol niaethat indicate would 10% the IRR of as An rate of generates. interest investment thought real an rate intuitively the that internal than be growth the larger can of compute international which rate also (e.g., annual (IRR), We which funders return rates. 1998–2018, potential of lower during other face Kenya if donors) in conservative rate is interest real median h anetmtsuea nuldson aeo 0,the 10%, of rate discount annual an use estimates main The ramn (λ Treatment .0 .0 .4 13,768 0.043 0.004 −0.001 −0.57 1 1 ulsample Full ) .0 .3 13,760 0.033 −0.007 .0 .3 13,761 0.230 −0.002 .639 13,807 3.99 −0.46 13,707 9 −3 λ 1 rmEq. from oto Number Control https://doi.org/10.1073/pnas.2023185118 o h full the for 1) PNAS | f9 of 7

SUSTAINABILITY ECONOMIC SCIENCES SCIENCE 300 Implied Social IRR = 36.7% limited earnings or consumption gains far smaller than those $305 observed in KLPS could justify subsidies for mass deworming 250 given its very low cost. Consumption 200 Gains Discussion 150 This study provides causal evidence on the long-run effects of child health investments on adult living standards and labor mar- 100 Implied Social IRR = 40.7% ket outcomes. Individuals who received deworming as children $80 50 experience substantial increases in adult consumption, hourly Earnings Gains $7.99 (Needed for Social IRR = 10%) earnings, nonagricultural employment, and urban residence. 0 Deworming Teacher These findings add to growing evidence that the PSDP had Assume Gain = $0 after Year 25 Costs and Benefits (2017 USD PPP) −50 Cost Costs meaningful positive effects on recipients (24, 31). Even ignor- 0 5 10 15 20 25 30 35 40 45 50 ing spillovers and making other conservative assumptions, the Years Since Start of PSDP (1998) social rate of return appears to be very high. Notable strengths of the study include the 84% effective respondent follow-up rate Fig. 2. Deworming costs, benefits, and rate of return. This figure presents after 20 y, and the use of a PAP for the most recent round of the costs and benefits of deworming over time, and calculated social IRR. Costs and benefits in the figure are reported in 2017 USD PPP terms. survey data. For additional details and alternative assumptions, see SI Appendix, Table The study has several limitations. The main full sample treat- S12 and section D. Total costs include the direct cost of providing mass ment effect estimates for the primary outcomes are not statisti- school-based deworming from the NGO Deworm the World plus the costs cally significant at conventional levels, although some estimated of additional teachers, based on documented educational gains and the effects are significant at such levels among the demographic approach of ref. 31. We calculate teacher costs as average educational gains subgroups mentioned in the PAP. The original experiment was per student per year as a result of deworming (from ref. 31) times annual carried out as a stratified list randomization rather than using teacher salary costs per pupil (USD PPP 267.88, based on an estimate of a computer random number generator; see refs. 24 and 31 for annual teacher salary [USD PPP $12,055] from the upper tier of monthly details. teacher salaries from refs. 42 and 43 of and a pupil–teacher ratio of 45, as in ref. 31). On average, from 1999 to 2007, students attended school for From a policy perspective, it is important to consider exter- an additional 0.15 y. We assume no earnings gains in the first 10 y after nal validity. Intestinal worm infections are widespread globally, receiving deworming medication. We use the estimate of treatment effects with high infection rates in many parts of Africa, South Asia, for annual individual earnings measured 10, 15, and 20 y after the start and Latin America, and even a possible (and unfortunate) resur-

