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WORKING PAPER · NO. 2021-69

Aid Fragmentation and CorruptionTravers B. Child, Austin L. Wright, and Yun XiaoJUNE 2021

Aid Fragmentation and Corruption∗

Travers B. Child† Austin L. Wright‡ Yun Xiao§

June 10, 2021

Abstract

Effectiveness of development aid is widely perceived to suffer in the presence of multipledonors with overlapping responsibilities. We test existing theory on aid fragmentation bystudying aid provision under numerous donors throughout Afghanistan from 2006-2009.Our study leverages granular military data on aid and conflict, and household survey dataon corruption and public opinion. We conduct the first micro-level analysis of aidfragmentation. When delivered by a single donor, aid appears to curtail corruption, boostpublic opinion, and reduce conflict. But under donor fragmentation, the benefits of aid aresignificantly reduced. Our results suggest under high volumes of aid provision,fragmentation facilitates corruption and thereby erodes aid’s ability to win hearts andminds in the fight against insurgents. At moderate levels of aid, however, fragmentationmay actually benefit the quality of institutions. Our findings remain stable whenaccounting for a rich set of observable confounds. Moreover, we obtain robust estimateswhen correcting for bias likely arising from the omission of unobservable factors.

Keywords: Aid, Corruption, Public opinion, Conflict, Afghanistan

JEL Codes: F35, D73, D74

∗For helpful comments we thank Eric Bartelsman, Erwin Bulte, Richard Carney, Jiahua Che, GabrieleCiminelli, Chris Elbers, David McKenzie, Thu Nguyen, Remco Oostendorp, Paul Pelzl, Mounu Prem, Toh WenQiang, Jacob Shapiro, Martin Wiegand, Frank Yu, Zhang Yu, and seminar participants at the Tinbergen Institute,Vrije Universiteit Amsterdam, Paris School of Economics (HiCN Workshop), Asia School of Business, and ChinaEurope International Business School (CEIBS). Conclusions reached from the ANQAR data are not attributableto NATO/RS nor to US Forces Afghanistan (USFOR-A), and interpretations offered are not necessarily sharedby RS/NATO/USFOR-A. Generous research support was granted by CEIBS.†Finance Group, China Europe International Business School. Email: t.b.child@ceibs.edu.‡Harris School of Public Policy, The University of Chicago. Email: austinlw@uchicago.edu.§University of Amsterdam and Tinbergen Institute. Email: y.xiao@uva.nl.

We’re invariably going to get it wrong.

Let’s be honest, it’s almost impossible to avoid

unintended consequences of our work here.

Development aid donor interview in Kabul

November 2013

1 Introduction

Conflicted states have received enormous amounts of foreign aid in recent decades. From 2000 to

2012, official development assistance (ODA) to fragile states grew more than 10% per year, and

totalled over USD 450 billion. Among the top 10 ODA recipients in 2010, nine were currently

in conflict. The US government alone has doled out more than USD 120 billion on development

in Afghanistan, and over USD 80 billion in Iraq.1 Donor nations expect foreign aid to improve

stability in fragile states, in addition to furthering development. To this end, policymakers and

scholars cite various channels through which aid may impact security.2 First, aid may improve

local economic conditions, providing alternative careers for would-be insurgents. Second, aid may

strengthen state institutions, improving the state’s capacity to deter or pre-empt attacks. Third,

aid may attract predation from government officials or rebels, thereby eroding state institutions

or directly financing insurgency. Finally, follow-on effects from the aforementioned channels run

through public opinion. If aid boosts support for government, it facilitates broader cooperation

in the fight against insurgents; if aid degrades public support, conflict can intensify. Taken

together, aid’s stabilizing potential ultimately hinges on its effectiveness at improving economic

conditions, state institutions, and public support for government. But the effectiveness of aid in

this regard is highly uncertain ex ante.3

Aid effectiveness has been a key topic of development summits since the Monterrey Consensus

of 2002 in which donors prioritized aid delivery alongside the traditional metric of ‘dollars spent’.

Numerous summits on aid effectiveness have since taken place4, and paperbacks on the topic1Aid figures are from OECD (2013), OECD (2015), SIGIR (2013), and SIGAR (2018).2Empirical evidence suggests the relation between aid and conflict is unclear. Aid has proven successful in

some settings (e.g. Berman et al., 2011b; Sexton, 2016), but either inconsequential (e.g. Berman et al., 2011a;Child, 2014) or even harmful (e.g. Crost et al., 2014; Nunn and Qian, 2014; Khanna and Zimmermann, 2017;Child, 2019) in others.

3For a history of studies examining the impact of aid on growth, see Easterly (2003). Literature on aid andstate institutions is discussed later in this introduction.

4For corresponding official reports, see OECD (2005, 2008, 2009, 2011); United Nations (2015); OECD/UNDP(2016).

1

have sparked broad public debate (Easterly, 2006; Moyo, 2010). Many discussions have centered

around the harmonization of donor efforts. But cooperation between donors has been challenging

under the prevailing landscape of ‘donor fragmentation’ - the multiplicity of donors sharing

overlapping responsibilities within a common geographical area.5

Donor fragmentation is widely perceived to negatively moderate the effectiveness of aid,

and thereby limit the quality of institutions. First, donor proliferation makes coordination

challenging, and a replication of development efforts can ensue (Halonen-Akatwijuka, 2007;

Easterly and Pfutze, 2008). Second, greater competition by aid providers restricts spending

opportunities, leading donors to select poor quality projects.6 Third, greater competition

implies fewer trustworthy partners for project implementation. Scrutiny by donors is relaxed

by necessity (Djankov et al., 2009), facilitating both rebel and elite capture of development

resources (Gibson et al., 2005; Acharya et al., 2006). Fourth, fragmentation erodes each

donor’s sense of accountability and responsibility for development outcomes. This has the

combined effect of (i) further relaxing scrutiny, (ii) further decreasing quality standards, and

(iii) introducing a greater willingness to undermine local preferences in the pursuit of

self-interest (Knack and Rahman, 2007; Knack and Smets, 2013). Finally, all of the above

contribute to normalize poor standards of conduct, which then inspire or incentivize poor

governance within the burgeoning state apparatus (see Isaksson and Kotsadam, 2018).

Notwithstanding the above, the presence of foreign donors may instead foster exemplary

norms of professional conduct when aid volumes are not intractable. When aid provisions are

maintained at relatively moderate rates, competition is not pronounced and coordination is

facilitated, thereby attenuating the mechanisms above. Under these conditions, good conduct

by donors is more likely to prevail and donor proliferation may actually strengthen institutions.

In this case donors may serve to monitor the development sector and public officials (Kimura

et al, 2012; Gibson et al 2015). New foreign contacts may offer licit opportunities for growth

(e.g. trade, FDI) acting as substitutes to displace rent-seeking by opportunistic government

agents (Dreher and Michaelowa, 2010). Donors may also bring experience, ideas, and innovation

which strengthen development processes and outcomes (Gehring et al, 2017). So even though5Donor fragmentation is especially prevalent within fragile states. In 2009 the average developing nation

received aid from over 20 separate donors; but in Afghanistan and Kenya 37 unique donors were documented. InEthiopia and Palestine, 19 unique donors accounted for less than 10% of aid in a single development sector (Frotand Santiso, 2010; OECD, 2013).

6One aid worker disclosed his agency spent over $100k on equipment prone to resale by corrupt governmentofficials, because no viable projects were available. The individual ultimately quit his job in protest of thisdecision (field interview, Kabul, November 2013).

2

existing theory suggests the moderating role of donor fragmentation is largely detrimental, it is

nevertheless reasonable to also expect direct benefits from fragmentation when aid volumes are

relatively contained.

While theoretically compelling, the above arguments have been subject to little empirical

scrutiny. Using granular data from Afghanistan, we offer the first micro-level analysis of aid

fragmentation and its effects. Our analysis exploits rich spatiotemporal variation in aid,

corruption, public opinion, and conflict. We assemble panel data at the district-quarter level of

aggregation, spanning 330 districts from July 2008 to December 2009 in our main

specifications. Donor fragmentation is operationalized using two measures of sector

concentration - the Herfindahl index, and a donor count. We estimate a fixed effects model

permitting donor fragmentation to moderate aid’s effectiveness at reducing corruption,

boosting public opinion, and improving security.

Our results suggest aid strengthens the quality of state institutions in the absence of

fragmentation (i.e. in the presence of a single donor). But as the donor landscape becomes

fragmented, those beneficial effects vanish. Confirming earlier conjecture by theoreticians and

practitioners, we discover a moderating force through which donor fragmentation dampens the

quality of institutions. In a reversal of priors, however, our evidence suggests donor

fragmentation also positively affects institutions when considered at moderate levels of aid.

Our micro-level evidence therefore suggests the direction of fragmentation’s total effect

depends on the volume of aid provision. A similar picture emerges when examining the impact

of aid and fragmentation on public opinion and conflict outcomes. Household opinions of

reconstruction and development are directly boosted by aid. But under voluminous aid

disbursements, the effects of fragmentation become deleterious. Nonfragmented aid is effective

at inducing stability, but donor fragmentation appears to attenuate that effectiveness and may

lead to damaging consequences.

Our analysis leverages data from three unique sources. Aid data are gleaned from a rare

hardcopy source of NATO C3 Agency’s Afghanistan Country Stability Picture. This source

contains over 30,000 development projects from approximately 40 different donors. We also

invoke newly declassified conflict microdata provided by US Central Command.7 Our records

include more than 200,000 precisely georeferenced and time-stamped events including close

combat attacks, IED explosions, and indirect fire engagements. Subjective measures of7These data implicitly address concerns about collection biases in media-derived conflict measures.

3

institutional quality and public opinion are gleaned from the Afghanistan Nationwide

Quarterly Assessment Research (ANQAR) surveys sponsored by ISAF HQ and Resolute

Support HQ. We operationalize the quality of state institutions using household assessments of

corruption and misuse of power among government officials. These data were accessed through

a pilot partnership agreement between NATO and the authors.

