HIAS-E-94

Decentralized Targeting of Agricultural Credit Programs:
Private versus Political Intermediaries
Pushkar Maitra(a) , Sandip Mitra(b) , Dilip Mookherjee(c) and Sujata Visaria(d)(a) Department of Economics, Monash University, Clayton Campus, VIC 3800, Australia(b) Sampling and Oﬃcial Statistics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India(c) Department of Economics, Boston University, 270 Bay State Road, Boston, MA 02215, USA.
(d) Department of Economics, Lee Shau Kee Business Building, Hong Kong University of Science andTechnology, Clear Water Bay, Hong Kong

January, 2020

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Decentralized Targeting of Agricultural
Credit Programs: Private versus Political
Intermediaries⇤
Pushkar Maitra†, Sandip Mitra‡, Dilip Mookherjee§, Sujata Visaria¶
January 2020
Abstract
We compare two di↵erent methods of appointing a local commission agent as anintermediary for a credit program. In the Trader-Agent Intermediated Lending
Scheme (TRAIL), the agent was a randomly selected established private trader,while in the Gram Panchayat-Agent Intermediated-Lending Scheme (GRAIL),
he was randomly chosen from nominations by the elected village council. More
TRAIL loans were taken up, but repayment rates were similar, and TRAIL loans
had larger average impacts on borrowers’ farm incomes. The majority of this difference in impacts is due to di↵erences in treatment e↵ects conditional on farmer
productivity, rather than di↵erences in borrower selection patterns. The findings
can be explained by a model where TRAIL agents increased their middleman
profits by helping more able treated borrowers reduce their unit costs and increase
output. In contrast, for political reasons GRAIL agents monitored the less able
treated borrowers and reduced their default risk.
Key Words: Targeting, Intermediation, Decentralization, Community Driven Development, Agricultural Credit, Networks.
JEL Codes: H42, I38, O13, O16, O17⇤
Funding was provided by the Australian Agency for International Development, the International Growth Centre,
United States Agency for International Development, the Hong Kong Research Grants Council (GRF 16503014) and the
HKUST Institute for Emerging Market Studies (IEMS15BM05). We thank the director and sta↵ of Shree Sanchari forcollaborating on the project. Jingyan Gao, Arpita Khanna, Clarence Lee, Daijing Lv, Foez Mojumder, Moumita Poddar
and Nina Yeung provided exceptional research assistance, and Elizabeth Kwok provided excellent administrative support.
We thank audiences at numerous seminars and conferences for their comments and suggestions, which have significantly
improved the paper. We received Institutional Review Board clearance from Monash University, Boston University andthe Hong Kong University of Science and Technology. The authors are responsible for all errors.
†
Pushkar Maitra, Department of Economics, Monash University, Clayton Campus, VIC 3800, Australia.
Pushkar.Maitra@monash.edu.
‡
Sandip Mitra, Sampling and Official Statistics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108,
India. Sandip@isical.ac.in.
§
Dilip Mookherjee, Department of Economics, Boston University, 270 Bay State Road, Boston, MA 02215, USA.
dilipm@bu.edu.
¶
Sujata Visaria, Department of Economics, Lee Shau Kee Business Building, Hong Kong University of Science andTechnology, Clear Water Bay, Hong Kong. svisaria@ust.hk.

1Introduction

Public programs in developing countries are increasingly being implemented at the local
level. Communities are monitoring service providers, and local governments are delivering development and welfare programs. Despite the promise that decentralization can
improve targeting and implementation through better information and accountability,
a growing literature argues that these programs have a mixed record.1 Community-led
programs are often captured by the local elite (see, for example World Development
Report 2004; Mansuri and Rao 2013; Vera-Cossio 2018; Deserranno et al. 2018), andthere is evidence that local governments target their vote bank, rather than those who
stand to benefit the most (Stokes 2005; Robinson and Verdier 2013; Bardhan et al. 2015;
Bardhan and Mookherjee 2016; Devarajan and Khemani 2016; Dey and Sen 2016).
In this paper we present evidence on a new alternative designed in the context of an
agricultural credit program in West Bengal, India. The program o↵ered subsidized individual liability loans to smallholder farmers. Our approach, called Agent IntermediatedLending (AIL), delegates the selection of beneficiaries to an intermediary chosen fromwithin the community. It sought to leverage the intermediary’s specialized informationand connections with local residents, while avoiding the pitfalls associated with elite
capture.
During 2010–2013 we collaborated with a microfinance institution that implemented
the AIL scheme in 48 randomly chosen villages in two potato-growing districts of West
Bengal, India. We experimented with two di↵erent versions of the AIL scheme.2 In
the 24 villages randomly assigned to the Trader-Agent Intermediated Lending (TRAIL)
treatment, the agent was randomly chosen from a list of local private traders with atrack record of lending to, and selling and buying from farmers in the village. In theremaining 24 villages assigned to the Gram Panchayat Agent Intermediated Lending(GRAIL) treatment, the agent was randomly selected from a list of local individuals
provided by the elected village council in GRAIL. The agents then recommended village
households for individual liability loans designed to finance the cultivation of the local
1

In her survey of community-driven development (CDD) programs around the world, Casey (2018)
argues that although CDDs have successfully provided public goods in countries with weak states (forexample, Sierra Leone, Indonesia, Afghanistan and Sudan), they have generally failed to transform local
decision-making or increase empowerment.
2
In a di↵erent set of 24 randomly chosen villages in the same districts, we implemented a group-based
lending (GBL) approach, where borrowers self-selected into joint liability groups. Our previous research(Maitra et al. 2017) compares TRAIL with GBL. In the current paper we focus on a comparison of the
TRAIL and GRAIL schemes.

1

cash crop, potatoes. They earned as commission 75 percent of the interest payments
made by each borrower they recommended.3
The goal of this paper is to evaluate these alternative methods of devolution of beneficiary selection. Specifically, we ask if the programs had positive impacts on farmer
cultivation decisions, output and incomes. We examine whether the impacts di↵er acrossthe two approaches, and why. In particular, the agent’s identity, networks and motivations could have a↵ected the success of the intervention. Given their experience lending
and trading farm inputs and output within the community, the TRAIL agents may bebetter informed about borrowers’ farm productivity and reliability. They could also
expect to earn middleman profits from trading the farmers’ output, and so may havechosen to direct credit and business advice to their most productive farmer-clients.4
GRAIL agents, on the other hand, would have likely acted in line with the prioritiesof their political party, for example, selecting poor beneficiaries to further their party’s
pro-poor agenda. They may also have been motivated di↵erently vis-a-vis their beneficiaries’ projects: they were unlikely to benefit directly if their borrowers had large
harvests, and instead could have faced the blame if the borrowers’ projects failed andthey fell into economic distress. The agents would also likely impose informal sanctions
on defaulting borrowers. A TRAIL agent might withhold future business transactions,and the GRAIL agent might apply social or political pressure.
Our experimental design allows us to separate the e↵ect of receiving an AIL loan fromthe confound caused by endogenous borrower selection. In each village, the agent was
asked to recommend 30 village residents as potential borrowers, but only 10 randomly
chosen from this list were o↵ered the AIL loan. Among the recommended, a comparisonof those who were o↵ered the loan with those who were not, provides an estimate of thetreatment e↵ect of the loan, conditional on selection. Among those who were not o↵ered
the loans, the underlying di↵erences between those who were and were not recommended
allow us to separately estimate the selection e↵ect.
Our estimates show that TRAIL loans were more likely to be taken up than GRAIL
loans. Among the loans taken up, repayment rates were a similarly high 93 percent inboth schemes. In both schemes, beneficiaries borrowed more, cultivated more area andhad larger potato harvests. However, potato profits and farm incomes only increased for3
See Fuentes (1996), Varghese (2005) and Mansuri (2007) for theoretical arguments about credit
delivery using local agents and bank-money-lender linkages in developing countries.
4
The potato value chain in this region is characterized by large middlemen margins: Mitra et al.
(2018) estimate that in 2008 farmgate prices were 45% of wholesale prices, with middlemen earning atleast 50-70% of this gap.

2

beneficiaries in the TRAIL scheme (by 40 percent and 21 percent, respectively).5 Thisis because TRAIL farmers’ expansion of cultivation was accompanied by a reduction inunit production costs, whereas GRAIL farmers continued to produce at previous (high)costs per acre.
We develop a simple model with a decreasing returns Cobb-Douglas production function where farmers vary in a single productivity attribute, and at baseline, the allocationof informal credit is efficient. We use this model to estimate farmer productivity, andfind that the TRAIL agent selected more productive farmers than the GRAIL agent did.
And yet, this selection di↵erence accounts for less than 10 percent of the TRAIL–GRAIL
di↵erence in the average treatment e↵ects on farm income. We show that alternative
explanations such as superior selection on other farmer attributes (such as wealth orunit costs), and credit rationing cannot explain why the TRAIL scheme outperforms
the GRAIL. Instead, our analysis indicates that most of the di↵erence can be attributed
to the TRAIL scheme generating larger treatment e↵ects on farm income, conditional on
farmer productivity. Specifically, our findings suggest that a beneficiary in the TRAIL
scheme produced more output at significantly lower per-unit cost and earned larger
profit, than a GRAIL beneficiary of the same productivity. Since the loan product andhence borrower incentives were identical in the two schemes, this suggests that the key
di↵erence was the borrower’s relationship with their agent.
Accordingly, we extend our simple model of selection on a single productivity dimension to incorporate interactions between agents and borrowers. Since TRAIL agentsare also middlemen in the potato trade, they are motivated to increase their profitsby helping their clients produce and sell more(Mitra et al. 2018). In our model, they
advise farmers on ways to lower unit costs of production, inducing them to cultivate
more potatoes, and earn larger profits per kilogram. This advice is most e↵ective for themost able farmers. On the other hand, the GRAIL agent is motivated by the objectivesof the political party controlling the village council. His primary goal is to minimize
loan defaults, and accordingly he may intensively monitor poor, less able borrowers andinduce them undertake actions and expenditures that ensure crop success, but also lower
expected profits. As a result GRAIL borrowers would incur higher costs than TRAIL
borrower. We show that our model’s predictions match empirical patterns in default
rates and conditional treatment e↵ects on borrowers’ farm output and incomes, as wellas the frequency of their interactions with the agent and local traders.
5

These percentage e↵ects are mirrored in the absolute e↵ects on borrower profits and farm incomes,
since control farmers in both schemes had similar profits and incomes.

3Our paper contributes to the recent literature that examines the role that community agents play in development interventions. Several recent papers find that central
individuals in the network can help to e↵ectively target beneficiaries, di↵use information and increase program take-up (Bandiera and Rasul 2006; Alatas et al. 2012, 2016;
Fisman et al. 2017; Hussam et al. 2018; Berg et al. 2018; Beaman and Magruder 2012;
Debnath and Jain 2018; Beaman et al. 2018; Banerjee et al. 2013; Chandrasekhar et al.
2018). We bring to this literature the observation that villages tend to have a number of di↵erent networks: economic, political and social. The nodal agents of di↵erent
networks may have di↵erent information sets and di↵erent incentives to deliver development programs. This raises the question of which network to tap into. Further, theintervention itself may a↵ect network relationships, and thereby also a↵ect borrowers’
profits. Our results indicate the importance of looking beyond the architecture of local
networks, and incorporating the e↵ects of policy treatments on relationships within the
network. Our work also sheds light on the specific role that intermediaries play.6 We
provide evidence on interactions between the beneficiary and program intermediary, andquantify the importance of such interactions vis-a-vis beneficiary selection.
Our experiment provides a rare instance of a microcredit experiment that successfullyraised borrower production and incomes, while maintaining high repayment rates andtake-up. In a previous paper (Maitra et al. 2017), we found that the TRAIL scheme
also outperformed a traditional group-based micro-lending (GBL) scheme. We foundthat di↵erences in borrower selection accounted for at least 30-40% of the di↵erence inthe average treatment e↵ects across the two schemes. Instead, this paper finds that
selection di↵erences explain very little of the di↵erence in the outcomes of the TRAIL
and GRAIL schemes; accordingly we focus on the role of interactions between the agent
and farmers.7 Interestingly, the large profits that the traders earn as middlemen appear
to be the precise reason why their incentives are aligned with increasing farmers’ output.
The paper is organized as follows. In Section 2 we describe the two loan intervention
schemes that we will analyse in this paper. Section 3 describes our data and Section 4
6For example, Beaman and Magruder (2012) argue that members of social networks can be incentivized to help select and refer high ability workers to jobs. On the other hand, Heath (2018) finds
evidence that firms mitigate shirking by workers referred by employees that interact with them socially,
relying on these interactions to help weaken the e↵ect of limited liability constraints.
7
In another related paper (Maitra et al. 2019), we compare the distributive e↵ects of TRAIL, GRAIL
and GBL, by assessing their impacts on an Atkinson (1970) welfare function. We find that the TRAIL
scheme had significantly positive average treatment e↵ects on welfare that were consistently larger than
the average treatment e↵ects of the GRAIL and GBL schemes, irrespective of the degree of inequality
aversion.

4presents the estimates of the average treatment e↵ects of the two schemes on borrower
outcomes. In Section 5 we present statistics on the financial performance of the schemes.
To explain our empirical findings, in Section 6 we examine how and why selection patterns varied across the two schemes. We then decompose the di↵erence in averagetreatment e↵ects into the part that can be explained by these selection di↵erences, andthe part that is due to di↵erences in the treatment e↵ects, conditional on borrower productivity. Since we find that this latter part is substantial, in Section 7 we develop amodel of agent incentives and agent-farmer interactions and test its predictions. Section
8 concludes.

2

Empirical Context and Intervention Design

Our interventions were conducted across 48 randomly-chosen villages in the Hugli andWest Medinipur districts of the Indian state of West Bengal.8 The loans o↵ered throughthe AIL schemes were designed to facilitate the cultivation of potatoes, a high returnhigh-cost crop in this region (see Maitra et al. 2017, Table 2).
The 48 villages in our sample were randomly and evenly assigned to the two treatment arms. Our sample villages were at least 8 kilometres apart from one another; thishelped to avoid information and other spillovers across the treatment arms. Each village
belonged to the jurisdiction of a di↵erent village council or gram panchayat (GP). Panel
A of Table 1 shows that across the villages in the two treatment arms there are no significant di↵erences in village size, or number or landsize distribution of potato cultivators.
Our partner MFI had not operated in any of these villages before. In general, when ourintervention began in 2010, there was very little microfinance available in this area.
The institutional arrangements in this region are such that potato traders regularly
visit farmers in their fields, and often engage in repeated sales and credit transactions
with farmers. In each TRAIL village, our project team drew up a list of local traders
who had at least 50 clients or had been operating for longer than 3 years. One trader
was randomly drawn from this list and o↵ered the position of agent.9
In India, gram panchayats are village councils of between 8 and 15 members, formed
through direct elections once every five years. In the West Bengal village councils,
8This is a subset of the random sample of villages where Mitra et al. (2018) conducted a potato priceinformation intervention experiment in 2007–08.
9
If this person had refused to participate, we would have o↵ered the position to a second randomly
chosen trader from the list. In practice the first trader approached always accepted the contract.

