Aid Crowd-Out: The Effect of NGOs on

Government-Provided Public Services∗

(Preliminary Draft – Do not cite or circulate

without the authors’ permission)

Erika Deserranno†, Aisha Nansamba‡, and Nancy Qian§

September 5, 2019

Abstract

We document that NGO entry in rural communities with a pre-existing gov-ernment health worker reduces the number of government workers, governmenthealth delivery, as well as total health service delivery (from the government orthe NGO). This is consistent with the NGO offering stronger monetary incentivesfor commercial activities, which induces some government workers to move to theNGO and allocate their time away from health service provision.

Keywords: Foreign Aid, Aid Effectiveness.JEL: O01, O02.

∗We thank Ameet Morjaria, Jakob Svensson, Edoardo Teso for his insights; and participants ofthe UCSD Development Workshop, University of Wisconsin Development Workshop, the CEPR Con-ference on Development, and the Development Conference for Chicago Area Economists for usefulcomments.†Northwestern Kellogg MEDS, erika.deserranno@kellogg.northwestern.edu‡NGO Uganda§Northwestern Kellogg MEDS, BREAD, NBER, CEPR, nancy.qian@kellogg.northwestern.edu

1 Introduction

The efficacy of foreign aid is a central and one of the most controversial questions for

researchers and policy makers today. Well-known arguments have been made both for

and against the effectiveness of traditional aid (e.g., Burnside and Dollar, 2000; East-

erly, 2006). In response to the criticisms, particularly those regarding aid being tied to

the strategic objectives of donor countries (e.g., Alesina and Dollar, 2000; Kuziemko and

Werker, 2006), aid policy makers have given increasing support to non-government or-

ganizations (NGOs) (e.g., Buthe et al., 2012; Dreher et al., 2007; Nancy and Yontcheva,

2006).1 For example, Faye and Niehaus (2012) documents that donors use bilateral aid

to influence elections in developing countries, but that this strategic behavior is not

present for aid administered by NGOs. In the past twenty years, the number of NGOs

and overall aid from major donors such as USAID and the European bilateral agencies

channeled through NGOs have more than quadrupled (Pfeiffer et al., 2008). To re-

duce reliance on large donors, several of the largest NGOs have also created innovative

methods of generating revenues that we discuss later.

The ultimate goal of foreign aid is to create self-sustainable development in coun-

tries that are otherwise poor. One of the key challenges for many poor developing

nations that NGOs can help address is the deficit in public services relative to what the

population needs. NGOs fill this gap as the aid-recipient government builds capacity to

provide these services in the long-run. The success of NGOs in achieving this objective

is far from clear. Critics point out that NGOs are largely unregulated and, for the most

part, do not coordinate with each other or recipient-country governments.2 This could1Nancy and Yontcheva (2006) studies NGO aid allocation across countries and finds that it is

unrelated to donor strategic objectives and strongly associated with poverty in recipient countries. SeeButhe et al. (2012) for a discussion of the motivations of USAID that is administered by NGOs andDreher et al. (2007) for a discussion of the motivations of Swedish aid that is administered by NGOs.

2For a few examples, see Bromideh (2011) for a discussion of NGO performance in Iran, Rahman(2003) for a discussion of NGOs in South Asian countries and Pfeiffer et al. (2008) for a discussion ofNGOs in sub-Saharan Africa.

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create inefficiencies by replicating services and even harming the development of public

service delivery by competing with the recipient government for resources and reduce

the government’s capacity to provide services and, in turn, increase the country’s future

reliance on aid. A second concern stems from the fact that to achieve financial indepen-

dence, some NGOs ask field workers to perform parallel tasks: deliver free services to

needy individuals and sell households products using an parallel-task business model.

The latter has the advantage of providing income to field workers and the NGO, and

the disadvantage that monetarily incentivized activities can crowd out service delivery.3

In the context of health services, concerns over the efficacy of NGOs have been

articulated by medical doctors and public health experts, who call for NGOs to “Limit

hiring of public systems”, “Limit pay inequity between the public and private sectors”

and “Commit to joint planning [with the recipient government]” (Pfeiffer et al., 2008).

Farmer (2008) stated that “The NGOs that fight for the right to health care by serving

the African poor directly frequently do so at the expense of the public sector. Their

efforts too often create a local brain drain by luring nurses, doctors, and other profes-

sionals from the public hospitals to “NGO land,” where salaries are better.” NGOs are

aware of the potential challenges. For example, in discussing its parallel-task model,

BRAC, the world’s largest NGO, states: “Clearly there is a potential programmatic

trade-off here between increasing her sales and monthly income, while still ensuring

that the preventative and health education aspects of the program are being sufficiently

addressed” (Reichenbach and Shimul, 2011).

This study aims to make progress on understanding how NGOs can hinder (or help)

the development of public services in poor countries with rigorous empirical evidence

from a highly relevant context. We focus on basic health services in rural Uganda, where

the population is poor, where access to modern medical care is limited, and where infant3We provide a more detailed discussion in the Background Section.

2

mortality rates are amongst the highest in the world.4 This is also a context where the

government has been attempting to expand public provision of basic health services,

and where an NGO with similar aims entered soon after the government program was

rolled out. These features make it an ideal context for our research question.

Around 2009 and 2010, government workers began to provide basic health services

and products free of charge to their local communities. Like many public-sector workers,

they were volunteers.5 Approximately one year later, a large NGO randomly rolled out

similar health services in the same region. At this time, nearly half of the villages that

received an NGO worker had a pre-existing government worker. NGO health workers

provide similar services, also free of charge, as the government. In addition, NGO

workers sell products to households. These include the medicines that government

workers give for free, as well as other household products such as soap, oil or gloves.

The profits from sales partly go towards paying NGO village health workers, and partly

go towards funding the NGO more generally.

The main analysis investigates two dimensions of crowd-out: government health

workers (labor) and government-provided services. Our baseline equation estimates

the interaction effect of the pre-existence of a government worker and the entry of

the NGO on the number of government health workers and on the delivery of health

services. Since NGO entry is randomly assigned, the two variables are independent and

the interaction can be interpreted as causal. Note that we could equivalently restrict

our attention to a subsample of villages with a pre-existing government program and

simply estimate the uninteracted effect of randomly assigned NGO entry. We prefer the

interaction specification because the larger sample of villages provides more statistical4Infant morality in our sample is 69 per 1,000 live births. In the 2011 Ugandan Demographic and

Health Survey (DHS), they are 66 per 1,000.5In both poor and rich countries, local public services are often delivered by volunteers. For example,

Khan et al. (2015) studies volunteer tax collectors in Pakistan. In the United States, election pollworkers are often volunteers. Most of the traditional health worker or agriculture extension programslack explicit monetary incentives (Bhutta et al., 2010; Gilmore and McAuliffe, 2013).

3

precision.

The results for labor show that the arrival of the NGO in villages with a government

worker reduces the number of government workers on average: by one health worker

in every other village (i.e., half of the villages). The findings are consistent with NGO

entry crowding out the infant public sector, and the convention wisdom that higher

pay from the NGO induces volunteer government workers to switch to working for the

government. In the other half of the villages (with a pre-existing government worker),

the NGO is able to recruit a new person, increasing the total number of health workers

in the village by one.

The results on service delivery show that households in villages that have pre-

existing government workers and then receive the NGO are approximately 25.9 percentage-

points less likely to see a government health worker and 12.7 percentage-points less

likely to see any health worker. The former shows that NGO entry crowds out government-

provided services and is consistent with the shift in labor from the government to the

NGO. The latter shows that NGO entry reduces total service delivery, which is consis-

tent with time allocation data which show that NGO workers spend most of their time

on commercial activities and very little time providing health services.6

The main results support the concern that NGO entry crowds out government-

provided health services, and shows that the labor supply of health workers is an im-

portant channel. To assess the importance of the observed crowding out on health

outcomes, we examine infant mortality as an outcome and are able to provide useful

suggestive evidence. We find that in villages with a pre-existing government worker,

NGO entry has a positive, albeit imprecise effect on infant mortality. However, when we

decompose the villages into those where the NGO could not recruit a new worker (i.e.,

the government worker shifted to work for the NGO, the total number of workers was6See the Background section for a discussion about time allocation.

4

unchanged) and those where the NGO recruited a new person (i.e., the total number

of workers increased from one to two), we find highly suggestive evidence that NGO

entry increased infant mortality in the former and had no effect in the latter.

Our study focuses on crowding out, and documents the adverse effects of NGO entry

in villages that had a government worker. However, it is important to note that we also

find that in villages where there was no pre-existing government, NGO entry increases

the supply of health workers from zero to one, increase health services, and reduces

infant mortality.

The main caveat for our identification strategy is the concern of external validity –

the presence of a government worker, which is non-random, could be correlated with

factors that cause her to prefer to work for the NGO. We address this by carefully

controlling for a large number of potentially important base year characteristics (e.g.,

proxies for village-level or household-level socio-economic status, access to other medical

assistance, mortality), each interacted with the introduction of the NGO. Our results

are very robust. We also explore and rule out some obvious alternative mechanisms

(e.g., changes in the prices of drugs, relative demand for health care and household

products). See the paper for details.

The results of our paper should be cautiously interpreted as specific to the context

of our study. However, the finding that NGOs can distort the local labor market

is generalizable. Our results have several policy implications and motivate several

questions for future research, which we discuss in the Conclusion.

Our study contributes to the large literature on foreign aid. Several recent studies

have provided rigorous empirical evidence on the heterogeneous effect of aid across

contexts (e.g., Crost et al., 2014; Nunn and Qian, 2014).7 In focusing on NGOs, we add

to the recent study of Faye and Niehaus (2012), which is referenced earlier. Our results7See Easterly (2009) and Qian (2015) for literature overviews.

5

complement this earlier work by pointing to coordination with recipient governments as

an important dimension for improving NGO-administered aid. Studies that argue for

better coordination typically focus on donor coordination (e.g., Bigsten and Tengstam,

2015). Recent studies have also provided theoretical evidence that higher wages from

foreign aid can distort local labor markets (e.g., Knack and Rahman, 2004; Koch and

Schulpen, 2018).

In documenting how private spending can crowd out government spending in a poor

country, we contribute to the public economics literature, which has mainly focused on

developed countries and on how government spending crowds out private spending

(e.g., Gruber and Hungerman, 2007; Kingma, 1989; Payne, 1998).8 The labor supply

mechanism we study supports the recent paper by Wagnerly et al. (2019), which finds

that community health workers in Uganda who are randomly instructed to sell health

products (rather than distribute them free of charge) provide health services to fewer

households. We are also closely connected to a study by Baldwin et al. (2019) which find

that a participatory development program launched by an NGO caused improvements

in leadership by pre-existing institutions, but decreased investments in local public

goods through these institutions. A large number of studies have produced mixed

findings when evaluating the effect of NGO provided health services on mortality.9 The

heterogeneous effects we estimate – that NGOs reduce mortality in villages with no

government worker complements this literature.

The paper is organized as follows. Section 2 describes the context. Section 3 de-

scribes the data and descriptive statistics. Section 4 presents the main results on labor

supply and health service delivery. Section 5 presents the results on mortality. Section8In two well-known earlier studies, Kingma (1989) finds that U.S. government contributions to

National Public Radio crowds out private donations, while Payne (1998) finds little evidence thatgovernment spending crowds out private donations to U.S. homeless shelters. More recently, Gruberand Hungerman (2007) finds that U.S. government spending crowded out church spending on socialservices during the Great Depression.

9See Scott et al. (2018) for a review of the evidence.

6

6 concludes.

2 Background

2.1 Government Health Service Provision

Rural Uganda is one of the poorest regions in the world, where average per capita

income today is $522. In the 2011 Ugandan DHS data, infant mortality rates in rural

Uganda were estimated at 111, 66 and 30 per 1,000 live births, respectively; among the

highest in the world DHS (2011). According to the World Health Organization, the

main contributors of under-5 mortality in this context are neonatal mortality (60%),

diarrhea (14%), malaria (9%), pneumonia (6%) and other causes (11%). For neonatal

mortality, the main causes are birth asphyxia/trauma (30%), premature births (28%),

sepsis (18%), congenital anomalies (12%), acute respiratory infections (6%) and other

causes (6%) (WHO, 2012).

To respond to the scarcity of public health services and the shortage of health

workers, the Ugandan government founded the Village Health Team program which

hires community health workers to provide health services to their own community.