Following our main results, we strengthen identification by gathering data on numerous

factors potentially confounding the role of fragmentation. We provide evidence to rule out

confounding factors related to (i) characteristics of the aid initiative; (ii) human and physical

geography; (iii) levels of development; and (iv) security conditions. Even when allowing aid’s

effect to vary by all these criteria simultaneously, our results remain reasonably robust. To bound

the potential selection bias arising from the omission of unobservable factors, we follow Oster’s

(2019) implementation of a generalized Altonji et al. (2005) method. The approach involves

computing bias-adjusted effect sizes under empirically-founded assumptions regarding the degree

of selection on unobservables, and the proportion of outcome variance hypothetically explainable.

We calculate bounds for the (moderating and direct) effects of fragmentation on institutional

quality by observing coefficient and R2 movements when including observable controls. Under

this bias correction, the moderating role of fragmentation remains deleterious, and its direct

impact remains mostly beneficial. This exercise therefore provides plausible support for a causal

interpretation of our findings.

Altogether our results may be interpreted in the following manner. Nonfragmented aid

strengthens the quality of institutions, and therefore boosts public opinion and mitigates conflict.

Donor fragmentation also strengthens institutions at moderate levels of aid. Under high volumes

of aid, however, donor fragmentation hinders aid effectiveness at strengthening institutions,

thereby eroding public opinion and potentially facilitating conflict. Theoretical explanations

for these findings are expounded throughout the paper. In short, we suggest that under donor

fragmentation there transpires a degradation of ownership, accountability, and responsibility over

development outcomes and processes. This facilitates an ongoing culture of corruption within

state institutions, thereby nullifying aid’s otherwise beneficial impact on institutional quality,

and the indirect public opinion dividends that ensue (including those related to conflict).

Extending our analysis, we rule out two additional (non mutually exclusive) channels

potentially underpinning a relation between aid, fragmentation, and conflict. First, under poor

monitoring and accountability associated with donor fragmentation, aid resources may be

4

appropriated directly by insurgents. Second, economic inefficiencies spurred by donor

fragmentation may affect the opportunity cost of insurrection. Neither mechanism is supported

by our additional tests.

The remainder of this paper is structured as follows. The subsequent section contextualizes

our contribution with respect to related literature. Section 3 introduces all data and variables

used in the analysis. Section 4 examines the relation between aid, donor fragmentation, and the

quality of state institutions. Our identification strategy is developed in section 5. Sections 6

analyzes aid’s impact on public opinion and conflict. Section 7 concludes, and some alternative

channels of influence are discussed in Appendix B.

2 Contribution

The contribution of our paper lies at the intersection of three separate literatures. First, our

paper contributes significantly to the empirical understanding of aid fragmentation. This topic

has been central to high-level policy debate, and received considerable attention from

development theorists and practitioners. But to date, discussions on this issue have not

benefited from carefully identified studies permitting causal inference. Cross-sectional tests at

the country level suggest fragmentation renders aid ineffective (or counterproductive) at

boosting the quality of governance and economic growth (Knack and Rahman, 2007; Djankov

et al., 2009; Kimura et al., 2012). Gehring et al. (2017), however, casts doubt on those

findings. Crucially, all of these studies are conducted at the country level of analysis. But

coarse identification in this setting inhibits the reliability of estimates, and prevents the

exploration of underlying causal processes. We offer unique micro-level evidence on the effects

of aid fragmentation. Our results are consistent with theory suggesting fragmentation erodes

oversight, accountability, and responsibility over development processes and outcomes, and

thereby facilitates corruption (Gibson et al., 2005; Acharya et al., 2006; Knack and Rahman,

2007; Djankov et al., 2009; Knack and Smets, 2013). At the same time, we also provide

evidence supporting the far less prominent hypothesis that fragmentation may also confer

positive effects (Dreher and 2010; Kimura et al 2012; Gehring et al 2017).

Of equal importance, our paper speaks to literature examining the impact of foreign aid on

corruption and governance. The empirical record on this issue is mixed. Widely-cited findings

suggest aid increases corruption (Svensson, 2000; Alesina and Weder, 2002; Andersen et al., 2019)

and erodes the quality of institutions (Knack, 2001; Bräutigam and Knack, 2004; Djankov et al.,

5

2008; Busse and Gröning, 2009; Young and Sheehan, 2014). Yet evidence linking aid to improved

governance also exists (Tavares, 2003; Okada and Samreth, 2012; Kersting and Kilby, 2014;

Gibson et al., 2015; Dietrich and Wright, 2015; Jones and Tarp, 2016). Notably - most findings

in this vein of inquiry are based on country-level inference. To our knowledge, a single exception

is Isaksson and Kotsadam (2018), which finds Chinese aid projects fuel corruption at the local

level in Africa. Our paper extends this literature by offering additional results based on granular

spatiotemporal data.8 Our findings suggest locally nonfragmented aid reduces corruption, while

fragmentation diminishes those beneficial effects. By explicitly modeling fragmentation as a

source of heterogeneity, we are able to reconcile mixed results to date, on both theoretical and

empirical grounds.

Finally, our findings enhance the literature’s understanding of aid’s impact on conflict. Prior

work has yielded a scope of conditions under which aid delivery can reduce conflict. Chief among

them is the potential for aid to ‘win hearts and minds’ of community members. In order for aid

to be successful in this regard, it must first deliver outcomes, and also ‘do no harm’. But aid

fragmentation has been suggested to drain efficiency and perpetuate corruption. Hence, there

is a good reason to suspect fragmented aid will be unsuccessful at reducing conflict. Yet we

know little about the role of aid fragmentation in conflict settings, even though it is regarded as

highly relevant to the effectiveness of aid in general. Our study introduces donor fragmentation

as an important source of heterogeneity underlying the impact of aid on conflict. In doing so

we provide micro-level evidence that aid fragmentation can foster corruption, degrade public

opinion, and potentially intensify violence. Thus, our findings extend earlier work highlighting

public opinion as instrumental for aid’s effectiveness (see, e.g., Berman et al., 2011b; Beath et

al., 2018; and Child, 2019).

3 Data

Throughout the analysis, our primary unit of observation is the district-quarter. Our total

sample covers 398 districts over 15 quarters (from Q1 2006 to Q3 2009), containing 5,970

district-quarter observations.9 Aid volumes for a district-quarter are calculated as the average

number of concurrent projects being implemented. Fragmentation is calculated per day for8The richness of our data permits us to address a host of observable and unobservable factors potentially

confounded with aid provision at the subnational level.9We follow the 2005 Afghan Ministry of Interior administrative designation of 398 districts spanning 34

provinces.

6

each sector, then averaged across sectors and over time to obtain a district-quarter average.

Institutional quality and public opinion measures reflect average survey responses within a

district-quarter. Conflict levels are obtained by aggregating all significant activities over the

corresponding period. For ease of interpretation, aid, institutional quality, and conflict

measures are standardized to zero-mean and unit-variance. Fragmentation measures are

normalized to fall in the range [0, 1]. Conflict and aid measures are expressed in per-capita

terms, and winsorized at the 99.9th percentile.10 Population data is for 2011/12, and obtained

from the Central Statistics Organization of Afghanistan.

3.1 Aid Projects

Aid data is from NATO C3 Agency’s Afghanistan Country Stability Picture (ACSP). The

ACSP contains a comprehensive nationwide database of donor-funded reconstruction and

development projects between January 2002 and September 2009. These data include detailed

information on over 30,000 projects across 38 unique donors, documenting at least $28.2 billion

spent. For each project we have information on implementation start/end dates, sector, donor,

and location.11 The ACSP covers projects funded by USAID, World Bank, WHO, UN

agencies, military-led Provincial Reconstruction Teams (PRTs), and a host of other donors

(including individual countries, national development funds, multilateral agencies, and large

international NGOs). Projects span a number of sectors including education, health, security,

commerce and industry, agriculture, energy, water and sanitation, environment,

transportation, emergency assistance, capacity building, governance, and community

development. For each district-quarter we calculate the per-capita number of concurrent

projects being implemented on an average day.12 The spatial distribution of project volumes is

presented in Figure 1a. Because donor fragmentation is undefined in the absence of aid, we

restrict our sample to district-quarters with non-zero aid volumes. For the regression analysis,

Aid is standardized (to zero-mean, unit-variance) so that the direct effect of fragmentation is

interpretable when Aid takes the value of zero.10For descriptive purposes we scale these measures to the average-sized district (approximately 63,000

inhabitants).11Due to inconsistent transliteration of location names in the ACSP database, we invoke the Esri World

Gazetteer and digital mapping software to geolocate many projects in our sample.12Reliable cost data is available for only a subset of projects. Because fraud, waste, and abuse of development

funds is pervasive in our setting, we contend project counts are a more reliable metric of implementation volumes.

7

3.2 Donor Fragmentation

3.2.1 Donor Count

Our first measure of donor fragmentation relies on a simple count of donors active in each sector.

The more donors that are active, the greater is donor fragmentation. We begin by using the

day as our unit of analysis. By this measure, donor fragmentation on day t in sector j is the

following:

FDtj = 1− 1/

∑d∈N

Ddtj

where d indexes the set of donors N , and Ddtj indicates whether donor d is active in sector

j on day t. We impose FDtj = 0 if

∑d∈N Ddtj = 0. Accordingly, FD

tj ∈ [0, 1). To arrive at an

overall measure of donor fragmentation across sectors on day t, we take the weighted average:

FDt =

∑j∈J

[Ptj∑j∈J Ptj

]FDtj (1)

where J is the set of sectors, and Ptj is the amount of projects underway in sector j on day

t. Finally, we compute a quarterly measure, FDq , by taking a simple average across days in the

quarter. Effectively, FDq then reflects the average extent of contemporaneous overlapping

development responsibilities within-sector, during quarter q. The measure is constructed

separately for each district i, such that we have FDiq for incorporation into our later regression

analysis. Figure 1b reflects a heatmap of average fragmentation across districts in our sample.