5

candidates tend to be affiliated with state-level political parties.10 Many government
schemes are implemented locally by village councils, and local party affiliates are often
involved in both identifying beneficiaries and delivering benefits.11
In the villages assigned to the GRAIL treatment, we asked the village council tosuggest as potential GRAIL agents individuals who had lived in the village for at least
3 years, were personally familiar with village farmers, and had a good local reputation.
Given the background we described above, we expected the GP to recommend politicalaffiliates experienced at delivering government benefits. One randomly drawn personfrom this list was o↵ered the contract.12
In each intervention arm, the agent was asked to recommend 30 potential borrowers,
from the land-poor households in the village.13 Ten of these 30 recommended individuals
were randomly chosen in a lottery that we conducted in the office of the local government.14 The MFI then approached the selected individuals in their homes and o↵ered
them the loans.
Our partner MFI was limited to disbursing hte program loans and collecting repayment. It did not select borrowers or monitor them subsequently. The loans were fundedthrough an external grant held by the principal investigators of this project.
The first loan cycle began with disbursals in October–November 2010, to coincidewith the planting season for potatoes. Borrowers were individually liable for the repayment of their loans. The scheme featured “progressive lending” to generate dynamic
repayment incentives. In cycle 1 the loan size was capped at Rs. 2000 (equivalent to
US $30), repayable as a single lumpsum at the end of four months, at 6 percent interest.
The loan size in each subsequent cycle was 133% of the principal amount repaid. Borrowers who repaid less than 50% of principal in any cycle, were terminated. We did not
10

West Bengal has a long history of cadre-based mass mobilization of voters through political rallies
and campaigns. From 1977 to 2011, the Communist Party of India (Marxist) led a left-wing coalition
government in West Bengal state, and held the majority of the village council seats. This long dominanceended when the Trinamool Congress captured the majority of state assembly seats in the 2011 stateelections. After the 2013 village council elections most village councils also had a Trinamool Congress
majority.
11
There is considerable evidence that local governments in West Bengal direct benefits towards swing
voters (Bardhan et al. 2015; Dey and Sen 2016).
12
Only 1 of the 24 individuals selected in this way refused the position, citing a religious taboo on
participating in a credit scheme. He was replaced by a second randomly drawn person from the list.
13
Specifically, we required that recommended households owned no more than 1.5 acres of cultivableland.
14
The list of recommended borrowers was kept confidential so as to avoid any spillovers on informal
credit access or other relationships for households that had been recommended but were not eventuallychosen to receive the loan.

6want to create any pressure for borrowers to sell their harvest prematurely to meet their
repayment obligation; therefore borrowers could also repay in potato “bonds”; we thencalculated the rupee value of the repayment at the prevailing price of potato bonds.15
Although the stated purpose of the loans was agricultural, we did not monitor or restrict
how households spent the funds.16
Agents had both monetary and non-monetary incentives to participate in the scheme.
At the end of each loan cycle, they received from the MFI a commission equal to 75% of
the interest paid by all borrowers whom they had recommended. This high commission
rate was meant to incentivize the agent to select productive borrowers who would repay
the loan and benefit from it, and to discourage collusion between the agent and potential
applicants.17 In addition, the agent put down a deposit of Rs. 50 per borrower, which
was refunded if his borrower survived in the program for two years. If more than onehalf of the borrowers recommended by an agent defaulted on their loans, the agent was
terminated and earned no further commissions. All agents who survived in the programfor 2 years also received a paid holiday to a seaside resort. In conversations during ourfield visits, some TRAIL agents remarked that they expected the scheme to increase
their prominence in the village, or to boost their business. GRAIL agents may also haveviewed the scheme as an extension of government anti-poverty programs, or expected
the public to view their political party more favorably because of association with thescheme.

3

Data and Selected Descriptive Statistics

We conducted surveys with a sample of 50 households per village. These households
were selected as follows. In each village, all 10 households assigned to receive the loan
(Treatment households) were included in the sample. Of the 20 households that theagent recommended but did not receive the loan, we included a random subset of 10
households; they are referred to as Control 1 households. We also included 30 households
randomly chosen from the pool of the non-recommended, hereafter referred to as Control
2 households.
The first round of surveys was conducted in December 2010, two months after the15

Farmers can store their harvest in cold storages for a maximum of 11 months. Potato “bonds” arereceipts from the cold store facility that are often traded between farmers and traders.
16
Survey respondents reported to us their actual use of loan funds in our detailed four-monthly
household surveys.
17
Maitra et al. (2017) presents a model of borrower selection that illustrates this mechanism.

7Cycle 1 loans were disbursed. Surveys were repeated every four months. In each sample
household, we ensured that the same person responded to the survey in each round.
There was no attrition in the sample over the eight survey rounds. In Panel B of
Table 1, we present summary statistics of selected household-level characteristics for thissample. These household characteristics do not jointly explain assignment to treatment(p = 0.996).
Table 2 compares the characteristics of the TRAIL and GRAIL agents. Over 95%
of the TRAIL agents were business persons and reported owning a shop or business.
GRAIL agents were either farmers, or had a salaried government job. TRAIL agentswere wealthier and had higher incomes, but were less likely to have studied beyond
primary school. On the other hand GRAIL agents were more involved in civil society
and politics; 30% were members of a village organization, 17% were political party
workers, and 13% had been members of the local government. None of the TRAIL
agents were directly involved in politics in this way.
Table 3 presents information about the relationships between agents and village
residents. Both types of agents were well-known in the village. More than 90% of the
non-recommended Control 2 households said they knew the agent, and 98% of these 90
percent said they saw him at least once a week. However Control 2 households were lesslikely to have close social ties with the agent, as depicted by belonging to the same casteor religious category, or visiting the agent’s house on special occasions.
The main distinction between TRAIL and GRAIL agents is in their economic links
with sample households. Nearly a fifth of Control 2 households and a quarter of Control
1 households in TRAIL villages reported that the agent was an important source ofcredit or inputs, or an important buyer of their crop output. The GRAIL agent wassignificantly less likely to fill these roles.18 In survey round 1 in December 2010, we also
asked households whether they had transacted with the agent in the product, credit
or labour markets at any point in the previous three years. A third or more of TRAIL
households had bought from the agent, and a tenth to a quarter had borrowed or worked
for the agent. Households in GRAIL villages were less likely to have engaged with theiragent in this way. This suggests that the TRAIL agents were better informed about thecultivation and borrowing activities of village residents.
Table A1 in the Appendix summarizes the characteristics of the loans that sample
households held at the time that our intervention began. Two-thirds of the households
18

We note however that about 10% of sample households in both treatment arms reported that they
had worked for the agent.

8

reported that they had an outstanding loan, the bulk of which were for production
purposes. Of the agricultural borrowing, nearly two-thirds was from traders or moneylenders, for a mean duration of 4 months and an average annual interest rate of
25%. Credit cooperatives accounted for nearly one quarter of the loans, but loans fromcommercial banks and from microfinance institutions were rare. Cooperatives and commercial banks charged much lower interest rates, but generally required collateral, andso were inaccessible to poor households.

4

Average Treatment E↵ects of the Schemes

We start by examining the impacts of the TRAIL and GRAIL schemes on borrower
outcomes. Since only a random subset of the recommended household were o↵ered theloans, the average di↵erence in the outcomes of the Treatment and Control 1 households
is an estimate of the treatment e↵ect of the loan, conditional on the household being
selected into the scheme. The average di↵erence in the outcomes of the Control 1 andControl 2 households is an estimate of the selection e↵ect. Our regression specification
takes the form:
yivt =+

0+

1 TRAILv +2 (TRAILv ⇥ Control 1iv ) +4 (GRAILv ⇥ Control 1iv ) +3 (TRAILv ⇥ Treatmentiv )5 (GRAILv ⇥ Treatmentiv )(1)+ Xiv + I(Yeart ) + "ivt
Here yivt denotes the outcome variable of interest for household i in village v at timet. The omitted category is the Control 2 households in GRAIL villages. The average
ˆ2 andtreatment e↵ects of the TRAIL and GRAIL schemes are estimated by ˆ3
ˆ5
ˆ4 respectively.19 The coefficient ˆ2 is the di↵erence in outcomes of Control 1 andControl 2 borrowers in TRAIL villages, and so represents the TRAIL selection e↵ect;
analogously, ˆ5 measures the GRAIL selection e↵ect. The set Xiv contains measures of
the household’s landholding, religion and caste, and the age, education and occupationof the oldest male in the household.20 I(Yeart ) denotes two year dummies to control for
19

All treatment e↵ects are intent-to-treat estimates because they compare the outcomes of households
assigned to the Treatment and Control 1 groups, regardless of whether the Treatment households
actually took the loan.
20
In our study the administrative definition of a village corresponds to a collection of hamlets orparas. Households within the same hamlet tend to be more homogenous, are more likely to interact witheach other, and arguably experience correlated shocks to cultivation and market prices. The results are9secular changes over time.21 Standard errors are clustered at the hamlet level.

4.1

Treatment E↵ects on Agricultural Borrowing

Columns 1 and 2 of Table 4 present the average treatment e↵ects on agricultural borrowing. The TRAIL scheme increased total agricultural borrowing for selected households
by Rs. 2868 (or 137%) over the three-year study period, which is statistically significant at the 1% level. The GRAIL scheme caused a similar, significant, increase of
Rs. 2754 (or 143%). The point estimates on non-program agricultural borrowing aresmall and statistically insignificant, suggesting that the TRAIL and GRAIL loans did
not crowd out agricultural loans from other sources. Thus Treatment households took
the additional subsidized credit, but did not substitute away from their more expensive
pre-existing informal loans, possibly because they wanted to sustain their traditional
informal credit relationships.

4.2

Treatment E↵ects on Farm Incomes

Columns 3, 4 and 5 of Table 4 present the estimates of average treatment e↵ects on
aggregate farm and non-farm income. In column 3, farm value-added is computed as
the sum of the value-added for the four major crops grown in this region: potatoes,
paddy, sesame and vegetables.22 The TRAIL loans caused a statistically significant
21% increase in average farm value-added, whereas GRAIL loans had a non-significant
impact, estimated at 1.3%. In column 4, non-agricultural income is calculated as thesum of rental, sales, labour and business income. The average treatment e↵ect estimatesfor non-agricultural income are imprecise, possibly because these incomes are measured
with greater error. Nevertheless, column 4 indicates that neither loan scheme increased
non-agricultural incomes significantly. Column 5 shows that aggregate income increasedby 11.3% in the TRAIL scheme, and decreased by 10% in the GRAIL scheme; thisdi↵erence is statistically significant at the 10% level.
robust to clustering at the village-level instead. See Panel B of Table A3.
21
In Panel A of Table A3 we present the results from running equation (1) without the set of controls
Xiv . The key e↵ects are qualitatively similar, although less precise.
22
The separate results for paddy, sesame and vegetables are available on request.

10

4.3

Treatment E↵ects on Potato Cultivation

Recall that the AIL loans were designed specifically to enable the cultivation of potatoes, the highest-value cash crop in the region. Table 5 shows that the TRAIL loans
led to large and statistically significant increases in potato cultivation.23 The e↵ect isconcentrated on the intensive margin: although the TRAIL Treatment households were
just as likely to cultivate potatoes as the Control 1 households (column 1), they planted
an additional 0.09 acres (or 27%, column 2), harvested 950 kg (or 26%, column 3) more,and earned an additional Rs. 3900 (or 27%, column 5) in sales revenue.24 Simultaneously they incurred an additional Rs. 1846 (or 22%, column 4) in production costs, sothat on net, value-added increased by Rs. 2060 (or 36%, column 6). When we subtract
the imputed cost of family labor, this works out to a statistically significant Rs. 1907
or 40% increase in profit (column 7).25
The households that the GRAIL agent recommended were less likely to cultivate
potatoes in the absence of the loan: potatoes were planted in only 64% of Control
1 household-years in GRAIL villages, unlike the 72% in TRAIL villages. However,the loans increased the likelihood that a GRAIL borrower cultivated potatoes by 13%
points (or 20%, Column 1, p < 0.001). Correspondingly, acreage (column 2) and output(column 3) increased significantly (23% and 24%, respectively). Revenue increased by
19% (column 5), but since the cost of production increased by a larger 28% (column 4),
the GRAIL loans had a non-significant e↵ect on value-added and imputed profit.26 The
di↵erence in the average treatment e↵ects on imputed profits (column 7) across the two
schemes is statistically significant at 10%.
23

We aggregate the data from the four-monthly surveys into a household-year level dataset capturing
the amount of land households planted with potatoes, harvested quantities, sales, revenues, production
costs, value-added and imputed profits. This allows us to align the costs of cultivation with the revenues,
since harvested potatoes can be stored and then sold at di↵erent points in the year.
24
Households reported to us the quantity and price of each potato sale. When they held potatoes for
self-consumption, we imputed the sales revenue by pricing the potatoes at the median sale price in thevillage.
25
To calculate the shadow cost of family labour, we price the reported family labor time (male, female
and child labor separately) spent on the crop at the median wage reported for hired labor of that typein that village in that year for that crop.
26
Since we analyse a large number of dependent variables, we correct for the increased chance offinding statistically significant results. Following Hochberg (1988), we report a conservative p-value for
an index of variables in a family of outcomes taken together (see Kling et al. 2007). The variables arenormalized by subtracting the mean in the control group and dividing by the standard deviation in thecontrol group; the index is the simple average of the normalized variables. To adjust the p-value of thetreatment e↵ect for an index, the p-values for all indices are ranked in increasing order, and then eachoriginal p-value is multiplied by m k + 1, where m is the number of indices and k is the rank of theoriginal p-value. If the resulting value is greater than 1, we assign an adjusted p-value of > 0.999.

11

Thus, while both schemes led borrowers to plant more potatoes, only TRAIL borrowers earned larger profits as a result. This is mainly because they lowered their unit
costs by 6%, whereas GRAIL borrowers’ unit costs increased by 2%. The di↵erence isstatistically significant (p < 0.05).27

5

Loan Performance

In Table 6 we examine the take-up and repayment rates for the program loans. In column 1, the dependent variable is the likelihood that a household eligible to take the loan
in a given cycle actually took the loan.28 The sample means in Panel A show that eligible TRAIL Treatment households accepted 94% of the program loans o↵ered, whereas
eligible GRAIL Treatment households took 87% of the loan o↵ers. The di↵erence isstatistically significant at the 1% level. This result holds (p < 0.01) even in Panel B
when we control for household characteristics following the regression specification:
yhvt = ↵0 + ↵1 GRAILv + Xivt + "hvt(2)where Xivt includes the landholding, religion, caste, and the age and educational attainment of the oldest male member of the household, and a vector of loan cycle dummies
to control for seasonal or other time-varying changes in take-up and repayment rates.
We define a loan to be in default if the repayment amount due was not paid fullyby the due date. The average default rate over the three-year intervention period is an
identically low 7% in both schemes (Table 6, Panel A, column 2). The regression result inPanel B confirms that there is no di↵erence in the loan default rates in the two schemes.
Despite the fact that the scheme did not increase their incomes significantly, GRAIL
Treatment households repaid their loans at the same high rates as TRAIL Treatment
households.