While the program was founded in 2001, it was not implemented for many years in many

areas in Uganda because of the lack of funding. In the North and Central Ugandan

regions that we study, government workers were hired around mid-2009.10

The main job of government workers is to provide the following health services during

home visits to members of their community: (i) health education (e.g., benefits of a

hospital delivery, methods of disease prevention), (ii) post-natal check ups, (iii) basic

medical advice and referrals to health clinics that are usually located in more urban10See the “Ugandan Annual Health Sector Performance Report 2008/2009” and the “Village Health

Team, Strategy and Operational Guidelines” (MoH, 2010). A survey of 150 government workers inNorth Uganda indicate that 87% of them were hired between 2009 and 2010 (Kimbugwe et al., 2014).

7

areas. The latter include helping patients decide when it is useful to travel to urban

areas for medical attention, as well as coordinating the visit to ensure that professional

medical staff is on site during the visit. The latter is important because of the high level

of absenteeism in Ugandan public health facilities. For example, Nyamweya et al. (2017)

documents a 48% average rate of staff absenteeism in Ugandan public health facilities,

which creates excessively long lines, which for pregnant women, increases the risk of

having to deliver in the health facilities without assistance from a health professional.

Government workers also provide basic medicines, such as ACT (artemisinin com-

bination therapy for malaria), ORS (oral rehydration solution), Zinc, antibiotics, and

deworming tablets free of charge, as well as distribute free bed nets during national

malaria campaigns.

The government recruits workers locally. To be eligible for the community health

worker position, a person must satisfy three conditions: village residence, age above

eighteen, and be able to read and write. Among the eligible candidates, the government

prioritizes those who are good community mobilizers and communicators, trustworthy

and willing to work for the community, and who have experience in the health sector

(Turinawe et al., 2015).11 Once hired, government workers are given five days of basic

training, a uniform that makes them easily identifiable (e.g., a t-shirt with the official

logo), and free medical products to disperse to the community. They are not paid or

given any incentives and are therefore generally considered to be motivated by altruism

(e.g., Kasteng et al., 2015; Deserranno, 2019).

The government program was rolled out in all villages. It aimed to employ two work-

ers per village. However, in our study area, only 57% of the villages had a government11The exact selection procedure varies from one village to another. In some villages, the worker

is appointed by a government official. In some others, the worker is appointed by the communitymembers, either by the village chief (LC1) or through a popular votes (Turinawe et al., 2015; Kimbugweet al., 2014). In North Uganda, 78% of the hired government workers had prior experience working as avolunteer in the health sector as a Community Drug Distributor or a Condom Distributor (Kimbugweet al., 2014).

8

worker in 2010, one year after the government program was rolled out; and no village

had more than one worker. In the other villages, the government was either unable

to recruit or retain a health worker (i.e., the recruited worker had stopped delivering

health services by 2010). To understand the recruitment and staffing of government

worker, we conducted in-depth interviews with government workers in our study areas

at all levels of the bureaucracy. The presence of a government worker in 2010 is not

random. According to higher-level government officials as well as community health

workers, the key limitation is the labor supply of those who are both qualified and

willing to work as volunteer workers.

Each government worker is affiliated with a nearby health facility: she refills her

stock of health products, attends occasional meetings, and reports to the person “in-

charge” at the health facility. District-level health officials that we interviewed stated

that each health facility is responsible for keeping track of resignations of affiliated

community health workers and finding a replacement, but are severely under-staffed,

and in practice, neither keep track of community health workers nor replace those that

drop out of the program. In other words, there is no accurate record of government

workers at a given point in time. This creates an important difficulty for NGOs, when

the latter attempts to avoid hiring government workers. We discuss this more in the

next section.

2.2 The NGO

The NGO we study has the same aims and provides the same services as the government

program. It entered our study area in June of 2010 by rolling out its program in a sub-

sample of 66 randomly selected villages and is the only other source of local healthcare

services. At this time, the government program had already been in place for at least

six months.

9

NGO workers, like government ones, are recruited locally and tasked to provide

similar free basic health services to the community. They mainly differ from govern-

ment ones in that they also sell household products from which they receive piece-rate

(whereas government workers earn no income). As we discussed in the introduction,

the motivation of the “parallel-task” model is to provide financial sustainability for the

NGO and increase its independence from donors. This model is used by several of the

largest NGOs today, including Living Goods, Grameen, BRAC, VisionSpring, Solar-

Sister, HealthStore Foundation and HoneyCare Africa. NGO workers, like government

workers, are easily identifiable from wearing their NGO uniforms.

Specifically, NGO workers buy products from the NGO at a price that is slightly

above the wholesale price, and then sell to households at a retail price that is set by the

NGO to be equivalent to the market price in that location. The difference between the

wholesale price and the buying price for the health worker goes towards the revenues

of the NGO at large. The difference between the buying price and the retail price

constitutes the income of the health worker.

The NGO and government programs differ in two other ways. First, the NGO

provides more training: workers receive ten days of initial training (rather than five)

and attend a monthly one day refresher training session, where they receive further

training and discuss the gaps in coverage and the quality of care. This could ostensibly

result in the NGO providing higher quality care than the government. Second, the

government workers distribute drugs for free, while the NGO workers sell similar drugs

for a small fee. Past studies have noted that NGOs may provide higher quality products

(Bjorkman-Nyqvist et al., 2016, Forthcoming).

In our study area, the NGO was able to successfully recruit in all the villages it

entered, whereas the government was only able to do so in half of the villages. Since

the NGO looks for individuals with the same skills and follow the same hiring criteria

10

as the government, government workers who apply to work for the NGO are typically

more competitive than other applicants. Based on the interviews that we conducted

with NGO recruiters, the NGO attempts to avoid hiring government workers. But this

is difficult to implement because of the lack of information on government workers and

the incentives for applicants to hide their role as government worker. There are also

some cases where the NGO knows that the applicant works for the government, but

still hires her because she is the only able and willing candidate.

2.3 Time Allocation of Government vs. NGO workers

Both government workers and the NGO workers are part-time employees who typically

maintain other daily occupations such as farming or small shopkeeping. Government

workers work on average ten hours per week delivering health services, including the

dispersal of free drugs (Mays et al., 2017). To the best of our knowledge, there are no

disaggregated time allocation data for government health workers.

For NGO workers, we have such data from two sources. First, Deserranno (2019)

finds that NGO workers work approximately fourteen hours a week, half of which is

devoted to health services. This roughly implies that NGO workers provide 30% fewer

hours of health services than government workers. The second source is Reichenbach

and Shimul (2011), who interviewed 660 NGO workers. Table 1 shows that over one

month, NGO workers supply a total of 49 hours, where 37% is spent on providing

health services (which includes attending refresher training) and 63% is spent on selling

medicines or health commodities. Because the time allocated for refresher training

includes visits to the branch office to resupply the products they sell, 37% is the upper

bound of health-related activities. The lower bound can be obtained if we attribute the

time attending refresher training to market activities. When we do this, we find that

NGO workers spend 21% of their total effort on health related activities and 79% of

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their time on market activities.

The time allocation data should be interpreted as merely suggestive and can under

or over-state the true time spent providing health services. On the one hand, we assume

that the time spent by the NGO worker selling similar drugs and other products is not

a part of health care provision, where as, for government workers, we assume that

distributing drugs is part of health services provision.12 On the other hand, the self-

reported NGO allocation of time to health services is probably an upper bound of

actual service delivery because patient visits are also used to sell products and monthly

training visits are also used to refill stocks.

Health services provided by government workers can be crowded in or crowded out

by the introduction of the NGO. On the one hand, the presence of the NGOs can com-

plement existing government-provided services if there are fixed costs in provision, such

as recruiting and training workers, or if there are positive externalities. For example,

increasing general awareness of the benefits of basic health services could increase the

supply of individuals who are willing to work to deliver these services and the demand

for these services for both the government and the NGO. This could, in turn, lead to

an increase in the efficacy of health care for both organizations – e.g., a mother who is

aware of the benefits of pre-natal check ups is more likely to take up an offered check

up from a government health workers as well as the NGO.

On the other hand, the NGO can crowd out health care delivery in several ways. The

NGO distorts the local labor market by providing the monetarily incentivized activity

of selling goods to health workers, which can crowd out more altruistically motivated

health service provision. NGOs are aware of this problem. For example, BRAC, the

world’s largest NGO, which uses the parallel-task model said in an evaluation of the

community health program in 2011, “There is a perception among the NGO staff that12We make this assumption because we lack disaggregated data for time allocation for government

health workers.

12

women are more commercial-minded and very much motivated by financial incentives

as opposed to non-financial incentives” (Reichenbach and Shimul, 2011).

The potentially negative effects of this distortion on the supply of health services can

be exacerbated by the fact that total compensation is much higher for the NGO than

the government such that government workers may switch to working for the NGO. If

the government is unable to recruit a replacement, then the introduction of the NGO

can reduce the total supply of health services unless if the NGO worker sufficiently

increases the total supply of hours on the extensive margin to offset the share of hours

spent away from health service delivery on the intensive margin.

The discussion so far focuses on time allocation that is unadjusted for quality. For

example, the NGO worker may be more efficient at identifying and examining patients,

such that she can provide more services within a given time frame than the government

worker. Similarly, if the drugs sold by the NGO (and are given for free by the govern-

ment) are higher quality, the effect of the increase in prices on health may be offset by

the increase in quality. We will return to this when we present the results. We will

discuss quality later when relevant for our main results.

3 Data

3.1 Data Sources

The main analysis uses survey data that cover 127 villages in twelve geographical areas

of North/Center Uganda.13 Data on mortality and access to health services were col-

lected in May, 2010, approximately six months after the government program was rolled

out and one month before the NGO program rolled out. Data were collected again in13The areas are: Kajjansi, Kisaasi, Abaita Ababiri, Ggaba, Kangulumira (Central Uganda) and

Kitgum, Padibe East, Orom, Palabek Kal, Patongo, Pader, Rackoko (North Uganda).

13

December, 2012. The paper will sometimes refer to 2010 as the base year and 2012 as

the end year. In each wave, there is a village-level and a household-level survey. The

former is answered by the village head. The latter is answered by a randomly selected

20% of the households. The survey questions change slightly over time. We will discuss

this when relevant. We supplement the survey data with census data collected during

February to April, 2010. These include variables such as household size, mortality and

occupation for all households in each village.14 We will discuss other data sources when

they become relevant.

3.2 Descriptive Statistics

Figure 1 documents the retail price (what households pay) and the profit margin for the

NGO worker for the products they sell. The medicines that distributed free of charge

by government workers (oral rehydration salts, pain reliever, zinc, antimalarials, cold

capsules, deworming tablets) are sold at very low retail prices and provide negligible

profits to the NGO agent. This suggests that the increase in the price of drugs is likely

to play a small role for understanding the tradeoffs of government and NGO workers.

The products that provide the highest profits to the NGO workers are, on average,

less related to the most concerning health outcomes: fortified oil, cotton, soap, fortified

flour and toothpaste. This means that the NGO worker is mostly monetarily incen-

tivized to sell products that have less of a direct impact on health than those provided

by the government worker.

The data provide several other interesting statistics (not presented in tables). Con-

sistent with our sample being rural and poor, 76% of the households surveyed in 2010

report that medical services are too expensive and 51% report that health facilities

are too far. Interestingly, 31% of households report staff absenteeism in public health14These census data were used by the NGO for sampling in the household-level survey.

14

facilities as another constraint to the access of health services.

In addition to the surveys discussed earlier, we have access to an internal survey

conducted by the NGO for its community health workers in January, 2012, eighteen

months after the NGO rolled out.15 Several interesting facts emerge from the self-

reported data from health workers (not presented in tables). First, 21% of all NGO

workers used to work for the government as health workers. None are from villages

with no pre-existing government program. In contrast, in villages with pre-existing

government programs, 40% had switched from the government to the NGO. Even more

strikingly, if we examine villages with a government worker in 2010, but no government

worker in 2012, 82% of the pre-existing government workers switched to the NGO

after the latter entered the community. This is consistent with the concern that NGOs

compete with the government for labor and workers are likely to move to the NGOs that

pays a higher income. Second, NGO workers report earning 14.2 USD per month, or

38% of the average monthly income in Uganda in 2013, which is a considerable income

for a part-time job. Finally, the fraction of households that has purchased commodities

(soap, oil, salt) from the NGO, 28%, is higher than that which has purchased medical

products (antimalarials, oral rehydration salt) from the NGO worker (9% and 3%,

respectively).

4 Results

4.1 Labor Supply

We begin our analysis by investigating whether the introduction of the NGO crowds

out government-provided public health services. The first outcome we examine is the15The survey includes NGO workers in 2012, and does not include any government workers in 2012.

It will include government workers from 2010 only if they switched to work for the NGO.