3.2.2 Herfindhal-Hirschman Index

The above donor count measure reflects how many organizations operate simultaneously in the

same sector. That measure does not, however, reflect the distribution of participation shares

across donors. Importantly, theory predicts complications in aid delivery are exacerbated as

multiple donors enter the stage on relatively equal terms. By contrast, a fragmented donor

landscape highly skewed towards a single dominant player may not degenerate into loss of

ownership, accountability, monitoring, and coordination. Accordingly, we adopt a measure

based on the commonly invoked Herfindahl-Hirschman Index (HHI) of industry concentration

(Hirschman, 1945; Herfindahl, 1950). By this measure, donor fragmentation on day t in sector

j is:

8

FHtj = 1−

∑d∈N

(Pdtj

Ptj

)2

where Pdtj is the number of projects being undertaken by donor d on day t in sector j. The

more equal (skewed) are the participation shares of contributing donors, the higher (lower) is

donor fragmentation. Again, we impose FHtj = 0 if Ptj = 0, so FH

tj ∈ [0, 1). Just as in Equation

1 above, we take a weighted average across sectors to obtain a development-wide measure FHt .

Then, we again average over time to obtain a quarterly measure, FHq (all constructed at the

district level, yielding FHiq ). The spatial distribution of the HHI fragmentation measure closely

resembles Figure 1b. For our regression analysis, we normalize both FHiq and FD

iq to fall in the

[0, 1] range. The distribution of non-zero fragmentation (both measures) is depicted in Figure

3.

3.3 Corruption and Public Opinion

Subjective measures of corruption and public opinion are based on Afghanistan Nationwide

Quarterly Assessment Research (ANQAR) surveys sponsored by ISAF HQ and Resolute

Support HQ. These data were accessed through a pilot partnership agreement between NATO

and the authors. Polling was conducted every three months across the country, from

September 2008 until the present. During our sample period, the interviews were carried out

exclusively by the Afghan Center for Socio-Economic and Opinion Research (ACSOR).13

Interviews were proportionally distributed across districts according to CSO population data.

For each survey wave, settlements were selected randomly within each district, and 10

households were interviewed per settlement (using random walks and kish grids to select

respondents).14

We use the first five waves of the ANQAR survey (beginning July 2008 until December

2009), each of which has a sample size of more than 8500 households. The surveys collect

information on demographics and various domains of public opinion. We calculate district-level

average responses to questions about: government effectiveness at reducing corruption

(Corruption); governor and police chief abuse of power (Misuse of Power); and government13ACSOR serves as the implementing partner for numerous clients in Afghanistan, including the Asia

Foundation and Gallup International. Considerable efforts are made to ensure sponsors of ACSOR surveys(e.g. ISAF) remain anonymous to household respondents. Interviewers are local to the province they work in,having strong familiarity with the area’s culture and dialects.

14Further detail regarding sampling design and methodology of the ANQAR surveys is available upon request.See also Condra and Wright (2019).

9

effectiveness at delivering reconstruction and development (Good Job R&D). The resulting

variables and corresponding survey questions are listed in Table 1. Each question is asked for

varying levels of government authority. In our analysis we invoke the average response for each

level of authority, and an index reflecting the average response across all levels. Heatmaps

depicting index averages for each ANQAR outcome category are offered in Figures 2a-2c.

Table 2 reports summary statistics for key variables in the analysis.15

Because our analysis relies on subjective measures of corruption (i.e. Corruption, Misuse of

Power), some discussion around this matter is warranted. First, it is important to note that

Afghanistan is one of the most corrupt environments for public officials on record.16 In this

setting, when government officials are not actively suppressing corruption, they are effectively

(if tacitly) supporting corruption. Second, our results are estimated using temporal variation

in perceptions of corruption. If public opinion contends government effectiveness at reducing

corruption has declined from one period to the next, it is sensible to attribute this change in

perceptions to a rise in corruption itself. Moreover, if public perceptions contend that misuse

of power has increased, it is not unreasonable to assume those perceptions are adequately

rooted in some objective basis. Thus, throughout this study we interpret the subjective

measures Corruption and Misuse of Power as reflections of institutional quality. Nevertheless,

we acknowledge the limitations of survey data relative to objective measures of corruption used

elsewhere in this literature.17

3.4 Conflict Events

Our conflict microdata is newly declassified, and provided by the US Central Command. These

data track a number of significant conflict events (SIGACTS), including close combat (Direct

Fire), indirect fire engagements (Indirect Fire), and improvised explosive devices (IED

Explosion). Precise definitions for SIGACTS variables are provided in Table 1. Our records

include more than 200,000 precisely georeferenced and time-stamped SIGACTS from

2003–2014 across Afghanistan.18 For each district-quarter we aggregate events within each of15ANQAR data cover 330 districts across 5 waves, yielding measures of institutional quality and public opinion

for 1,083 district-quarter observations in total.16Transparency International’s Corruption Perceptions Index measures perceived levels of public-sector

corruption across 180 countries, drawing on expert and business surveys. Afghanistan ranked 176th and 179thin 2008 and 2009, respectively. See https://www.transparency.org for details.

17For the sake of brevity, we occasionally refer to effects on ‘corruption’ and ‘institutional quality’ in ouranalysis. These references are made with the explicit understanding that our outcomes capture only publicperceptions of these phenomena, strictly speaking.

18For additional details, see Condra et al. (2018) and Fetzer et al. (2021).

10

the abovementioned categories. The spatial distribution of an aggregate conflict index is

offered in Figure 2d. It is noteworthy that conflict and fragmentation are both spatially

concentrated (in Afghanistan’s Southern and Western regions, respectively). This underscores

the importance of controlling for district level effects, and primarily exploiting time variation

within a restricted geographical space to elicit our estimates of interest.

4 Aid Fragmentation and the Quality of State Institutions

The first step in our analysis is to estimate the effect of aid provision and donor fragmentation on

the quality of state institutions. To this end, we estimate the following model including district

and quarter fixed effects (with errors clustered at the district level):

Yiq = αi + β0Piq + β1PiqFiq + β2Fiq + γq + εiq (2)

Here Yiq captures public perceptions of corruption, or misuse of power among governors and

police chiefs (both catalogued in Table 1). Following the notation of section 3.2, Piq captures

the average number of concurrent aid projects disbursed in district i during quarter q. This

aid measure is standardized (zero-mean, unit-variance) to enable interpretation of β2.19 Fiq

captures donor fragmentation - either FDiq or FH

iq , as described in section 3.2.20 Thus β0 measures

the direct impact of aid on the quality of institutions, absent of donor fragmentation. The

parameter β2 then captures the direct impact of fragmentation on institutions, at average levels

of aid provision. Finally, β1 captures our effect heterogeneity of interest - how aid volumes

moderate the impact of donor fragmentation. Alternatively, β1 may be regarded as estimating

the effectiveness of aid in an environment characterized by maximal fragmentation, relative to

non-fragmented aid.

4.1 Corruption

In Table 3 we estimate Equation 2, with Yiq being district-quarter averages of Corruption. In

panels A and B we operationalize donor fragmentation with the donor count and Herfindahl

index, respectively. In column 1 our outcome is the corruption perceptions index, and we

find that aid generally improves assessments of corruption-reducing initiatives. A standard-19Absent of aid provision, the concept of fragmentation is meaningless (by construction, no aid implies no

fragmentation). So without standardizing aid, β2 would be uninterpretable.20Notably, Piq and Fiq are calculated as averages over the quarter, whereas Yiq is based on end-of-quarter

survey data. In that sense, Equation 2 does not estimate strictly contemporaneous effects.

11

deviation increase in aid leads to a nearly 1-standard-deviation decline in mean perceptions

of corruption. In columns 2, 3, and 4 we find appraisals of national, provincial, and district

government initiatives each positively influenced by aid provision.

These findings lend support to theoretical work suggesting aid can improve the quality of

institutions in a number of ways. First, aid programs may directly introduce governance reforms,

including those related to law enforcement and judicial organs (Brautigam and Knack 2004;

Jones and Tarp 2016). Second, aid can alleviate budget constraints otherwise inhibiting (i)

the development of well-functioning bureaucracies and legal systems, or (ii) training and salary

support for government officials prone to graft (Knack 2001). Third, aid inflows may defuse

distributional conflicts and attenuate the sacrifices of public officials resistant to modernization

(see, e.g., Casella and Eichengreen 1996). Fourth, aid conditionality can prompt improved

governance in exchange for continued outlays (Crawford 1997).

Our Panel A results also suggest donor proliferation reduces corruption at moderate levels

of aid (i.e. when standardized aid is nil). As mentioned in section 1, this may be attributed to

a number of factors. First, the presence of foreign donors can foster exemplary norms of

professional conduct adopted by government officials (Isaksson and Kotsadam, 2018). Second,

donors can serve a monitoring role with respect to aid providers and public officials (Kimura et

al, 2012; Gibson et al 2015). Third, foreign contacts may offer licit growth opportunities as

substitutes for rent-seeking by government officials (Dreher and Michaelowa, 2010). Fourth,

donors may bring experience, ideas, and innovation, thereby strengthening development

processes (Gehring et al, 2017).

But when aid volumes become intractably large, the beneficial effects of donor

fragmentation are reversed. The interaction terms of Panels A and B provide evidence for such

impact heterogeneity. Fragmented aid is significantly less successful at dampening corruption

across all specifications in Table 3. The total (direct plus moderating) effects of fragmentation

calculated for different levels of aid are depicted in Figure 4. Under moderate provisions of aid,

fragmentation is beneficial to institutional outcomes. But once aid volumes exceed around 0.7

standard deviations above average, the net effect of fragmentation on corruption turns

detrimental.