6

Selection-based Explanation

Next we examine the mechanisms that underlie our empirical findings. We start byexamining how much of the di↵erence in the average treatment e↵ects in the two schemes
27

TRAIL borrowers also faced a smaller decline in output price (0.6% instead of 3.6%), but thisdi↵erence is not statistically significant (p = 0.14).
28
In loan cycles 2 and beyond, a Treatment household was only eligible to borrow if it had repaid atleast 50% of the amount previously outstanding.

12can be explained by selection di↵erences. This requires us to estimate a measure of theunderlying heterogeneity among farmers, based on the following simple model. The
key assumptions are that (a) farmers are heterogenous in ability, (b) more able farmers
have higher TFP and lower unit costs of production, (c) the production function follows
the Cobb-Douglas form and exhibits decreasing returns to scale, and (d) the informal
credit market is efficient because lenders have perfect information about borrower ability
and compete in Bertrand fashion. In particular, borrowers are not credit-rationed. The
program loans provide them an extra line of credit at an interest rate below the prevailingmarket rate, and induce them to expand borrowing and cultivation scale.

6.1

Estimation of Ability: Control Households

Consider farmer i in village v in year t who produces according to the function:
Yivt = Pvt Ai

1
1

l1 ↵
↵ ivt

(3)

Pvt denotes a village-level yield or price shock, livt is the farmer’s chosen scale of cultivation, and ↵ 2 (0, 1). Farmer ability Ai follows a common, given, distribution both in
GRAIL and in TRAIL villages. The cost of production per unit area ci is constant for
any given farmer, and decreases in farmer ability. Assume for now that Pvt , Ai and ci
are una↵ected by the treatment.29
A farmer in village v, year t who does not receive a program loan (henceforth, acontrol farmer) borrows from informal lenders. All informal lenders face the cost ofcapital ⇢vt .30 Since these lenders compete in the Bertrand fashion, the farmer pays
expected interest cost ⇢vt .
To cultivate potatoes, the farmer must incur a fixed cost F > 0. Accordingly, theccontrol farmer chooses l = livt
to maximize
Pvt Ai

l1 ↵
1 ↵

29

⇢vt ci lF

The current model assumes that farmer productivity and costs are correlated with a measure ofexogenous ability. This is a special case of the model in our previous paper (Maitra et al. 2017). In
Section 7 we allow the trader and agent to endogenously determine the extent to which they help andmonitor the farmers, and these in turn a↵ect the values of Ai and ci .
30
In the presence of default risk, the informal interest rate also depends on, and is decreasing in, thefarmer’s ability, so that the expected interest cost of the borrower is ⇢vt .

13

For control farmers with a sufficiently high ability, this yields
c
log livt
=

1
Ai
1
log
+ [Pvt
↵
ci
↵

⇢vt ]

(4)

Observe that log Acii , or the ratio of TFP to per-unit cost, is increasing in farmer ability.
This serves as an exogenous measure of relative productivity. In equation 4 this ratio
is scaled relative to ↵; if ↵ is close to 1 then it is also an absolute measure of theratio of TFP to per-unit cost. Accordingly, among control farmers, productivity can be
estimated as the household fixed e↵ect in a household-year level panel regression, wherethe (log) scale of potato cultivation (acreage or output) is regressed on farmer, village
and year dummies.
Equation (4) applies only to farmers with ability above the threshold ✓vt . These
farmers eventually earn profits that cover the fixed cost F . Farmers with ability below
this threshold would choose not to cultivate potatoes. Our data show that roughly
30 percent of Control 1 and Control 2 group farmers planted potatoes in at most oneof the three years in our study period; we cannot estimate household fixed e↵ects forthese households. We call these households non-cultivators, and assign to them thelower endpoint of the estimated productivity distribution among the cultivators. Thisis an upper bound to their true latent productivity. None of the comparisons below area↵ected if we replace this upper bound with any lower estimate. Throughout we will
use our estimate of productivity as a proxy for underlying unobserved ability.
In Table 7 we present the results from regressing each cultivator household’s productivity estimate, obtained from the household fixed e↵ects regression following equation(4), on household characteristics.31 As the bottom panel of Table 7 shows, there is wide
variation in the estimated productivity level across households: the 75th percentile ismore than three times the size of the 25th percentile. Although the productivity estimate correlates positively with landholding and having a male household head, theR-squared indicates that variation in observable characteristics can only explain 16%of the variation in household productivity.32 This justifies our working hypothesis that
community-based agents have local information about farmers that an outsider would
31

Table A2 presents the di↵erences in demographic characteristics between the cultivators and noncultivators. Cultivators own more land, are more likely to be upper caste Hindus, have larger households,
are more likely to be male-headed, and the oldest male in the household is likely to be older and is more
likely to have completed primary school.
32
A LASSO estimator performs only slightly better than the ordinary least squares estimator. Under
the Extended Bayesian Information Criterion the selected LASSO model has an R-squared of 0.23.

14

be unable to observe.

6.2

Estimation of Ability: Treatment Households

We cannot use the same procedure as in Section 6.1 to estimate the productivity oftreated households, because the program loan would have a direct impact on treatedhouseholds’ scale of cultivation. Also, treated households could have engaged di↵erently
with their agent than control households. Instead, to recover their productivity estimate,
we employ the Order-Preserving Assumption (OPA), which states that the treatment did
not change households’ rank order in terms of productivity levels. In Section 7.1 we will
provide theoretical justification for this assumption.33 Under this assumption, in eachtreatment arm, we can rank Treatment farmers by cultivation scale, and assign to themthe counterfactual productivity estimate Ai of the farmer in the Control 1 distribution
at the same rank, as estimated in Section 6.1. This gives us the correct estimate for thelatent productivity of this farmer.

6.3

Di↵erences in Selection

We can now examine whether the TRAIL and GRAIL agents systematically recommended households of di↵erent productivity levels. We focus on Control 1 and Control
2 households; neither group received program loans.
In Panel A of Figure 1, we compare the distributions of the productivity estimates forControl 1 and Control 2 households.34 The figure on the left shows that in TRAIL villages, the cumulative distribution function for Control 1 households first-order stochastically dominates that for the Control 2 households. A two-sample Kolmogorov-Smirnov
test rejects the null hypothesis that the two distributions are identical (p = 0.005).35
Thus, the TRAIL agent positively selected borrowers on productivity. The figure on
the right shows that the GRAIL agent also selected borrowers positively (K-S test
p = 0.011).36
33
Athey and Imbens (2006) use a similar assumption in order to identify treatment e↵ects in non-linear
di↵erence-of-di↵erence settings.
34
The flat segment in the bottom end of the plotted CDFs depicts the upper bound of the estimatesfor non-cultivators.
35
Since our productivity estimates are generated variables, we also simulate 2000 bootstrap samples
and run the Kolmogorov-Smirnov test for each Control 1 vs Control 2 CDF comparison. In 87 percentof the simulations, we can reject the null hypothesis that the two distributions are identical.
36
The Kolmogorov-Smirnov test rejects the null hypothesis that the two distributions are identical in
83 percent of our bootstrap simulations.

15

When in Panel B we again present the cumulative distribution functions for Control
1 households, we see that the graph for TRAIL households first-order stochasticallydominates that for GRAIL households. A two-sample Kolmogorov-Smirnov test rejects
the null hypothesis that the two distributions are identical (p = 0.06).37 Thus, the
TRAIL agent selected more productive borrowers than the GRAIL agent.
For what follows, it is convenient to group sample households into productivity
classes, or bins. Accordingly, we place all non-cultivator households in Bin 1. Among
the rest, we use a median split to create Bins 2 and 3. Figure A2 shows that the GRAIL
agent selected more Bin 1 borrowers (34.5% versus 27.3%), and fewer Bin 3 borrowers(32.3% versus 39.7%).38

6.4

Explaining Selection Di↵erences

What explains this di↵erence in selection patterns? The TRAIL agent’s experience lending and trading with village residents could have given him more or better information
about their productivity levels. Alternatively, he might have had di↵erent incentives
than the GRAIL agent. For example, the prospect of increasing his middleman profits might incentivize him to help farmers increase their crop volumes. Conversely, the
GRAIL agent would likely share the motives of the political party controlling the village
council. If this party pursues a pro-poor welfarist ideology, this might motivate the
GRAIL agent to select asset-poor (and possibly) low-ability farmers. Alternatively, if
the party is opportunistic and uses welfare programs clientelistically to mobilize votes,
then too the GRAIL agent would recommend poor households for the loan scheme.39
We examine whether GRAIL agents used the loan recommendations for political
gains. In our final survey round, we asked survey respondents to participate in a straw
37

The Kolmogorov-Smirnov test rejects the null hypothesis that the two distributions are identical in
74 percent of our 2000 bootstrap simulations. Figure A1, in the Appendix, presents descriptive statistics
on household productivity for TRAIL and GRAIL Control 1 households. TRAIL households had higher
mean and maximum productivity estimates; the 25th, 50th and 75th percentiles or the distribution of
productivity were also higher for TRAIL Control 1 households.
38
The Bin 3 borrowers here could be thought of as equivalent to the gung-ho (GE) entrepreneurs in
Banerjee et al. (2019). Indeed, the Bin 1 borrowers are akin to the non-GE entrepreneurs in that they
do not cultivate potatoes absent the AIL loan. However, while Banerjee et al. (2019) argue that theoverall di↵erences in later outcomes between GE and non-GE entrepreneurs are driven by selection, we
find in this paper that TRAIL and GRAIL borrowers have di↵erent outcomes conditional on selection,and that this di↵erence is caused by di↵erences in the agents’ non-program incentives. See Section 6.5.
39
It is a standard feature of the literature on political clientelism that political parties target swing
voters among the poor. This is because poor voters sell their vote for a lower price (see, for example,
Stokes 2005; Kitschelt and Wilkinson 2007).

16

poll. Survey investigators gave them a sheet of paper resembling a ballot, and asked
them to mark the symbol of their preferred political party, and then fold and place thepaper in a box.40 If the straw poll vote indicates the political party that the respondent
supports, then we can interpret a straw vote for the political party that was incumbent
in 2010 as support for the political party of the GRAIL agent. We use data from theWest Bengal State Election Commission to identify the incumbent political party in each
local village council in 2010. We run the regression:
Iiv = ⇠0 + ⇠1 Treatment iv + ⇠2 Control 1iv + Xiv + "iv(5)

where the dependent variable Iiv takes the value 1 if the respondent i in village v voted
for the incumbent party in the straw poll. The set of controls Xiv includes the same
household characteristics included in equation (1). We run the regression separately for
TRAIL and GRAIL villages. In Table 8 we see that the estimated treatment e↵ect ⇠ˆ1 ⇠ˆ2is positive and statistically significant in GRAIL villages (column 2), but insignificant
in TRAIL villages (column 1).
Hence in GRAIL villages, those who were randomly selected to receive the program
loan were 8% more likely to express support for the incumbent party than comparable
households that were not o↵ered the loan. Column 4 shows that the point estimate of
the treatment e↵ect is largest for the lowest-ability GRAIL households. This is consistent with opportunistic selection by the GRAIL agent, since clientelistic transfers aremost likely to mobilize votes among the poor. It is also consistent with the incumbent
following a pro-poor welfarist policy and thereby inducing greater gratitude among poor
beneficiaries, that they express by voting for the party.

6.5

The Role of Selection in Explaining ATE Di↵erences

We have seen that the TRAIL agent selected more productive borrowers than the GRAIL
agent did. However it does not follow that this superior selection alone caused thelarger impacts on borrower outcomes. In what follows, we examine how much of thedi↵erential impact of the TRAIL scheme on profits can be explained by di↵erences
in borrower productivity levels, and how much by di↵erential e↵ects conditional on
productivity. Specifically, we will decompose the di↵erence in the ATEs of the TRAIL
and GRAIL schemes into the component attributable to di↵erences in productivity, and40

Respondents were assured the response would be kept confidential and used only for research
purposes. Less than 1% of households refused to participate.

17

the component due to di↵erences in conditional treatment e↵ects.
In Table 9 we estimate bin-specific heterogenous treatment e↵ects (HTEs) using thefollowing specification:
yivt =
+
+

3
X

k=1
3
X
k=1
3
X
k=1

d ik +⇠1k Bin3
X
i=1

d ik ) +⇠2k (Control 1iv ⇥ Bin

d ik ⇥ GRAILv +⇠4k Bin

3
X
k=1

3
X
k=1

d ik )⇠3k (Treatmentiv ⇥ Bin

d ik ⇥ GRAILv )⇠5k (Control 1iv ⇥ Bin(6)

d ik ⇥ GRAILv ) + X0 ivt + "ivt
⇠6k (Treatmentiv ⇥ Bin

Since household productivity is an estimated regressor, we bootstrap standard errors
with 2000 iterations.41 In both schemes, the HTEs on value-added (column 7) are larger
for borrowers of higher productivity, which indicates that a scheme that selects more ableborrowers will likely generate a larger average treatment e↵ect. Comparing the TRAIL
and GRAIL HTEs for any given productivity bin, we also see that the point estimates
are larger for the TRAIL scheme, although Panel C indicates that the di↵erences are
not statistically significant.42
Table 10 shows the results of the decomposition. The penultimate row indicates thatholding everything else constant, if the TRAIL agents had followed the GRAIL agents’
selection patterns, then the average treatment e↵ect of the TRAIL scheme on potato
value-added would have been 8.38% smaller than our actual estimate. The last row
shows that if the GRAIL agents had kept their own selection pattern but their selectedborrowers had had the same HTEs as the TRAIL borrowers in that productivity bin,then the gap in the ATEs would have narrowed by 75%.
We infer that selection di↵erences accounted for only a small fraction of the observed ATE di↵erence. This conclusion is robust to using the continuous measure of
productivity rather than the three discrete bins: with continuous productivity, selection
41

For households that did not cultivate potatoes in any study year, we replace the value of potato area
cultivated, output produced or profits earned with zero, thus we continue to include these households
in the estimating sample. However the treatment e↵ects on unit costs are only estimated in the subset
of observations where potatoes were cultivated.
42
Here we are abstracting from any heterogeneity of treatment e↵ects within productivity bins. Ourfindings are similar when we consider continuous productivity levels instead. In Figure A3, the verticaldi↵erence in (smoothed) value-added between Treatment and Control 1 households gives us a visual
estimate of the treatment e↵ect at that productivity level. As the left panel shows, the vertical di↵erence
for TRAIL households is larger at larger productivity levels. The right panel shows that the GRAIL
treatment e↵ects are nearly always zero.