of deworming and pooled across rounds (λ1t from Table 1, annual indi- gence in the rural US South (44). The ubiquity of the infections vidual earnings). We assume no per capita consumption gains in the first suggests that this study’s findings have relevance for many other 15 y after receiving deworming medication. As for earnings, we use the esti- settings. At the same time, the degree to which school-based mate of annual per capita consumption expenditures measured 15 and 20 y mass deworming generates positive long-run benefits is plausi- after the start of deworming and pooled across rounds from Table 1, annual bly linked to the extent of infection. The study setting featured per capita consumption. For both earnings and per capita consumption, we assume zero gains after the last observed 5-y period (25 y after receiving high baseline infection prevalence, at over 90%, and a large share treatment). The dotted line at USD PPP 7.99 shows the average treatment of children with intense infections. The PSDP intervention also

effect (λ1t ) needed from year 10 to year 25 in order to generate a social began during the strong 1997–1998 El Nino–Southern˜ Oscilla- IRR of 10%. A return of 10% represents the median real interest rate from tion event, which brought torrential rains and flooding to the 1998 to 2018 (based on Kenyan government bond rates and inflation rates). region, and the related deterioration in hygiene and sanitation The annualized social IRR for earnings gains is 40.7% and, for consumption likely contributed to elevated worm infection levels. Deworm- gains, is 36.7%. Assuming a discount rate of 10%, the NPV from observed ing treatment impacts would presumably have been smaller had earnings gains is USD PPP 230.71, and, for consumption gains, is USD PPP worm infection levels been lower. The intensity and timing of the 467.90. El Nino˜ event may also have contributed to the heterogeneity of impacts across demographic subgroups that we document. The analysis does not resolve the issue of exactly why and deworming is likely to be a cost-effective policy in Kenya. The through what channels deworming affected adult outcomes. dotted horizontal line in Fig. 2 shows the magnitude of average Since changes to health, education, social activity among school- annual treatment effects needed to attain an annualized IRR mates, marital choices, and income levels may all affect each of 10% is USD PPP 7.99. We also calculate the NPV and IRR other in various directions, the impacts should not be inter- of additional government tax revenue generated by deworming preted strictly as all reflecting deworming’s direct health effects, by multiplying earnings or consumption gains by the average but rather are likely to be the result of a cumulative process Kenyan tax rate. of interaction among these factors.## Our examination of het- The estimated deworming consumption and earnings gains erogeneous treatment effects by gender and age sheds some are both an order of magnitude larger than the USD PPP 7.99 light on the importance of certain factors, but cannot defini- needed to attain the social IRR of 10% noted above (Fig. 2 and tively adjudicate between channels. Further research is needed SI Appendix, Table S12), and are also far larger than the gains to understand how institutional and contextual factors interact needed to attain a fiscal IRR of 10% (USD PPP 29.12 and 48.21, with child health investments, to better understand mechanisms respectively; SI Appendix, Table S12). The social and fiscal NPV (1). Another area of ongoing debate is whether child health and estimates are positive for both the consumption and earnings nutrition investments must fall within a “critical” early period effects, and for annual discount rates of 10%. In the most conser- of development for long-term gains to accrue (45). Our find- vative scenario, focusing on earnings gains and the 10% discount ings indicate that even health programs focused on school-age over 25 y, the social NPV is USD PPP 230.71, and the fiscal NPV children can yield substantial benefits, consistent with recent US is USD PPP 16.74 (SI Appendix, Table S12, NPV). The implied findings (2). social and fiscal IRR estimates in this case are 40.7% and 15.5%, with values higher if we allow gains to persist beyond year 25 (SI Appendix, Table S12, IRR). If we focus on consumption and con- ##To be clear, we do not expect that child deworming treatment would have a direct sider gains out to 25 y, the social and fiscal IRR estimates are impact on respondents’ adult worm loads decades later, given worms’ relatively short 36.7% and 19.6%, respectively. The results imply that even quite average lifespan in the human body.

