community-mediated exchange and collective loan defaults...
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Working Paper 2020/04/STR
(Revised version of 2019/06/STR)
Community-Mediated Exchange and Collective Loan Defaults in Microfinance: Evidence from Indian
Demonetization
Arzi Adbi INSEAD, [email protected]
Matthew Lee
NYU Stern School of Business, [email protected]
Jasjit Singh INSEAD, [email protected]
Draft: December 1, 2019
Firms serving base-of-the-pyramid markets often employ community-mediated exchange strategies that build and maintain exchange relationships with customers collectively, through community interactions. Prior research suggests that such strategies may engender positive peer influence that leads customers to collectively fulfill exchange obligations. However, we argue that community-mediated exchange can also have an opposite, adverse effect when systemic economic distress affects an entire community of customers simultaneously. We test this proposition through a quasi-experimental design leveraging India’s 2016 demonetization policy, a shock that revoked the legal tender status of 87 percent of the country’s currency in circulation and imposed significant economic hardship on its population. Using proprietary data on approximately two million borrowers from a leading Indian microlender, we find strong evidence suggesting that a sharp increase in loan default rates following demonetization was a collective phenomenon shaped by peer influence within community-level microfinance lending centers. Post-demonetization, all borrowers defaulted (“collective default”) in 21.6 percent of centers, compared with just a 3.7 percent collective default in a simulated scenario in which individual defaults were equally common, but determined through independent individual-level decisions, absent peer influence. Further, the likelihood of collective default was greater in centers with greater homogeneity of borrower religion and lending cohort, both likely indicators of peer relationship strength. Our findings contribute to research on community-mediated exchange strategies by showing how their consequences for firms may differ according to external and social conditions.
Keywords: Peer Relationships; Social Capital; Interpersonal Networks; Economic Distress; Demonetization; Policy Shock; Natural Experiment; Emerging Markets; Microfinance.
Electronic copy available at: http://ssrn.com/abstract=3334887
We are grateful to the microfinance firm that shared proprietary data for this research. We thank Paul Adler, Matthew Bidwell, Vivek Choudhary, Jerry Davis, Martin Gargiulo, Dean Karlan, Brayden King, Leena Kinger Hans, Phanish Puranam, Prothit Sen, Mike Toffel, Balagopal Vissa, Tyler Wry, Eric Zhao, and seminar participants at the 2019 SMS Conference, the 2019 AOM Conference, the 2019 ARCS Conference, the 2019 AIB Conference, and the 2018 SMS India Special Conference for their suggestions. We also thank INSEAD Emerging Markets Institute for financial support.
Working Paper is the author’s intellectual property. It is intended as a means to promote research to
interested readers. Its content should not be copied or hosted on any server without written permission
from [email protected]
Find more INSEAD papers at https://www.insead.edu/faculty-research/research
Copyright © 2020 INSEAD
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1. INTRODUCTION
Recent research calls attention to firm strategies that address economically impoverished, “base-of-the-
pyramid” (BOP) markets (George et al. 2012, Luo and Kaul 2019, Mahoney et al. 2009, Prahalad 2006,
Simchi-Levi 2018, Wry and Zhao 2018). Two-thirds of the world’s population, largely located in the
emerging markets of South Asia, Sub-Saharan Africa and Latin America, still lives on less than $8 of
income per day (Rosling et al. 2018). BOP markets present attractive business opportunities in aggregate.
However, they have several distinctive features that that challenge the creation and management of
exchange relationships: BOP customers have relatively low individual purchasing power, many live in
hard-to-reach rural areas, and their vulnerability to economic distress creates substantial operational and
financial risks. In an influential article, Prahalad and Hammond (2002) suggested that to overcome these
challenges, BOP firms might “aggregate demand, making the community – not the individual – the network
customer” (p. 7). Indeed, many BOP firms build and manage relationships through aggregated communities
of exchange partners: while still transacting directly with individuals, such firms share information with,
monitor, and complete financial transactions with their customers in collective social settings. Evidence of
such approaches — which we refer to as “community-mediated exchange” — can be observed in BOP
settings as diverse as microfinance (Doering 2018), micronutrient supplement (Suchdev et al. 2010),
agricultural trading (Chen et al. 2013), water and sanitation products (Evans et al. 2014), and electrification
(Schnitzer et al. 2014).
Prior research generally suggests that beyond reducing cost, community-mediated exchange
engenders peer influence that is beneficial for firms, as peers educate, monitor, and support one another,
particularly in rural communities where peer relationships are already powerful sources of social influence
and support (Collins et al. 2009, Prahalad 2006). For instance, One Acre Fund, which sells seeds and
provides training to rural farmers, operates a network of 800 community-based “rural market points” where
a field officer delivers products, collects payments, registers new customers, and provides technical
assistance to farmers in the community. In this structure, the field officer can visit the rural market point
weekly to interact with all local farmers together. These weekly meetings create relationships that facilitate
information flow and mutual support among farmers, allowing customer management to be more efficient
than if the individual customer relationships were built and managed independently.
We argue here, however, that community-mediated exchange strategies may also unwittingly
facilitate adverse business effects for the firms that employ them. Our central argument is that the effect of
community mediation on the fulfillment of exchange obligations depends on the overall economic
conditions the customers face — in particular, the pattern of economic distress that threatens some
customers’ ability to fulfill such obligations. Specifically, we argue that while community mediation is
likely to promote fulfillment of exchange obligations when economic distress is idiosyncratic – that is,
2
relatively uncorrelated across individual customers – conditions of systemic economic distress that
simultaneously increase all customers’ likelihoods of digressing are also likely to promote non-fulfillment
of exchange obligations. Examining systemic economic distress is particularly in important in BOP settings,
where physical, macroeconomic, and political sources of systemic distress are relatively common and
individuals are relatively vulnerable.