15

number of government health workers. To illustrate the empirical strategy and under-

lying variation, Table 2 first presents a simple four-by-four difference-in-differences grid

view of the raw data. We divide villages according to whether there was a government

worker before the NGO roll out, and whether the NGO randomly selected the village

to be part of its program. In each cell, we present the number of government, NGO

and total (the sum of the government and NGO) workers in 2012. Column (1) shows

that in villages with no government worker in 2010, there were no government workers

in 2012, regardless of NGO entry. Column (2) shows that in villages with a government

worker in 2010, there were on average 0.95 government workers in villages that were not

assigned to be part of the NGO program, and 0.53 government workers in villages that

were assigned to be part of the NGO program. Column (3) is the difference between

columns (1) and (2). The bottom row of column (3) is the difference-in-differences. It

shows that NGO entry reduced the number of government workers by -0.58, i.e., approx-

imately one per every two villages. For brevity, we do not discuss the other statistics

in the table, which have analogous interpretations to that of government workers.

This difference-in-differences variation can be characterized in the following regres-

sion:

yi = α + β(Govi ×NGOi) + δGovi + γNGOi + ΓXi + λa + εi. (1)

The number of health workers in village i in 2012, yi, is a function of: the interaction

of a dummy that takes a value of 1 if the village receives a government health worker

in 2009, Govi, and a dummy that takes a value of 1 if it is designated to participate in

the NGO program in 2010, NGOi; the uninteracted dummy variables; and area fixed

effects, λa.16

The main benefit of estimating this regression relative to examining the raw data16We include area fixed effects because the level of our main outcome variables (labor supply, mor-

tality, etc) varies substantially across areas and because the NGO random assignment was stratifiedby area.

16

is that it allows us to estimate the precision of the relationships and also to control for

potentially confounding factors. β is analogous to the difference-in-differences shown

in Table 2. It captures the differential effect of the NGO in villages with vs. without a

government worker. Govi is not randomly assigned. But NGOi is randomly assigned,

which means that the two variables are independent. Thus, the interaction term can

be interpreted as causal. We are mainly interested in β and β + γ. The latter term

is the effect of NGO entry in villages with a pre-existing government worker and can

be interpreted causally (see the following discussion for the intuition). If NGO entry

crowds out government health workers, then β + γ < 0.

Note that instead of estimating the interaction specification with all villages, we

could alternatively estimate the uninteracted effect of random NGO entry on whether

there is a government worker in 2012 using a sample of villages with a pre-existing

government program. Conceptually, this uninteracted NGO coefficient is similar to

β + γ from our baseline specification. It has a causal interpretation because NGO is

randomly assigned. The main advantages of our interaction specification relative to

the simpler alternative is that it maximizes statistical power by pooling the villages

with and without pre-existing government programs.17 At the same time, the earlier

differences-in-difference grid. Table 2 shows clearly that the alternative strategy would

produce similar results – column (2) shows that the number of government workers

declines by -0.58 after NGO entry in villages with a government program.

The main caveat for interpretating the our baseline results regards external validity

– there may be omitted factors which vary by the presence of the government program

which confound our estimates. We will discuss this in detail after we present the main

results.17Note that the uninteracted estimates using a subsample of villages with a government program is

statistically significant for examining labor supply and the delivery of health services. These resultsare available upon request. However, for the estimates on mortality, the pooled sample is required forstatistical precision.

17

Table 3 presents the estimates. In column (1), we first examine the number of NGO

workers as the outcome variable. The uninteracted coefficient for NGO show that NGO

entry into a village with no government workers increase the number of health workers

by almost exactly one worker (0.987). The estimate is statistically significant at the

1% level. The interaction coefficient is equal to zero, which means that the effect of

NGO entry on the number of NGO workers is the same in villages with and without a

pre-existing government worker. Note that these estimates are similar to the patterns

shown in Table 2, but the exact magnitudes will differ slight because the regression

controls for area fixed effects.

Column (2) tests crowding out and examines the number of government health

workers as the outcome. The uninteracted government coefficient is 0.791, which means

that the villages with the government program but no NGO had almost one government

worker in 2012. The interaction effect is -0.446, which means that NGO entry reduces

the number of government workers by approximately “half” a worker relative to villages

that had a pre-existing government worker. Both estimates are statistically significant

at the 1% level. The uninteracted NGO coefficient is small and statistically insignificant.

Thus, the sum of the uninteracted NGO coefficient and the interaction coefficient is -

0.466. This is shown at the bottom of the table in row [1]. The p-value in row [2] shows

that it is statistically significant at the 1% level. This is consistent with NGO entry

crowding out government health workers.

In column (3), we examine the total number of health workers from either the NGO

or the government. This variable can take the values of zero, one or two in our sample.

The uninteracted government and NGO coefficients indicate that each program, without

the presence of the other, results in almost one additional health worker relative to a

village with neither program.18 The interaction effect shows that villages with both18The fact that the estimates are less than one reflects attrition: 21% of government workers and

2% of NGO workers dropped out between the two waves of the survey, 2010 and 2012. This suggests

18

programs have on average half a worker less than villages with just one program. All

three coefficients are statistically significant at the 1% level. The sum of the interaction

coefficient and the uninteracted NGO coefficient is positive. The p-value at the bottom

of the table shows that the joint estimates is statistically significant at the 1% level. The

estimates in column (3) imply that NGO entry on average increases the total number

of health workers in all villages, but the increase is smaller by almost “half” a worker

in villages with a pre-existing government health worker.

Taken together, the estimates in columns (1)-(3) show that in villages with a govern-

ment worker, the introduction of the NGO has an effect on the intensive and extensive

margins of the labor supply of health workers. On the intensive margin, the NGO

induces government workers to shift to working for the NGO. Recall from the NGO in-

ternal survey discussed earlier that 82% of NGO worker in villages with a pre-existing

government program report as being former government workers. On the extensive

margin, the entry of the NGO increases the total number of health workers by approx-

imately half a person per village, or one person per every two villages. In other words,

when the NGO arrives at two villages with government workers, in one village, govern-

ment workers will work for the NGO, and in the second village, the NGO will hire an

additional worker while government workers will continue to work for the government.

To check our assumption that the government and NGO are the only providers

of modern health services – i.e., there are no other substitutes in villages, we also

examine the presence of a traditional healer and drugstore as outcomes. Traditional

healers provide non-Western medical treatments (e.g., herbalist, spiritual healer) and

drug stores sell health products and also provide basic medical advice. The worker in

the latter is typically also the owner. The mean dependent variables at the top of the

that the NGOs monetary incentives were successful not only in increasing the rate at which vacanciesare filled (as in Deserranno (2019) and Dal Bó et al. (2013)) but also in increasing the retention rates(as in Deserranno (2019)).

19

table show that there is approximately one traditional healer per every five villages,

and one drug store per every two villages. Because the skills of traditional healers and

drugstore owners are so different from what the NGO looks for in health workers, it

is unlikely that the arrival of the NGO will affect the presence of these other entities.

The regression estimates in columns (4) and (5) show that this is indeed the case.

4.2 Service Delivery

We use two proxies for the delivery of health services. The first is a survey question

for whether a household has sought medical advice from a community health worker in

the past year and the identity of the service provider. Table 4 presents the results. The

number of observations is much larger than the earlier analysis since this analysis uses

a household level survey. To address the possibility that the error terms are correlated

with villages, we cluster the standard errors at the village level for all household-level

regressions.

Column (1) examines whether households have obtained services from the NGO.

The estimates show that NGO entry increases the probability of getting health advice

from the NGO by 31.5 percentage-points. This estimate is statistically significant at

the 1% level. The interaction effect is small and insignificant. Thus, the effect of NGO

entry on services from the NGO does not vary depending on the pre-existence of the

government program.

Column (2) examines services from the government worker. The uninteracted gov-

ernment coefficient shows that having the government program with no NGO entry is

associated with 42.8 percentage-points higher probability of obtaining help from the

government worker. The interaction term shows that this probability declines by 24

percentage-points when the NGO enters. More importantly, the sum of the interaction

coefficient and the uninteracted NGO coefficient is negative, -0.259, and statistically

20

significant at the 1% level. This means that NGO entry reduces the probability that

households obtain medical advice from government health workers by 25.9 percentage-

points in villages that already had a government health worker relative to a village with

no government worker.

That the NGO crowds out government-provided services is not surprising after find-

ing that it reduces the number of government health workers. We next examine the

effect of NGO entry on total health service delivery (from any provider) in villages with

a pre-existing government worker. This is ‘ambiguous ex ante. On the one hand, the

increase in the total number of health workers on average (Table 3 column 3) could

increase service delivery. If NGO workers are better trained, they may also be more

efficient and be able to provide more services within a given time frame. On the other

hand, if NGO workers spend a large part of their time selling goods instead of pro-

viding health services, then the shift of workers out of the government program into

the NGO program could reduce service delivery. Our empirical estimates will capture

the net of these positive and negative forces on service delivery. Alternatively, one

can ask whether the increase in services from NGO workers (Table 4 column 1) offsets

the decline in service from government workers (Table 4 column 2) in villages with a

pre-existing government worker.

Table 4 column (3) shows that having a government worker or an NGO worker

increases the probability of receiving medical attention by 36.1 and 28.8 percentage-

points respectively. However, the interaction coefficient indicates that the introduction

of the NGO in a village with a government worker reduces the probability that a

household will receive medical attention by 41.5 percentage-points relative to a village

without a government worker. Moreover, the sum of the uninteracted NGO coefficient

and the interaction term is negative, -0.127, and statistically significant at the 1% level,

which means that in villages with a government health worker, NGO entry reduces the

21

probability that households obtain any health worker advice by 12.7 percentage-points.

This is strong evidence that NGO entry crowded out all services. It implies that

the reduction in time allocated to providing health services caused by selling products

is not sufficiently offset by the increase in the total number of health workers or the

possible improvement in the quality of NGO health worker.

Columns (4)-(7) show that we find no effect on health advice from other sources

in the village (traditional healers and drug stores), or sources in more urban areas

(government hospital or health centers, and private clinics that administer Western

medicine).

The second measure of service delivery we examine pertains to post-natal care. This

is motivated by the fact that the core mission of both government and NGO workers

is to reduce infant mortality by improving these services. The mortality data are only

available for households which have given birth during the interim between the two

waves of surveys (2010, 2012), for whom such care is most relevant. Since giving birth

is potentially an outcome of the provision of NGO and government health workers, we

first need to establish that fertility is not endogenous to the presence of government

and/or NGO workers.19 Table 5 column (1) examines the presence in the household of a

woman who delivered in the past year as the outcome variable. We find no relationship

between this proxy for fertility and our main right-hand-side variables. Therefore, in

columns (2)-(5), we will restrict our sample to households with at least one woman who

delivered in the past year. This results in the sample size decreasing from 2,747 to 407

households.

Columns (2)-(4) examine three variables which may be affected by government work-

ers and NGO workers: whether the woman delivered in a hospital, whether the delivery19We can alternatively estimate a multinomial logit model with the full sample. See Appendix Table

A.2. The results are similar.

22

was assisted by a health professional, and whether the mother received post-natal care

within two days of birth.20 We find no effect on having a hospital delivery, but statisti-

cally significant effects on whether the delivery was assisted by a health profession (e.g.,

hospital doctor or nurse). The comparison between the estimates for hospital delivery

and a hospital delivery supervised by a doctor or a nurse is consistent with the high

rate of absenteeism from hospitals and the fact that one of the tasks of the community

health workers is to coordinate for the delivery to take place when the nurse and doctor

is present.21

In column (4), we examine the probability of having had a follow up visit within two

days of birth for women who delivered in the past year, which is important for reducing

infant mortality and is used as a key measure of access to health services by the WHO

and UNICEF (Jones et al., 2003).22 The estimates show a negative and statistically

significant interaction effect of −0.216.

In column (5), we examine whether mothers breastfeeding. Ex ante, we do not

expect any effect because almost all mothers already breastfeed in this context. Our

results are consistent with expectations. However, we do find an effect on whether

the child is breastfed immediately after birth, a less common behavior which the WHO

recommends. The estimates show a negative and statistically significant interaction

effect of −0.188 (column 6).

Importantly, row [1] of columns (3)-(4) and (6) show the sum of the interaction

coefficient and the uninteracted NGO coefficient. These are all negative, which imply

that in villages with a government worker, the introduction of the NGO reduced the

probability that a child was delivered by a health professional, received a post-natal20These numbers are in line with data from the 2011 Uganda DHS, which indicate that among

women who delivered in rural Uganda, 53% of them were assisted by a health professional, and 30%received post-natal check-ups within two days of births.

21Recall the discussion from the Background section.22See WHO (2014, 2016).

23

visit within two days or was immediately breastfed. However, the joint statistic is not

statistically significant.