These results add nuance to near-consensus views regarding the damaging impact of donor

fragmentation on aid effectiveness. Conditional on high levels of aid disbursement, our

evidence empirically supports the theoretical arguments expounded in section 1. Greater

12

competition by aid providers limits spending opportunities, leading donors to accept partners

with poor standards of conduct (Djankov et al., 2009). Lax scrutiny can in turn facilitates

rebel and elite capture of development resources (Gibson et al., 2005; Acharya et al., 2006).

Fragmentation erodes donor accountability and oversight, decreasing quality standards, and

compromising public interests (Knack and Rahman, 2007; Knack and Smets, 2013). Finally, all

of the above contribute to poor standards of conduct which inspire or incentivize poor

governance by public officials (Isaksson and Kotsadam, 2018). Alternatively put, the rent

seeking famously associated with aid (e.g. Svensson 2000; Alesina and Weder 2002) is

exacerbated once oversight collapses under the weight of donor fragmentation. Foreign aid

typically shifts government accountability from the citizenry to international donors (Rajan

and Subramanian 2007; Kersting and Kilby 2014; Young and Sheehan 2014). Under donor

fragmentation, however, the commensurate lack of oversight and accountability implies no

single donor offers a check on corrupt practices (Knack, 2001). Altogether, our evidence

suggests both aid and fragmentation lead to improved governance under the right conditions.

But when combined at relatively high levels, the impact of fragmented aid can be deleterious.

4.2 Misuse of Power

Next we conduct analogous tests examining the misuse of power among governors and police

chiefs, by again estimating Equation 2. There is considerable evidence across both panels of

Table 4 that aid is effective at reducing the misuse of power. A standard-deviation increase in

aid generally provokes more than a one-half-standard-deviation decline in the perception of power

abuses. Similar to Table 3, Panel A also provides evidence that donor fragmentation has some

direct positive impact on the quality of institutions. Why these effects do not appear in Panel B

may be understood by considering that the sheer number of donors (rather than overlapping aid

shares) best captures the direct channels of influence discussed above. Importantly, both panels

provide evidence that donor fragmentation mitigates aid’s capacity to reduce corruption in the

form of power abuses. Roughly, our point estimates suggest an increase in fragmentation equal

to the interquartile range would neutralize almost all of aid’s beneficial effects in this domain.

The total effect of fragmentation on the misuse of power is depicted in Figure 5. Although

conceivably beneficial at moderate levels of aid, we observe significantly damaging effects as aid

reaches one standard deviation above the mean.

To reiterate, earlier work suggests aid can exacerbate corruption by (i)

13

introducing/reinforcing poor standards of conduct (Isaksson and Kotsadam, 2018); or (ii)

constituting a honey pot altering incentives for good governance (Svensson, 2000; Bräutigam

and Knack, 2004). Based on existing theory, we suggest aid fragmentation results in a loss of

accountability among donors in Afghanistan, enabling them to pursue selfish interests and

practice poor oversight. Such behaviour surely (i) constitutes a bad organizational norm, and

(ii) changes the calculus for public officials engaging in corruption. Thus, the ineffectiveness of

fragmented aid is consistent with either mechanism above countervailing aid’s otherwise

beneficial impact on institutions.

5 Identification

5.1 Selection on Observables

Thus far we have examined donor fragmentation as both a moderating and direct factor

influencing the effectiveness of aid. To this end we exploit rich spatiotemporal variation in

fragmentation - the source of which remains undocumented. Variation in fragmentation is the

outcome of myriad decision factors spread across the many donors in our study. If our

identification were to rely on exogenous variation from a specific policy discontinuity, our

LATE would be estimated at the margin of the corresponding donor’s engagement (subject to

location, sector, or donor specific idiosyncracies). So in the absence of a general shock to donor

incentives, we adopt an alternative identification strategy.

We assemble data on a host of observable factors which conceivably confound the role of

donor fragmentation. We then condition our estimates on these factors, identifying our effects

of interest from the residual variation in fragmentation. Our main analysis relies on both cross-

sectional and panel variation, so we include both time-invariant and time-varying controls. In

choosing candidate confounders we seek omitted variables which correlate with fragmentation,

moderate aid’s effectiveness, and influence outcomes directly. We invoke theoretically motivated

confounders across four qualitative domains (below). We test for the importance of each domain

as a source of selection bias by estimating a variant of Equation 2 in which aid’s impact is

permitted to vary over the range of confounds in that domain. This model takes the general

form:

Yiq = αi + β0Piq + β1PiqFiq + β2Fiq + β3PiqCiq + β4Ciq + γq + εiq (3)

14

where Y is the corruption or misuse of power index, and C is a column vector containing

confounds within a particular domain.21 In the first column of Table 5 (6) we reproduce the

specification of column 1 from Table 3 (4). The subsequent four columns of Tables 5–6 control

for the various candidate confounders motivated below.

First in column 2 we check whether donor fragmentation is confounded with important

characteristics of the aid effort. For example, when total aid volumes are large, they are often

delivered by multiple donors. So it is possible our heterogeneous effects thus far reflect decreasing

marginal returns to aid. Hence, we include a quadratic aid term as one control in column

2. Additionally, we include indicators for education spending, military-delivered aid, and US-

delivered aid, as these have been previously highlighted as important characteristics.22 Control

variables (Ciq) included in column 2 are defined in Panel A of Table A1, and summary statistics

are offered in Panel A of Table A2.

Next in column 3 we permit aid’s effect to vary accross the landscape of human and

physical geography. The Southern and Eastern regions of Afghanistan are comparatively

unstable, and aid’s effect has been shown to vary accordingly (Beath et al., 2018). Meanwhile,

we know from Figure 1b that fragmentation also exhibits spatial concentration. Accordingly,

we allow latitudinal and longitudinal coordinates to moderate the impact of aid. We also

include ethnicity shares to account for the influence of traditional culture and conservatism on

institutional development and aid disbursement. We include measures of population and urban

coverage to account for differences between urban and rural environments. And because all

aforementioned geographical characteristics are time invariant, we additionally allow for

region-specific trends in the form of region-quarter fixed effects. Variable definitions (summary

statistics) are again bracketed together in Table A1 (A2).

In column 4 we account for the level of development and demand for donor engagement,

as these are likely to influence disbursements and institutional quality. To this end we invoke

proxies for food shortage, educational attainment, and health services from Child (2019). We also

include survey-based measures of job availability, price fluctuations, and financial and physical

well-being. Finally, we account for the incidence of displaced persons and natural disasters.

Donor engagement is likely to intensify during periods of heightened need, while aid effectiveness

may be strained under challenging operational environments.21The subscripts of C depend on the dimensions of variation of its components. For time-invariant elements of

C, the corresponding term β4Ci is of course subsumed within the district fixed effect αi. But even if all elementsof C are time-invariant, β3 can still be identified.

22See, e.g., Berman et al. (2011b), Beath et al. (2018), and Child (2019).

15

Finally in column 5 we control for security conditions as an important source of confound.

In this respect we include a survey-based measure of local security. We also proxy for

government/coalition force prevalence by tallying friendly fire incidents. Lastly we include an

indicator for military base presence in the district. Each of these measures have clear relevance

for aid effectiveness and also for the spatiotemporal selection of donors.

By virtue of the battery of controls assembled, we are able to rule out many alternative

interpretations for our results. When examining columns 2-5 of Tables 5-6, we can see Panel A

results are generally robust, even when controlling simultaneously for all domains in column 6.23

These results offer support for a causal interpretation of our main findings. From Panel B of

Table 5 we recognize the limits of our findings. Once including geographical controls in column

3, the moderating effect of fragmentation becomes imprecisely estimated. Rather than due to

confoundedness, however, this loss in precision could also be a consequence of the declining

variation available to identify our effect of interest in the presence of many controls. Notably,

along similar lines the direct effect of fragmentation also becomes imprecisely estimated when

including geographical controls in columns 3 and 6 of Panel A in Table 5.24

5.2 Selection on Unobservables

The previous section follows an established tradition among studies based on observational data.

Observable omitted variables are included in the baseline specification to shed light on omitted

variable bias. When the treatment effect changes relatively little (and remains statistically

significant), it is common to conclude omitted variable bias is not of major concern. However,

there are at least two problems with that line of reasoning. First, in order to determine the

size of bias outstanding, one must consider how much outcome variance remains to be explained

after the inclusion of observable controls. Second, to understand how much effect sizes are

likely to change under the hypothetical inclusion of unobservable controls, one must make some

assumptions regarding the importance of unobservable relative to observable confounds.

Seminal work by Altonji et al. (2005) operationalizes the above considerations by calculating

bias-adjusted effect sizes. To generate such estimates, they assume the degree of confound arising23To conserve space, the coefficients β3 and β4 (from Equation 3) remain untabulated.24Readers interested in the cross-sectional determinants of fragmentation may refer to Table A3. There we

regress average fragmentation on the abovementioned potential confounds (measured prior to the sample period).Results suggest many of these variables are important determinants of fragmentation. With these factors alone,we are able to explain approximately 40% of the cross-sectional variation in donor fragmentation. Further detailsare provided in the notes of Table A3.

16

from unobservable factors is equal to that stemming from observables.25 Under the Altonji et al.

(2005) framework, omitted variable bias from unobservables is informed by coefficient movements

following the inclusion of observables. An estimated effect whose magnitude changes a lot under

small changes to R2 (when adding controls) is unlikely to remain relevant under the Altonji et al.

(2005) adjustment. By contrast, estimated effects which remain stable while observable controls

explain a large portion of residual outcome variation are likely to survive the bias adjustment.

Oster (2019) provides a blueprint for implementing a general version of the Altonji et al.