We empirically analyze these issues in the context of microfinance, a sector where community-
mediated exchange is widely practiced. We leverage a natural, quasi-experimental design (Dunning 2012,
Shadish et al. 2002) based on the Government of India’s introduction of a “demonetization” policy: on
November 8, 2016, the Government unexpectedly and immediately voided the legal tender status of 500
and 1000 Indian Rupee currency notes (US$7 and US$14 respectively), thus temporarily suspending the
value of a significant proportion of Indians’ liquid wealth and leading to widespread economic distress
(Banerjee et al. 2018, Chodorow-Reich et al. 2019).1 We therefore analyze how the influence of community
mediation varies with the introduction of systemic economic distress by analyzing how the loan repayment
behavior of approximately two million borrowers from a large Indian microfinance firm varies before and
after demonetization. We find strong evidence that peer influence within communities of borrowers shaped
the pattern of increased loan defaults following demonetization: defaults were far more localized within
lending centers than would be expected if borrowers in a center had made their loan repayment decisions
independently (i.e., in the absence of intra-center peer influence). We further find that this tendency towards
collective default was strongest in lending centers that were religiously most homogeneous, and those where
the borrowers were most concentrated in the same lending cohorts, both indicators of peer relationship
strength.
This paper is, to our knowledge, the first to demonstrate conditions under which community-
mediated exchange strategies (often relied on by BOP businesses to improve financial performance and
mitigate business risk under normal conditions) can lead to adverse effects on business outcomes. We
conclude the paper with a discussion of implications for future research as well as for the practical
development and management of exchange relationships with BOP populations.
2. COMMUNITY-MEDIATED EXCHANGE IN MICROFINANCE
We define community-mediated exchange as the creation and management of exchange relationships
through aggregated communities of exchange partners. Relative to direct exchange, community-mediated
exchange reduces the frequency of interaction with exchange partners and thus, associated costs.
1 The government’s stated goals of demonetization were: (1) eradication of “black money”, (2) removal of counterfeit
currency, (3) curtailment of terrorism funding, and (4) a move to a cashless economy (Sanyal 2018). The 500 and
1000 Rupee notes affected by demonetization accounted for 87 percent of the currency value in the Indian economy.
3
Furthermore, community-mediated exchange strategies may benefit the firms that employ them by
reinforcing exchange relationships through social capital, or the information, norms, and other resources
accessible through a durable social network of institutionalized relationships of mutual acquaintance
(Bourdieu and Wacquant 1992, Coleman 1990). While these benefits may largely be due to existing social
capital among a firm’s exchange partners, firms relying on this approach often deliberately facilitate
structured, routine interaction among community members, thus building social capital among them
(Sanyal 2009).
The center-based lending approach employed by many microfinance firms is typical of community-
mediated exchange strategies. In this model, often employed to serve populations for whom individual
credit scoring and collateral are impractical, borrowers are organized into a lending center where they meet
collectively for loan repayment and information sharing (Armendáriz and Morduch 2010, Banerjee and
Duflo 2011). In some (but not all) microlending contracts, borrowers are also jointly liable for the
repayment of the other borrowers in a group of borrowers. Community meetings often also involve training
and socialization regarding the importance of fiscal responsibility, meant to promote timely repayment of
their loans and other desired behaviors.2 Research suggests that center-based lending is less costly for the
lending organization and also increases repayment rates (Dowla and Barua 2006, Feigenberg et al. 2013).
Previous studies further show that the business benefits of this approach arise to a significant degree from
informal relations among exchange partners, that have been shown to exist even absent formal joint liability
(de Quidt et al. 2016, Giné and Karlan 2014).
3. SYSTEMIC ECONOMIC DISTRESS AND MICROLOAN DEFAULTS
This section examines conditions under which community-mediated exchange might unintentionally
increase loan defaults in microfinance.3 In general, we conceptualize a firm’s exchange partners as subject
to a pattern of economic distress that may, in any given repayment period, lead them to fail to fulfill their
exchange obligations; in the context of microfinance, this is the failure to make timely loan repayment.
Microfinance borrowers, possessing limited liquidity and wealth (Banerjee and Duflo 2011, Collins et al.
2009), are vulnerable to external sources of economic distress that would decrease their ability to make
loan repayment. Consequently, a baseline expectation is naturally that microfinance borrowers face some
risk of economic distress in a given period that affects their likelihood of loan default.
2 The microcredit model that relies on community-mediated exchange is also commonly referred to as “group lending”
(Karlan and Zinman 2011). This approach of lending to BOP populations in emerging markets rose to popularity
especially after the success of financial institutions like Grameen Bank and BRAC in Bangladesh. Since then,
microcredit has also evolved in a number of directions (such as increased reliance on technology for evaluating
creditworthiness or tracking payments), but center-based lending still remains a dominant model for microcredit. 3 Following the terminology used in recent research in microfinance (Canales and Greenberg 2016, Doering 2018),
the terms “loan default” and “missed payment” are used interchangeably in this paper.
4
We differentiate between two types of economic distress: idiosyncratic economic distress and
systemic economic distress. These differ based on whether an incident of economic distress is limited to a
specific exchange partner within a community (idiosyncratic), or if it encompasses all exchange partners
within a firm’s community of exchange partners (systemic). Thus, idiosyncratic economic distress results
from causes that are uncorrelated across individuals, such as personal medical emergencies or individual
loss of employment. By contrast, systemic economic distress results from causes that affect the entire
community. For example, systemic economic distress could arise from a poor harvest in an agricultural
community (e.g. due to inadequate rainfall), a natural disaster (e.g. a flood or a drought) or a regulatory
change (e.g. the demonetization policy our empirical analysis exploits).4
Our central argument is that in community-mediated exchange, failures to fulfill exchange
obligations resulting from systemic economic distress are likely to be amplified by peer influence within
the community. Prior research in microfinance suggests that community-mediated exchange positively
affects repayment through two social mechanisms (Carpena et al. 2013, Feigenberg et al. 2013). First, peer
connections enable visibility, and therefore facilitate mutual peer monitoring: when default rates are low,
norms of on-time repayment emerge, and those who default are subject to embarrassment and social
exclusion (Khanna 2018). The threat of social stigma thus creates social pressure on borrowers for timely
loan repayment (Lin et al. 2013, Thorne and Anderson 2006). Second, peers provide informal, mutual
insurance (de Quidt et al. 2016): when one borrower faces financial distress, a peer would feel the social
obligation to help with a scheduled repayment of the financially-constrained borrower (Armendáriz and
Morduch 2010). Peer relationships within lending centers therefore enable normative enforcement of
repayment and shield borrowers from unexpected economic distress through pooling of risk.