Interestingly, Table 5 also shows that the government health program on its own

is positively associated with post-natal services. Households located in villages with

a government health worker and no NGO have a substantially higher rate of being

delivered by a health professional (column 3) and receiving a post-natal visit within

two days of birth relative to villages with no health worker. These large positive effects

are consistent with two studies of the effect of the government health worker program

in rural Uganda (Ayiasi et al., 2016; Waiswa et al., 2015).23

Note that NGO and government health workers also provide pre-natal services.

However, the pre-natal service questions were only posed to women who were pregnant

at the time of the survey (276). Thus, we do not examine these outcomes.

4.3 Robustness

4.3.1 Correlates of Government Presence

The main concern for our preferred interpretation is one of external validity – villages

with government programs may differ from other villages in ways that interact with

NGO entry to affect the outcomes of interest. One way to investigate this concern is to

examine the correlation between characteristics of the village and households measured

in 2010, before the NGO rolled out, and the interaction term. In other words, we

estimate our baseline equation, equation (1), with these characteristics as outcome

variables. We focus on variables that reflect access to health services, the demand for

health services, and socio economic variables. This addresses the concern, for example,

that government health workers were more likely to be present in poorer villages, where23Ayiasi et al. (2016) document that the presence of a government health worker in a village increases

the likelihood that a woman receives three or more antenatal care visits by 19.7%. Waiswa et al. (2015)show similarly large effects on postnatal visits and institutional deliveries.

24

the health worker would be more eager to take up the opportunity for additional income

from the NGO.

Table 6 Panel A presents the estimates when the outcome variables are the presence

of a government health worker, traditional healer, drug store, government hospital

within ten kilometers, government health center within ten kilometers, and a private

clinic within ten kilometers. The interaction coefficient in column (6) is only significant

at conventional levels for the presence of a private clinic. It shows that, in 2010, villages

with both the government program and the NGO are further away from private clinics

than villages with neither program.

Panel B presents the estimates when the outcomes are the number of households in

the village, the number of children under five years of age per households, the number

of infants per household, the number of children under five years of age who died in the

past year, the age of the female household head, the share of households who conduct

non-agricultural business as their main activity, the share of household heads who

completed primary education, and two measures of wealth (assets and food security).24

The interaction coefficient in column (6) is only significant at conventional levels for the

number of households in the village and the share of household heads who completed

primary education. It shows that, in 2010, villages with only the government program

has fewer households and fewer educated household heads than villages with neither

program, while villages with both programs have a similar number of households and

educated household heads as villages with neither program.

We examine whether our results are robust to controlling for the presence of a private

health clinic and the number of households per village, as well as their interaction with

NGO entry in the next section.25

24Assets is measured as the number of assets owned by a household, out of a list of 11 commonhousehold assets. Food security takes value 1 to 4, where 1 indicates “deficit of food the whole year,”2 indicates “occasional deficit,” 3 indicates “neither deficit nor surplus” and 4 indicates “food surplus.”

25We also examine other potential correlates in a similar manner. None are statistically significantly

25

4.3.2 Additional Controls

To alleviate the identification concern raised in the previous section and to be conser-

vative, we will control for a large number of potentially confounding factors in addition

to those which the previous section shows are positively associated with the interaction

term. We categorize the additional controls into four groups. The first group comprises

of variables that can influence the effectiveness of a community health worker: distance

to nearest government hospital, distance to the nearest government health center, dis-

tance to the nearest private clinic, the presence of a drug store and the presence of a

traditional healer. Access to these resources could help or hinder the effectiveness of

the NGO. For example, on the one hand, proximity to a government clinics could allow

referral made by the NGO to be more effective (i.e., the patients are more likely to go

to a nearby clinic than a far away one). On the other hand, the presence of a traditional

healer may be correlated with community members not believing in Western medicine,

which lowers take up of the NGO’s services. Note that these factors can influence the

effects on service delivery and mortality, but it is hard to see how they would affect the

labor supply of health workers.

Second, we consider demographic variables: the number of households in the com-

munity, the number of children under five per household and the number of infants

per household. These variables could affect the labor supply of and demand for health

workers. For example, it may be easier to find additional health workers in larger vil-

lages. Alternatively, the demand for services may be larger in more populous villages

or villages with more young children.

Third, we consider base year morbidity: the number of children under five who died

in the past year and the number of children under one who died in the past year. The

variables could be correlated to the demand for health workers, and reflect underlying

correlated with the interaction term. These are not reported and available upon request.

26

well-being (e.g., income) of the population. For example, a village with higher child

mortality rate may face a higher disease burden (e.g., in places with more rainfall

and mosquitoes), where it is more difficult to reduce mortality. Or, a village with a

higher mortality rate may be poorer, where there is a lower supply of community health

workers or where government workers is more motivated to work for higher pay with

the NGO.

Finally, we consider household-level demographic variables: the age of the female

household head and the percent of households involved in farming as the main activity,

the share of household heads who completed primary education, assets and food secu-

rity. These factors may affect the availability of health workers for government workers

and NGO. The age of the female household head may also affect beliefs in Western

medicine.

All variables are measured in the base year of 2010. When we include controls for

the interactions of the NGO indicator variable, the uninteracted NGO coefficient is no

longer meaningful. Thus, Table 7 presents the estimates evaluated at the mean values

of all of the controls. They show that the main results are very robust and are unlikely

to be driven omitted variables.

4.3.3 Non-Linear Estimation

The main results are estimated with a Linear Probability Model. We can alternatively

use a Logistic Model for the dummy variable outcomes of the presence of health workers

or whether a household received pre-natal care, and a Poisson model to estimate the

effect on the total number of workers. The estimates are shown in Appendix Tables

A.1. Similarly, one may be concerned that for the examination of post-natal care in

Table 5, we restrict the sample based on a potential outcome. Although we document

that this not the case, we also alternatively using a Multinomial Logistics Model. See

27

Appendix Table A.2. All of the non-linear estimates are consistent with those from the

main regressions.

4.4 Alternative Mechanisms

We interpret the main results on labor supply and the reduction of total health service

delivery as being consistent with NGO entry crowding out government workers because

it provides better pay and more incentives to workers to allocate time away from pro-

viding health services. The most obvious alternative explanation is one of demand: the

demand for health services relative to the products sold by the NGO is lower in villages

with a government program. The robustness exercises discussed earlier addresses this

by controlling for variables that are likely to be correlated with the demand for health

services, such as child mortality rates and access to health services in the base year.

Here, we address this concern by dividing the sample into villages that had a drug store

in 2010 with those that did not. Drug stores sell many of the same products as the

NGO. Thus, if the main results are driven by differential demand for goods (relative to

health services), then we should find a smaller effect in villages that had a drug store.

Appendix Table A.3 presents the results. We examine the total number of health

workers and total health care delivery as outcome variables, and estimate the triple

interaction effect of the presence of the government program in 2010, NGO entry and

the presence of a drug store in 2010. This specification controls for the full set of

double interactions, uninteracted coefficients and area fixed effects. We find that the

triple interaction is statistically zero, which means that the presence of a drug store

does not affect our main results. This goes against differential demand as an alternative

hypothesis.

28

5 Mortality

To understand whether the crowding-out has real-world consequences, we examine

under-one and under-five child mortality, the reduction of which is a focal point for

both government workers and the NGO workers. Ex ante, the effect of the shift to the

NGO on community health outcomes is ambiguous. On the one hand, the reduction

in labor supply and service delivery could increase mortality.26 On the other hand,

the NGO may provide higher quality care, which can offset the negative effects. Our

empirical strategy estimates the net effect of the reduction in supply and any change

in the quality.

Table 8 columns (1)-(3) examine infant mortality using different measures. Column

(1) uses the household-level survey to examine a dummy that equals one if at least

one infant died within the household since 2010. The results in column (1) show that

the presence of government workers in a village with no NGO reduces the probability

of infant mortality by 2.6 percentage-points, relative to villages with no health worker

of any type.27 The introduction of the NGO in a village with no government worker

reduces mortality by 2.3 percentage-points. In contrast, the introduction of the NGO

in a village with a government worker does not reduce mortality. If anything, the sum

of the uninteracted NGO coefficient and the interaction coefficient implies that in those

villages the NGO increases mortality by 0.6 percentage-points (−0.023+0.029 = 0.006).

However, the p-value at the bottom of the table shows that the joint estimate is not

statistically significant. The lack of precision is not altogether surprising given that

mortality is an extreme and relatively rare event. We conservatively interpret the26Appendix Table A.4 documents the negative correlation between receiving health services provided

by government and NGO health workers and infant mortality, as well as the lack of a relationshipbetween receiving services from other health care providers and infant mortality.

27In our sample, the mean in villages with neither program is 4.6 percentage-points. Thus, theestimated reduction in mortality is large in magnitude and in line with Brenner et al. (2011) whichdocument a 53.2% decline in mortality as a result of the same government program in an experimentthat takes place in 116 villages of Southwest Uganda.

29

results as showing that the NGO, at best, brings no valued added to villages with a

government worker in terms of reducing infant mortality.

We observe a similar pattern in columns (2) and (3), when we normalize the outcome

variable by the number of births. In column (2), we calculate the household-level infant

mortality ratio as the number of children who were born between the two waves of

surveys and who died below age of one during this period divided by the number

of children who were born during this period.28 In column (3), we follow a similar

approach as column (2) but aggregate all numbers at the village level rather than

at the household level and then multiply the denominator by 1,000. One caveat of

the approach in columns (2) and (3) is that any child born before 2012 but who dies

before the age of one after 2012 increases the infant mortality ratio denominator by one

without increasing the nominator, thus causing us to underestimate infant mortality.

To address this problem, in columns (4) and (5) we exclude from the analysis any child

who is less than one year old in 2012, in addition to any child who would have been

less than one in 2012 had he/she not died.29

As with the estimates in column (1), we always find that the introduction of the

NGO increases mortality for villages that had a pre-existing government program (the

joint coefficient at the bottom of the table is positive), but the joint coefficient is not

statistically significant.

Appendix Table A.5 includes additional controls similar to Table 7. The results are28The latter is measured as the sum of: (a) the number of children who were born during this period

and who died below age of one during this period, and (b) the number of children who were bornduring this period and who did not die during the trial period (i.e., children less than three years atin 2012).

29This further reduces the sample size and exacerbates the sample selection problem. An alternativemethod to estimating infant mortality rate without reducing the sample size would be to express theinfant-mortality ratio in terms of the “infant-years of exposure to the risk of dying under the age ofone” (e.g., a child who is three months old in 2012 is exposed 1/4 [3/4] of a year while a child bornafter 2010 and who is more than one year old in 2012 is exposed a full year). This approach, which isused in Bjorkman-Nyqvist et al. (Forthcoming), cannot be used in this paper as we only observe childage in completed years but not in months. This data limitation also restricts our analysis to studyinginfant (<1 year) mortality but not neonatal (<1 month) mortality.

30

very robust.

5.1 Decomposition

From the previous section, we see that although each regression coefficient is large in

magnitude and statistically significant, the joint coefficients are small in magnitude and

imprecise. As we discussed earlier, this is consistent with the fact that mortality is a

relatively rare event. It may also be due to the fact that the baseline specification does

not distinguish between villages where the government worker switched to working for

the NGO (i.e., one total worker in 2012) and where the NGO recruited an additional

worker (i.e., two total workers in 2012). Recall from Section 4.1 that each type of villages

comprise approximately half of the villages that the NGO entered. If the increase in

mortality is due to a decline in health services, then we would expect the decline in

health services and increase in mortality to be more prominent in villages where the

government worker switched to working for the NGO.

We examine this by decomposing our estimates according to whether there was one

or two health workers in 2012. Since there is never more than one government worker

and never more than one NGO worker, this is very similar to decomposing according to

whether the government worker switched to working for the NGO or if the NGO was

able to hire a new person.30 Also note that in villages with the both programs and only

one worker in 2012, the worker is always an NGO worker. Since the number of workers

is an outcome variable, this exercise should be cautiously interpreted as suggestive.

Table 9 presents the results. We estimate an equation similar to the baseline, except

that we replace the interaction of the presence of the government program in 2010 and

NGO entry with two triple interactions: the government program, NGO entry and30In villages with both programs in 2012, there are very few situations with one worker who is not

the original government worker (i.e., there are very few cases where the government resigns as a healthworker and the NGO hires a new person).

31

the presence of one worker in 2012; and the government program, NGO entry and the

presence of two workers in 2012. Columns (1) - (3) examine health service delivery by

the NGO worker, government worker, or any worker. Column (1) shows that the effect

of NGO entry on medical advice from NGO workers does not vary depending on the

pre-existence of the government worker or the number of total health workers in 2012.