(2005) method.26 Table 7 reports results following her approach. Column 1 reports baseline

results from the first columns of Tables 5–6. Column 2 reports results from the fully controlled

models estimated in the final columns of Tables 5–6. In column 3 of Table 7 we report bias-

adjusted effects for both the moderating and direct effect of fragmentation. In Panels A1–B1

we can see that bias-adjusted estimates for the moderating effect of fragmentation (β1 from

Eqn 3) retain their original sign. This suggests unobservable omitted variable bias is unlikely to

account for the moderating effects documented in our study. A similar finding emerges (albeit

less robust) when bias-adjusting the direct effect of fragmentation (β2) in Panels A2–B2.

Next in column 4 we relax the assumption of equal selection betweeen unobservables and

observables (δ = 1, in Oster’s notation). Instead we calculate the relative selection on

unobservables (δ) required to nullify our effects of interest (i.e. to yield β = 0). Generally

speaking, selection on unobservables would need to considerably exceed selection on

observables in order to extinguish the moderating effect of fragmentation.27 As Oster (2019)

points out, this is unlikely to obtain when observable controls are carefully selected on a

theoretical basis. Given we possess rich data on observable controls, we find it unlikely they

collectively remain less important than unobservables in this study. It is also worth mentioning

that our ‘observable’ controls also include unobservable region-quarter fixed effects.25“Roughly speaking, this condition states that the part of an outcome that is related to the observables has

the same relationship with [the treatment] as the part related to the unobservables” (Altonji et al., 2005). Thisassumption is generally regarded as conservative since observable factors are typically chosen with an aim toreduce omitted variable bias.

26Oster (2019) relaxes an (implicit) assumption of Altonji et al. (2005) that 100% of outcome variance ishypothetically explainable. Oster (2019) demonstrates about 60% of RCT results in top economics journalswould not survive the correction. Because randomized treatment assignment is by construction uncorrelatedwith unobservables (in expectation), Oster (2019) instead suggests an alternative benchmark permitting 90%of RCT results to pass the test. That benchmark assumes the maximal R2 is 30% larger than the R2 of theresearcher’s fully controlled model. Still less than half of observational studies in top economics journals survivethe adjustment under her benchmark.

27Note that interpretating bounded δ is problematic when β changes direction or grows in magnitude underthe inclusion of observable controls.

17

6 Aid Fragmentation, Public Opinion, and Conflict

6.1 The Role of Public Opinion

Next we examine the impact of aid and fragmentation on public opinion of reconstruction and

development efforts.28 Table 8 presents estimates from Equation 2 invoking various public

opinion measures as the dependent variable. In Panel A of Table 8, aid appears to positively

affect community appraisals of national, provincial, and district government efforts. These

effects are consistent with our documented benefits of nonfragmented aid on the quality of

state institutions. In particular, a standard-deviation increase to nonfragmented aid leads to

an approximately one-half-standard-deviation improvement to public opinion of development

efforts (albeit imprecisely estimated in Panel B). In district-quarters characterized by high

donor fragmentation, however, the effectiveness of aid in boosting public support is nullified. It

therefore appears public sentiment responds to aid in a way consistent with its effectiveness at

strengthening institutions (even though the direct effects of fragmentation have little

consequence in this respect).

Our results suggesting nonfragmented aid boosts public opinion in Afghanistan are consistent

with the empirical findings of Beath et al. (2018). But since corruption persists relatively

unabated in the presence of fragmented aid, it is perhaps unsurprising this type of aid does

not yield public opinion dividends (a finding consistent, by contrast, with Böhnke and Zürcher

(2013)). Hearts and minds theory contends aid reduces conflict iff it is well-received by the

community (United States Army, 2006). From this perspective, the inability of fragmented

aid to galvanize public support for development initiatives implies it is unlikely to generate

support for hard counterinsurgency efforts, nor to reduce conflict by extension. In the following

subsection we test these conjectures.29

6.2 The Stabilizing Potential of Aid

According to hearts and minds theory, community support is crucial to determining the

success of counterinsurgency efforts. If community members are dissatisfied with public goods28See again Table 1 for variable definitions.29This discussion interprets public opinion as a function of aid’s ability to strengthen institutions. But

community appraisals of development efforts may be based as much on efficiency considerations. Fragmentation-spurred inefficiencies may be perceived by the community as wasteful, and therefore reflect poorly on thegovernment and coalition allies. If this degrades community support for development (and hard counterinsurgencyefforts by extension), fragmentation could also indirectly strengthen the insurgency through this alternativechannel.

18

provision (due for example to corruption in government), they are more likely to side with

rebels. Under such a scenario, community members would refrain from sharing intelligence

with government/coalition forces. Public appraisals of aid’s effectiveness are therefore expected

to heavily influence aid’s ability to promote stability.

In Table 9 we test the impact of aid and fragmentation on three conflict outcomes. The

impact of nonfragmented aid on each type of event is negative and significant at the 10%

threshold. These findings are consistent with those of Berman et al. (2011b) and Sexton

(2016). Effect sizes are small, but consistent with reasonable expectations regarding the

marginal impact of foreign aid on civil conflict. By allowing for heterogeneous effects of aid

across the level of donor fragmentation, we yield an interesting result. Under donor

fragmentation in Panel B, aid’s stabilizing effects on indirect fire engagements are significantly

reversed.30 Notably, indirect fire attacks require relatively less physical and human capital

investments than close-quarters combat operations (i.e. direct fire or IEDs). Thus, our findings

imply fragmentation is unlikely to enable conflict by facilitating rebel capture of aid resources.

Further evidence against that interpretation is provided in section B.1.31 More broadly, it can

be shown that the total impact of fragmented aid on conflict is statistically indistinguishable

from zero. By most accounts, nonfragmented aid appears effective at inducing stability, but

donor fragmentation tends to nullify that success. In this respect our findings extend earlier

work (e.g. Berman et al., 2013; Sexton, 2016; and Child, 2019) by elucidating a novel

dimension of aid influencing its effectiveness at ‘winning hearts and minds’.

7 Conclusion

Our findings suggest that non-fragmented aid in Afghanistan curtails corruption, boosts public

opinion, and reduces conflict. In the presence of donor fragmentation, however, the beneficial

impact of aid is obstructed. Donor fragmentation benefits the quality of institutions under

moderate levels of aid provision. But fragmentation carries deleterious effects, by contrast,

when aid volumes are high. This effect heterogeneity survives a strict bias-adjustment to

account for selection on both observable and unobservable confounding factors. Our

identification strategy therefore lends credence to a more causal interpretation of findings. We

suggest aid fragmentation degrades accountability over development processes and outcomes,30The total effect of fully fragmented aid on indirect fire incidents is positive and significant.31In section B.2 we subsequently rule out improved economic conditions as a channel connecting aid,

fragmentation, and conflict.

19

and thereby facilitates corruption. The persistence of poor quality state institutions precludes

public support for counterinsurgency, and conflict may persist unabated.

While policymakers have long emphasized the negative implications of aid fragmentation,

this paper offers the first micro-level evidence of its effects. Given the paucity of theoretical and

empirical research on this topic, we hope our results help carry this major policy interest further

into the academic domain. With more nuanced theory development and broader goegraphical

analyses, additional new insights can be generated to guide decisionmakers at various levels of

aid provision.

20

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23

Figure 1: Spatial distribution of aid and fragmentation

(34.4,186.7](19,34.4](12.7,19](9.4,12.7](6.05,9.4](4.2,6.05](2.9,4.2](2,2.9](1.1,2][0,1.1]

(a) Aid

(.26,.71](.18,.26](.12,.18](.06,.12](.04,.06](.03,.04](0,.03][0,0]

(b) Fragmentation

Note: Subfigures map spatial distributions of sample-wide district averages of aid and fragmentation. Aid dataare from NATO C3 Agency’s Afghanistan Country Stability Picture (ACSP). Each shade corresponds to onedecile. Darker shades indicates higher deciles. Subfigure (b) depicts Donor Count measure of fragmentation.

Figure 2: Spatial distribution of corruption, public opinion, and conflict

(3.2,4](3,3.2](2.9,3](2.8,2.9](2.7,2.8](2.6,2.7](2.5,2.6](2.3,2.5](2.1,2.3][1.3,2.1]No data

(a) Corruption

(3.2,3.8](3.1,3.2](3,3.1](2.9,3](2.8,2.9](2.7,2.8](2.6,2.7][2.1,2.6]No data

(b) Misuse of Power

(3.5,4.2](3.3,3.5](3.2,3.3](3.1,3.2](3,3.1](2.9,3](2.8,2.9](2.7,2.8](2.5,2.7][1.8,2.5]No data

(c) Good Job R&D

(22.3,148.7](11.6,22.3](5.2,11.6](2.5,5.2](1.1,2.5](.7,1.1](.4,.7](.2,.4](0,.2][0,0]

(d) Conflict

Note: Subfigures map spatial distributions of sample-wide district averages of corruption, public opinion, andconflict. Survey data are from the Afghanistan Nationwide Quarterly Assessment Research (ANQAR) surveyssponsored by ISAF HQ and Resolute Support HQ. Conflict microdata are provided by US Central Command.Each shade corresponds to one decile. Darker shades indicates higher deciles. All subfigures depict index averagesof corresponding measures.

24

Figure 3: Distribution of non-zero donor fragmentation

Note: Figure depicts empirical distributions of non-zero donor fragmentation. Sample includes 398 districtsacross Afghanistan, and spans 15 months. Left (right) panel corresponds to Donor Count (Herfindahl Index)measure. Data is from NATO C3 Agency’s Afghanistan Country Stability Picture (ACSP).

Figure 4: Heterogeneous effects of fragmentation on corruption

Note: Subfigures plot point estimates and 90% confidence intervals for the heterogeneous effects of fragmentationon corruption. The range of the x-axis [−0.6, 3] approximates the observed density of standardized aid (withrange [−0.58, 9.97]). Figure reports total effects for the Donor Count measure of fragmentation. Total effects arecalculated as β1Piq + β2, where coefficients are estimated from Equation 2, and aid volumes read off the x-axis.