However, we assert that the positive effects of norms and mutual insurance on repayment are likely
to require that economic distress be idiosyncratic, rather than systemic. For norm enforcement to increase
repayment, peer repayment rates must be sufficiently high such that non-repayment leads to costly social
stigma. For informal, mutual insurance to increase repayment, peers must have the financial capacity to
subsidize a distressed borrower’s loan repayments. Both of these assumptions seem reasonable under
normal conditions, where economic distress is idiosyncratic and relatively rare among individual
4 Our general argument does not depend on the exact cause of the systemic distress. However, given our empirical
context, it is worth noting that there is a well-documented link between Indian demonetization and systemic economic
distress. Numerous expert commentaries and media reports have described how the policy had a particularly
devastating impact on BOP populations, who rely the most on cash for their savings and everyday transactions. When
the demonetization shock made the cash people held invalid (with replacement currency notes generally not becoming
available for at least a few weeks), entire communities suffered severely as poor people’s capacity to pay for goods,
services and commitments (including microfinance loan repayments) was exogenously and simultaneously reduced.
5
borrowers.5 Conditions of systemic economic distress that affect the capacity of all borrowers in a
community to fulfill exchange obligations, however, threaten both of these assumptions. When all
borrowers in a community face economic distress simultaneously, the aforementioned social mechanisms
through which peer monitoring normally promotes regular loan repayment may no longer function: a
reduction in the repayment capacity of all borrowers might disrupt norms of repayment and dissipate the
social stigma associated with default. Furthermore, by simultaneously decreasing all borrowers’ economic
capacity, systemic economic distress diminishes the ability of one borrower to subsidize another.
Under conditions of systemic economic distress, not only might peer influence encouraging loan
repayment be weakened, but peer influence might adversely affect loan defaults. Just as norms of repayment
diffuse through strong social relationships and visibility of repayment behavior under normal conditions,
norms of non-repayment (i.e., social acceptability of a loan default) may diffuse through the same social
relationships under conditions of systemic economic distress. Similarly, relationships that enable mutual
insurance and promote repayment following idiosyncratic economic distress may also, under conditions of
systemic economic distress, diminish repayment as all of the closely connected borrowers that are
struggling to repay may even coordinate their defaults to minimize backlash any one of them would suffer
as a result (Zhang and Liu 2012).
Based on the above arguments, we propose that reliance on peer monitoring and social pressure
leads to a unique business risk in the form of increasing the likelihood of local contagion in loan default,
such that a peer-connected community of borrowers becomes more likely to exhibit a “collective default”
than if the defaults were independently determined for individual borrowers. In other words, under
conditions of systemic economic distress, we expect loan defaults to be clustered among socially-connected
borrowers in local communities, rather than being evenly distributed across different communities:
Hypothesis 1 (H1). Systemic economic distress will increase collective loan defaults among microfinance
borrowers; that is, defaults will be concentrated to a greater degree within lending centers.
If peer relationships contribute to collective defaults under conditions of systemic economic
distress, this effect should also be strongest when social ties among peers are strongest. Social homogeneity
within communities of individuals leads to stronger relational social capital in the form of personal trust
between individuals (Coleman 1988), and such enhanced trust among individuals can influence the quality
of peer monitoring and may even give rise to collusive activity (Karlan 2007). Communities composed of
diverse individuals in terms of demographic characteristics, such as religion, are likely to possess weaker
social connections than more homogeneous groups (Fisman et al. 2017, Shu et al. 2012, Yenkey 2015).
5 Formal models also generally assume that external causes of loan default are idiosyncratic; that is, uncorrelated
between borrowers (see, e.g. de Quidt et al. 2016).
6
Strength of social connections in collective entities can also be influenced by whether or not the individuals
belong to the same cohort (McPherson et al. 2001, Smith et al. 2014); especially in the context of
microfinance communities, lending cohorts can influence the level of solidarity among the local community
of borrowers (Khanna 2018). Research in both organization theory (Burt 2011, Hasan and Bagde 2013,
Reagans 2011) and economics (Alesina and Ferrara 2005, Easterly and Levine 1997) notes that diversity
impedes cooperation and coordination. Thus, one would expect greater diversity to reduce the likelihood
of collective behavior (Levine et al. 2014). Hence, we predict:
Hypothesis 2 (H2). Diversity within the local borrower base will moderate the increase in collective loan
defaults resulting from systemic economic distress, i.e., a lending center with greater borrower diversity
will experience a smaller increase in the likelihood of collective loan default.
4. RESEARCH SETTING AND DATA
To empirically examine the effects of systemic economic distress, we focus on microfinance borrower
behavior in the months surrounding India’s 2016 demonetization policy. Microfinance borrowers typically
operate in a cash-based economy (Alatas et al. 2012, Bandiera et al. 2017), with limited access to formal
banking and credit (Collins et al. 2009, Chakravorti 2017). Their reliance on microloansis evidence of their
limited liquidity and wealth. Following demonetization, this set of conditions contributed to severe
economic distress: according to one expert, “Liquidity has been sucked out. You have stopped market
transactions for 70 per cent of the economy. The poor will suffer more. To withdraw his/her own money,
the poor person has to stand in the queue. Ninety per cent of the poor’s liquidity is in cash, so they have no
cash.” (Sanyal 2018: p. 39). Demonetization was a nationwide policy that affected entire communities of
borrowers simultaneously. Thus, we argue that the resulting economic distress was systemic in nature.6
We analyze a large, proprietary dataset of individual loan repayment outcomes obtained from a
leading publicly-listed Indian microfinance firm. Our field interviews with the firm’s management as well
as outside experts working in the Indian microfinance sector suggest that the firm’s microlending model as
well as the typical profile of its customer base were representative of the Indian microfinance sector more
generally. At the time of demonetization, our partner firm had operations in 15 Indian states that together
constituted almost 75 percent of the country’s total rural population (the primary target market for
microfinance lending). Consistent with the focus on lending to women documented in the prior literature
6 Demonetization has been used as an exogenous shock in a few recent studies in both management (Natarajan et al.
2019) and economics research (Banerjee et al. 2018, Chodorow-Reich et al. 2019), but these studies pursue research
questions distinct from our study. Natarajan et al. (2019) use demonetization as an exogenous shock to conduct a post-
hoc analysis of the effects of an unfavorable environment for managers in their study of how middle managers’
decision making shapes resource allocation. Banerjee et al. (2018) combine a field experiment with demonetization
to study how information should be disseminated to large populations and Chodorow-Reich et al. (2019) use
demonetization as a large scale natural experiment to study the effects of monetary policy on economic activity.