Column (2) shows that the reduction in services from the government health worker is

driven by villages where there is only one health worker in 2012 – i.e., the NGO was

unable to recruit a new person. The triple interaction with one worker is -0.506 and

statistically significant at the 1% level, while the triple interaction with two workers is

small and insignificant.

In column (3), the triple interaction with one worker is -0.545 and the triple interac-

tion with two workers -0.275. Both are statistically significant at the 1% level. At the

bottom of the table, we present the sum of the uninteracted NGO coefficient and the

triple interaction with one worker (-0.255), the sum of the uninteracted NGO coefficient

and the triple interaction with two workers (0.015), and their p-values. The first joint

estimate is statistically significant at the 1% level. The latter is insignificant. This

implies that in villages where there was a government program, NGO entry reduced

the total service delivery if it could not recruit an additional worker, and had no effect

if it successfully recruited an additional worker.

Columns (4)-(6) examine infant mortality outcomes. A comparison of the two triple

interaction terms show that the increase in infant mortality is more prominent in vil-

lages with a government program and where the NGO could not recruit a new worker.

The triple interaction with one worker is positive and statistically significant and the

triple interaction with two workers small and insignificant for all mortality outcomes.

Moreover, the joint statistics at the bottom of the table show that the sum of the un-

interacted NGO coefficient and the triple interaction with one worker is positive and

32

statistically significant at the 10% level for all mortality outcomes. This implies that

the increase in mortality in villages with a pre-existing government program is driven

by villages where the NGO could not recruit a new worker.

Taken together, these results are consistent with our interpretation that in village

with a government worker, NGO entry produced heterogeneous effects depending on

their ability to recruit a new worker. In places where they successfully recruited a

worker, causing there to be two health workers by 2012, there was no change in total

health service delivery or mortality. In places where they could not and employed

the government worker, total service delivery declined and infant mortality increased.

Recall that this decomposition exercise should be interpreted as suggestive rather than

causal.

5.2 Drug Prices

Since the government community health worker gives out the drugs for free, while the

NGO sells the same drugs at a low price, the crowding out of the government worker

can also increase drug prices, and through this potentially increase infant mortality

if households cannot afford this higher price. We examine drug prices for the most

relevant diseases as dependent variables and find no evidence to support this alternative

mechanism (see Appendix Table A.6).

5.3 Other Health Outcomes

In our investigation of the possible health outcomes of the crowding out of labor and

health services, we focus on infant mortality, which is the focal point of both the NGO

and government programs. However, health workers also provide advice and medicines

for many other health conditions (e.g., malaria, basic hygiene, etc.). We investigate

whether NGO workers in village with government program improved these other out-

33

comes. We examine a large number of variables that capture better preventive health

behavior (e.g., children washing their hands, sleeping under bednets, couples using con-

traceptives) and disease incidence (cough, diarrhea, worms, tuberculosis, malaria) as

the outcome in our baseline specification. Appendix Table A.7 shows that we find no

effect.

The main interventions that reduce infant mortality (education, pre- and post-natal

checkups, coordination with urban health facilities) are very time consuming. In con-

trast, there are other activities that would benefit the health of community members

which are less time consuming and perhaps more complementary to selling goods (e.g.,

advising couples to use/buy condoms). The results in Appendix Table A.7 show that

the NGO entry does not have any impact on these other activities, whether the village

has a pre-existing government worker or not.

6 Conclusion

This paper presents novel empirical evidence that NGOs can crowd out government-

provided community health services and reduce total health services. When the NGO

arrives to villages with volunteer government health workers, government workers shift

to work for the NGO in half of the cases. In the other half of the cases, the NGO is

able to recruit a new person. In the former scenario, the NGO entry reduces the total

supply of health services and the evidence is highly suggestive that it increases infant

mortality. It is important to keep in mind that our results do not say that NGOs are bad

for developing countries. Recall that we show that in villages without any government

health worker, NGO entry increases service delivery and reduces infant mortality. Taken

together, the empirical findings portray a nuanced picture of heterogeneous effects:

NGO entry provides benefits in places with no government-provided services, but can

34

hinder its own goals in places where the government services have already begun to be

established.

The generalizable insight that NGO entry can distort the local labor market (which

may, in turn, adversely affect the local population) is a concern that has been voiced

by researchers in public health and aid workers in the field for many years.31 We are

the first to provide rigorous evidence.

The empirical estimates should be cautiously interpreted as specific to the context

of this study. The two key features that are particularly important to keep in mind are

the novelty of the government program and the limited supply of able and willing health

workers. One could imagine that the entry of the NGO into a location with a mature

program may attract fewer workers from the government because there is more worker

loyalty. Time may also allow the government to build up the supply of potential health

workers (e.g., because there is a better understanding about the importance of the job,

and more people have knowledge about good health practices). For understanding the

magnitude of the findings, it is important to keep in mind that government workers are

all volunteers and do not receive any pay. It is very likely that if the income gap between

government and NGO workers is smaller, the magnitude of crowding out would also be

smaller. At the same time, note that the context that we study is very relevant for policy

makers. Hindering the development of public services in their infancy can impede the

process towards sustainable development and the practice of using volunteers to provide

local public services is common to developing (and developed) countries (e.g., Cowley

and Smith, 2014; Finan et al., 2015; Banuri and Keefer, 2016; Deserranno, 2019).

For understanding the external validity of our results, note that the dual-task model

is popular amongst NGOs today. It is mostly used in the context of community based

agents: e.g., community health workers provide health services and sell health products,31See the Introduction for references.

35

or agricultural extension workers train farmers and sell fertilizer and seeds. The NGO

incentivizes sales and not services because the former is easier to monitor. As we

discussed in the Introduction, this business model provides revenues that help reduce

the reliance of NGOs on large donors. Our findings illustrates the challenges this

business model poses for the NGO.

For policymakers, our results have several implications. The clearest one is that

aid-recipient governments and NGOs should coordinate their efforts. Towards this end,

the recipient government should improve record keeping of the location of government-

provided workers and share these data with NGOs. NGOs should experiment with

alternative compensation schemes to minimize the crowding out of altruistic activities

by money-making activities. Some NGOs that use the parallel-task model has recently

started to do this. For example, BRAC has begun to provide monetary incentives for

health services.

This study suggests several avenues for future research. First, problems highlighted

by our results are directly related to tradeoffs illustrated in the seminal model of multi-

tasking by Holmstrom and Milgrom (1991). Our findings suggest that NGO-delivered

aid is a natural context to test this model. Second, future studies should explore whether

aid crowds out or crowd in the development of native capacity in other contexts. For

example, policymakers and aid workers have expressed much concern that food aid,

if not thoughtfully targeted, can reduce farmgate food prices and negatively affect

farmers in recipient countries (Janzen, 2015; Levinsohn and McMillan, 2005). Finally,

the results emphasize the importance of heterogeneous effects for understanding the

effect of foreign aid and NGO effectiveness. Specifically, our findings suggest that

understanding the determinants of local supply is an important question for future

study.

36

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41

Tab

le1:

NGO

Tim

eAllo

cation

AcrossTa

sks

Act

ivity

% h

ealth

wor

kers

w

ho p

rovi

ded

Tim

e (m

inut

es)

per a

ctiv

ity

# of

activ

ities

pr

ovid

ed

Tota

l tim

e pe

r ac

tivity

(2) x

(3)

Tota

l tim

e pe

r act

ivity

as

% o

f tot

al ti

me

per

mon

th (4

)/29

20 x

100

(1)

(2)

(3)

(4)

(5)

Tota

l34

911

129

2010

0%Pr

egna

ncy

iden

tific

atio

n98

%25

717

56%

Atte

ndin

g de

liver

y an

d pr

ovid

ing

new

born

care

97%

287

196

7%Re

ferr

al to

clin

ic o

r hos

pita

ls88

%21

1123

18%

Atte

ndin

g re

fres

her t

rain

ing

94%

231

246

216

%Se

lling

med

icin

es99

%23

4610

5836

%Se

lling

hea

lth co

mm

oditi

es97

%21

3879

827

%

Not

es:

Sam

ple

= 66

0 N

GO

com

mun

ity h

ealth

wor

kers

inte

rvie

wed

by

the

NG

O in

200

9. W

orke

rs w

ere

aske

d (a

) whi

ch a

ctiv

ities

they

per

form

ed a

s a

heal

th w

orke

r in

the

last

mon

th, (

b) h

ow m

uch

time

was

ded

icat

ed to

eac

h of

thes

e ac

tiviti

es. R

efre

sher

trai

ning

s are

org

aniz

ed o

n a

mon

thly

bas

is in

th

e N

GO

bra

nch

and

are

aim

ed to

"ref

resh

" wor

kers

' hea

lth k

now

ledg

e an

d to

allo

w w

orke

rs to

repl

enis

h th

eir s

tock

of h

ealth

pro

duct

s.

42

Tab

le2:

Allo

cation

ofGovernm

entan

dNGO

Workers

AcrossVillag

esin

2012

No

Gov

in 2

010

Gov

in 2

010

Diff

eren

ce (2

) - (1

) (1

)(2

)(3

)N

o N

GO

0, 0

, 0 [2

4]0.

95, 0

, 0.

95 [3

7]0.

95, 0

, 0,9

5N

GO

0 , 1

, 1

[30]

0.53

, 1, 1

.53

[36]

0.53

, 0, 0

.53

Diff

eren

ce

0, 1

, 1-0

.58,

1, 0

.62

-0.5

8, 0

, -0.

38

Notes: E

ach

cell

repo

rts t

he #

Gov

hea

lth w

orke

rs, #

NG

O h

ealth

wor

kers

, Tot

al #

(NG

O +

G

ov) h

ealth

wor

kers

in 2

012.

The

num

ber o

f vill

ages

is st

ated

in b

rack

ets.

"No

Gov

" and

"G

ov" i

n th

e co

lum

n tit

les r

efer

s to

whe

ther

a v

illag

e ha

d a

gove

rnm

ent h

ealth

wor

ker i

n 20

10. "

NG

O" a

nd "N

o N

GO

" in

the

row

title

s ref

er to

whe

ther

a v

illag

e w

as ra

ndom

ly

assi

gned

to b

e pa

rt o

f the

NG

O p

rogr

am.

43

Tab

le3:

Results

onLa

borSu

pply

ofCom

mun

ityHealthWorkers

NG

O =

{0, 1

}G

ov =

{0, 1

}#

Tota

l (G

ov o

r NG

O)

= {0

, 1, 2

}Tr

aditi

onal

hea

ler

= {0

, 1}

Dru

g st

ore

= {0

, 1}

(1)

(2)

(3)

(4)

(5)

Mea

n D

ep.V

ar.

0.51

20.

425

0.94

50.

236

0.51

2

Gov

-0.0

480.

791*

**0.

791*

**-0

.003

-0.0

02(0

.046

)(0

.074

)(0

.074

)(0

.135

)(0

.101

)

NG

O

0.98

7***

-0.0

200.

980*

**0.

027

-0.0

11(0

.016

)(0

.027

)(0

.027

)(0

.122

)(0

.096

)

Gov

× N

GO

-0

.016

-0.4

46**

*-0

.446

***

-0.1

200.

013

(0.0

20)

(0.0

99)

(0.0

99)

(0.1

58)

(0.1

22)

Obs

erva

tions

127

127

127

127

127

R-sq

uare

d0.

973

0.70

90.

798

0.21

00.

663

[1] G

ov ×

NG

O +

NG

O c

oef

0.97

1-0

.466

0.53

4-0

.093

0.00

2[2

] Gov

× N

GO

+ N

GO

p-v

alue

<0.0

01<0

.001

<0.0

010.

339

0.97

5

Dep

ende

nt V

aria

bles

: Pre

senc

e of

hea

lth p

rovi

ders

in th

e vi

llage

in 2

012

Not

es: O

bser

vatio

ns a

re a

t the

vill

age

leve

l. A

ll re

gres

sion

s inc

lude

are

a fix

ed e

ffect

s. Ro

bust

New

ey-W

est s

tand

ard

erro

rs a

re re

port

ed in

the

pare

nthe

ses.

*** p

<0.0

1, **

p<0

.05,

* p<

0.1

44

Tab

le4:

Results

onHealthServiceAccess

(1)

(2)

(3)

(4)

(5)

(6)

(7)

NG

OG

ovG

ov o

r NG

OTr

aditi

onal

he

aler

Dru

g st

ore

Gov

hos

pita

l/

heal

th ce

nter

Priv

ate

clin

ic

Mea

n D

ep.V

ar.

0.23

50.

313

0.45

70.

014

0.14

30.