18

di↵erences explain 17.5% of the di↵erence in ATEs, whereas di↵erences in HTEs explain
82.5%.

6.6

Alternative Selection-based Explanation: Multidimensional
Screening with Credit Rationing

A potential concern with the selection model we described above is that in the presenceof credit constraints, wealth levels a↵ect credit access and thereby productivity (Galor
and Zeira 1993; Banerjee and Newman 1993; Moll 2014; Kaboski and Townsend 2012;de Mel et al. 2008). If, in reality, borrowers di↵er both in ability and wealth, then evenbetween two farmers of the same ability, the wealthier farmer would cultivate more.
Further, if returns to scale are increasing, then the wealthier farmer would be moreproductive at the margin (Banerjee et al. 2019). Some part of the larger treatmente↵ect of the TRAIL scheme could then be explained by the fact that the TRAIL agent
selected wealthier farmers. If there are pecuniary scale economies, then these farmers’
larger scale of cultivation could also explain their lower unit costs. In addition, farmers
could di↵er in the extent to which this happens: lower-cost farmers could face larger
reductions in unit cost as they expand their scale of cultivation. If cost and ability
are not perfectly correlated, then our sole focus on ability di↵erences might incorrectly
attribute too little of the ATE of the TRAIL scheme to selection.
In Section A1 in the Appendix, we present a model where farmers di↵er on three
dimensions: ability, wealth and cost. Assuming constant elasticity forms of the production function and the unit cost function, and assuming also that all farmers are credit
rationed, we can use panel data on farmers’ unit costs and revenue to back out theseelasticities and the three dimensional “type” of each control farmer. Specifically, theproduction function islog yivt = log ai + µ log livt + 2vt(7)
where ai denotes ability, livt denotes area cultivated, yivt denotes revenue, 2vt denotes avillage-year productivity shock and µ represents returns to scale. The cost function islog uivt = log ci + ⇣ log livt + log qvt(8)

where uivt denotes cost per acre, ci denotes the farmer’s cost type, and qvt denotes avillage-year cost shock, and ⇣ represents elasticity of unit cost with respect to the scaleof cultivation, representing pecuniary scale e↵ects. The credit-rationed farmer’s total
19

cultivation cost (Civt ) is determined by his financial access according to the equation:log Civt = log wi + log vt(9)where wi represents a measure of the farmer’s wealth, and vt is a village-year shockto the supply of credit. The three dimensions of type: ability, cost and wealth can be
estimated as farmer fixed e↵ects in the panel regressions corresponding to these threeequations. As shown in the Appendix, the model predicts that the treatment e↵ect ofQµexpanding credit access by one percent equals the “quasi-profit” ⇡ivt
⌘ [ 1+⌫
Rivt Civt ]of Control 1 household i in village v in year t, where Rivt denotes the revenue earned
by farmer i in village v in year t, and Civt is the cultivation cost, as defined above.
Taking expectations of this with respect to unit mean village-year shocks, the predicted
treatment e↵ect for a farmer is a function of the three dimensions of the farmer’s type,according to the expression:⇡iQ = ai [µ
wi 1+⇣]ciwi1(ci ) 1+⇣

(10)

We refer to the quasi-profit as the “reduced form estimate” of the treatment e↵ect, andto the expression in equation (10) above as the “structural estimate”.
To examine if this model can explain our empirical results, we follow equations (7),(8) and (9) and estimate the three dimensions for each control household in the TRAIL
and GRAIL villages. The cumulative distribution functions for each of the three types
are presented in Figure A4. We see in Panel B that control farmers in TRAIL and
GRAIL villages have almost identical ability distributions. In Panel A the cumulative
distribution functions for cost type cross, although a larger fraction of TRAIL farmers
have low unit costs over a wide range. Similarly, the CDFs for wealth cross, although alarger fraction of TRAIL farmers have high wealth over a wide range.
The corresponding estimates of ⇣ and µ are presented in Table 11. Row 4 displays“reduced form” estimates of average quasi-profit in the two schemes, using observed
revenues and costs of Control 1 subjects, adjusted using the estimated elasticities. Thisis multiplied by the proportional change ( ) in the scale of cultivation (Row 2) togenerate a “reduced form” estimate of the ATEs predicted, in Row 5. The corresponding“structural estimates” of selected farmers’ quasi-profit based on their estimated types
is shown in Row 6, and the corresponding predicted percentage e↵ect on potato valueadded in Row 7.
20

We see that the model’s reduced form estimate of the TRAIL ATE (Rs. 2732) isvery similar to our actual empirical estimate (Rs. 2059, see Table 5 column 7). However
for the GRAIL ATE the model produces a vast overestimate (Rs. 3481 v. Rs. 492).
Similarly, the structural estimate of the TRAIL scheme’s e↵ect on value-added (36.48%)
is similar to the 35.92% that we obtained in Table 5, but the estimate for the GRAIL
scheme’s e↵ect (33.48%) is nearly four times as large as the estimated GRAIL ATE(8.45%).
Rows 8 through 12 show how the predicted ATEs are modified when we replace theestimated returns to scale elasticity (µ) with an IV estimate.43 The predicted percentagee↵ects on potato value-added are not significantly altered, so the predicted GRAIL
ATE continues to be larger than the predicted TRAIL ATE. Hence allowing for credit
rationing and extending the dimensions of heterogeneity to include wealth and unit costdoes not help explain our empirical result that the TRAIL scheme generated a larger
ATE than the GRAIL scheme.
Below we present an alternative model that instead emphasizes di↵erences in the
TRAIL and GRAIL outcomes conditional on borrower selection.

7

A Model Of Agent-Farmer Interactions

In this model, TRAIL and GRAIL agents interact with program borrowers so as to influence their cultivation, purchase and sales choices and crop outcomes. Interactions takethe form of conversations about the weather, market prices, cultivation techniques andharvesting times. Through these conversations agents might monitor farmers’ actions,and provide technical advice and marketing assistance.
Agents are motivated to engage with farmers because of their program as well as
non-program incentives. Since the TRAIL agent is also a trader, he has a stake inboth the upside and downside of the farmer’s cultivation project. Not only does anabundant harvest ensure that he receives his TRAIL commission, it also earns him alarger middleman profit. On the downside, a failed crop wipes out both his commission
and his profit. The GRAIL agent is a political appointee and so has incentives alignedwith his village council. The elected village leaders may benefit if the scheme is perceived
to be a success, and so the GRAIL agent may want to ensure that poor borrowers repay
43

This elasticity estimate is discussed in more detail in Section 7. It is obtained from a regression(equation (14) below) of log output per acre on log area cultivated and village year dummies using
Treatment and Control households, with treatment dummies as an instrument for area cultivated.

21

their loans. This gives him a stake in the success of the crop, but conditional on crop
success, he has no additional benefit from a large harvest.
In turn these di↵erent motivations will shape the specific manner in which the agents
engage with the farmer: either helping to increase the harvest size, or monitoring to
reduce default risk.

7.1

Assumptions and Predictions

Farmers vary in intrinsic ability, denoted by the single dimension ✓. Their crop is successful with probability p(✓, m) where p✓ > 0, pm > 0, pmm < 0, p✓m < 0: lower ability
farmers are riskier, and monitoring (m) reduces their risk by more. In the event of success, output is a(✓, m)f (l), where a✓ > 0, am < 0, fl > 0, fll < 0, ff”0 is non-increasing,and p varies relatively little with ✓.44 Expected TFP A(✓, m) ⌘ a(✓, m)p(✓, m) satisfies
Am < 0. The farmer’s cultivation cost is c(h, m)l, where l is area cultivated, h is the levelof help he receives, and ch < 0, chh > 0, cm > 0. We also assume chm = 0; this simplifies
the analysis but is not critical. Whereas monitoring increases unit costs and lowers risk
and expected productivity, help has no e↵ect on risk and lowers unit cost. For instance,
monitoring induces the farmer to act to lower the chance of crop failure, such as byspending more on pesticides. These activities are expensive and time consuming, and so
the farmer’s unit cost rises and productivity fall. On the other hand, the agent can help
the farmer lower costs or raise quality of inputs, by providing valuable business advice
about the brands to purchase and where to purchase them. We also assume that therelevant functions are smooth with well-behaved curvature, so that optimal allocations
are interior, satisfying suitable first and second order conditions.
Traders and farmers enter into bilateral interlinked credit-cum-output contracts,
where the trader provides the farmer credit for working capital, help and monitoring, andthe farmer cultivates the specified area, and then sells his harvest to the trader. Bothparties are risk-neutral. Farmers have zero wealth, and traders have unlimited access tocredit at a constant cost ⇢. The contracts are the outcome of an efficient equilibriumin a frictionless contract market where traders know farmers’ ability. Traders incur interaction costs of T (h + m) and credit costs of ⇢ per rupee loaned, and earn per unit
return of ⌧ when they sell the farmer’s crop on an external market. The farmer cannot
repay the loan if the crop fails, but otherwise repays at interest rate r, and receives a44This is in comparison to how much TFP a varies with ✓. This assumption ensures that treatmente↵ects of the TRAIL scheme increase in ✓. See Maitra et al. (2017) for further details.

22

lumpsum side payment s from the trader when the contract is signed. Hence a control
farmer of type ✓ enters into a contract specifying (lc (✓), mc (✓), hc (✓), rc (✓), sc (✓)), where(l, m, h) = (lc (✓), mc (✓), hc (✓)) maximizes joint expected payo↵s of the farmer-traderpair:(1 + ⌧ )A(✓, m)f (l) (1 + ⇢)c(h, m)l(11)
T [m + h]
The interest rate rc (✓) is then set to “decentralize” the efficient scale decision lc (✓) to thefarmer, so that the farmer selects the efficient area lc (✓) in his own self-interest: l = lc (✓)
maximizes A(✓, mc (✓))f (l) p(✓, mc(✓) (1+rc (✓))cc(✓) (h, m)l.45 The side-payment depends
on the relative bargaining power of the trader and the farmer. The details of the model
are presented in Section A2 in the Appendix.
In an optimal contract, traders do not monitor control farmers (mc (✓) ⌘ 0), butthey do help them (hc (✓) > 0). Monitoring is inefficient because it lowers expected
productivity and is costly for both farmer and trader, and produces no benefits sinceneither party is risk-averse. Help is efficient because it raises expected productivity, andso it is provided as long as T is not too large. The help induces the farmer to plant
more area (l), and earn greater profit per acre. More able farmers receive more help,
because per unit of trader’s time, help generates a greater expected return when the
farmer has higher ability. Hence we obtain the following testable predictions for control
farmers:(i) Higher ability farmers are less likely to default, and pay a lower interest rate;(ii) Higher ability farmers produce more output, and incur lower unit costs.
In TRAIL villages, a trader is selected as the agent. Suppose a farmer of type ✓is o↵ered the TRAIL loan. This allows him to supplement his informal loan from thetrader at interest rate rT < ⇢, and thus expand acreage by lt . We assume he cannot
reduce the area that was already agreed upon before the scheme was introduced, while
trader help and monitoring decisions can be freely modified. The farmer repays the
TRAIL loan only if his crop succeeds. The TRAIL agent receives a commission < 1
per rupee interest repaid. The trader-farmer pair then modify their contract decisions
by choosing (lt , mt , ht ) = (lt (✓), mt (✓), ht (✓)) to maximize(1+⌧ )A(✓, m)f (lc (✓)+lt ) [(1+⇢)lc (✓)+{1+rT (1)}p(✓, m)lt ]c(ht , mt )tt
T (h +m )

(12)
45

c
Note that 1 + rc (✓) = (1+⌧1+⇢)p(✓,0) , so r (✓) decreases in ✓.

23

Let the resulting outcomes for TRAIL treated farmer of type ✓ be denoted lT (✓) ⌘lc (✓) + lt (✓), mT (✓) = mt (✓), hT (✓) = ht (✓). The variables lT (✓) and hT (✓) are increasingin ✓, while mT (✓) ⌘ 0. Hence the Order-Preserving Assumption (OPA) holds. The
model then predicts:(iii) Among treated farmers in the TRAIL scheme, those with higher ability produce
more and receive more help, and incur lower unit costs;(iv) In the TRAIL scheme, a treated farmer produces more, earns more profit andincurs lower unit costs than a control farmer of the same ability level.
In the GRAIL scheme, a third party who is not a trader is appointed as the agent.
The GRAIL agent earns expected payo↵ v(✓)p(✓, m)
G m where v(✓) represents a
welfare weight on the farmer, weighted by the default rate, and m is the extent to which
the agent monitors the farmer. The welfare weight is decreasing in ✓, reflecting the
pro-poor ideological or opportunistic objectives of the political party the GRAIL agent
represents. These objectives are achieved as long as the farmer does not default on
the GRAIL loan.46 The commission enters into the welfare weight. This may bias the
GRAIL agent in favor of high ability farmers, because they borrow more, cultivate larger
areas and produce more. We assume that this consideration for personal financial gain is
outweighed by the political considerations which create a bias in the opposite direction.
Not being a trader, the GRAIL agent is not in a position to provide business advice andhelp to the farmer, but can monitor him. The cost of monitoring of the GRAIL agent
is G .
Given a farmer of type ✓ selected for a GRAIL loan, the GRAIL agent’s monitoringlevel mG (✓) will maximize v(✓)p(✓, m)
G m. Given this monitoring level, the farmer
renegotiates his contract with his trade partner. It is easy to check the trader willcontinue to have no incentive to monitor the farmer. Hence, given mG (✓), the revised
contract will select supplementary area cultivated lg = lg (✓) and revised help hg = hg (✓)
to maximize their joint payo↵(1 + ⌧ )A(✓, mG (✓))f (lc (✓) + lg )[(1 + ⇢)lc (✓) + p(✓, m)(1 + rT )]c(hg , mg )lg

Gh

(13)

Let the resulting GRAIL treated outcomes be denoted (lG (✓) ⌘ lc (✓)+lg (✓), mG (✓), hG (✓)).
mG (✓) 0 = mT (✓) and mG (✓) is decreasing. We find that the Order Preserving As46

Here we simplify by assuming that the farmer’s profits do not enter the GRAIL agent’s objective.
More substantively, the GRAIL agent worries about only the downside risk faced by the farmers they
select, but does not obtain any benefit if the farmer avoids a default and earns a large profit.

24

sumption holds in the GRAIL scheme. We obtain the following predictions for GRAIL
treated farmers:(v) The GRAIL agent interacts more with low ability agents. GRAIL borrowers areless likely to default on their loans than TRAIL borrowers of the same ability level.
(vi) If the production function has constant elasticity, a GRAIL treated farmer cultivates a smaller area, receives less help, achieves a smaller reduction in unit costs,
and a smaller increase in expected profits, than a TRAIL treated farmer of thesame ability.
This model thus explains larger heterogenous treatment e↵ects for the TRAIL scheme,
which can account for a larger TRAIL ATE even in the absence of any selection di↵erences. This is mainly because TRAIL treated farmers receive more help. In turn, this isdue to the di↵erent non-program objectives of the TRAIL and GRAIL agents. TRAIL
agents farmers want the borrowers to produce more, so that they can earn larger middleman profits. The help is more e↵ective at raising crop output if the farmer is more able.
On the other hand, the GRAIL agent monitors treated farmers so as to reduce default
risk. This raises their unit cost and lowers their productivity, and so GRAIL treatedfarmers produce less and earn smaller profits than TRAIL treated farmers. These effects are larger, the less able the farmer. Observe finally that the model also explains
the di↵erent selection incentives of the two agent types: the TRAIL agent benefits morefrom more able farmers, while the GRAIL agent benefits more from less able farmers.