7
on microfinance (e.g., Bulte et al. 2017, Canales and Greenberg 2016, Karlan and Zinman 2011), our partner
firm made loans exclusively to low-income women. By relying on peer monitoring within a community of
microfinance borrowers, the firm’s lending model required no collateral from the borrowers.
Our sample consists of the entire population of 2,036,108 borrowers who had active loans with the
firm at the time of demonetization. In line with microfinance regulation in India, all of these borrowers
belonged to a segment that could be considered a part of the BOP: they could not have a monthly household
income that exceeded Rupees 12,000 (US$171). Consistent with similar samples employed in prior
microfinance research (Banerjee et al. 2013), these borrowers generally relied on income from activities
like agriculture or microenterprise (such as a small tailoring or retail shop) for their livelihood.
Loan contracts in our data had a median loan amount of Rupees 25,000 (US$357), which borrowers
were contractually obligated to repay in regular installments over an average duration of two years.
Borrowers with a consistently good repayment record were offered the opportunity to renew their loans,
typically with a loan amount that increased over loan cycles. The loan repayments, which were due every
two weeks, were collected simultaneously for all borrowers that belonged to the same community and were
assigned to the same microfinance “center.” Centers typically contained approximately 15 people resident
in the same village or neighboring villages. Within each center, borrowers were divided into three to five
groups, with members within a group being jointly liable for one another’s repayment.
While the women belonging to a center often typically knew one another well even prior to their
taking on a loan, center meetings further nurtured these social relationships over and above their normal
day-to-day interactions. Administration of loans followed a highly routinized procedure consistent with
standard processes in the microfinance industry. In the center meeting, all borrowers belonging to the center
had an opportunity to interact with one another as well as their loan officer (also known as “community
service officer”, an employee of the microfinance firm). The agenda for these meetings consisted of not
only collecting the loan repayments but also sharing of recent successes and failures in each borrower’s
life. Each meeting also included joint recitation by borrowers of a script that pledged commitment to
ensuring the economic success of all borrowers belonging to the center. This way of reinforcing mutual
commitment or “solidarity” among BOP borrowers is common in microfinance (Khanna 2018: p. 108) and
indicative of the firm’s intent to build social capital within each community of borrowers. Interviews with
staff and first-hand observations by the first author at the center meetings indicated that borrowers identified
closely with the entire community of women who belonged to the same center, and not just to those who
were part of their formal joint-liability group.7
7 While our empirical analysis starts with a center-level investigation, we subsequently also test for the possibility of
the group-level joint liability arrangement influencing our main findings (see Section 7).
8
Figure 1 shows the organization structure of our partner firm. At the time of demonetization, the
firm operated 536 branches, each serving borrowers within a geographic area of approximately 25
kilometers in radius. Each branch employed approximately six loan officers, each of which managed a
portfolio of about 40 centers within a close geographic proximity of a few square kilometers.
[Insert Figure 1 here]
5. BASELINE ANALYSIS OF INDIVIDUAL LOAN DEFAULTS
Before testing our hypotheses related to peer influence and collective defaults at the center-level, it is
helpful to explore the overall effect of demonetization on loan defaults. We do so in this section by
introducing regression models predicting individual loan defaults post-demonetization versus a pre-
demonetization baseline.
5.1 Empirical Strategy for Individual Borrower-Level Analysis
We analyze a dataset consisting of eight months of repayment data for the 2,036,108 borrowers in our
sample. The dataset includes over 15 million borrower-month observations, with the longitudinal panel
being slightly unbalanced over time due to origination of some new loans after July 2016 (but before
November 2016) and the termination of others after November 2016 (but before February 2017). These
data enable a comparison of repayment rates in the approximately four-month period right before
demonetization (July 2016 to October 2016) with those in the approximately four-month period after
demonetization (November 2016 to February 2017). Analysis of a relatively narrow time window around
demonetization (announced on November 8, 2016) minimizes the likelihood that the results may be driven
by time-varying conditions unrelated to our event of interest (Angrist and Pischke 2009, Dunning 2012).
Following recent research that studies microfinance loan repayment behavior (Canales and
Greenberg 2016, Doering 2018), we construct an outcome variable Missed Payment – set to one if the
borrower did not make the scheduled repayment, and zero otherwise.8 In order to carry out longitudinal
analysis, we set an indicator variable Post to zero for observations in the months prior to November 2016
(the month of demonetization), and one for November 2016 onwards. To estimate more specific period
effects, we also construct dummy variables Pre4 (July 2016), Pre3 (August 2016), Pre2 (September 2016),
Pre1 (October 2016), Post1 (November 2016), Post2 (December 2016), Post3 (January 2017) and Post4
(February 2017). Table 1 shows the definitions and descriptive statistics for key borrower-level variables.
[Insert Table 1 here]
8 Although the underlying repayment frequency was fortnightly, we only have data aggregated to the monthly level
and use that for constructing our variable Missed Payment (set to one if there was payment outstanding at the end of
the month). We do not expect this to affect our findings. As a robustness check, additional analysis reported in the
supplementary material also employs an outcome Total Arrears (the total monetary amount outstanding with a
borrower).
9
Self-reported income data in low-income segments of the population are known to be unreliable
for measuring poverty levels (Alatas et al. 2012). Therefore, following recent literature (Arndt et al. 2017),
we capture a household’s poverty level using the indirect (but relatively more reliable) measure of whether
the borrower lives in a non-brick house (extreme poverty) or a brick house (less extreme poverty).