25

Figure 5: Heterogeneous effects of fragmentation on misuse of power

Note: Subfigures plot point estimates and 90% confidence intervals for the heterogeneous effects of fragmentationon misuse of power. The range of the x-axis [−0.6, 3] approximates the observed density of standardized aid (withrange [−0.58, 9.97]). Figure reports total effects for the Donor Count measure of fragmentation. Total effects arecalculated as β1Piq + β2, where coefficients are estimated from Equation 2, and aid volumes read off the x-axis.

26

Tab

le1:

Outcomevariab

les

Nam

eRan

geSu

rvey

Question/VariableDescription

Corruption

a

GoA

[1-5]

How

welld

oestheGovernm

entof

Afgha

nistan

doitsjobredu

cing

corrup

tion

intheGovernm

ent?

ProvGov

[1-5]

How

welld

oestheGoverno

rof

this

prov

ince

dohisjobredu

cing

corrup

tion

inhisad

ministration?

DistGov

[1-5]

How

welld

oestheDistrictGoverno

rdo

hisjobredu

cing

corrup

tion

inhisad

ministration?

Misuseof

Pow

erb

ProvGov

[1-3]

Doyoube

lieve

thefollo

wingpe

rson

smisusetheirpo

wer:Provinc

ialG

overno

r?ProvPC

[1-3]

Doyoube

lieve

thefollo

wingpe

rson

smisusetheirpo

wer:Provinc

ialP

oliceChief?

DistGov

[1-3]

Doyo

ube

lieve

thefollo

wingpe

rson

smisusetheirpo

wer:DistrictGoverno

r?DistPC

[1-3]

Doyoube

lieve

thefollo

wingpe

rson

smisusetheirpo

wer:DistrictPoliceChief?

GoodJo

bR&D

c

GoA

[1-5]

How

welld

oestheGovernm

entof

Afgha

nistan

doitsjobat

developm

entan

dreconstruc

tion

inAfgha

nistan

?ProvGov

[1-5]

How

welld

oestheGoverno

rof

this

prov

ince

dohisjobat

developm

entan

dreconstruc

tion

oftheprov

ince?

DistGov

[1-5]

How

welld

oestheDistrictGoverno

rdo

hisjobat

developm

entan

dreconstruc

tion

ofthedistrict?

SIGACTS

DirectFire

Directfireoccurs

whe

nlethal

effects

arede

livered

onatarget

that

isvisibleto

theaimer

orfiringun

it,an

duses

the

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27

Table 2: Summary statistics

(1) (2) (3) (4) (5)N mean s.d. min max

Aid 5,156 15.66 27.15 0.01 286.17Fragmentation (Donor Count) 5,156 0.11 0.22 0 1Fragmentation|Frag>0 (Donor Count) 1,457 0.38 0.25 0.0008 1Fragmentation (Herfindahl Index) 5,156 0.08 0.18 0 1Fragmentation|Frag>0 (Herfindahl Index) 1,457 0.29 0.22 0.0005 1

Corruption 1,083 3.67 0.60 1.49 5GoA 1,083 3.78 0.61 1.39 5Prov Gov 1,083 3.63 0.68 1.50 5Dist Gov 1,083 3.61 0.67 1.30 5

Misuse of Power 1,082 1.85 0.33 1 2.79Prov Gov 1,082 1.84 0.41 1 3Prov PC 1,082 1.85 0.37 1 3Dist Gov 1,082 1.83 0.36 1 3Dist PC 1,082 1.89 0.38 1 3

Good Job R&D 1,083 3.02 0.45 1.66 4.82GoA 1,083 3.10 0.61 1.05 4.9Prov Gov 1,083 3.00 0.67 1.10 4.9Dist Gov 1,083 2.88 0.65 1.05 4.9

Direct Fire 5,156 4.52 14.89 0 190.39Indirect Fire 5,156 2.68 10.77 0 164.4IED Explosion 5,156 1.66 5.40 0 63.02

Note: For aid and conflict data, sample spans 15 quarters. For institutional qualityand public opinion data, sample spans 5 quarters. Cross-sectional coverage alsovaries by data source. Aid project data are from NATO C3 Agency’s AfghanistanCountry Stability Picture (ACSP). Corruption, Misuse of Power, and Good JobR&D outcomes are from Afghanistan Nationwide Quarterly Assessment Research(ANQAR) surveys sponsored by ISAF HQ and Resolute Support HQ. Conflictmicrodata are provided by US Central Command. GoA, Prov Gov, and Dist Gov referto government authorities at the national, provincial, and district level, respectively.Prov PC and Dist PC refer to police chiefs at the provincial and district level,respectively. Summary statistics for index averages in each category are reported inthe header rows of each panel.

28

Table 3: Aid fragmentation and corruption

(1) (2) (3) (4)Corruption GoA Prov Gov Dist Gov

Panel A: Donor CountAid -0.971*** -1.064*** -0.869*** -0.768***

(0.281) (0.292) (0.269) (0.266)Aid × Fragmentation 1.213*** 1.045** 1.169*** 1.136***

(0.405) (0.419) (0.363) (0.395)Fragmentation -0.828** -0.616* -0.763** -0.897**

(0.338) (0.321) (0.331) (0.347)R2 0.086 0.078 0.071 0.083

Panel B: Herfindahl IndexAid -1.143*** -1.350*** -1.005*** -0.836**

(0.361) (0.361) (0.346) (0.340)Aid × Fragmentation 2.071** 2.408*** 1.850** 1.519*

(0.884) (0.896) (0.817) (0.838)Fragmentation -0.408 0.0163 -0.379 -0.728

(0.517) (0.513) (0.499) (0.505)R2 0.082 0.079 0.066 0.078

Observations 1,083 1,083 1,083 1,083Districts 330 330 330 330

Note: Dependant variables are based on survey questions in Table 1, and describedin section 3.3. Each column of Panels A and B represents a separate regression basedon Equation 2. District fixed effects and year-quarter fixed effects are included. GoA,Prov Gov, and Dist Gov refer to government authorities at the national, provincial,and district level, respectively. Panel A (B) invokes the Donor Count (HHI ) measureof fragmentation. *, **, *** denote statistical significance at the 10, 5, and 1% level.Robust standard errors in parentheses are clustered at the district level.

29

Table 4: Aid fragmentation and misuse of power

(1) (2) (3) (4) (5)Misuse of Power Prov Gov Prov PC Dist Gov Dist PC

Panel A: Donor CountAid -0.600*** -0.309 -0.550*** -0.513** -0.771***

(0.209) (0.246) (0.199) (0.232) (0.213)Aid × Fragmentation 1.128*** 0.809** 1.129*** 1.078*** 1.002***

(0.273) (0.354) (0.336) (0.293) (0.283)Fragmentation -0.624** -0.842*** -0.343 -0.509* -0.487

(0.279) (0.310) (0.300) (0.298) (0.303)R2 0.082 0.060 0.066 0.076 0.060

Panel B: Herfindahl IndexAid -0.617** -0.213 -0.498* -0.629** -0.872***

(0.273) (0.323) (0.270) (0.284) (0.267)Aid × Fragmentation 1.313* 0.264 1.138 1.738** 1.603**

(0.765) (1.029) (0.842) (0.730) (0.771)Fragmentation -0.427 -0.827 -0.279 -0.158 -0.201

(0.463) (0.662) (0.473) (0.462) (0.467)R2 0.076 0.053 0.060 0.073 0.058

Observations 1,082 1,082 1,082 1,082 1,082Districts 330 330 330 330 330

Note: Dependant variables are based on survey questions in Table 1, and described in section 3.3. Each columnof Panels A and B represents a separate regression based on Equation 2. District fixed effects and year-quarterfixed effects are included. Prov Gov and Dist Gov refer to government authorities at the provincial and districtlevel, respectively. Prov PC and Dist PC refer to police chiefs at the provincial and district level, respectively.Panel A (B) invokes the Donor Count (HHI ) measure of fragmentation. *, **, *** denote statistical significanceat the 10, 5, and 1% level. Robust standard errors in parentheses are clustered at the district level.

30

Table 5: Selection on observable factors - Corruption

(1) (2) (3) (4) (5) (6)Panel A: Donor CountAid -0.971*** -0.921 -19.368 1.997 1.428 -12.844

(0.281) (0.601) (29.937) (3.173) (0.938) (32.158)Aid × Fragmentation 1.213*** 0.995** 0.939** 0.995** 1.285*** 0.845*

(0.405) (0.413) (0.396) (0.425) (0.434) (0.438)Fragmentation -0.828** -0.650** -0.514 -0.811** -0.739** -0.384

(0.338) (0.330) (0.360) (0.335) (0.329) (0.330)R2 0.086 0.104 0.192 0.127 0.092 0.241

Panel B: Herfindahl IndexAid -1.143*** -0.975 -22.248 1.874 1.424 -14.119

(0.361) (0.618) (29.885) (3.392) (0.951) (32.216)Aid × Fragmentation 2.071** 1.971* 1.827 1.920* 2.634** 1.980

(0.884) (1.034) (1.398) (1.122) (1.076) (1.533)Fragmentation -0.408 -0.181 0.080 -0.430 -0.116 0.258

(0.517) (0.548) (0.735) (0.574) (0.588) (0.775)R2 0.082 0.103 0.190 0.125 0.090 0.240

Observations 1083 1083 1083 1083 1082 1082Aid characteristics Yes YesGeography Yes YesDevelopment Yes YesSecurity Yes Yes

Note: Dependant variable is Corruption (index). Each column of each panel represents a separateregression based on Equation 3. District fixed effects and year-quarter fixed effects are included in allspecifications. We include four sets of potentially confounding factors (in both levels and interactionswith Aid). Variable definitions, sources, and summary statistics for these variables are reported inTables A1 and A2. Variables from SIGACTS and ANQAR are time-variant, whereas variables fromother data sources are measured cross-sectionally prior to Wave 1 of ANQAR. *, **, *** denotestatistical significance at the 10, 5, and 1% level. Robust standard errors in parentheses are clusteredat the district level.