7.2

Testing Predictions of the Model

Before testing the predictions, we test the underlying assumption of decreasing returns
to scale. We regress per acre productivity as a double-log-linear function of log area
cultivated, farmer fixed e↵ects and village-year shocks
log[

Rivt
] = log ai + (µlivt1) log livt + log qvt + eivt

(14)

The elasticity (µ 1) with respect to area cultivated can be estimated by instrumental
variables on the sample of Treatment and Control 1 farmers, with the treatment dummy
as the instrument. We have already seen that treatment has a significant positive e↵ect
on area cultivated, while the randomization ensures that the exclusion restriction issatisfied. We obtain an IV estimate of (µ 1) = .094, which is statistically insignificant
25

(p-value of the one-sided test of the null hypothesis (µ 1) = 0 is 0.81). Hence we haveweak evidence for decreasing returns to scale.
To test prediction (i) about the variation of control farmers’ interest rates withability, Column 1 of Table 12 presents the OLS regression results of interest rates paid on
informal loans by Control 1 households, using the pooled sample of TRAIL and GRAIL
households. We restrict our sample to informal loans taken before our intervention
began, to avoid potential contamination from the intervention on borrowers’ interest
rates.47 The coefficient estimate of productivity is negative (p = 0.11) indicating thatfor control households, there is a decline in interest rate as productivity increases. The
average control household in Bin 1 reported taking loans at 21% interest per annum.
This is significantly higher than the 15% that Bin 2 (p = 0.03) and the 16% that Bin 3households reported (p = 0.04).48 Hence the evidence confirms prediction (i).
Next consider prediction (ii) about how output and unit cost vary with ability among
control farmers. Columns 2 and 3 of Table 12 present OLS regression results of potato
ouput (in kg) and input cost per acre in potato cultivation (in Rs.) on the productivity
estimate. The regressions control for year dummies, and pertain to all Control 1 andControl 2 households. Consistent with prediction (ii), column 2 shows that output
is increasing in productivity, while column 3 shows that unit costs are decreasing in
productivity.
Now turn to prediction (iii) for TRAIL treated farmers. At each four-monthly survey
interview, we asked sample households whether in the previous three days they had
spoken with the local trader or the agent (separately) about cultivation, the harvest,or output sales. Since in the TRAIL scheme the agent is also a trader, we include
interaction with the trader as well as the agent (in case the agent is a di↵erent trader)
to measure number of interactions with traders.49 In column 4 of Table 12 we see that
TRAIL Treatment households’ interacted more with the agent if they had higher ability.
Columns 5 and 6 of Table 12 present the OLS regressions of quantity of potato cultivatedand input cost per acre in potato cultivation on productivity for TRAIL Treatment
47
Since only 10 households in each village received the program loans, we do not believe there were
any spillover or general equilibrium e↵ects.
48
These averages are presented in the left panel of Figure A6. The right panel of this Figure uses
continuous variation in productivity. Recall that we do not have a continuous measure of productivity
for those who did not cultivate potatoes; for this group we compute the mean informal interest rate andplot it as a single point in the right panel of Figure A6. For Bins 2 and 3, we run a locally-weighted
polynomial regression of the mean interest rate on farmer productivity.
49
Since the GRAIL agent is not a trader, when we measure the number of interactions with the
GRAIL agent we will not include interactions with the trader.

26

households. Consistent with prediction (iii), more productive TRAIL Treatment farmers
interacted more with the agent, and produced more potatoes, at a lower input cost per
acre.
Table 9 provides evidence about prediction (iv). The TRAIL HTEs on potato
acreage, potato output and input cost per acre in potato cultivation (see columns 2,
3 and 10 respectively) conform to prediction (iv), and most are statistically significant.
Finally, we test the predictions about the GRAIL scheme, and its comparison with
the TRAIL scheme. Start with prediction (v). Column 11 of Table 9 presents the
HTEs on agent interactions in TRAIL and GRAIL. As the results in Panel B show,
they are decreasing in ability in the GRAIL scheme indicating that the GRAIL agent
interacts more with low ability farmers. On the other hand they are increasing in ability
in the TRAIL scheme, indicating that the TRAIL agent increases his interaction withhigher-ability farmers more.
Figure 2 presents the default rates on program loans by TRAIL and GRAIL treatedhouseholds by productivity bin. TRAIL Treatment households in productivity Bin 1
defaulted on 9.3 percent of their loans, whereas GRAIL Bin 1 Treatment households
defaulted at a significantly lower 5 percent (p = 0.03). In Bins 2 and 3 the di↵erencesin default rates across TRAIL and GRAIL go the other way, although they are notstatistically significant. This is consistent with prediction (v).
To check prediction (vi), consider the results presented in Panel C of Table 9. For
every productivity bin, TRAIL treatment e↵ects on acreage, output and value-added
exceed the corresponding GRAIL treatment e↵ects (although the di↵erences are notstatistically significant): see columns 2, 3 and 7. For every productivity bin, the TRAIL
treatment e↵ects on unit costs are lower than the corresponding GRAIL treatment e↵ects
and the di↵erence is statistically significant for Bin 3. See column 11. This is consistent
with prediction (vi).

8Concluding Comments

This paper finds evidence that appointing private traders as intermediaries to delivercredit, as in the TRAIL scheme, resulted in significant impacts on borrower production
and farm incomes. When the local elected council appointed the intermediary, as in
the GRAIL scheme, this did not have a significant impact on borrower incomes, though
it did increase production. The di↵erence was due to the di↵erent impacts on cultivation costs: TRAIL treated households reduced their unit costs of production while the27

GRAIL treated households did not. Further, although the TRAIL agent selected moreproductive farmers to participate in the scheme than the GRAIL agent did, this could
only explain very little of the di↵erence in outcomes. Instead, the TRAIL agents’ interaction with treated farmers were far more important: they increased their interactions
with treated farmers, and to a greater extent with more productive farmers. GRAIL
agents also interacted more with treated farmers, but to a greater extent with less productive farmers; this appears to have caused them to default less than their TRAIL
counterparts. The results are consistent with a model of endogenous agent-farmer interactions where the TRAIL agent helped treated farmers reduce production costs byproviding helpful advice, while the GRAIL agent monitored treated farmers more intensively, thus reducing their default rates and raising production costs. In this model, the
GRAIL agent resembles a private holder of debt, motivated primarily to reduce borrower
default risk, while the TRAIL agent is like an equity holder who shares risk evenly withthe farmer and is motivated to provide useful information and advice. Although the
TRAIL agent did not monitor the treated farmers, TRAIL and GRAIL loans still had
similar repayment rates, because the TRAIL borrowers were more able farmers to start
with, and so were less likely to default. The GRAIL agent selected a larger proportionof low ability farmers with higher ex ante risk, and then monitored them intensively to
lower these risks.
The more general lesson is that the role of the intermediary was not confined to
selecting beneficiaries and achieving better targeting. The di↵erences in the nature of
network connections with the intermediary mattered significantly for the eventual successof the program. In particular the endogenous change in this relationship resulting fromthe intervention accounts for most of the di↵erences in impacts on beneficiary incomes.
Policy interventions that employ local intermediaries could benefit from considering thismechanism at the time of intervention design.

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30

31

Panel B: Productivity estimates
Selected households

Notes: In Panel A, the sample is restricted to Control 1 and Control 2 households in TRAIL and GRAIL villages. Productivity is computed using thelogarithm of acreage under potato cultivation. The null hypothesis that the distributions of estimated productivity for Control 1 and Control 2 households
are equal is rejected by a two sample Kolmogorov-Smirnov (KS) test with p value = 0.005 for TRAIL and 0.011 for GRAIL. In Panel B, the sample
is restricted to Control 1 households in TRAIL and GRAIL villages. Productivity is computed using the logarithm of acreage under potato cultivation.
The null hypothesis that the distributions of estimated productivity for TRAIL Control 1 and GRAIL Control 1 households are equal is rejected by a two
sample Kolmogorov- Smirnov (KS) test with p value = 0.06.

Panel A: Productivity Distribution
Selected v. Non-selected households

Figure 1: Selection on Productivity: TRAIL and GRAIL

Figure 2: Default Rates on TRAIL and GRAIL Loans, by Productivity Bin

Notes: The height of each bar measures the fraction of program loans that were not repaid fully by thedue date. The sample is restricted to Treatment households in TRAIL and GRAIL villages with at most
1.5 acres of land. p-value of di↵erence between default rate between TRAIL and GRAIL for each Bin
presented. Productivity is computed using the logarithm of the acreage under potato cultivation.

32

Table 1: Randomization

TRAIL
(1)

GRAIL
(2)

TRAIL — GRAIL(3)

276.04(201.59)164.63(130.30)252.21(238.36)160.75(168.39)

23.83

15.96
(18.98)113.88(103.22)
25.58(16.27)10.88(7.39)
1.38(1.79)
0.00(0.00)

27.96(75.63)99.67(78.00)24.63(25.20)12.83(17.11)
1.17(1.95)
0.04(0.20)-12.000.407(0.015)0.441(0.015)0.340(0.015)0.456(0.013)0.052(0.001)0.564(0.015)0.124(0.010)0.447(0.015)0.420(0.015)0.415(0.015)0.343(0.015)0.443(0.013)0.052(0.002)0.608(0.015)0.126(0.010)0.475(0.015)-0.013

Panel A: Village level characteristics
Number of Households
Number of Potato Cultivators
Of which:
Landless
Own 0

1.25 acres

Own 1.25

2.50 acres

Own 2.50

5.00 acres

Own 5.00

12.50 acres

Own more than 12.50 acres

3.88

14.21
0.96
-1.96
0.21
-0.04

Panel B: Household level characteristics
Head: More than Primary School
Head: Cultivator
Head: Labourer
Landholding (Acres)
Area of house and homestead (Acres)
Separate toilet in house
Own a motorized vehicle
Own a Savings Bank Account

0.026-0.0030.013
0.000-0.044-0.002-0.028Notes: Panel A uses data from the houselisting exercise we carried out in 2007.
Since 2 of the villages from the 2007 sample had to be replaced due to Maoist
violence, Panel A uses a sample of only 46 villages. Panel B uses household survey
data from 2010 for all 48 villages for sample households that owned no more than
1.5 acres of land. Standard errors in parentheses.

33

Table 2: Agent Characteristics

Male
SC/ST
Non-Hindu
General caste
Primary Occupation: Cultivator
Primary Occupation: Shop/businessPrimary Occupation: Other
Owns agricultural land
Total owned land
Has pucca house
Educated above primary school
Weekly income (Rupees)
Village society member
Party hierarchy member
Panchayat member
Self/family ran for village head

GRAIL
(1)

TRAIL
(2)

Di↵erence(3)

1.00(0.00)
0.208(0.085)0.125(0.069)0.667(0.098)0.375(0.101)
0.208(0.095)0.417(0.690)2.63(0.198)4.08(0.248)0.375(0.101)
0.958(0.042)1102.895(138.99)
0.292(0.095)0.167(0.078)0.125(0.069)0.083(0.058)0.958(0.042)0.083(0.058)0.083(0.058)0.833(0.078)0.042(0.042)0.958(0.042)0.000(0.000)3.29(0.244)5.04(0.292)0.458(0.104)0.792(0.085)1668.75(278.16)0.083(0.058)0.000(0.00)
0.000(0.00)
0.000(0.00)

0.042(0.042)0.125(0.102)
0.042(0.090)-0.167(0.125)0.33(0.109)-0.667(0.104)0.125(0.690)-0.667(0.314)-0.958(0.383)-0.083(0.145)0.167(0.094)-565.855(336.78)
0.208(0.111)0.167(0.079)0.125(0.069)0.083(0.058)Notes: Sample consists of 24 agents in TRAIL villages and 24 agents in
GRAIL villages. Standard errors in parenthesis.

34

0.109(0.312)0.870(0.339)0.33(0.020)0.14(0.015)0.10(0.012)0.17(0.016)0.18(0.016)0.18(0.016)0.12(0.013)0.45(0.021)0.90(0.013)0.98(0.007)0.31(0.020)0.171(0.377)0.877(0.331)0.41(0.023)0.25(0.020)0.13(0.016)0.21(0.019)0.24(0.020)0.25(0.020)0.10(0.014)0.53(0.023)0.96(0.009)1.00(0.002)0.45(0.024)(2)

0.020(0.142)0.556(0.527)0.05(0.009)0.04(0.008)0.09(0.012)0.08(0.012)0.08(0.011)0.02(0.006)0.08(0.011)0.61(0.020)0.91(0.012)0.98(0.006)0.27(0.019)(3)

0.027(0.163)0.615(0.506)0.07(0.012)0.09(0.013)0.11(0.014)0.10(0.014)0.09(0.014)0.04(0.009)0.07(0.012)0.68(0.022)0.97(0.008)1.00(0.003)0.39(0.023)(4)

GRAIL
Non-recommended
Recommended

0.089(0.02)
0.315(0.133)0.28(0.02)
0.10(0.01)
0.01(0.02)

0.08(0.12)
0.10(0.02)
0.16(0.02)
0.04(0.02)-0.16(0.03)-0.01(0.02)-0.01(0.01)
0.04(0.03)(5)

0.143(0.02)
0.261(0.107)0.34(0.03)
0.16(0.02)
0.02(0.02)

0.11(0.02)
0.14(0.02)
0.21(0.02)
0.03(0.02)-0.15(0.03)-0.01(0.02)
0.00(0.00)
0.06(0.03)(6)Di↵erence (TRAIL–GRAIL)†
Non-recommended
Recommended

Notes: The TRAIL agent was a randomly selected trader in the village. The GRAIL agent was selected by the local government. Recommended households include Treatment
and Control 1 households. Non-recommended households include Control 2 households. Sample restricted to all households with 1.5 acres of land in TRAIL and GRAIL villages.‡ :The farmer belongs to same the religion category (Hindu/Non-Hindu) or caste category (Scheduled caste/Scheduled tribe, or General) as the agent. | : Conditional on selling to
the agent. ⇤ : Conditional on knowing the agent. † : The di↵erences in columns 5 and 6 denote TRAIL—GRAIL for the specific sub-group (Non-recommended or Recommended)households.