Specifically, the variable NonBrickHouse is set to one for those living in a non-brick house, and to zero for
those in a brick house. Almost two-thirds of our borrowers live in non-brick houses (Table 1).
Recall that our unit of analysis for examining individual loan defaults is the borrower-month. To
test the baseline prediction that demonetization (an exogenous source of economic distress) will increase
the likelihood of loan defaults, we estimate the following linear probability model: 9
MissedPaymenti,t = α + β1 Postt + μi + εi,t (1)
Here, β1 represents the effect of demonetization on the probability of borrower i’s loan default in
month t, and μi denotes borrower fixed effects (to account for time-invariant borrower characteristics). We
estimate heteroskedasticity-robust standard errors, clustered at the loan officer level for conservative
inference.10 We subsequently also estimate a model that includes the monthly indicators:
MissedPaymenti,t = α + β1 Post1t + β2 Post2t + β3 Post3t + β4 Post4t + β5 Pre2t + β6 Pre3t + β7 Pre4t
+ μi + εi,t (2)
The month immediately before demonetization, i.e., October 2016, is the reference (omitted)
month. Coefficients β1 to β4 estimate the change in probability of default in each of the four months
following demonetization relative to the reference month. To test for differences based on a borrower’s
poverty level, we also separately estimate equation (2) for the subsamples of the poorest borrowers
(NonBrickHouse = 1) and the relatively less poor borrowers (NonBrickHouse = 0). We also conduct a
formal test of the difference in the strength of the effect between the poorest versus the relatively less poor
borrowers in the form of a single regression model with interaction terms (explained in detail below).
5.2 Results from Individual Borrower-Level Analysis
Observed loan default rates show a stark increase from about 2.2 percent just prior to demonetization
(October 2016) to 39.1 percent at the end of the month of demonetization (November 2016). Figure 2 shows
this pattern reflects a nationwide increase in defaults. Prior to demonetization, each of the 15 states in which
9 Recent work has emphasized the benefits of using a linear probability model instead of a logistic model when
employing fixed effects regressions (Bennett et al. 2013: p. 1733, Chatterji et al. 2019). Linear models do not suffer
from the potentially severe incidental parameters problem logistic models might face (Wooldridge 2002: p. 454-457,
Angrist and Pischke 2009). However, we have ensured that our findings are robust to employing logistic regressions
(without fixed effects). 10 We cluster standard errors at the loan officer level rather than the borrower level for more conservative inference
(as all borrowers in a center are served by the same loan officer). In results not reported here, all our main findings
were qualitatively similar even if we clustered at the branch level, which is an even higher level of aggregation.
10
our partner firm operated showed a loan default rate of under 10 percent, but in the month of demonetization
most states exhibit a large increase in loan defaults.11
[Insert Figure 2 here]
Detailed trends in the loan default rates across the eight-month period are shown in Figure 3. While
overall loan defaults were negligible prior to demonetization, they increased sharply post-demonetization
(Figure 3A). Further, while pre-demonetization loan default rates for the poorest (NonBrickHouse = 1) and
the relative less poor (NonBrickHouse = 0) borrowers are not meaningfully different, the post-
demonetization default rates are much larger among the poorest borrowers (see Figure 3B).
[Insert Figure 3 here]
Table 2 shows the results of estimating equation (1) described above to investigate the impact of
demonetization on the probability of a borrower i missing a payment in a given month t, while accounting
for borrower fixed effects. Consistent with Figure 3A, Column 1 in Table 2 shows a sharp increase in
Missed Payment by 43 percentage points (95% CI [0.414, 0.437], p < 0.001) post-demonetization compared
to pre-demonetization.12 Column 2 shows the more detailed temporal pattern, with October 2016 (i.e., Pre1)
as the reference month, as per equation (2). Each of the four months after demonetization shows a large
increase in the probability of loan defaults relative to the reference, providing strong support for the baseline
prediction that economic distress leads to an increase in loan defaults.
[Insert Table 2 here]
Columns 3 and 4 in Table 2 replicate the above analysis, per equation (2), separately for the sub-
samples of the poorest (NonBrickHouse = 1) and relatively less poor (NonBrickHouse = 0) borrowers.
Consistent with Figure 3B, the estimates from the regression analyses indicate a greater increase in the
probability of default for the poorest borrowers than for the relatively less poor borrowers.13
6. CENTER-LEVEL ANALYSIS OF COLLECTIVE LOAN DEFAULTS
We now test the main hypotheses of the paper (Hypotheses 1 and 2) regarding the extent to which the
increase in loan defaults documented above is concentrated within a subset of the lending centers rather
than being evenly distributed across centers. For these analyses, the unit of analysis is the center rather than
the individual, and the outcome of interest is collective defaults rather than individual defaults.
11 While this is not our focus, we should note that there appear to be significant differences across some states. For
example, Uttarakhand and Uttar Pradesh witnessed loan default rates exceeding 50 percent. Such differences might
be driven by varying geographic, economic, or social conditions across states. As explained later, in order to ensure
that our findings are not driven by unobserved heterogeneity across regions, our examination of drivers of loan defaults
relies on an identification strategy and econometric model that accounts for most such cross-regional differences. 12 The results remain consistent in robustness analysis where, instead of Missed Payment, Total Arrears is used as the
outcome variable (see Table S1 in the supplementary material). 13 These differences are statistically significant when formally tested by employing a single regression with interaction
terms (see Table S2 in the supplementary material).
11
6.1 Empirical Strategy for Center-Level Analysis
To measure collective defaults at the center level, we construct an indicator variable ZCC (“zero collection
center”) that is set to one if and only if all borrowers in a given center missed their payments in a given
month.14 Table 3 summarizes the definitions and descriptive statistics of variables used in our center-level
analysis. These center-level variables are aggregations of the same individual-level data analyzed in the
previous section (see Table 1).
[Insert Table 3 here]
We begin by analyzing the effect of demonetization on the likelihood of collective loan defaults at
the center level. To do so, we estimate the following linear probability model:
ZCCj,t = α + β1 Postt + μj + εj,t (3)
Unlike in prior equations (1) and (2), the unit of analysis in Equation (3) is the center-month: j
denotes the center and t denotes the month. The coefficient μj denotes use of center fixed effects, accounting
for center-specific time-invariant factors. We employ robust standard errors clustered at the loan officer
level, as this is more conservative than if the clustering had also been performed only at the center level (as
one loan officer serves multiple centers).