384

0.33

7

Gov

-0

.093

**0.

428*

**0.

361*

**-0

.002

-0.0

10-0

.042

0.00

1(0

.038

)(0

.049

)(0

.057

)(0

.006

)(0

.038

)(0

.047

)(0

.037

)

NG

O

0.31

5***

-0.0

190.

288*

**0.

003

-0.0

13-0

.022

-0.0

02(0

.043

)(0

.020

)(0

.046

)(0

.003

)(0

.025

)(0

.043

)(0

.033

)

Gov

× N

GO

-0

.016

-0.2

40**

*-0

.415

***

0.01

0-0

.006

-0.0

220.

014

(0.0

55)

(0.0

65)

(0.0

69)

(0.0

10)

(0.0

38)

(0.0

53)

(0.0

49)

Obs

erva

tions

2,74

72,

747

2,74

72,

747

2,74

72,

747

2,74

7R-

squa

red

0.19

30.

418

0.27

00.

031

0.15

90.

252

0.20

6

[1] G

ov ×

NG

O +

NG

O c

oef

0.29

9-0

.259

-0.1

270.

013

-0.0

19-0

.044

0.01

2[2

] Gov

× N

GO

+ N

GO

p-v

alue

<0.0

01<0

.001

0.00

70.

180

0.50

10.

133

0.74

4

Notes: O

bser

vatio

ns a

re a

t the

hou

seho

ld le

vel.

All

regr

essi

ons i

nclu

de a

rea

fixed

effe

cts.

Stan

dard

err

ors a

re cl

uste

red

at th

e vi

llage

leve

l. **

* p<0

.01,

**

p<0.

05, *

p<0

.1

Dep

ende

nt V

aria

ble:

Hou

seho

ld S

ough

t Med

ical

Adv

ice

from

the

Follo

win

g in

the

Past

Yea

r (20

12) =

{0, 1

}

45

Tab

le5:

Results

onNatal

andPost-Natal

Services

All (1)

(2)

(3)

(4)

(5)

(6)

Pres

ence

of a

w

oman

who

de

liver

ed in

the

past

yea

r D

eliv

ered

in a

ho

spita

l

Del

iver

y w

as

assi

sted

by

a he

alth

pr

ofes

sion

al

Rece

ived

pos

t-na

tal v

isit

with

in

two

days

of b

irth

Chi

ld b

reas

tfed

Chi

ld b

reas

tfed

imm

edia

tely

afte

r bi

rth

Mea

n D

ep.V

ar.

0.14

80.

744

0.53

30.

265

0.99

50.

428

Gov

0.

028

-0.0

530.

355*

**0.

224*

**-0

.014

0.03

2(0

.026

)(0

.067

)(0

.106

)(0

.080

)(0

.012

)(0

.094

)

NG

O

0.01

40.

045

0.20

0*0.

128*

*-0

.018

-0.0

10(0

.017

)(0

.062

)(0

.105

)(0

.063

)(0

.019

)(0

.095

)

Gov

× N

GO

-0

.053

-0.0

49-0

.257

**-0

.216

*0.

027

-0.1

88*

(0.0

34)

(0.0

88)

(0.1

16)

(0.1

10)

(0.0

21)

(0.1

09)

Obs

erva

tions

2,74

740

740

740

740

740

7R-

squa

red

0.09

80.

143

0.10

50.

181

0.04

40.

161

# of

Clu

ster

s12

710

710

710

710

710

7

[1] G

ov ×

NG

O +

NG

O c

oef

-0.0

39-0

.004

-0.0

57-0

.088

0.00

9-0

.198

[2] G

ov ×

NG

O +

NG

O p

-val

ue0.

175

0.94

40.

300

0.30

40.

287

<0.0

01

Dep

ende

nt V

aria

ble:

Hea

lth B

ehav

ior a

nd P

ost-N

atal

Car

e =

{0, 1

}H

ouse

hold

s with

a w

oman

who

del

iver

ed in

the

past

yea

r+

Notes: O

bser

vatio

ns a

re a

t the

hou

seho

ld le

vel.

Sam

ple

rest

rictio

ns a

re st

ated

in th

e co

lum

n he

adin

gs. + In

0.2

% o

f the

hou

seho

lds,

mor

e th

an o

ne

wom

an d

eliv

ered

in th

e pa

st y

ear i

n w

hich

case

we

colla

pse

wom

an-le

vel d

ata

at th

e ho

useh

old

leve

l. A

ll re

gres

sion

s inc

lude

are

a fix

ed e

ffect

s. St

anda

rd e

rror

s are

clus

tere

d at

the

villa

ge le

vel a

nd a

re p

rese

nted

in p

aren

thes

es. *

** p

<0.0

1, **

p<0

.05,

* p<

0.1

46

Tab

le6:

Villag

e-Le

velC

orrelates

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep

ende

nt V

aria

bles

Mea

n D

ep.V

ar.

Coe

f.St

d. E

rr.

Coe

f.St

d. E

rr.

Coe

f.St

d. E

rr.

Obs

.R2

A. P

rese

nce o

f a H

ealth

Car

e Pro

vide

r in

2010

= {0

, 1}

Gov

Hea

lth W

orke

r0.

575

-0.0

85(0

.072

)12

70.

479

Trad

ition

al H

eale

r 0.

480

-0.0

82(0

.155

)-0

.076

(0.1

32)

0.10

0(0

.186

)12

70.

217

Dru

g St

ore

0.67

7-0

.064

(0.0

53)

-0.0

22(0

.020

)0.

089

(0.0

67)

127

0.78

6G

ov H

ospi

tal w

ithin

10

km

0.55

9-0

.051

(0.1

41)

-0.0

44(0

.108

)-0

.023

(0.1

58)

127

0.36

4G

ov H

ealth

Cen

ter w

ithin

10k

m0.

717

-0.1

08(0

.127

)-0

.042

(0.1

17)

0.14

2(0

.145

)12

70.

416

Priv

ate

Clin

ic w

ithin

10k

m0.

835

-0.0

66(0

.046

)-0

.041

(0.0

56)

0.15

4*(0

.089

)12

70.

557

B. H

ouse

hold

Com

posit

ion

and

Mor

talit

y in

201

0

# H

Hs i

n th

e vi

llage

182.

071

-98.

536*

*(4

3.47

3)-7

3.31

4(4

4.87

4)97

.596

*(5

0.99

3)12

70.

374

# in

fant

s per

HH

0.29

1-0

.007

(0.0

19)

0.01

3(0

.018

)-0

.035

(0.0

28)

127

0.38

7#

infa

nts p

er H

H w

ho d

ied

in p

ast y

ear

0.04

1-0

.010

(0.0

10)

-0.0

07(0

.007

)0.

003

(0.0

17)

127

0.42

4A

ge o

f fem

ale

HH

hea

d31

.160

0.13

3(0

.411

)0.

441

(0.3

14)

-0.4

85(0

.549

)12

70.

760

% H

Hs i

nvol

ved

in fa

rmin

g as

mai

n ac

tivity

0.56

80.

054

(0.0

40)

0.05

9*(0

.035

)-0

.045

(0.0

51)

127

0.90

6%

HH

hea

ds w

ho co

mpl

eted

prim

ary

educ

atio

n0.

381

-0.1

00**

*(0

.036

)-0

.060

(0.0

38)

0.09

0*(0

.050

)12

70.

726

Num

ber o

f ass

ets o

wne

d (o

ut o

f 11)

5.58

30.

011

(0.1

77)

-0.0

78(0

.162

)0.

116

(0.2

68)

127

0.87

2Fo

od se

curit

y (1

to 4

)2.

217

-0.0

21(0

.050

)0.

026

(0.0

51)

0.00

1(0

.066

)12

70.

934

Gov

NG

O

Gov

× N

GO

Not

es: O

bser

vatio

ns a

re a

t the

vill

age

and

year

leve

l. Ea

ch ro

w is

one

regr

essi

on. A

ll re

gres

sion

s inc

lude

are

a fix

ed e

ffect

s. Ro

bust

New

ey-W

est s

tand

ard

erro

rs a

re re

port

ed in

pa

rent

hese

s. **

* p<0

.01,

** p

<0.0

5, *

p<0.

1

47

Tab

le7:

Rob

ustnessto

Con

trols

(1)

(2)

(3)

(4)

# To

tal h

ealth

wor

kers

(G

ov o

r NG

O) =

{0, 1

, 2}

HH

soug

ht m

edica

l adv

ice

from

any

hea

lth w

orke

r in

the

past

yea

r = {0

, 1}

Del

iver

y w

as a

ssist

ed b

y a

heal

th p

rofe

ssio

nal

= {0

, 1}

Rece

ived

pos

t-nat

al v

isit

with

in tw

o da

ys o

f birt

h =

{0, 1

}

Mea

n D

ep.V

ar.

0.94

50.

457

0.53

30.

265

Gov

0.70

9***

0.43

0***

0.43

2***

0.42

8***

(0.1

29)

(0.0

72)

(0.0

90)

(0.1

12)

NG

O

0.94

7***

0.33

2***

0.20

3**

0.26

7***

(0.0

80)

(0.0

43)

(0.0

90)

(0.0

86)

Gov

× N

GO

-0

.417

**-0

.471

***

-0.3

66**

*-0

.546

***

(0.1

67)

(0.0

85)

(0.1

21)

(0.1

41)

Obs

erva

tions

127

2,74

740

740

7R-

squa

red

0.85

10.

298

0.16

30.

313

Uni

t of O

bser

vatio

nV

illag

eH

ouse

hold

Hou

seho

ld

Hou

seho

ld

Stan

dard

Err

orN

ewey

-Wes

tC

lust

er V

illag

eC

lust

er V

illag

eC

lust

er V

illag

e

[1] G

ov ×

NG

O +

NG

O c

oef

0.53

0-0

.139

-0.1

63-0

.279

[2] G

ov ×

NG

O +

NG

O p

-val

ue<0

.001

0.01

30.

045

0.00

1

Dep

ende

nt V

aria

bles

Not

es: T

his t

able

pre

sent

s coe

fficie

nts e

valu

ated

at t

he m

ean

valu

e of

all

of th

e co

ntro

l var

iabl

es. A

ll re

gres

sions

cont

rol f

or a

rea

fixed

effe

cts a

nd th

e fo

llow

ing

addi

tiona

l con

trol

s, w

hich

are

all

mea

sure

d in

201

0 an

d de

mea

ned,

and

thei

r int

erac

tions

with

the

pres

ence

of a

gov

hea

lth w

orke

r in

2010

: vi

llage

size

and

com

posit

ion

(# o

f HH

, # o

f inf

ants

per

HH

). H

H ch

arac

teris

tics:

%H

Hs i

nvol

ved

in fa

rmin

g, %

HH

hea

ds w

ho co

mpl

eted

prim

ary

educ

atio

n), p

roxi

es fo

r wea

lth (a

vera

ge fo

od se

curit

y, a

vera

ge n

umbe

r of a

sset

s ow

ned

by a

HH

), ac

cess

to h

ealth

pro

vide

rs (p

rese

nce

of g

ov h

ospi

tal

with

in 1

0km

, pre

senc

e of

gov

hea

lth ce

nter

with

in 1

0km

, pre

senc

e of

priv

ate

clini

c with

in 1

0km

, pre

senc

e of

a d

rug

stor

e, p

rese

nce

of a

trad

ition

al

heal

er),

mor

talit

y (#

of c

hild

ren

<1 p

er H

H w

ho d

ied

in p

ast y

ear)

. ***

p<0

.01,

** p

<0.0

5, *

p<0.

1.

48

Tab

le8:

Results

onInfant

Mortality

(1)

(2)

(3)

(4)

(5)

At l

east

one

di

ed

#die

d as

shar

e of

birt

hs+

# di

ed p

er 1

,000

bi

rths

+

#die

d as

shar

e of

birt

hs++

#

died

per

1,0

00

birt

hs++

Mea

n D

ep.V

ar.

0.04

00.

053

69.0

690.

088

117.

215

Gov

-0

.026

**-0

.063

***

-76.

710*

**-0

.073

**-1

16.0

57**

*(0

.012

)(0

.021

)(2

7.31

4)(0

.033

)(4

3.36

2)

NG

O

-0.0

23**

-0.0

57**

*-5

8.25

1**

-0.0

72**

-74.

677*

(0.0

11)

(0.0

20)

(28.

351)

(0.0

32)

(39.

469)

Gov

× N

GO

0.

029*

0.06

2**

68.0

36*

0.08

8**

116.

083*

*(0

.016

)(0

.025

)(3

4.85

5)(0

.041

)(5

8.53

9)

Obs

erva

tions

2,74

71,

408

127

936

127

R-sq

uare

d0.

021

0.03

70.