Household has been selling Potatoes to Agentfor longer than one year|

Household Sold Potatoes to Agent (% of Transactions)

Worked for Agent

Borrowed from Agent

Bought from AgentIn the last 3 years household has

Employers

Output buyers

Input suppliers

Moneylenders

Agent is one of the two most importantHousehold member invited by Agent on special occasions

Respondent meets Agent at least once a week⇤

Respondent knows Agent

Agent and Household belong to same caste/religion category‡

(1)

TRAIL
Non-recommended
Recommended

Table 3: Pre-Intervention Social and Economic Interactions with the AgentTable 4: Average Treatment E↵ects on Agricultural Borrowing, Farm Value
Added, Non Agricultural Income and Total Household Income

Agricultural LoansAll
Non-Program†

Farm
Value Added

IncomeNon Agricultural Household

(Rs.)

(Rs.)

(Rs.)

(Rs.)

(Rs.)

(1)

(2)

(3)

(4)

(5)

2868
(658.90)2096
136.83-448.50(634.80)2096-21.392147.874(723.935)
10336.511
20.7791506.785(3655.543)
33617.764
4.482

5612.479(4125.591)
49765.238
11.278Mean GRAIL C1% E↵ect GRAIL

2754(593.40)
1931
142.62-104.90(551.30)
1931
-5.43

138.905(929.675)
10501.064
1.323-4326.522(3225.654)
37170.751-11.640-5434.789(4496.421)
56082.489-9.691TRAIL v. GRAIL
Treatment

114.50(890.60)-343.6(842.6)

2008.969(1183.349)

5833.307(4879.593)

11047.268(6110.838)

Sample Size

6,156

6,156

6,156

6,159

6,156

TRAIL Treatment
Mean TRAIL C1% E↵ect TRAILGRAIL Treatment

Notes: Treatment e↵ects are computed from regressions that follow equation (1) in the text and are
run on household-year level data for all sample households in TRAIL and GRAIL villages with at most
1.5 acres of land. Regressions also control for the religion and caste of the household, age, educational
attainment and occupation of the eldest male member of the household, household’s landholding, a setof year dummies and an information village dummy. % E↵ect: Treatment e↵ect as a percentage of themean of the relevant Control 1 group. † : Non-Program loans refer to loans from sources other than the
TRAIL or GRAIL schemes. Standard errors in parentheses are clustered at the hamlet level. .

36

37

0.130
(0.029)
0.641
20.33

-0.090(0.040)6,150

GRAIL Treatment

TRAIL v. GRAIL
Treatment

Sample Size

6,150

0.024
(0.041)

0.069(0.029)
0.296
23.25

0.093
(0.028)
0.336
27.58

6,150

175.452(457.836)

770.916(340.084)
3236.730
23.82

946.368(302.239)
3646.124
25.96

(3)

(Kgs)

(Acres)(2)

Production

Acreage

6,150-161.092(1067.944)

2007.117(779.852)
7070.653
28.39

1846.025(722.561)
8481.751
21.76

(4)

Cost of
Production
(Rs.)

3,820

0.146
(0.145)-0.176(0.110)4.800-3.665-0.030(0.099)4.627-0.650(5)(Rs./Kgs)

Price

6,150

1397.405(1838.410)

2500.996(1377.891)
12964.776
19.29

3898.401(1204.271)
14285.467
27.29

(6)

(Rs.)

Revenue

6,150

1566.839(979.876)

492.395(744.349)
5827.663
8.45

2059.235(631.241)5732.357
35.92

(7)

Value
Added
(Rs.)

6,150

1716.404(941.279)

190.292(720.068)
4941.511
3.85

1906.696(600.595)
4733.770
40.28

(8)

ImputedProfit(Rs.)

6150-837.707(1923.972)

3886.833(1389.776)
13454.993
28.8883049.126(1316.601)
16288.501
18.719(9)(Rs.)

Input Cost

4038

-3462.985(1499.369)

554.147(1098.176)
47510.733
1.166-2908.838(1015.032)
49076.876-5.927(10)

Input Cost
Per acre(Rs./Acres)

6150

0.038
(0.083)0.142(0.062)0.180(0.054)

(11)

Index
Variable

Notes: Treatment e↵ects are computed from regressions that follow equation (1) in the text and are run on household-year level data for all sample households
in TRAIL and GRAIL villages with at most 1.5 acres of land. The dependent variable in column 1 takes the value of 1 if the household cultivated potato in thatyear, 0 otherwise. The dependent variables in columns 2—9 take the actual value reported by the household if it did, or take the value zero if it did not cultivate
potatoes in that year. In column 10, households that did not cultivate potatoes in a year are dropped from the estimating sample. Regressions also control for the
religion and caste of the household, age, educational attainment and occupation of the eldest male member of the household, household’s landholding, a set of year
dummies and an information village dummy. % E↵ect: Treatment e↵ect as a percentage of the mean of the relevant Control 1 group. Imputed profit = Value Added– shadow cost of labour. In column 11, the dependent variable is an index of z-scores of the outcome variables in the panel; the p-values for treatment e↵ects inthis column are computed according to Hochberg (1988)’s step-up method to control for the family-weighted error rate across all index outcomes. Standard errors
in parentheses are clustered at the hamlet level. .

Mean GRAIL C1% E↵ect GRAIL

Mean TRAIL C1% E↵ect TRAIL

0.040(0.028)0.715
5.63

TRAIL Treatment(1)

Cultivate

Table 5: Average Treatment E↵ects in Potato Cultivation

Table 6: Loan PerformanceTake-up(1)

Default(2)

0.937(0.006)0.872(0.009)0.065(0.011)0.070(0.007)0.070(0.008)0.000(0.010)Panel A: Sample Means
TRAIL
GRAIL
Di↵erence (TRAIL–GRAIL)

Panel B: Regression Results
GRAIL-0.066(0.011)

0.005(0.010)R2
Sample Size

0.08
2667

0.05
2422

Notes: The sample consists of household-cycle
level observations of Treatment households in
TRAIL and GRAIL villages with at most 1.5 acresof landholding. The dependent variable in column 1
takes value 1 if the household took the program loanin the particular cycle, provided the household was
eligible for the loan in that cycle. The dependent
variable in column 2 takes value 1 if a borrowing
household fails to fully repay the amount due on
the loan taken in that cycle on the due date. The
regression specification In Panel B is given by equation (2). Regressions also control for landholding,religion and caste of the household and age and educational attainment of the oldest male in the household. Robust standard errors.

38

Table 7: Variation of Productivity with
Observable Household Characteristics

Regression Results
Landholding
Non Hindu
Low Caste
Household Size
Female-headed Household
Age of Oldest Male
Oldest Male: Completed Primary School
Constant

Sample Size
R-squared

1.103(0.115)-0.157(0.147)-0.110(0.120)0.019(0.021)-0.457(0.217)-0.004(0.003)0.080(0.084)1.212(0.188)

1,000
0.163

Descriptives on farm productivity
Mean of Productivity
SD of Productivity
Minimum Productivity
Productivity Quartile 25%
Productivity Quartile 50%
Productivity Quartile 75%
Maximum Productivity

1.707
1.148
-1.294
0.811
2.014
2.638
3.702

Notes: The dependent variable is the householdproductivity estimate. Standard errors in parentheses are clustered at the hamlet level. The etimating sample includes cultivator Control 1 andControl 2 households in TRAIL and GRAIL villages with at most 1.5 acres of land.

39

Table 8: E↵ect of Treatment on Voting Patterns in Straw Poll

Average Treatment E↵ect
TRAIL
GRAIL

Treatment E↵ect

(1)

(2)

0.0241(0.0496)0.0782(0.0340)

Treatment E↵ect: Bin 1
Treatment E↵ect: Bin 2Treatment E↵ect: Bin 3

Selection E↵ect-0.0649(0.0447)

Selection E↵ect: Bin 2
Selection E↵ect: Bin 3

1,011

(3)

(4)

0.0915(0.0868)-0.0741(0.0805)
0.0568(0.0564)0.130(0.0697)0.0309(0.0702)0.0135(0.0743)-0.133(0.0610)-0.0291(0.0738)-0.0343(0.0594)

0.0217(0.0580)0.117(0.0664)0.105(0.0718)

1,021

1,044

0.0825(0.0369)

Selection E↵ect: Bin 1

Sample Size

Heterogeneous Treatment E↵ect
TRAIL
GRAIL

1,026

Notes: Estimating sample includes all sample households in TRAIL and GRAIL villages withatmost 1.5 acres of land. OLS regression results presented. Standard errors in parenthesis
clustered at the hamlet level. Estimating sample includes all sample households in TRAIL and
GRAIL villages with at most 1.5 acres of land. Productivity computed using logarithm of acreage
under potato cultivation.

40

41

6,150

6,150

6,150

6,150

3,820

0.47
(0.25)
0.14(0.24)
0.16(0.19)-0.21(0.16)-0.27(0.22)-0.12(0.10)

0.27(0.19)-0.13(0.11)
0.03(0.16)

Price(Rs./Kgs)
(5)

6,150

817.11(1135.25)
679.19(1650.43)
118.38(3235.01)

923.13(754.42)3022.03(1214.53)
4499.38(2438.55)

1740.24(799.82)3701.22(1116.50)
4617.76(2127.12)

Revenue
(Rs.)
(6)

6,150

1010.06(652.15)1009.97(997.51)1542.63(2147.61)

30.37(436.82)551.22(776.04)
1291.41(1705.78)

1040.43(461.01)
1561.19(616.88)2834.05(1296.70)

Value Added
(Rs.)
(7)

6,150

1195.10(622.94)
1157.03(962.31)1767.44(2137.38)-265.08(419.17)301.55(752.09)900.73(1713.97)

930.02(439.59)1458.58(590.20)2668.16(1268.49)

Imputed Profit
(Rs.)
(8)

6,150

-477.89(1197.94)-372.38(1921.03)-4162.92(3134.61)

1882.20(782.34)
4002.05(1350.70)
6777.32(2271.26)

1404.31(867.58)3629.66(1380.78)
2614.40(2162.73)

Input Cost
(Rs.)
(9)

4,038

-8469.90(5981.13)-439.27(2374.94)-5290.21(2061.19)

6768.55(2949.59)-1881.26(1708.23)
1552.87(1561.18)-1701.34(5217.85)-2320.53(1624.29)-3737.34(1334.26)

Input Cost (Per acre)(Rs./Acres)
(10)

2760

-1.42
(0.54)-0.31(0.31)
0.20(0.12)

1.47(0.54)
0.36(0.30)
0.12(0.03)

0.04(0.03)
0.06(0.08)
0.32(0.13)

Agent
Interaction(11)

Notes: The estimating equation follows equation (6) in the text. The estimating sample includes all sample households in TRAIL and GRAIL villages with at most 1.5 acresof land. The dependent variable in column 1 takes the value of 1 if the household cultivated potato in that year, 0 otherwise. The dependent variables in columns 2—9 takethe actual value reported by the household if it did, or take the value zero if it did not cultivate potatoes in that year. In column 10, households that did not cultivate potatoes
in a year are dropped from the estimating sample. In column 11, agent-farmer interaction is measured by the number of times in the past year the responder talked to theagent about agricultural cultivation related matters. In this column the sample includes Treatment and Control 1 households in TRAIL and GRAIL villages with at most 1.5
acres of land. The regressions also control for the religion and caste of the household, age, educational attainment and occupation of the eldest male member of the household,household’s landholding, a set of year dummies and an information village dummy. Productivity is computed using the logarithm of the acreage under potato cultivation.
Standard errors in parentheses are clustered at the hamlet level.

Sample Size

-195.94(608.30)-328.49(887.63)-1395.52(1773.03)

Panel C: TRAIL – GRAIL Di↵erence
Bin 1
-0.18
0.01
96.79
(0.05)(0.02)(269.90)
Bin 2
-0.11
0.00
64.88
(0.03)(0.04)(388.58)
Bin 3
0.00
0.00
-292.36(0.00)(0.06)(751.62)696.46(444.71)2132.54(638.20)
1814.68(1252.25)

Cost (Total)
(Rs.)
(4)

892.40(392.02)
2461.04(622.18)3210.20(1254.91)

0.03(0.02)
0.08(0.03)
0.11(0.05)

Panel B: GRAIL
Bin 1
0.26
(0.04)
Bin 2
0.12(0.02)
Bin 3
0.00(0.00)

383.82(179.66)
909.28(274.25)1126.38(488.62)

Output(Kgs)(3)

287.04(191.71)844.40(277.39)1418.74(570.73)

0.04(0.02)
0.09(0.03)
0.11(0.04)

Acreage(Acres)(2)

Panel A: TRAIL
Bin 1
0.08
(0.03)
Bin 2
0.01(0.02)
Bin 3
0.00(0.00)

Cultivate
(0/1)
(1)

Table 9: HTEs by Productivity Bin. Potato Cultivation and Interaction with Agent

42

0.27
0.33
0.40

0.34
0.33
0.32

(2)

(1)

(3)

-0.07
0.00
0.07

T
k

T
k

G
k

2059.21040.41561.22834.1(4)

TRAIL HTEs
(TkT )

492.4

30.4
551.2
1291.4(5)

GRAIL HTEs
(TkG )

denotes the proportion of TRAIL households in Bin k;
T
G
T
P
(( k
k )⇥Tk
ATE due to selection computed as 3k=1.
ATE

Notes:% of ATE due to Selection% of ATE due to CTE

ATE

Bin 1
Bin 2
Bin 3

G
k

T
k

G
T
k ) ⇥ Tk

-74.1
-4.5
209.8

(7 = (3) ⇥ (4))

( kT

TkG )

8.38
75.46

348.2
335.2
498.9

(8 = (2) ⇥ (6))

GTk ⇥ (TkG
k denotes the proportion of GRAIL households in Bin k. % of

1566.81010.11010.01542.6(6)

TRAIL–GRAIL HTEs
(TkT TkG )Table 10: Decomposition of ATE Di↵erences in Value-added from Potato Cultivation;
TRAIL v. GRAIL

Table 11: Estimates from the Multidimensional Screening Model

Variable

(1)

GRAIL

(1)

(2)

⇣

-0.076

(2)

(3)

TRAIL

0.21

µ (Fixed E↵ect Estimate† )

0.28

1.39

Reduced Form Estimates:
(4)
(5)

µ
Quasi Profit: 1+⇣
Rivt CivtµPredicted ATE: [ 1+⇣
Rivt Civt ] ⇥13008.29
2731.7412432.64
3481.14

1.35

1.46

36.48

33.48

Structural Estimates:
µ

(6)

i 1+⇣
[ai [ w
]
c
i

wi

1

(ci ) 1+⇣]⇥

(7)

Percentage E↵ect on Value Added(8)µ (IV Estimate‡ )0.90

Reduced Form Estimates:
(9)
(10)

µ
Quasi Profit: 1+⇣
Rivt CivtµPredicted ATE: [ 1+⇣
Rivt Civt ] ⇥5432.67
1140.865557.38
1556.07

1.31

1.48

35.27

33.95

Structural Estimates:
µ

(11)
(12)

i 1+⇣
[ai [ w
]
c
i

wi

1

(ci ) 1+⇣]⇥

Percentage E↵ect on Value Added

Notes: † : Estimate of µ obtained from a household fixed e↵ects regression of log quantity of potato cultivates on log acreage and village–
year dummies. Sample includes Control 1 and Control 2 households
in TRAIL and GRAIL villages with at most 1.5 acres of land. ‡ : Estimate of µ obtained from a regression of average productivity on log
acreage, household fixed e↵ects and village-year dummies, with thetreatment dummy as the instrument.
defined as the proportional
increase in the cost of cultivation owing to the treatment defined by
the treatment e↵ect as a proportion of the Mean for Control 1 households in column 4 of Table 5. R and C defined by the mean Revenue
and Cost of cultivation for Control 1 households in columns 6 and 4of Table 5. Sample includes Treatment and Control 1 households in
TRAIL and GRAIL villages with at most 1.5 acres of land.