The coefficient β1 in Equation 3 estimates the average post-demonetization increase in the
probability of a center receiving no collections at all (i.e., ZCC = 1). However, this approach is insufficient
to test Hypothesis 1 because an increase in the average individual-level likelihood of default would be
expected to increase the incidence of zero collection centers, even absent any peer influence due to
community mediation. Therefore, to estimate the extent to which joint defaults are collective defaults
resulting from peer influence, we need a counterfactual that accounts for the impact of increased likelihood
of individual default on the likelihood of center-level ZCC, even absent peer influence.
We develop such a counterfactual by adopting the “dartboard approach” employed by Ellison and
Glaeser (1997).15 Specifically, we construct the counterfactual scenario by randomly reassigning “missed
payment” status to individuals within a loan officer, while keeping the total number of individual defaults
14 We estimate collective default using a binary variable (which takes a value of one only in the extreme case of all
borrowers in a center missing their payment) rather than a continuous variable (based on the fraction of borrowers in
a center missing their payment) in part because the former is not sensitive to dynamics that operate only within joint
liability groups a center is comprised of (see Figure 1 for the nested structure). In our research design, a continuous
variable would lead to a local effect being detected even if the effect in reality operates only within joint liability
groups and not across groups in a center. A binary variable, by construction, measures defaults across joint liability
groups. We provide further analysis related to this distinction in Section 7. 15 Ellison and Glaeser (1997) estimate geographic clustering of factories in the U.S. by comparing observed geographic
proximity among U.S. factories to a simulated (counterfactual) scenario constructed by randomly “throwing darts”
(equal to the number of factories) to assign locations on the map of U.S. A comparison of distances among actual
factory locations with that among factories in the simulated scenario thus provides a measure of geographic clustering.
12
within each loan officer the same as in actual data (for each given month). This provides a baseline rate for
incidence of zero-collection centers that would be expected to occur in the absence of peer influence.16
Using this simulated scenario, we calculate a new variable ZCCSimulated as a baseline for detecting
the presence of collective default arising specifically from peer influence within centers.17 Because each
loan officer covers a small geographic area (typically just a few square kilometers), this counterfactual
accounts for any alternative reasons for observing zero-collection centers that might be either region-
specific (such a bad harvest or local scarcity of cash) or loan officer-specific (such as leniency in
collections).18
Figure 4 illustrates our empirical approach using a stylized (hypothetical) example of a loan officer
serving just two centers, each with 15 borrowers. It assumes that, in actual data, all 15 borrowers in Center
1 default (so ZCC = 1 for Center 1), but only five of the 15 borrowers in Center 2 do so (so ZCC = 0 for
Center 2). In order to construct the counterfactual scenario for this case, we randomly reassign all 20 of
these observed loan defaults within the loan officer across the centers within the same loan officer — with
the likelihood of the reallocation once more leading to a 15-5 split (the only way to realize a zero collections
center in this example) is rather low (with Figure 4 depicting a 10-10 split instead for illustration). Any
allocation other than a 15-5 split (including a 10-10 split as shown) would lead both centers to have
ZCCSimulated = 0. Implementing the process illustrated here for the entire dataset (separately for each
month) thus allows coming up with indicators ZCC and ZCCSimulated for all centers, facilitating a
calculation of clustering of defaults that accounts for some centers seeing zero collections purely by chance.
[Insert Figure 4 about here]
Analogous to our use of equation (3) for actual data, we use the following model to estimate the
increase in frequency of zero collection centers following demonetization for the counterfactual scenario:
ZCCSimulatedj,t = α + β1 Postt + μj + εj,t (4)
16 Naturally, repeating the randomized assignment process leads to slightly different counterfactuals. However, given
the large sample size (over two million borrowers), we did not expect the results to be very sensitive to a specific
draw. Nevertheless, to be sure, we repeated the simulation several times. The findings remained remarkably consistent. 17 We also conducted a more elaborate simulation to also explicitly account for differences in individual-level
characteristics that might drive default behavior. First, using the full dataset, we ran a linear regression predicting loan
defaults based on observed individual characteristics as well as fixed effects for the loan officer, the month, and the
loan officer-month combination. Next, we used these estimates to predict individual borrower probabilities of default.
Finally, we simulated defaults based on these predicted probabilities to come up with the counterfactual rate of zero-
collection centers. Results were very similar to those produced by ZCCSimulated in our reported analysis. 18 By relying on a conservative counterfactual that uses randomized reassignment of defaults only within sets of centers
served by the same loan officer (and hence located close by), our identification strategy is likely able to account for
most (but not all) possible heterogeneity across locations. Specifically, we cannot rule out the possibility of there
sometimes being important unobserved differences even among neighboring centers served by the same loan officer.
13
Comparing the estimates of β1 across equations (3) and (4) enables comparison of the effect of
demonetization in actual data (ZCC) versus the simulated data (ZCCSimulated).19
To test Hypothesis 2, we investigate the effects of the interaction between demonetization and intra-
center diversity on the probability of ZCC. Our first diversity measure is based on the religious affiliation
of borrowers, as prior research suggests that people from same religion tend to share stronger social ties
(Fisman et al. 2017, Yenkey 2015). As noted in Table 3, we construct the variable Diversity by Religion by
using the Blau Index (Blau 1977, Budescu and Budescu 2012), standardized by dividing it by the
theoretically maximum possible value of (k-1)/k, where k is the number of categories.20 Being in the same
lending cohort is another important indicator of social connectedness among microfinance borrowers, as
borrowers who join the local borrower community at the same time and go through the same history of
center meetings and loans repayments together are more likely to develop stronger ties with one another.