248

0.05

80.

256

[1] G

ov ×

NG

O +

NG

O c

oef

0.00

60.

005

9.78

50.

016

41.4

06[2

] Gov

× N

GO

+ N

GO

p-v

alue

0.57

10.

693

0.60

00.

529

0.32

8

Uni

t of o

bser

vatio

nH

ouse

hold

Hou

seho

ldVi

llage

Hou

seho

ldVi

llage

Stan

dard

Err

orC

lust

er V

illag

eC

lust

er V

illag

eN

ewey

-Wes

tC

lust

er V

illag

eN

ewey

-Wes

t

Dep

ende

nt V

aria

ble:

Infa

nt (U

nder

-1)

Mor

talit

y

Not

es: A

ll re

gres

sion

s con

trol

for a

rea

fixed

effe

cts.

*** p

<0.0

1, **

p<0

.05,

* p<

0.1

+ In C

ol. 2

, we

calc

ulat

e th

e ho

useh

old-

leve

l inf

ant m

orta

lity

ratio

as (

# ch

ildre

n bo

rn 2

010-

2012

and

who

die

d be

low

age

one

201

0-20

12)/

(# ch

ildre

n bo

rn

2010

-201

2). T

he la

tter i

s # ch

ildre

n b

orn

2010

-201

2 an

d d

ied

belo

w a

ge o

ne 2

010-

2012

+ #

child

ren

born

201

0-20

12 a

nd

did

not d

ie d

urin

g 20

10-2

012

(i.e.

, chi

ldre

n le

ss th

an 3

yea

rs in

201

2). C

ol. 3

follo

ws a

sim

ilar a

ppro

ach

as C

ol. 2

but

ag

greg

ates

all

num

bers

at t

he v

illag

e le

vel r

athe

r tha

n at

the

hous

ehol

d le

vel a

nd fu

rthe

r mul

tiplie

s the

den

omin

ator

by

1,0

00. ++

Col

. 4-5

repl

icat

e th

e ap

proa

ch o

f Col

. 2-3

but

exc

lude

s fro

m th

e ra

tio a

ny ch

ild w

ho, i

n 20

12, i

s les

s tha

n on

e ye

ar o

ld o

r who

wou

ld h

ave

been

less

than

one

had

he/

she

not d

ied.

49

Tab

le9:

Decom

position

oftheInfant

MortalityResults

(1)

(2)

(3)

(4)

(5)

(6)

NG

OG

ovG

ov o

r NG

OA

t lea

st o

ne d

ied

#die

d as

shar

e of

bi

rths

+ #

died

per

1,0

00

birt

hs+

Mea

n D

ep.V

ar.

0.23

50.

313

0.45

70.

040

0.05

369

.069

Gov

-0.0

79**

0.50

1***

0.39

7***

-0.0

33**

*-0

.073

***

-87.

941*

**(0

.038

)(0

.052

)(0

.060

)(0

.012

)(0

.022

)(2

8.05

0)

NG

O

0.31

6***

-0.0

150.

290*

**-0

.023

**-0

.057

***

-58.

983*

*(0

.043

)(0

.017

)(0

.046

)(0

.011

)(0

.021

)(2

8.58

5)

Gov

× N

GO

× 1

hea

lth w

orke

r in

2012

(NG

O)

-0.0

68-0

.506

***

-0.5

45**

*0.

053*

*0.

096*

**11

0.54

9**

(0.0

55)

(0.0

47)

(0.0

71)

(0.0

21)

(0.0

31)

(43.

006)

Gov×

NG

O ×

2 h

ealth

wor

kers

in 2

012

(NG

O +

Gov

)0.

040

0.04

9-0

.275

***

0.00

30.

035

22.9

83(0

.068

)(0

.060

)(0

.070

)(0

.016

)(0

.025

)(3

3.61

3)

Obs

erva

tions

2,74

72,

747

2,74

72,

747

1,40

812

7R-

squa

red

0.19

60.

488

0.28

50.

024

0.04

20.

302

Uni

t of o

bser

vatio

nH

ouse

hold

Hou

seho

ldH

ouse

hold

Hou

seho

ldH

ouse

hold

Villa

geSt

anda

rd E

rror

Clu

ster

Vill

age

Clu

ster

Vill

age

Clu

ster

Vill

age

Clu

ster

Vill

age

Clu

ster

Vill

age

New

ey-W

est

[1] G

ov ×

NG

O x

1 w

orke

r + N

GO

coe

f0.

248

-0.5

21-0

.255

0.03

00.

039

51.5

67[2

] Gov

× N

GO

x 1

wor

ker +

NG

O p

-val

ue<0

.001

<0.0

01<0

.001

0.07

20.

090

0.09

4[3

] Gov

× N

GO

x 2

wor

kers

+ N

GO

coef

0.35

60.

034

0.01

5-0

.020

-0.0

22-3

5.99

9[4

] Gov

× N

GO

x 2

wor

kers

+ N

GO

p-v

alue

<0.0

010.

553

0.77

00.

070

0.08

50.

025

Notes: T

he u

nit o

f obs

erva

tion

is st

ated

at t

he b

otto

m o

f the

tabl

e. A

ll re

gres

sion

s con

trol

for a

rea

fixed

effe

cts.

*** p

<0.0

1, **

p<0

.05,

* p<

0.1

+ In C

ol. 5

, we

calc

ulat

e th

e ho

useh

old-

leve

l inf

ant m

orta

lity

ratio

as (

# ch

ildre

n bo

rn 2

010-

2012

and

who

die

d be

low

age

one

201

0-20

12)/

(# ch

ildre

n bo

rn 2

010-

2012

). Th

e la

tter i

s # ch

ildre

n b

orn

2010

-201

2 an

d di

ed b

elow

age

one

201

0-20

12 +

# ch

ildre

n bo

rn 2

010-

2012

and

did

not

die

dur

ing

2010

-201

2 (i.

e., c

hild

ren

less

than

3 y

ears

in 2

012)

. Col

. 6 fo

llow

s a

sim

ilar a

ppro

ach

as C

ol. 5

but

agg

rega

tes a

ll nu

mbe

rs a

t the

vill

age

leve

l rat

her t

han

at th

e ho

useh

old

leve

l and

furt

her m

ultip

lies t

he d

enom

inat

or b

y 1,

000.

Dep

ende

nt V

aria

ble

HH

soug

ht m

edic

al a

dvic

e fr

om th

e fo

llow

ing

= {0

, 1}

Infa

nt M

orta

lity

50

Figure

1:UnitPrice

&Marginpe

rProdu

ctSo

ldby

theNGO

Not

es: T

his f

igur

e pr

esen

ts th

e lis

t of p

rodu

cts s

old

by th

e N

GO

wor

kers

, ran

ked

from

low

est t

o hi

ghes

t uni

t m

argi

n. T

he u

nit m

argi

n eq

uals

the

pric

e at

whi

ch th

e he

alth

wor

ker s

ells

the

prod

ucts

in h

er co

mm

unity

("

heal

th w

orke

r pric

e") m

inus

the

pric

e at

whi

ch sh

e bu

ys th

e pr

oduc

ts fr

om th

e N

GO

("N

GO

pric

e").

Pric

es

are

expr

esse

d in

Uga

ndan

Shi

lling

s (1$

=3,7

13 U

GX)

.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

PRICE AND MARGIN (IN UGX)

NG

O P

RIC

Ehe

alth

wor

ker P

RIC

EU

NIT

MA

RGIN

51

Tab

leA.1:Lo

gitEstim

ation

Pois

son

(1)

(2)

(3)

(4)

(5)

(6)

(7)

NG

OG

ovN

GO

or G

OV

Del

iver

y w

as

assi

sted

by

a he

alth

pr

ofes

sion

al

Rece

ived

pos

t-nat

al

visi

t with

in tw

o da

ys o

f birt

h

At l

east

one

infa

nt

died

# To

tal h

ealth

w

orke

rs (G

ov o

r N

GO

) = {0

, 1, 2

}

Mea

n D

ep.V

ar.

0.23

50.

313

0.45

70.

533

0.26

50.

040

0.94

5

Gov

-0

.118

2.69

3***

2.09

6***

1.55

1***

2.11

6***

-0.7

25**

16.7

74**

*(0

.320

)(0

.291

)(0

.300

)(0

.482

)(0

.799

)(0

.298

)(0

.205

)

NG

O

2.55

6***

-0.3

891.

844*

**0.

850*

1.54

4*-0

.633

**16

.934

***

(0.2

99)

(0.3

09)

(0.2

49)

(0.4

66)

(0.7

96)

(0.2

76)

(0.1

92)

Gov

* N

GO

-0

.692

*-0

.906

*-2

.464

***

-1.1

10**

-1.9

82**

0.78

0*-1

6.49

7***

(0.3

67)

(0.4

68)

(0.3

54)

(0.5

09)

(0.9

22)

(0.4

08)

(0.2

10)

[1] G

ov ×

NG

O +

NG

O c

oef

1.86

4-1

.295

-0.6

20-0

.261

-0.4

380.

147

0.43

7[2

] Gov

× N

GO

+ N

GO

p-v

alue

0.00

00.

000

0.00

90.

270

0.34

20.

602

0.00

0

Obs

erva

tions

2,74

72,

747

2,74

740

735

72,

747

127

Uni

t of o

bser

vatio

nH

ouse

hold

Hou

seho

ldH

ouse

hold

Hou

seho

ldH

ouse

hold

Hou

seho

ldVi

llage

Stan

dard

Err

orC

lust

er V

illag

eC

lust

er V

illag

eC

lust

er V

illag

eC

lust

er V

illag

eC

lust

er V

illag

eC

lust

er V

illag

eN

ewey

-Wes

t

Logi

t

Notes:

All

regr

essi

ons c

ontr

ol fo

r are

a fix

ed e

ffect

s. **

* p<0

.01,

** p

<0.0

5, *

p<0.

1

HH

soug

ht m

edic

al a

dvic

e fr

om th

e fo

llow

ing

= {0

, 1}

Del

iver

y, P

ost-n

atal

Car

e, M

orta

lity

= {0

, 1}

1

Tab

leA.2:Multino

mialL

ogit

Estim

ation

(1)

(2)

(3)

(4)

12

34

Mea

n D

ep.V

ar.

0.30

60.

306

0.30

60.

306

Gov

-0

.298

0.42

50.

969

1.72

0(0

.327

)(0

.357

)(1

.042

)(1

.072

)

NG

O

-0.8

47**

0.57

21.

279

2.64

6**

(0.4

23)

(0.4

34)

(1.1

50)

(1.0

32)

Gov

× N

GO

0.

432

-0.7

11-1

.508

-2.4

03**

(0.4

56)

(0.4

46)

(1.1

08)

(1.1

31)

Obs

erva

tions

2,74

72,

747

2,74

72,

747

Dep

ende

nt V

aria

ble

Notes: O

bser

vatio

ns a

re a

t the

hou

seho

ld le

vel.

All

estim

ates

incl

ude

area

fixe

d ef

fect

s. St

anda

rd

erro

rs a

re cl

uste

red

at v

illag

e le

vel.

*** p

<0.0

1, **

p<0

.05,

* p<

0.1

Mul

tinom

ial l

ogit

(0 to

4)

0 =

No

wom

an d

eliv

ered

1

= W

oman

del

iver

ed, n

ot a

ssis

ted

by a

hea

lth p

rofe

ssio

nal,

with

out p

ost n

atal

care

2

= W

oman

del

iver

ed, a

ssis

ted

by a

hea

lth p

rofe

ssio

nal,

with

out p

ost n

atal

care

3

= W

oman

del

iver

ed, n

ot a

ssis

ted

by a

hea

lth p

rofe

ssio

nal,

with

pos

t nat

al ca

re

4 =

Wom

an d

eliv

ered

, ass

iste

d by

a h

ealth

pro

fess

iona

l, w

ith p

ost n

atal

care

2

Tab

leA.3:Heterog

eneous

Effe

ctsby

DrugStore

(1)

(2)

# To

tal h

ealth

wor

kers

(G

ov o

r NG

O) =

{0, 1

, 2}

HH

soug

ht m

edic

al a

dvic

e fr

om a

ny h

ealth

wor

ker i

n th

e pa

st y

ear =

{0, 1

}

Mea

n D

ep.V

ar.

0.94

50.

457

Gov

× N

GO

× D

rug

Stor

e-0

.042

0.22

7(0

.194

)(0

.142

)

Obs

erva

tions

127

2,74

7R-

squa

red

0.80

00.