8 of 9 | PNAS Hamory et al. https://doi.org/10.1073/pnas.2023185118 Twenty-year economic impacts of deworming Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 3 .J Wammes J. L. 13. Kirwan P. and 12. health huge brings children “Deworming Project, Priorities Control Disease 11. Silva N. 10. 4 .Guernier V. 14. 3 .Romn h mat fhowr rdcto nteAeia ot.Arepli- A South. American the in eradication hookworm of impacts the The in Roodman, eradication D. hookworm 23. from Evidence development: and Disease affect Bleakley, deworming H. mass 22. “Does Miguel, E. Kremer, M. Hsu, E. Hicks, H. J. Croke, K. 21. Deworm- Garner, P. Donegan, S. Soares-Weiser, K. Maayan, N. Taylor-Robinson, D. 20. Organization, Health World 17. Larocque helminth R. and microbiome 16. intestinal the between Interactions Harris, L. N. Zaiss, M. M. 15. 9 .Ahuja A. 19. (2017) Organization Health World 18. oadMlk,JmeMCsad rcOheg atPcno Kristianna Pecenco, Karing, Matt Anne Ochieng, Eric Kannell, Luben, McCasland, Leah Daniel Lowe, Jamie Layna Janani, Malaki, Liu, Runjiu Ronald Maryam Lees, Jonas Fischer Huang, Andrew Heil, Layvant, Anton Yue Michelle Esha Duhon, Casaburi, Madeline Luna Lorenzo DeFilippis, Hjort, Evan Brown, Chen, Christina Lisa Bonds, Chaudhuri, Stephanie Bergquist, cao ACKNOWLEDGMENTS. (https://dataverse.harvard.edu/dataverse/KLPS). Dataverse Harvard in Availability. Data The bolster children. effectiveness. further their cost would deworming’s benefits for intergenerational outcomes any life are of experienced existence improve effects gains could education such the women that that by possible The also suggest generation. is it document next plausible; we the impacts on effects economic inves- to deworming be possible will tigate direction future interesting another themselves, wnyya cnmcipcso deworming of impacts economic Twenty-year al. et Hamory .H .Gyt,S roe,C .Khma .Hl,D .Bny vlaino efficacy of Evaluation Bundy, A. D. Hall, A. Kihamia, M. C. Brooker, S. Guyatt, L. H. 9. Stoltzfus fitness, R. Physical 8. Pertet, A. Kinoti, N. S. Data Adams, J. Replication E. Latham, C. disease Baird, M. and infection Stephenson, of S. S. numbers L. Global Kremer, Brooker, 7. S. Jasrasaria, M. R. Smith, J. Walker, Pullan, R. M. 6. Miguel, E. Hamory, J. to trials 5. controlled randomized Using Miguel, E. Kremer, M. intervention Huang, nutrition Y. 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Bundy A. salaries D. 45. new releseases TSC McKenna M. as 44. teachers for Windfall Oduor, gender A. Tuko. Kenya (2018). 43. 2020 teachers 2020 for system grading and “USAID salaries TSC New Development, Nyanchama, V. 42. International for early Agency of effects US the 41. in differences gender and for inference plan Multiple Anderson, Pre-analysis L. M. economic Walker, “Twenty-year for 40. report M. plan Pre-analysis Walker, Miguel, M. Miguel, E. E. Layvant, M. Kremer, 39. M. Hicks, H. J. Baird, impacts S. 20-year “the 38. for plan Pre-analysis Miguel, E. Kremer, M. Hicks, H. J. effects. sustainability. Baird, of neighborhood S. illusion of The Miguel, 37. analysis E. Experimental Kremer, M. Katz, F. L. 36. Liebman, B. J. Kling, R. J. 35. Orr gen- L. the and 34. investment capital Human Hassan, N. M. Rosenzweig, R. M. Pitt, M. M. 33. Eds., child Glewwe, a P. of Grosh, impacts Long-run M. work: at Worms 32. Miguel, E. and Kremer, M. Hicks, H. Humphreys’ J. Baird, childhood S. Macartan early of 31. effects long-term on the estimate to Comment externalities Exploiting Ozier, Hicks, O. H. 30. J. Kremer, M. Miguel, take E. should public 29. the What wars: worm the Mapping Sandefur, J. in Clemens, health M. and 28. education on impacts Identifying Worms: Kremer, M. edu- Miguel, and E. health 27. of Reanalysis Hargreaves, R. J. Hayes, J. R. Aiken, M. A. Davey, educa- and C. health of 26. Re-analysis Hayes, J. R. the