Therefore, we construct our second diversity variable Diversity by Cohort using a similar approach as for
constructing Diversity by Religion. We then investigate the effect of both of these diversity variables on the
likelihood of center-level collective defaults by estimating the following linear probability model:
ZCCj,t = α + β1 Postt × Diversity by Religionj + β2 Postt × Diversity by Cohortj + β3 Postt
+ β4 Diversity by Religionj + β5 Diversity by Cohortj + β6x Postt × Xj + β7x Xj + μl + εj,t (5)
As before, the unit of analysis for these models is the center-month. But, since we are now interested
in identifying the effect of center-level diversity variables (time invariant in our data), we can no longer
employ center fixed effects. Instead, we take two steps to account for heterogeneity across centers. First,
we directly control for observable center-level characteristics, referred to as Xj above.21 Second, we now
employ fixed effects at the the loan officer level (denoted by μl), which is just one level of aggregation
higher than the center and still accounts for most geographic differences associated with a loan officer’s
jurisdiction, which typically is just a few neighboring centers, as well as any unobserved officer-specific
characterisics. The coefficient β1 represents the effect of Diversity by Religion on the relative (post-
demonetization vs. pre-demonetization) change in the probability of a given center receiving zero
collections (ZCC = 1). Similarly, β2 represents the effect of Diversity by Cohort on the relative (post-
19 In our analysis, we also formally compare estimates for the two cases by pooling the actual and simulated data and
carrying out a single estimation that now also employs an indicator for which data each given observation is from. 20 Blau Index (before standardization) in our case is 1 – Σpk
2, where pk represents the proportion of borrowers at the
center from religion k, with k ranging from 1 to 4 for the four religions in our data: Hindus, Muslims, Sikhs, and
Christians. Given four categories, the theoretical maximum possible is ¾. Thus, Diversity by Religion = (1 – Σpk2)/(¾).
For example, a center of 16 borrowers with four from each religion implies a Diversity by Religion equal to one. 21 The center-level controls are the proportion of borrowers from each religion, proportion of borrowers from each
cohort, proportion of borrowers living in non-brick houses, the average borrower age at the center, and the size of the
center (log-transformed). Note that equation (5) also includes interactions of Post with each of these control variables.
14
demonetization vs. pre-demonetization) change in the probability of a given center receiving zero
collections (ZCC = 1).
6.2 Results from Center-Level Analysis
Before getting to the specific effect of demonetization on collective default behavior, it is helpful to examine
the distribution of loan defaults across centers even pre-demonetization. Figure 5A depicts this distribution
for the actual as well as simulated data for October 2016, the month immediately preceding demonetization.
During this month, the proportion of centers that were zero collection centers (i.e., none of the local
borrowers made their repayment in time) was greater in the actual data (1.1 percent) than the simulated data
(0.0 percent). At the other extreme of the distribution, 90.0 percent of the centers were “full collection
centers” (i.e., all borrowers in the center made on-time repayments), but the corresponding proportion was
significantly lower at 78.7 percent in the simulated data. These observations are consistent with a view that,
irrespective of whether we consider the kind of behavior that is desirable or undesirable by the business,
individual loan repayment behavior is not independently determined: Center-level peer relationships are
likely to be significantly responsible in shaping these overall patterns in loan defaults.
[Insert Figure 5 here]
Comparison of the periods just before and just after demonetization shows that, from October 2016
(Figure 5A) to November 2016 (Figure 5B), the proportion of zero collection centers increased from 1.1
percent to 19.5 percent. Similarly, consistent with the overall pattern of a drastic increase in defaults, the
proportion of full collection centers dropped from 90.0 percent to 34.0 percent. Importantly, the proportion
of centers falling at either extreme in the actual data continues to be significantly larger than that in the
simulated data. The actual distribution of loan defaults across centers post-demonetization has fat tails, with
a large proportion of borrowers falling at the extreme ends. In contrast, the simulated distribution has a
much larger proportion of the centers falling in between the extremes.22 Once more, this stark contrast
between the actual versus simulated data is consistent with collective behavior operating at the center level
being an important driver of loan defaults. It is also worth noting that, compared with the analogous gap
pre-demonetization (Figure 5A), the gap between the incidence of zero collection centers between the actual
data (19.5 percent) and simulated data (1.1 percent) is much greater post-demonetization (Figure 5B),
symptomatic of an increase in collective defaults due to systemic economic distress that resulted from the
demonetization shock.23
22 Considering the entire statistical distribution of outcomes, a formal Kolmogorov-Smirnov test comparing the two
distributions (actual vs. simulated) rejects the null hypothesis of the two distributions being equivalent (p < 0.001). 23 Unlike the distribution of collective defaults at the center level (Figure 5), an analogous analysis of collective
defaults considered at the loan officers area level shows a far less concentration at the two extremes of zero collections
and full collections. This is once more consistent with center-level (peer) effects being an important driver of behavior.
15
Figure 6 shows, for the entire eight-month period, the frequency of zero collection centers as a
proportion of all centers in actual data as well as simulated data. The proportion of ZCC increases from
about one percent pre-demonetization to about 20 percent post-demonetization, a significantly larger
increase than that observed for ZCCSimulated. This large gap once more represents the difference between
the increase in observed incidences of collective default versus the increase that would be expected if the
borrowers were making independent decisions.24
[Insert Figure 6 here]
The econometric analysis corresponding to our above discussion appears in Table 4. We start with
regression models described as equations (3) and (4) described earlier, employing linear probability models
with center-month as the unit of analysis (and including center fixed effects). The estimated effect of Post
in Column 1, which predicts ZCC, suggests a 22 percentage point increase (95% CI [0.208, 0.225], p <
0.001) in the probability that a center will have zero collections post-demonetization versus pre-
demonetization. Column 2, which uses the simulated data instead to analogously predict ZCCSimulated,
shows a corresponding post-demonetization increase of only 4 percentage points (95% CI [0.034, 0.041],
p < 0.001). This gap between the two columns again provides strong evidence in support of Hypothesis 1
(in line with Figure 6).25
[Insert Table 4 here]
Columns 3 and 4 test for the possibility of more nuanced patterns over time by replacing Post with
separate indicator variables for each month. The first three months (captured respectively by indicators
Pre4, Pre3, and Pre2) in our data show no material difference relative to the month just prior to
demonetization (October 2016, the omitted category). But in each of the four months after demonetization
(captured respectively using Post1, Post2, Post3 and Post4) there is a much larger increase in the probability
of a center ending up as a zero collections center in the actual data (ZCC in Column 3) versus that in the
simulated data (ZCCSimulated in Column 4), once more supporting Hypothesis 1.