31

Table 6: Selection on observable factors - Misuse of Power

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

Panel A: Donor CountAid -0.600*** -0.259 -9.813 3.126 0.264 9.108

(0.209) (0.422) (23.942) (2.559) (0.657) (33.877)Aid × Fragmentation 1.128*** 1.315*** 1.074*** 1.172*** 1.152*** 1.114***

(0.273) (0.277) (0.354) (0.287) (0.285) (0.362)Fragmentation -0.624** -0.596** -0.650* -0.689** -0.595** -0.652**

(0.279) (0.269) (0.341) (0.276) (0.283) (0.326)R2 0.082 0.109 0.176 0.090 0.084 0.206

Panel B: Herfindahl IndexAid -0.617** -0.302 -11.878 3.406 0.297 8.484

(0.273) (0.418) (23.903) (2.679) (0.654) (33.953)Aid × Fragmentation 1.313* 2.040** 1.305 1.528 1.340 1.735

(0.765) (0.926) (1.352) (1.112) (0.920) (1.498)Fragmentation -0.427 -0.149 -0.435 -0.458 -0.399 -0.432

(0.463) (0.489) (0.706) (0.544) (0.493) (0.730)R2 0.076 0.103 0.171 0.084 0.078 0.204

Observations 1082 1082 1082 1082 1081 1081Aid characteristics Yes YesGeography Yes YesDevelopment Yes YesSecurity Yes Yes

Note: Dependant variable is Misuse of Power (index). Each column of each panel representsa separate regression based on Equation 3. District fixed effects and year-quarter fixed effects areincluded in all specifications. We include four sets of potentially confounding factors (in both levelsand interactions with Aid). Variable definitions, sources, and summary statistics for these variablesare reported in Tables A1 and A2. Variables from SIGACTS and ANQAR are time-variant, whereasvariables from other data sources are measured cross-sectionally prior to Wave 1 of ANQAR. *, **,*** denote statistical significance at the 10, 5, and 1% level. Robust standard errors in parenthesesare clustered at the district level.

32

Tab

le7:

Coefficientstab

ility

(1)

(2)

(3)

(4)

Baselineβ1(s.e.)

[R2]

Con

trolledβ1(s.e.)

[R2]

Bias-ad

justed

β1

Bou

nded

δ

Pan

elA1:

Don

orCou

ntCorruption(ind

ex)

1.21

3(0.405

)[0.086]

0.84

5(0.438

)[0.241

]0.46

21.630

Misuseof

power

(ind

ex)

1.12

8(0.273

)[0.082

]1.11

4(0.362

)[0.206

]1.09

72.97

0Pan

elB1:

Herfin

dahl

Index

Corruption(ind

ex)

2.07

1(0.884

)[0.082]

1.98

0(1.533

)[0.240

]1.54

21.237

Misuseof

power

(ind

ex)

1.31

3(0.765

)[0.076

]1.73

5(1.498

)[0.204

]4.07

0N/A

Baselineβ2(s.e.)

[R2]

Con

trolledβ2(s.e.)

[R2]

Bias-ad

justed

β2

Bou

nded

δ

Pan

elA2:

Don

orCou

ntCorruption(ind

ex)

-0.828

(0.338

)[0.086

]-0.384

(0.330

)[0.241]

-0.055

1.13

5Misuseof

power

(ind

ex)

-0.624

(0.279

)[0.082

]-0.652

(0.326

)[0.206

]-0.675

N/A

Pan

elB2:

Herfin

dahl

Index

Corruption(ind

ex)

-0.408

(0.517

)[0.082

]0.25

8(0.775

)[0.240

]1.01

5N/A

Misuseof

power

(ind

ex)

-0.427

(0.463

)[0.076

]-0.432

(0.730

)[0.131

]-0.438

N/A

Not

e:In

column1,

baselin

eβ1,β2,stan

dard

errors,an

dR

2aretakenfrom

thefirst

columns

ofTab

les5an

d6.

Incolumn2,

controlle

dβ1,β2,stan

dard

errors,an

dR

2aretakenfrom

thelast

columns

ofTab

les5an

d6.

BorrowingOster’s

notation

,δgaug

estheconfou

nding

role

ofun

observab

lesrelative

toob

servab

les.

Wead

optOster

(2019)

assumptions

toelicit

thebias-adjustedβforδ=

1,an

dthebo

unde

dδforβ=

0(incolumns

3an

d4,

respectively).

33

Table 8: Aid fragmentation and public opinion

(1) (2) (3) (4)Good Job R&D GoA Prov Gov Dist Gov

Panel A: Donor CountAid 0.538** 0.682*** 0.582** 0.539*

(0.271) (0.251) (0.264) (0.295)Aid × Fragmentation -0.650** -0.762** -0.755** -0.938***

(0.284) (0.301) (0.320) (0.345)Fragmentation 0.195 -0.0674 0.282 0.662**

(0.256) (0.304) (0.239) (0.319)R2 0.074 0.047 0.068 0.066

Panel B: Herfindahl IndexAid 0.510 0.661** 0.463 0.400

(0.326) (0.309) (0.316) (0.353)Aid × Fragmentation -0.478 -0.767 -0.247 -0.262

(0.848) (0.718) (0.862) (0.935)Fragmentation -0.105 -0.401 0.211 0.627

(0.410) (0.430) (0.464) (0.459)R2 0.070 0.042 0.062 0.056

Observations 1,083 1,083 1,083 1,083Districts 330 330 330 330

Note: Dependant variables are based on survey questions in Table 1, and describedin section 3.3. Each column of Panels A and B represents a separate regression basedon Equation 2. District fixed effects and year-quarter fixed effects are included. GoA,Prov Gov, and Dist Gov refer to government authorities at the national, provincial,and district level, respectively. Panel A (B) invokes the Donor Count (HHI ) measureof fragmentation. *, **, *** denote statistical significance at the 10, 5, and 1% level.Robust standard errors in parentheses are clustered at the district level.

34

Table 9: Aid fragmentation and conflict

(1) (2) (3)Direct Fire Indirect Fire IED Explosion

Panel A: Donor CountAid -0.197* -0.0406* -0.117***

(0.101) (0.0242) (0.0117)Aid × Fragmentation 0.154 0.0307 0.0497

(0.136) (0.0335) (0.0483)Fragmentation -0.0346 0.00602 -0.0809

(0.102) (0.0573) (0.0861)R2 0.070 0.033 0.099

Panel B: Herfindahl IndexAid -0.178* -0.0411** -0.108***

(0.0926) (0.0203) (0.0156)Aid × Fragmentation 0.288 0.158** 0.0160

(0.303) (0.0752) (0.199)Fragmentation -0.0884 -0.0561 -0.157

(0.140) (0.0768) (0.128)R2 0.069 0.034 0.100

Observations 5,156 5,156 5,156Districts 395 395 395

Note: Dependant variables are based on conflict microdata, and described insection 3.4. Each column of Panels A and B represents a separate regression basedon Equation 2. District fixed effects and year-quarter fixed effects are included.Panel A (B) invokes the Donor Count (HHI ) measure of fragmentation. *, **, ***denote statistical significance at the 10, 5, and 1% level. Robust standard errors inparentheses are clustered at the district level.

35

Table A.1: Variable definitions for potential confounds

Name Source Description

Panel A: AidAid ACSP Mean concurrent projects (see section 3.1)Education ACSP Indicator for ongoing education project(s)PRT ACSP Indicator for military-led PRT donor activityUSA ACSP Indicator for US donor activity

Panel B: GeographyLongitude Esri Longitudinal coordinates of district centerLatitude Esri Latitudinal coordinates of district centerPashtuns ANQAR Share of Pashtun householdsTajiks ANQAR Share of Tajik householdsHazaras ANQAR Share of Hazara householdsUzbeks ANQAR Share of Uzbek householdsTurkmen ANQAR Share of Turkmen householdsArabs ANQAR Share of Arab householdsNuristanis ANQAR Share of Nuristani householdsBalochis ANQAR Share of Balochi householdsPopulation CSO log of district population (1000s)Urban NRVA Urban share of district populationRegion ACSP Categorical indicators for North, East, South, West, and Central

regions

Panel C: DevelopmentHunger Child (2019) Unable to satisfy food needs of household (times/year); based on

NRVAEducation Child (2019) Measure of educational attainment; percentile rank of first

principal component score based on relevant questions from NRVAHealth services Child (2019) Measure of access to health services; percentile rank of first

principal component score based on relevant questions from NRVAJobs AF Availability of jobs? [1 = very good, ..., 4 = very bad]Price Change NRVA Any change to price of goods in the last 3 months that are not

based on the season? [1=Y; 0=N]Finances AF Financial wellbeing of household better? [1 = better, ..., 3 =

worse]Health AF Health wellbeing of your family membersDisplacement SIGACTS Event where persons have been internall displaced as a result of

armed conflict, generalized violence, violations of human rights,or natural disasters

Disaster SIGACTS Events where a natural event has caused significant loss of life ordamage to property and infrastructure (e.g. severe weather, rockfall, earthquakes, and flooding)

Panel D: SecuritySecurity AF Security situation in your area? [1 = excellent, ..., 4 = poor]Friendly fire SIGACTS Conflict event occurring accidentally between friendly forces (e.g.

Afghan National Army fires upon Afghan National Police)Military Base Gehring

et al. (2019)Indicator for any military base in the district

Source: ACSP: Afghanistan Country Stability Picture. Esri: GIS mapping software. ANQAR:Afghanistan Nationwide Quarterly Assessment Research. CSO: Central Statistics Organization ofAfghanistan. NRVA: National Risk and Vulnerability Assessment survey, 2007-2008. AF: Asia Foundationsurvey, 2007. SIGACTS: Conflict microdata provided by US Central Command.