274

Uni

t of O

bser

vatio

nVi

llage

Hou

seho

ldSt

anda

rd E

rror

New

ey-W

est

Clu

ster

Vill

age

Notes

: All

regr

essi

ons c

ontr

ol fo

r are

a fix

ed e

ffect

s and

the

full

set o

f int

erac

tion

term

s an

d un

inte

ract

ed te

rms:

gov,

NG

O, d

rug

stor

e, g

ov ×

dru

g st

ore,

ngo

× d

rug

stor

e, g

ov

× N

GO

. ***

p<0

.01,

** p

<0.0

5, *

p<0.

1

Dep

ende

nt V

aria

ble

3

Tab

leA.4:Association

betw

eenHealthServices

andInfant

mortality

(1)

(2)

(3)

(4)

(5)

(6)

Gov

-0.0

13-0

.100

**(0

.009

)(0

.045

) N

GO

-0.0

30**

*-0

.087

**(0

.010

)(0

.043

) G

ov o

r NG

O-0

.025

***

-0.1

52**

*(0

.009

)(0

.051

)

Obs

erva

tions

2,74

740

72,

747

407

2,74

740

7R-

squa

red

0.02

00.

069

0.02

30.

067

0.02

30.

097

Gov

-0.0

16-0

.048

**(0

.014

)(0

.022

) N

GO

-0.0

55**

*-0

.053

**(0

.012

)(0

.021

) G

ov o

r NG

O-0

.042

***

-0.0

80**

*(0

.014

)(0

.025

)

Obs

erva

tions

1,40

835

71,

408

357

1,40

835

7R-

squa

red

0.02

70.

064

0.04

10.

070

0.03

50.

092

Expl

anat

ory

Var

: HH

so

ught

med

ical a

dvice

fr

om th

e fo

llow

ing

in

the

past

yea

rPa

nel A

. Dep

ende

nt V

aria

ble

-- A

t lea

st o

ne in

fant

die

d

Pane

l B. D

epen

dent

Var

iabl

e --

# of

infa

nts w

ho d

ied

as a

shar

e of

birt

hs

Not

es:

Odd

num

bere

d co

lum

ns co

mpr

ise o

f all

hous

ehol

ds. E

ven

num

bere

d co

lum

ns co

mpr

ise o

f H

Hs w

ith w

oman

who

del

iver

ed in

pas

t yea

r. +I

n 0.

2% o

f the

hou

seho

lds,

mor

e th

an o

ne w

oman

de

liver

ed in

the

past

yea

r in

whi

ch ca

se w

e co

llaps

e w

oman

-leve

l dat

a at

the

hous

ehol

d le

vel.

All

regr

essio

ns in

clude

are

a fix

ed e

ffect

s. St

anda

rd e

rror

s are

clus

tere

d at

the

villa

ge le

vel a

nd a

re

pres

ente

d in

par

enth

eses

. ***

p<0

.01,

** p

<0.0

5, *

p<0.

1

4

Tab

leA.5:Results

onInfant

Mortality–Rob

ustnessto

Con

trols

(1)

(2)

(3)

(4)

(5)

At l

east

one

die

d #d

ied

as sh

are

of

birt

hs+

# di

ed p

er 1

,000

bi

rths

+

#die

d as

shar

e of

bi

rths

++

# di

ed p

er 1

,000

bi

rths

++

Mea

n D

ep.V

ar.

0.04

00.

053

69.0

690.

088

117.

215

Gov

-0.0

56**

*-0

.123

***

-119

.947

***

-0.1

43**

*-1

68.0

99**

(0.0

21)

(0.0

38)

(43.

991)

(0.0

54)

(72.

064)

NG

O

-0.0

43**

-0.0

98**

*-8

9.51

7**

-0.1

19**

*-1

28.3

96**

(0.0

18)

(0.0

32)

(38.

904)

(0.0

44)

(60.

598)

Gov

× N

GO

0.

062*

*0.

121*

**11

3.71

0**

0.15

8**

190.

433*

(0.0

25)

(0.0

43)

(53.

903)

(0.0

62)

(95.

937)

Obs

erva

tions

2,74

71,

408

127

936

127

R-sq

uare

d0.

033

0.06

70.

423

0.09

50.

397

Uni

t of O

bser

vatio

nH

ouse

hold

Hou

seho

ldVi

llage

Hou

seho

ldVi

llage

Stan

dard

Err

orC

lust

er V

illag

eC

lust

er V

illag

eN

ewey

-Wes

tC

lust

er V

illag

eN

ewey

-Wes

t

[1] G

ov ×

NG

O +

NG

O c

oef

0.01

90.

023

24.1

930.

039

62.0

38[2

] Gov

× N

GO

+ N

GO

p-v

alue

0.14

20.

238

0.31

70.

266

0.24

1

Dep

ende

nt V

aria

ble:

Infa

nt (U

nder

-1) M

orta

lity

Not

es: A

ll re

gres

sion

s con

trol

for a

rea

fixed

effe

cts a

nd th

e fo

llow

ing

addi

tiona

l con

trol

s, w

hich

are

all

mea

sure

d in

201

0 an

d de

mea

ned,

an

d th

eir i

nter

actio

ns w

ith th

e pr

esen

ce o

f a g

ov h

ealth

wor

ker i

n 20

10: v

illag

e si

ze a

nd co

mpo

sitio

n (#

of H

H, #

of i

nfan

ts p

er H

H).

HH

ch

arac

teris

tics:

%H

Hs i

nvol

ved

in fa

rmin

g, %

HH

hea

ds w

ho co

mpl

eted

prim

ary

educ

atio

n), p

roxi

es fo

r wea

lth (a

vera

ge fo

od se

curit

y,

aver

age

num

ber o

f ass

ets o

wne

d by

a H

H),

acce

ss to

hea

lth p

rovi

ders

(pre

senc

e of

gov

hos

pita

l with

in 1

0km

, pre

senc

e of

gov

hea

lth

cent

er w

ithin

10k

m, p

rese

nce

of p

rivat

e cl

inic

with

in 1

0km

, pre

senc

e of

a d

rug

stor

e, p

rese

nce

of a

trad

ition

al h

eale

r), m

orta

lity

(# o

f ch

ildre

n <1

per

HH

who

die

d in

pas

t yea

r). *

** p

<0.0

1, **

p<0

.05,

* p<

0.1

+ In C

ol. 2

, we

calc

ulat

e th

e ho

useh

old-

leve

l inf

ant m

orta

lity

ratio

as (

# ch

ildre

n bo

rn 2

010-

2012

and

who

die

d be

low

age

one

201

0-20

12)/

(# ch

ildre

n bo

rn 2

010-

2012

). Th

e la

tter i

s # ch

ildre

n b

orn

2010

-201

2 an

d d

ied

belo

w a

ge o

ne 2

010-

2012

+ #

child

ren

born

201

0-20

12 a

nd d

id n

ot d

ie d

urin

g 20

10-2

012

(i.e.

, chi

ldre

n le

ss th

an 3

ye

ars i

n 20

12).

Col

. 3 fo

llow

s a si

mila

r app

roac

h as

Col

. 2 b

ut a

ggre

gate

s all

num

bers

at t

he v

illag

e le

vel r

athe

r tha

n at

the

hous

ehol

d le

vel a

nd fu

rthe

r mul

tiplie

s the

den

omin

ator

by

1,00

0. ++

Col

. 4-5

repl

icat

e th

e ap

proa

ch o

f Col

. 2-3

but

exc

lude

s fro

m th

e ra

tio a

ny ch

ild

who

, in

2012

, is l

ess t

han

one

year

old

or w

ho w

ould

hav

e be

en le

ss th

an o

ne h

ad h

e/sh

e no

t die

d.

5

Tab

leA.6:Results

onDisease

Treatm

ent

(1)

(2)

(3)

Mal

aria

Dhi

arre

aPn

eum

onia

Mea

n D

ep.V

ar. (

1,00

0 U

gand

an S

hilli

ngs)

5.64

33.

418

3.35

7

Gov

-1.2

19-2

.019

*-1

.434

*(1

.115

)(1

.074

)(0

.839

)

NG

O

0.31

0-1

.276

-0.4

89(1

.279

)(1

.434

)(1

.105

)

Gov

× N

GO

0.

006

0.98

30.

596

(1.5

78)

(1.5

71)

(1.2

06)

Obs

erva

tions

2,23

61,

977

1,81

9R-

squa

red

0.10

50.

078

0.10

3

[1] G

ov ×

NG

O +

NG

O c

oef

0.31

7-0

.294

0.10

7[2

] Gov

× N

GO

+ N

GO

p-v

alue

0.72

50.

579

0.81

5

Dep

ende

nt V

aria

ble:

Pric

e of

trea

tmen

t (m

edic

atio

n an

d te

sts)

Notes: O

bser

vatio

ns a

re a

t the

hou

seho

ld a

nd y

ear l

evel

. All

regr

essi

ons i

nclu

de a

rea

fixed

effe

cts.

Stan

dard

err

ors a

re cl

uste

red

at

the

villa

ge le

vel.

*** p

<0.0

1, **

p<0

.05,

* p<

0.1

6

Tab

leA.7:Results

onOther

HealthOutcomes

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep

ende

nt V

aria

bles

Mea

n D

ep.V

ar.

Coe

f.St

d. E

rr.

Coe

f.St

d. E

rr.

Coe

f.St

d. E

rr.

Obs

.R2

A. H

ealth

Beh

avio

r -- A

ll ho

useh

olds

Chi

ldre

n sl

eep

unde

r bed

net

0.72

50.

064*

*(0

.030

)-0

.003

(0.0

33)

-0.0

64(0

.049

)2,

747

0.08

4C

hild

ren

drin

k tr

eate

d w

ater

(e.g

., bo

iled,

0.

722

0.03

1(0

.032

)0.

008

(0.0

26)

-0.0

15(0

.047

)2,

747

0.21

0C

hild

ren

was

h ha

nds b

efor

e fo

od a

nd a

fter

0.87

80.

024

(0.0

25)

-0.0

02(0

.022

)-0

.040

(0.0

41)

2,74

70.

084

Chi

ldre

n ar

e fu

lly im

mun

ized

0.90

50.

012

(0.0

29)

0.01

9(0

.022

)-0

.027

(0.0

28)

2,04

50.

236

Cou

ple

uses

cont

race

ptiv

es (e

.g.,

cond

om, p

ill,

ster

iliza

tion)

0.46

9-0

.070

(0.0

46)

-0.0

25(0

.034

)0.

047

(0.0

57)

2,51

00.

036

Aver

age

stan

dard

ized

effe

ct o

f out

com

es a

bove

0.01

4(0

.046

)0.

011

(0.0

37)

-0.0

40(0

.063

)1,

915

0.12

1

B. D

iseas

e Inc

iden

ce --

Hou

seho

lds w

ith a

t lea

st o

ne ch

ild u

nder

5 y

ears

of a

geC

ough

0.59

3-0

.019

(0.0

39)

0.01

3(0

.028

)0.

013

(0.0

43)

1,78

30.

204

Dhi

arre

a0.

284

-0.0

44(0

.039

)-0

.011

(0.0

35)

0.01

0(0

.047

)1,

783

0.18

1W

orm

s0.

348

-0.0

08(0

.036

)-0

.007

(0.0

33)

-0.0

18(0

.046

)1,

783

0.27

5TB

0.03

80.

003

(0.0

26)

0.02

2(0

.014

)-0

.030

(0.0

26)

1,78

30.

111

Mal

aria

0.47

10.

001

(0.0

33)

0.00

3(0

.032

)-0

.013

(0.0

44)

1,78

30.

119

Aver

age

stan

dard

ized

effe

ct o

f out

com

es a

bove

-0.0

28(0

.064

)0.

023

(0.0

44)

-0.0

36(0

.069

)1,

783

0.15

8

Gov

NG

O

Gov

× N

GO

Not

es: O

bser

vatio

ns a

re a

t the

hou

seho

ld a

nd y

ear l

evel

. Sam

ple

rest

rictio

ns a

re st

ated

in th

e pa

nel h

eadi

ngs.

The

aver

age

stan

dard

ized

effe

ct is

an

equa

lly

wei

ghte

d av

erag

e of

z-s

core

s of i

ts co

mpo

nent

s. In

Pan

el B

, mor

e th

an o

ne u

nder

-5 ch

ild li

ves i

n th

e ho

useh

old

in 3

4% o

f the

hou

seho

lds,

in w

hich

case

we

colla

psed

child

-leve

l dat

a at

the

hous

ehol

d le

vel.

Each

row

is o

ne re

gres

sion

. All

regr

essi

ons i

nclu

de a

rea

fixed

effe

cts.

Stan

dard

err

ors a

re cl

uste

red

at th

e vi

llage

leve

l. **

* p<0

.01,

** p

<0.0

5, *

p<0.

1

7