Hargreaves, R. in J. health Calum, and D. Aiken, education M. on A. impacts 25. Identifying Worms: Kremer, M. Miguel, E. 24. n osntncsaiyrflc h iw fayo u udr.This funders. Registry, our RCT of authors Association any the Economic of American of views the responsibility the Pop- on the #AEARCTR-0001191. reflect registered Berkeley solely is necessarily the is study not and content does SES-0962614), and The NIH and Center. US (SES-0418110 Givewell, ulation Awards Foundation, NSF Dioraphte R03-HD064888), US and the R01-HD090118, R01-HD044475, from (R01-TW05612, Awards funding sugges- Poverty helpful and for Association (Innovations for collaborators Action), Series, Economic our Conference acknowledge American Development Seminar gratefully We 2020 Pacific tions. Economics and the Eco- Meeting for Development at Carlo Annual Centre participants Virtual Collegio Finance, conference Maryland, and Research and Uni- Economics of Policy for University, University Institute nomic Stanford Cruz, Einaudi Oklahoma, Santa and Francisco, Alberto of California, San California, of University Sebastian of the versity Oxford, Fafchamps, at University of participants Marcel Berkeley, seminar University thank California, and of We Rosenberg, Josh University project. Roodman, Emaan KLPS David Galiani, the Shah, research Tun- excellent on Jonas Nachiket providing for Smith, others, assistance Sethi, among Emma Vinchery, Noor Paula Song, and Changcheng godden, Schellenberg, Sobharwal, Jon Somara Siddique, Rom, Adina Post, 21 ). (2018 from messages Key Alabama. 2021. March 8 Accessed for-teachers-as-tsc-releases-new-salaries. https://www.standardmedia.co.ke/education/article/2001249581/windfall- Standard. https://www. 2020. 2020). January 29 Accessed Development, International https://www.tuko.co.ke/281149-new-tsc-salaries-grading-system-teachers-2020.html. for Agency 2021. March 10 (US Accessed usaid.gov/documents/kenya-gender-fact-sheet. sheet” fact Training Early and Preschool, Perry Abecedarian, Projects. the of reevaluation A intervention: 2021. March 9 Accessed https://osf.io/t7rcs/. (2019). (2021). deworming” domains” of impacts Additional Kenya: in 2021. deworming March child 10 Accessed of https://doi.org/10.1257/rct.1191-11.1. impacts 20-year “The Accessed 2021. https://doi.org/10.1257/rct.1191-11.1. March 10 (2017). Kenya” in deworming child of Washington, Development, Urban Econometrica and Housing 2021. of March 10 Accessed https://www.huduser.gov/publications/pdf/mtofullreport.pdf. Department 2003). DC, US report, economy. brawn-based a in labor (2012). of division der Study Measurement Standards Living the 2000). of Press, Years University (Oxford 15 from Lessons Countries: oping (2015). study (2004) investment. health Kremer and Miguel deworming. the of 2021. March 9 discussions Accessed https://escholarship.org/uc/item/6ph9s61q. https://www. recent (2015). other deworming mass 2021. March about 9 Accessed debate about-mass-deworming. scientific the cgdev.org/blog/mapping-worm-wars-what-public-should-take-away-scientific-debate- from March Kre- away and 9 Miguel Accessed of replication https://escholarship.org/uc/item/8db127cm. to (2014). 2021. guide (2004) externalities, treatment mer of presence trial. the A stepped-wedge Kenya: quasi-randomized western cluster in a programme Epidemiol. deworming of school-based replication a statistical of impacts cational pure A Kenya: Western in programme deworming school-based replication. a of impacts tional externalities. treatment of presence Mvn oopruiy nei mat vlain (Technical evaluation” impacts Interim opportunity: to “Moving al., et .A.Sa.Assoc. Stat. Am. J. m .To.Md Hyg. Med. Trop. J. Am. 5119 (2015). 1581–1592 44, n.J Epidem. J. Int. m cn .Ap.Econ. Appl. J. Econ. 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