43

44
524

-0.007(0.004)0.001(0.002)0.196(0.012)2,876

907.774(40.407)679.859(23.831)486.300(139.244)

(2)

2,876

-182.203(194.776)-352.578(89.164)
42,641.482(705.384)

(3)

681

0.368
(0.068)-0.033(0.044)3.100(0.430)(4)500

1,219.652(133.88)685.131(48.367)
697.368(255.379)

(5)

500

-1,042.832(375.664)-101.632(168.138)
41,291.774(1090.398)(6)

TRAIL Treatment Households
Trader Interactions
OutputInput Cost(per acre)

Notes: OLS regression results presented. In column 1, the dependent variable is average interest rate paid on informal loans byhouseholds prior to the intervention. In columns 2 and 5, the dependent variable is the quantity of potato produced (in Kgs). In
columns 3 and 6, the dependent variable is input cost per acre in potato cultivation (in Rs.). In column 4, the dependent variable
is interactions with traders, measured as the number of times in the year the household reported engaging with local traders on
agricultural matters. Sample in columns 1–3 restricted to Control 1 and Control 2 households in TRAIL and GRAIL villages withat most 1.5 acres of land. Sample in columns 4–6 restricted to Treatment households in TRAIL villages with at most 1.5 acres ofland. In columns 2–6, the regressions also control for year dummies. Standard errors in parentheses are cluster-bootstrapped at thehamlet-level with 2000 iterations. Productivity is computed using the logarithm of the acreage under potato cultivation.

Sample Size

Constant

Productivity squared

Productivity(1)

TRAIL and GRAIL Control Households
Interest Rate
OutputInput Cost(per acre)

Table 12: Variation of Informal Interest Rate, Trader Interactions, Output and Input
Cost per acre by Productivity

Online Appendix
A1

Multidimensional Screening with Credit Rationing

Below we sketch the alternative model where agents screen farmers of multidimensional
types, in the presence of credit rationing. We use the predictions of this model toempirically examine whether this mechanism can provide an alternative explanation of
our result of larger average treatment e↵ects in the TRAIL scheme.
Farmers (indexed by i) can vary in three di↵erent dimensions of type: wealth, ability
and cost. All farmers face binding credit constraints in the informal credit market, so
their scale of cultivation is determined by their credit access, which is an increasing
function of their wealth type. Output is increasing in the scale of cultivation, witha constant elasticity µ which may exceed or be smaller than one, so we impose norestrictions on returns to scale. Output also depends on productivity, which depends
on the farmer’s ability type. Finally, unit costs of cultivation vary for a given farmer
with the scale of cultivation with a constant elasticity ⇣. Cultivation costs vary acrossfarmers according to the third dimension of heterogeneity, their cost type. We impose
no constraints on the joint distribution of wealth, ability and cost.
Since credit constraints bind, the scale of cultivation is not chosen by farmers anddepends on their wealth type, which is exogenously assigned. The farmer’s financing
constraint determines the upper bound on what the farmer can spend on cultivation
cost. Since financing constraints bind by assumption, the actual cultivation cost equals
the financing constraint. The farmers financial access (from self-financing as well as
access to credit) therefore determines the actual cultivation cost:log Civt = log wi + log vt + ✏4ivt(A1)

where Civt denotes the total cultivation cost of a control farmer i in village-year v, t, wi
is a measure of credit access or wealth, and vt represents village and year dummies forshocks to the supply of credit. Hence farmer fixed e↵ects in the panel regression (A1)
provide a measure of wealth.
Let uivt denote the unit cultivation cost of farmer i in village-year v, t. This in turndepends on the farmer’s cost type ci and the area cultivated livt (owing to pecuniary
scale economies or diseconomies), according tolog uivt = log ci + log qvt + ⇣ log livt + ✏3ivt(A2)

where log qvt denotes input price shocks in (v, t). On the other hand, the relationshipbetween total and unit cultivation costs is given by
Civt ⌘ uivt livt qvt(A3)

Given Civt the financial access of the farmer, (A2) and (A3) represent two equations in
1

two unknowns (unit cost, and scale of cultivation). Solving these, the scale of cultivation
is determined bylog livt =1[log civt
1+⇣

log cilog qvt ] =1[log wi1+⇣

log cilog qvt + log vt ] + ✏4ivt(A4)i.e., by the wealth and cost type of the farmer, in conjunction with village and year
shocks in the supply of credit and input prices.
Finally, we can estimate farmer ability ai as a fixed e↵ect in a panel production
function regression:
log yivt = log ai + µ log livt + 2vt + ✏2ivt(A5)
where yivt denotes revenue of farmer i in village v in year t, µ represents returns to scale,and 2vt denotes village and year dummies representing village level productivity shocks.
Selection Patterns
The three type variables are estimated as farmer fixed e↵ects in panel regressions (A1,A2, A5) respectively. We can then directly check selection in either TRAIL and GRAIL
on any of the three dimensions by comparing the corresponding distribution of eachdimension between control 1 and 2 subjects.
Predicted Treatment E↵ects
Under the assumption of no treatment e↵ects per se on farmer wealth, ability or cost,
we can estimate the treatment e↵ect of either program (TRAIL or GRAIL) and thedi↵erence between these treatment e↵ects implied by selection di↵erences on the three
dimensions: wealth, ability and cost.
Let > 0 denote the percent change in financial access resulting from the treatment,so that log Civt rises by . Dropping the regression error terms, the resulting expressions
for post-treatment log acreage and log unit cost are:log livt =log uivt =

1
[
1+⇣

⇣
[
1+⇣

+ log wi + log vt+ log wi + log vt ] +log qvt

log ci ]1[log ci + log qvt ]
1+⇣

(A6)
(A7)

Denoting (total) cost by Civt , the reduced form for log cost is therefore
log Civt ⌘ log livt + log uivt =+ log wi + log vt(A8)

Hence the treatment e↵ects on these three variables are:d log livt =1⇣, d log uivt =, d log Civt =
1+⇣
1+⇣
2

(A9)

The resulting treatment e↵ect on log revenues isd log Rivt = µd log livt =

µ
1+⇣

(A10)

and on farm profit isd⇧ivt ⌘ dRivt

dCivt = [

µ
Rivt
1+⇣

Civt ](A11)

The di↵erence in predicted treatment e↵ect between TRAIL and GRAIL is thus equalµto the di↵erence between average quasi-profit [ 1+⇣
Rivt Civt ] of the respective Control
1 subjects. To relate quasi-profit to underlying types of the farmers, observe that themodel impliesµµ
wi vt 1+⇣
wi vt
Rivt Civt = ai [
]
(A12)
1
1+⇣
ci qvt(ci qvt ) 1+⇣
so if we normalize the village year shocks to their unit mean, this reduces toai [µwi 1+⇣]ciwi1(ci ) 1+⇣(A13)

which we can calculate for each control 1 farmer from the estimated types on the three
dimensions ability (ai ), wealth (wi ) and cost (ci ).

3

A2

Model Of Agent-Farmer Interactions: Details

Control Farmers
A contract between farmer F of ability ✓ and trader T is represented by a scale ofcultivation l, help h, monitoring m, an interest rate r and a side-payment s. The first
three determine the size of the loan c(h, m)l. The farmer repays the loan if his crop
succeeds. Hence the farmer’s expected payo↵ (excluding fixed cost F ) isp(✓, m)[a(✓, m)f (l)(1 + r)c(h, m)l] + s(A14)

while the trader’s payo↵ is⌧ p(✓, m)a(✓, m)f (l) + [(1 + r)p(✓, m)(1 + ⇢)]c(h, m)lT (m + h)

s

(A15)

where ⌧ represents an exogenous middleman margin earned by the trader per unit output. An efficient contract maximizes the joint payo↵ given by(1 + ⌧ )A(✓, m)f (l)(1 + ⇢)c(h, m)lT [m + h](A16)

It is optimal for the trader to not monitor the farmer at all (mc (✓) = 0), sincemonitoring is costly, lowers expected productivity A and increases the production cost.
Next, observe that given a certain level of help h, the optimal scale of cultivation lc (✓, h)which maximizes(1 + ⌧ )A(✓, 0)f (l) (1 + ⇢)c(h, 0)l(A17)
is increasing in ✓ and h. Let the maximized value of the expression in equation (A17)be denoted by ⇧(h, ✓). Then help hc (✓) is chosen to maximize⇧(h, ✓)Th(A18)

By the Envelope Theorem, ⇧ is a supermodular function: the marginal return to help
increases with the farmer’s ability.50 Hence hc (✓) is increasing: higher ability farmers
receive more help, and end up with higher scale of cultivation, productivity, and lower
unit cost. This rationalizes our use of scale of cultivation as a proxy for ability and for
productivity among control farmers.
Observe also that the choice of scale of cultivation can be delegated to the farmer, if
the interest rate is set at
1+⇢
1 + rc (✓) =(A19)(1 + ⌧ )p(✓, 0)This interest rate adjusts the cost of capital up for default risk, and then subsidized by
the trader in order to induce the farmer to internalize the e↵ect of cultivation scale on
T’s profits. Hence we obtain predictions (i) and (ii).
50This is because ⇧h equals⇢ch (h, 0)lc (✓, h) which is rising in ✓.

4

TRAIL Treatment E↵ects
In TRAIL, a trader is appointed the agent, and recommends borrowers for TRAIL loans.
These loans are o↵ered at interest rate rT , which is lower than the informal cost of capitalfor traders ⇢. Agents earn a commission of 2 (0, 1) per rupee interest paid by theborrowers they recommended. We assume that any farmer whom the agent selects isalready committed to cultivating lc , financed by informal loans taken before the TRAIL
loan was o↵ered to him/her.51 As a result the TRAIL loan finances an increase in
the cultivation scale.52 This applies to farmers in productivity Bins 2 and 3; for thosein Bin 1 there are no pre-existing plans for cultivating potatoes. In what follows, we
present calculations for farmers in Bins 2 and 3; for those in Bin 1 we set the pre-existing
cultivation scale Lc (✓) to zero.
The efficient contract between T and F will now involve a supplementary cultivation
scale of lt , resulting in total scale of lT ⌘ lc + lt . The levels of monitoring and help will
be adjusted to mT , hT . Then the joint payo↵ of T and F is
(1 + ⌧ )A(✓, m)f (Lc (✓) + lt )[(1 + ⇢)Lc (✓) + {1 + rT (1)}p(✓, m)lt ]c(h, m)

T [h + m](A20)
where Lc (✓) ⌘ lc (✓, hc (✓)).
The TRAIL agent continues to find it optimal not to monitor the farmer: mT (✓) = 0.
Given help h, the treatment e↵ect on cultivation scale lt (✓, h) maximizes(1 + ⌧ )A(✓, 0)f (Lc (✓) + lt )[(1 + ⇢)Lc (✓) + p(✓, 0){1 + rT (1)}lt ]c(h, m)(A21)

and therefore it also maximizes(1 + ⌧ )a(✓, 0)f (Lc (✓) + lt )[{1 + rT (1)}lt ]c(h, m)(A22)

Using the same argument as used in Lemma 2 in Maitra et al. (2017), the cultivation
treatment e↵ect lt (., h) is increasing in ✓. The Envelope Theorem implies that the help
provided by the agent to the treated farmer hT (✓) must satisfy the first order condition[(1 + ⇢)Lc (✓) + {1 + rT (1)}p(✓, 0)lt (✓, hT (✓))]ch (hT (✓), 0) +T = 0.

(A23)

The corresponding second order condition implies that hT (✓) is increasing. Among
treated farmers the more able will receive more help, and thereby attain lower unit
costs, cultivate a larger scale, and produce higher output: hence the Order Preserving
Assumption holds in TRAIL.
We can also compare agent interactions between treated and control farmers withthe same ability ✓. Help hc (✓) provided to a control farmer with the same ability satisfies
51

This is in order to explain the lack of treatment e↵ects on informal borrowing.
Recall that in Table 4 we did not see any evidence that the TRAIL loans crowded out informalloans.
525the first order condition[(1 + ⇢)Lc (✓)]ch (hc (✓), 0) +T = 0.