36

Table A.2: Summary statistics for potential confounds

(1) (2) (3) (4) (5)N mean s.d. min max

Panel A: AidAid 367 -0.09 0.89 -0.57 6.66Education 368 0.44 0.43 0 1PRT 368 0.68 0.41 0 1USA 368 0.39 0.45 0 1

Panel B: GeographyLongitude 367 67.74 2.66 61.01 73.30Latitude 367 34.54 1.70 29.90 37.66Pashtuns 367 0.48 0.43 0 1Tajiks 367 0.29 0.35 0 1Hazaras 367 0.07 0.20 0 0.98Uzbeks 367 0.08 0.19 0 0.92Turkmen 367 0.03 0.12 0 0.91Arabs 367 0.01 0.03 0 0.27Nuristanis 367 0.02 0.14 0 0.99Balochis 367 0.01 0.07 0 0.80Population 367 3.75 0.81 0.69 8.10Urban 367 0.07 0.19 -0.04 1

Panel C: DevelopmentHunger 367 2.28 0.73 0 4.04Education 367 50.17 28.97 1 100Health Services 368 50.53 28.54 1 100Jobs 367 3.14 0.36 1.71 4.00Price Change 330 0.73 0.13 0.32 1Finances 367 1.98 0.33 1.15 2.93Health 367 1.85 0.32 1.03 2.75Displacement 367 0.01 0.04 0 0.34Disaster 367 0.01 0.10 0 1.50

Panel D: SecuritySecurity 367 2.29 0.47 1 3.70Friendly Fire 368 0.02 0.08 0 0.94Military Base 367 0.07 0.23 -0.04 1

Note: Aid, Education, PRT, USA, Displacement, Disaster, and Friendly Fire are districtaverages between July 2007 and June 2008. Remaining variables are either cross-sectionalor static. See Table A.1 for variable descriptions and sources.

37

Table A.3: Cross-sectional determinants of fragmentation

(1) (2)Donor count HHI

Aid 0.025* 0.003(0.014) (0.003)

Education -0.005 -0.007(0.015) (0.011)

PRT 0.072*** 0.046***(0.014) (0.011)

USA 0.063*** 0.035***(0.016) (0.011)

Longitude 0.004 0.004(0.003) (0.002)

Latitude -0.020*** -0.017***(0.007) (0.006)

Pashtuns -0.035 0.035(0.084) (0.027)

Tajiks 0.005 0.072**(0.084) (0.030)

Hazaras -0.012 0.054**(0.087) (0.027)

Uzbeks 0.008 0.066**(0.084) (0.032)

Turkmen 0.214* 0.263***(0.119) (0.100)

Arabs 0.138 0.301(0.258) (0.262)

Nuristanis -0.033 0.050*(0.084) (0.030)

Balochis -0.025 0.024(0.097) (0.050)

Population 0.036*** 0.016**(0.010) (0.007)

Urban 0.077 0.088**(0.047) (0.037)

Hunger 0.017** 0.013**(0.008) (0.006)

Education -0.000 -0.000(0.000) (0.000)

Health Services -0.000 -0.000(0.000) (0.000)

Jobs -0.011 -0.013(0.013) (0.010)

Finances 0.023 0.018(0.022) (0.013)

Health -0.024 -0.012(0.017) (0.012)

Displacement 0.287** 0.263***(0.112) (0.098)

Disaster -0.038 -0.025(0.035) (0.022)

Security -0.012 -0.013(0.013) (0.011)

Friendly Fire 0.086 0.090(0.100) (0.079)

Military Base 0.079** 0.074**(0.038) (0.030)

Observations 367 367R2 0.417 0.379

Notes: Tabular results are from estimating the cross-sectionalmodel: Fi = α + βCi + εi. Fragmentation outcomes, Fi, arecalculated as averages over the sample period July 2008 - September2009. Explanatory variables, Ci, are taken from the list ofconfounding factors discussed in section 5.1 and defined in TableA1. The items of Ci are measured as static prior to July 2008, orcalculated as an average of values during the four quarters precedingJuly 2008. Sample includes only districts having received a positiveamount of aid between July 2008 and September 2009. *, **, ***denote statistical significance at the 10, 5, and 1% level.

38

Appendix B: Alternative Channels

B.1 Rebel Financing

When foreign aid is distributed with little oversight and accountability, the appropriation of

development resources by anti-government elements may ensue. In that case aid would directly

finance the insurgency, thereby strengthening its capacity to carry out attacks (Wright, 2016;

Sonin and Wright, 2020). We test this channel by examining insurgents’ fundraising practices.

In particular, we examine aid’s impact on the incidence of terrorist group financing and illegal

checkpoints (both defined in the notes of Table B.1).

In Columns 1-2 of Table B.1 we estimate Equation 2 and find nonfragmented aid reduces

terrorist financing and illegal checkpoints. Donor fragmentation also appears to directly reduce

terrorist financing. Allowing for heterogeneity (through the interaction term) we find these

effects essentially unchanged under a fragmented donor landscape. As such, our evidence does

not suggest donor fragmentation enables anti-government forces to capture aid rents.

Moreover, as mentioned in section 6.2 - fragmented aid appears most likely to exacerbate

conflict through indirect fire engagements. If fragmented aid were associated with positive

shocks to rebel financing, we would instead expect more capital-intensive attacks to increase

(i.e. direct fire and IEDs).

B.2 Opportunity Cost of Insurrection

One prominent theory linking conflict to aid is the opportunity cost perspective. This theory

suggests aid improves local economic conditions, and thereby makes alternative career paths

more attractive for would-be insurgents. On theoretical grounds, aid fragmentation carries

an ambiguous impact on the amount of legitimate economic opportunities availed to marginal

insurgents. For example, while replication of projects or administrative duties is clearly inefficient

from a central planning perspective, it may actually have employment-increasing effects at the

local level. As such, pure efficiency considerations do not provide clear theoretical expectations

regarding the impact of aid on conflict. As such, in this subsection we empirically investigate

the potential relevance of this theory.

In Columns 3-6 of Table B.1 we introduce four outcomes related to employment: level of

legitimate employment; receipt of legitimate income; household income growth; and incidence

39

of terrorist recruitment.32 Results suggests aid provision generally reduces terrorist recruitment

(column 6), but we find no commensurate effect on legitimate employment outcomes (columns

3-5). Neither panel offers evidence of differential employment effects between fragmented and

nonfragmented aid. As such, we determine that opportunity cost mechanisms are unlikely to

explain the broad patterns demonstrated throughout this paper.

32Outcomes are defined in Table B.1 notes.

40

Tab

leB.1:Reb

elfin

ancing

andop

portun

itycost

(1)

(2)

(3)

(4)

(5)

(6)

Finan

ceIllega

lChe

ckpo

ints

EmploymentProvision

EmploymentIncome

IncomeIm

proved

Terrorist

Recruitment

Pan

elA:Don

orCou

ntAid

-0.080

6**

-0.095

0***

0.19

8-0.017

10.186

-0.0319**

(0.038

6)(0.034

2)(0.216

)(0.256)

(0.340)

(0.0135)

Aid*F

ragm

entation

-0.0492

0.08

24-0.474

-0.701

-0.342

-0.0197

(0.068

1)(0.058

5)(0.317

)(0.470)

(0.394)

(0.0320)

Frag

mentation

-0.143

**0.00

960

0.27

00.12

9-0.208

0.0896

(0.066

8)(0.052

2)(0.206

)(0.465)

(0.319)

(0.0748)

R2

0.03

10.01

60.02

60.02

10.201

0.009

Pan

elB:Herfin

dahl

Index

Aid

-0.097

2***

-0.072

2**

0.31

30.07

380.136

-0.0340***

(0.032

3)(0.031

8)(0.234

)(0.299)

(0.395)

(0.0127)

Aid*F

rag

0.04

02-0.0645

-1.116

*-1.456

-0.308

0.0184

(0.198

)(0.263

)(0.620

)(1.006)

(0.981)

(0.118)

Frag

-0.224

**-0.051

70.12

70.11

3-0.193

0.00230

(0.087

6)(0.048

8)(0.277

)(0.605)

(0.451)

(0.0801)

R2

0.03

10.01

60.02

90.02

30.200

0.008

Observation

s5,15

65,15

61,08

31,08

31,083

5,156

#district

395

395

330

330

330

395

Not

e:Dep

ende

ntvariab

lesin

columns

1,2,

and6aredistrict-qua

rter

SIGACTSeventaggregates,de

fined

asfollo

ws.

Fin

ance:activities

tied

tofund

ingof

illegal

events

orterroristactivities,suchas

mon

eylaun

dering

.Illega

lChe

ckpo

ints:checkp

ointsno

tap

proved

bytheGoA

,nor

officially

establishe

dby

ANDSF

orISAF/R

S.Ter

rori

stRec

ruitm

ent:

events

whe

reinsurgents

areactively

recruiting

individu

als.

Dep

enda

ntvariab

lesin

columns

3-5aredistrict-qua

rter

averages

ofANQAR

survey

respon

sesto

thefollo

wingqu

estion

s.Em

ploy

men

tPro

visi

on:How

satisfied

areyo

uwiththeprovisionof

jobs/employ

mentin

your

area?[1:very

dissastisfied

,...,5:

very

satisfied

].Em

ploy

men

tIn

com

e:Doesyour

family

currentlyha

veincomethroug

hem

ploymentor

othe

rmeans?

[1=Y;0

=N].

Inco

me

Impr

oved

:Has

your

family

’secon

omic

situationgotten

better,stayedthesameor

gotten

worse

compa

redto

12mon

thsago?

[1=

worse,

...,3=

better].

Eachcolumnof

Pan

elsA

andB

represents

asepa

rate

regression

basedon

Equ

ation2.

*,**,***de

note

statisticalsign

ificanceat

the10,5,

and1%

level.

Rob

uststan

dard

errors

inpa

renthe

sesareclusteredat

thedistrict

level.

Districtfix

edeff

ects

andyear-qua

rter

fixed

effects

areinclud

ed.

41