(A24)

Comparing (A23) and (A24), it is evident that hT (✓) hc (✓), so treated farmers obtain
more help. The reason is that they cultivate a larger area compared to control farmers
with the same ability, so the gains from unit cost reductions generate a larger reduction
in total cost, which motivates the agent to provide more help. In turn this implies treatedfarmers cultivate a larger area, produce more output and earn more profits comparedwith control farmers of the same ability. This is prediction (iv).
GRAIL Treatment E↵ects
In the GRAIL scheme, the political incumbent appoints an agent who is not a trader.
This agent does not lend, or trade in inputs or crop output, and so does not have the samebusiness-related incentives as a TRAIL agent. Instead, his objectives are political orideological, represented by welfare weight v(✓), and seeks to maximize v(✓)p(✓, m) G m.
The welfare weight also includes the commission earned by the agent. While this may
bias the agent in favor of selecting more able borrowers because they select larger loans
and are less likely to default, we assume this is outweighed by political considerations
which bias them in favor of less able farmers, so v is a decreasing function. The optimallevel of monitoring (positive if G is small enough) satisfiesv(✓)pm (✓, mG (✓)) =

G

(A25)

Since monitoring is more e↵ective when farmers are less able, and the welfare weights are
decreasing in ability, mG (✓) is decreasing in ability, and is greater that mT (✓) = 0. Thisimplies prediction (vi): the GRAIL agent interacts less with high ability farmers. And
default rates on GRAIL loans are lower than on TRAIL loans: p(✓, mG (✓)) p(✓, 0).
Monitoring by the GRAIL agent a↵ects the payo↵s of treated farmers and the trader
they contract with. Their joint payo↵ is given by(1 + ⌧ )A(✓, mG (✓) + m))f (Lc (✓) + lg ) [(1 + ⇢)Lc (✓)+{1 + rT }p(✓, mG (✓) + m)lg ]c(h, mG (✓) + m)
T [h + m](A26)

where lg denotes the additional area that the GRAIL treated farmer cultivates, and(h, m) continues to denote help and monitoring activities of the trader. The commission
does not enter this expression since it accrues to the GRAIL agent rather than the
trader. The trader has no incentive to monitor. Hence the contract involves a treatmente↵ect lg on area cultivated and help h which maximize(1+⌧ )A(✓, mG (✓)))f (Lc (✓)+lg ) [(1+⇢)Lc (✓)+{1+rT }p(✓, mG (✓))lg ]c(h, mG (✓))

6

Th
(A27)

lg (✓, h) must then maximize(1 + ⌧ )a(✓, mG (✓)))f (Lc (✓) + lg )[{1 + rT }lg ]c(h, mG (✓))

(A28)

while help hG (✓) minimizes[(1 + ⇢)Lc (✓) + {1 + rT }p(✓, mG (✓))lg (✓, hG (✓))]c(h, mG (✓)) +

Th

(A29)

Arguments similar to those used for TRAIL treated subjects imply that higher ability
farmers receive more help. To see this, note that if lg (✓; h) denotes the area treatmente↵ect in GRAIL for any given help h, the same argument (combined with mG (.) decreasing) implies lg (, , h) is increasing in ✓. Hence hG (✓) satisfies the first order condition[(1 + ⇢)Lc (✓) + {1 + rT }p(✓, mG (✓))lg (✓, hG (✓))]ch (hG (✓), mG (✓)) +

T = 0

(A30)

chm = 0 then implies that ch (hG (✓), mG (✓)) = ch (hG (✓), 0) and the second order condition for minimization of (A30) implies hG (.) is increasing. Hence the Order Preserving
Assumption is also satisfied in GRAIL: treated farmers of higher ability have lower unit
cost, cultivate larger area and produce more output. This is the second part of prediction(v). The first part follows from the greater monitoring in the GRAIL scheme.
Observe next that the HTE on area cultivated is higher in TRAIL, for any ✓. This follows from comparing maximization problems (A22) and (A28), and using a(✓, mG (✓)) a(✓, 0), {1 + rT } > {1 + rT (1)} and c(h, mG (✓)) c(h, 0).
To obtain prediction (vi), compare the first order conditions (A23) and (A30) for
help provided by the trader to treated farmers in TRAIL and GRAIL. Ifp(✓, 0){1 + rT (1)}lt (✓, hT (✓)) > p(✓, mG (✓)){1 + rT }lg (✓, hG (✓))

(A31)

more help will be provided to TRAIL treated farmers, who will then end up with lower
unit costs, higher output and profits than GRAIL treated farmers of the same ability(because the latter are less productive and incur higher unit costs).
Finally we show (A31) holds if the production function has constant elasticity f (l) =↵l where ↵ 2 (0, 1). Since A(✓, m) is falling in m and c(✓, m) is rising in m, it follows
thatA(✓, mG (✓))
A(✓, 0)(A32)Gc(✓, m (✓))
c(✓, 0)This impliesp(✓, mG (✓))
a(✓, 0)c(h, mG (✓))
[]p(✓, 0)a(✓, mG (✓))c(h, 0)(A33)

Since the right-hand-side of (A33) is larger than one:p(✓, mG (✓))
a(✓, 0)c(h, mG (✓)) 1 1 ↵[]p(✓, 0)a(✓, mG (✓))c(h, 0)
7

(A34)

From the respective first-order conditions for maximization of (A22) and (A28), andusing f (l) = l↵ , we havea(✓, 0)c(h, mG (✓))
Lc (✓) + lt (✓, 0)
1 + rT (1
=
[
]1 ↵
G
c
g
G
a(✓, m (✓))c(h, 0)L (✓) + l (✓, m (✓))
1 + rT)(A35)

The right-hand-side of this is smaller than[

Lc (✓) + lt (✓, 0) 1 + rT (1Lc (✓) + lg (✓, mG (✓))
1 + rT

)1 ↵
]

(A36)

Therefore[a(✓, 0)c(h, mG (✓)) 1 1 ↵Lc (✓) + lt (✓, 0) 1 + rT (1]<a(✓, mG (✓))c(h, 0)
Lc (✓) + lg (✓, mG (✓))
1 + rT)(A37)

Combining this with (A34) we obtain1<p(✓, 0){1 + rT (1)}(LC (✓) + lt (✓, 0))p(✓, mG (✓)){1 + rT }(LC (✓) + lg (✓, mG (✓)))
c

t

t

(A38)

L (✓)+l (✓,0)
l (✓,0)Since lg (✓, mG (✓))  lt (✓, 0) we have LC (✓)+lg (✓,mG (✓)))  lg (✓,mG (✓))) . So (A31) holds.

8

Figure A1: Comparing selection in TRAIL and GRAIL villages. Descriptive
Statistics on Productivity.

Notes: Sample restricted to Control 1 households TRAIL and GRAIL villages with at most 1.5 acres of
land.

9

Figure A2: Percentage of households in each Productivity Bin. TRAIL and
GRAIL

Notes: The height of the bars denote the fraction of households in each productivity Bin. Productivity
is computed using the logarithm of acreage under potato cultivation. Sample restricted to Control 1
households in TRAIL and GRAIL villages with at most 1.5 acres of land.

10Figure A3: Variation in Farm Value Added for Treatment and Control 1
groups by Productivity

Notes: Lowess plot of farm value added from potato cultivation on productivity presented. Separate
lowess plots presented for Treatment and Control1 households in TRAIL and GRAIL villages.

11

12

Cumulative Probability

0

.2

.4

.6

.8

1

Cost estimate

c.d.f. of TRAIL

0c.d.f. of GRAIL

5

10

0

.2

.4

.6

.8

1

-5
c.d.f. of TRAIL

c.d.f. of GRAIL

05Ability (output) estimate

Ability (output), Control 1

10

0

.2

.4

.6

.8

1

-4

Notes: Sample Restricted to Control 1 households in TRAIL and GRAIL villages with at most 1.5 acres of landholding.

-5

Cost, Control 1

Panel B: Wealth (Productivity)

Cumulative Probability

Panel A: Input Cost Per Acre

0
Wealth estimate
c.d.f. of TRAIL-2

2
c.d.f. of GRAIL

Wealth, Control 1

Panel C: Acreage

Figure A4: CDFs of the household fixed e↵ects on Unit Cost of Production, Acreage and Wealth

Cumulative Probability

4

13
i

µ

µ = 0.90.

i 1+⌫
Notes: Panel A presents the CDF of the structural estimates [{ai ⇡i ( A)cPanel A: µ = 1.39

1

Ai
ci1+⌫

}⇥

] for µ = 1.39. Panel B presents the corresponding CDF forPanel B: µ = 0.90

Figure A5: Distribution of Structural Estimate

Figure A6: Interest Rate on Informal Loans and Productivity.
Households Only
Panel A:
Average Informal Interest Rates

Control

Panel B:
Variation in Informal Interest Rates

Notes: The vertical axis measures the average interest rate paid on informal loans by households. The
horizontal axis shows the productivity estimate. In the left panel, we compute the average interest rate
for households in each productivity bin. The average interest rate paid on informal loans by households inproductivity Bin 1 is significantly higher than that paid by households in productivity Bin 2 (p value =0.03) and productivity Bin 3 (p value = 0.04). In the right panel we present the locally weighted
regressions in of interest paid on informal loans on productivity. The average interest rate paid byhouseholds in productivity Bin 1 is shown as a single point. The sample is restricted to Control 1 andControl 2 households in TRAIL and GRAIL villages with at most 1.5 acres of land. Productivity iscomputed using the logarithm of the acreage under potato cultivation.

14

Table A1: Baseline Credit Market characteristics

All Loans(1)Household had borrowed
Total Borrowing†

0.67
6352

(10421)

Proportion of Loans by Source‡
Traders/Money Lenders
0.63
Family and Friends
0.05
Cooperatives
0.24
Government Banks
0.05
MFI and Other Sources
0.03

Agricultural Loans
(2)

0.59
5054

(8776)

0.66
0.02
0.25
0.05
0.02

Annualized Interest Rate by Source (percent)
Traders/Money Lenders
24.93(20.36)
25.19
Family and Friends
21.28(14.12)22.66
Cooperatives
15.51(3.83)
15.70
Government Banks
11.33(4.63)
11.87
MFI and Other Sources
37.26(21.64)34.38(21.47)(16.50)(2.97)(4.57)(25.79)Duration by Source (days)
Traders/Money Lenders
125.08
Family and Friends
164.08
Cooperatives
323.34Government Banks
271.86
MFI and Other Sources
238.03(22.43)(104.25)(87.74)(91.49)(128.48)(34.05)(97.40)(90.97)(121.04)(144.12)122.80183.70327.25324.67272.80

Proportion of Loans Collateralized by Source
Traders/Money Lenders
0.02
0.01
Family and Friends
0.04
0.07
Cooperatives
0.79
0.78
Government Banks
0.81
0.83
MFI and Other Sources
0.01
0.01

Notes: Statistics are reported for all sample households in TRAIL
and GRAIL villages with at most 1.5 acres of land. All characteristics are for loans taken by the households in Cycle 1. Programloans are not included. For the interest rate summary statistics
loans where the principal amount is reported equal to the repayment amount are not included. To arrive at representative estimates for the study area, Treatment and Control 1 households are
assigned a weight of 30
N and Control 2 households are assigned aweight of N N30 , were N is the total number of households in theirvillage. † : Total borrowing = 0 for households that do not borrow.
‡: Proportion of loans in terms of value of loans at the household
level. All proportions are computed only over households thatborrowed. Standard deviations are in parentheses.

Table A2: Cultivators and Non-cultivators. Di↵erencesin Demographic Characteristics

Landholding
Non Hindu
Low Caste
Household Size
Female Headed Household
Age of Oldest Male
Oldest Male: Completed Primary School

Non Cultivators(1)

Cultivators
(2)

0.267
0.239
0.419
4.440
0.101
45.280
0.348

0.561
0.139
0.261
4.806
0.031
49.007
0.457

Notes: Households that cultivate potato at least 2 of the 3 survey years
are categorized as cultivators.

16

17

(3)

(Kg)

(Acres)(2)

Production

Acreage

0.15(0.03)
0.64
23.79

-0.11
(0.05)

6,237

GRAIL Treatment

TRAIL v. GRAIL
Treatment

Sample Size

6,237

-0.01
(0.05)

0.10(0.03)
0.30
34.29

0.09
(0.04)
0.34
27.21

6,237

-192.74(533.63)1127.11(378.38)
3236.7334.82

934.37(376.71)3646.12
25.63

6,237

-912.91(1195.00)

2755.10(824.74)
7070.65
38.97

1842.19(865.79)
8481.75
21.72(4)

Cost of
Production
(Rs.)

0.13
(0.03)
0.64
20.33

-0.09
(0.04)

6,153

GRAIL Treatment

TRAIL v. GRAIL
Treatment

Sample Size

Mean GRAIL C1% E↵ect GRAIL

Mean TRAIL C1% E↵ect TRAIL

0.04(0.03)
0.72
5.63

TRAIL Treatment

6,153

0.02
(0.04)

0.07(0.02)
0.30
23.25

0.09
(0.02)
0.34
27.58

6,153

175.14(383.88)770.95(273.22)3236.7323.82

946.10(256.12)3646.12
25.95

6,153

-161.77(913.29)2007.20(624.02)7070.65
28.39

1845.43(648.20)8481.75
21.76

Panel B: Regressions with SEs clustered at the village level

Mean GRAIL C1% E↵ect GRAIL

Mean TRAIL C1% E↵ect TRAIL

0.04(0.03)
0.72
6.25

TRAIL Treatment

Panel A: Regressions with no Additional Controls(1)

Cultivate

3,821

0.15
(0.16)-0.18(0.14)
4.80
-3.66

-0.03
(0.09)
4.63
-0.65

3,843

0.15
(0.15)-0.18(0.12)
4.80
-3.83

-0.03
(0.10)
4.63
-0.68

(5)

(Rs./Kg)

Price

6,153

1396.12(1561.45)

2501.15(1059.90)
12964.78
19.29

3897.28(1098.41)
14285.47
27.28

6,237

-193.88(2155.41)

4033.85(1554.91)
12964.78
31.11

3839.97(1495.02)
14285.47
26.88

(6)

(Rs.)

Revenue

6,153

1566.24(890.85)492.47(676.98)5827.668.45

2058.71(559.84)5732.36
35.91

6,237

737.87(1140.18)

1267.60(857.83)
5827.66
21.75

2005.47(752.46)5732.36
34.99

(7)

Value
Added
(Rs.)

6,153

1715.82(861.55)

190.36(652.96)4941.51
3.85

1906.18(544.32)4733.77
40.27

6,237

939.18(1092.87)

913.02(826.72)4941.51
18.48

1852.20(716.06)
4733.77
39.13(8)

Imputedprofit
(Rs.)

6,153

-839.04(1695.62)

3886.99(1174.36)
13454.99
28.89

3047.95(1189.33)
16288.50
18.71

6,237

-2431.54(2251.67)5459.37(1522.90)
13454.99
40.58

3027.82(1660.52)
16288.50
18.59

(9)

(Rs.)

Input Cost

4,041

6,153

0.04
(0.07)

0.14(0.05)-0.02-581.150.18(0.05)
0.05
365.12

6,237

-0.03
(0.10)

0.21(0.07)-0.02-869.230.18(0.07)
0.05
362.36(11)

Index
Variables

Continued . . .

-3461.26(1420.65)

550.94(1091.80)
47510.73
1.16-2910.32(900.72)49076.88
-5.93

4,061

-3604.50(1500.80)

700.20(1048.81)
47510.73
1.47-2904.30(1072.87)
49076.88
-5.92

(10)

Input Cost
per acre(Rs./acre)

Table A3: Robustness. Average Treatment E↵ects in Potato Cultivation.

18

(3)

(Kg)

(Acres)(2)

Production

Acreage(5)(Rs./Kg)

Production
(Rs.)
(4)

Price

Cost of

(6)

(Rs.)

Revenue

(7)

Added
(Rs.)

Value

(8)

profit(Rs.)

Imputed

(9)

(Rs.)

Input Cost(10)

per acre(Rs./acre)

Input Cost

(11)

Variables

Index

Notes: Treatment e↵ects are computed from regressions that follow a restricted version of equation (1) in the text and are run on household-year level data forall sample households in TRAIL and GRAIL villages with at most 1.5 acres of land. The dependent variable in column 1 takes the value of 1 if the household
cultivated potato in that year, 0 otherwise. The dependent variables in columns 2—9 take the actual value reported by the household if it did, or take the value
zero if it did not cultivate potatoes in that year. In column 10, households that did not cultivate potatoes in a year are dropped from the estimating sample.
Regressions also control for a set of year dummies and an information village dummy. % E↵ect: Treatment e↵ect as a percentage of the mean of the relevantControl 1 group. In Panel A, standard errors in parentheses are clustered at the hamlet level, while in Panel B, standard errors in parenthesis are clustered at thevillage level. .

(1)Cultivate

Robustness. Average Treatment E↵ects in Potato Cultivation