To test Hypothesis 2, Table 5 provides results obtained from equation (5), which examines the
effect of diversity among borrowers in a center on the relative (post-demonetization vs. pre-demonetization)
change in the probability of ZCC. The first two columns report the findings for the pre-demonetization and
24 Analogous to our analysis for zero collection centers, Figure S1 (in the supplementary material) shows the incidence
of full collection centers in the actual versus simulated data, using indicators FCC and FCCSimulated set to one in the
respective data if and only if every borrower at the center makes her repayment. Once more, we see a strong effect of
demonetization: FCC decreases post-demonetization (though by an extent smaller than FCCSimulated does). 25 For a formal test enabling a direct statistical comparison of the estimated coefficient for Post across actual versus
simulated data, we pooled the two datasets and merged ZCC and ZCCSimulated into a single outcome variable. The
regression now also included an interaction Post × Actual (where Actual is an indicator set to one for the observations
from the actual data, and to zero for those from the simulated data). Consistent with prior analysis, the coefficient for
the interaction Post × Actual represents a difference-in-differences estimate of 18 percentage points (p < 0.001).
16
post-demonetization periods separately. First, consistent with our earlier arguments, both religious and
cohort diversity are negatively associated with the likelihood that a center has zero collections. Second, also
consistent with our arguments, the magnitude of both of these effects is greater post-demonetization than
pre-demonetization. Column 3 further examines the latter finding by considering the data for the entire
eight months, and shows that the coefficients on the interaction terms Post × Diversity by Religion and
Post × Diversity by Cohort are negative, have large magnitude, and are statistically significant, once more
suggesting that greater diversity by religion or lending cohort attenuates the post-demonetization increase
in the probability of a center receiving zero collections.26 Figure S2 in the supplementary material illustrates
the size of these effects graphically by showing the effect of demonetization on the probability of zero
collections at a center for different values of the two diversity variables. All of the above results provide
consistent evidence in support of Hypothesis 2.
[Insert Table 5 here]
7. FURTHER ANALYSIS
So far, we have largely presented arguments for (and analysis supporting) peer influence within centers. In
further analysis, we incorporate the feature of our data that each borrower belongs to a formal joint-liability
group that is nested within a lending center. Therefore, addressing our arguments also requires that we
evaluate the possibility that our observed increase in collective defaults is reflective not of center-level
effects, but of the aggregation of group-level effects.
To account for this potentially confounding effect of group membership, we generated an
alternative counterfactual that randomized the assignment of groups (and their default status), while keeping
intact the pattern of individual defaults within groups. In other words, whereas our initial simulation
randomly reassigned individual defaults to centers within a loan officer’s jurisdiction, this new simulation
randomly assigned group defaults to centers within a loan officer’s jurisdiction. Thus, by controlling for
potential peer influence within groups (possibly due to joint liability), we can estimate the extent to which
the pattern of zero-collection centers is due to center-level effects over and above any group-level effects.
The alternative simulation described above allows us to calculate an alternate counterfactual
outcome variable, ZCCSimulated2.27 Similar to the formal econometric analysis reported earlier in Table
4, Table 6 shows the analysis with ZCC versus ZCCSimulated2 as the outcome variables. As before,
26 In addition, we also conducted an analogous analysis with FCC as the outcome variable instead of ZCC. The results
of this analysis indicate that greater diversity by religion attenuates the post-demonetization decrease in the probability
of a center receiving full collections, whereas greater diversity by lending cohort accentuates the post-demonetization
decrease in the probability of a center receiving full collections. 27 Analogous to the ZCC versus ZCCSimulated graph in Figure 6, Figure S3 (in the supplementary analysis) shows
the pattern of zero collection centers in the actual data (ZCC) versus our new simulation (ZCCSimulated2). The
proportion of ZCC continues to show a much larger increase than that observed even for ZCCSimulated2.
17
Column 1, which predicts ZCC, shows 22 percentage points increase (95% CI [0.208, 0.225], p < 0.001) in
the probability that a center will have zero collections post-demonetization. Column 2, which predicts
ZCCSimulated2, shows a corresponding increase of 9 percentage points (95% CI [0.085, 0.097], p < 0.001).
This large gap in the estimated effect of Post between Columns 1 and 2 suggests that peer relationships
within joint liability groups are related to, but not solely responsible for, the increase in collective defaults.28
Analogous to the corresponding columns in Table 4, Columns 3 and 4 in Table 6 examine the above
patterns over time by replacing Post with indicator variables for each month (again taking the month just
prior to demonetization as the omitted category). Once more, the months before demonetization show
indistinguishable effects, whereas in the four months after demonetization there is a much larger increase
in the probability of a center actually being a zero collections center (ZCC in Column 3) than in the
probability of it being a zero collections center in the new simulation (ZCCSimulated2 in Column 4). These
findings are again consistent with the argument that informal peer influence at the center level are driving
collective defaults independently of group-level effects.
[Insert Table 6 here]
8. DISCUSSION AND CONCLUSION
Previous work has convincingly documented the benefits of community-mediated exchange strategies for
promoting cost efficiency and desirable customer behavior (Prahalad 2006). But we argue that these effects
are contingent on external circumstances – specifically, the pattern of economic distress – that the firm’s
exchange partners face. We test these arguments using quasi-experimental evidence regarding loan
repayment behavior of microfinance customers under a scenario of systemic economic distress caused by
the 2016 Indian demonetization. Under normal conditions, the use of a community-mediated, center-based
approach in microfinance burnishes repayment by facilitating the development of norms and peer pressures
for repayment as well as providing mutual insurance that shields borrowers from idiosyncratic economic
distress. However, we find evidence that, following demonetization, loan defaults were localized within
centers to a greater degree than what would be expected if borrowers were making loan repayment decisions
independently. In particular, all borrowers in a center defaulted in 21.6 percent of centers, compared with
just a 3.7 percent rate that would have resulted if an equivalent number of loan defaults had occurred as a
result of independent individual decision-making. Further, this localization of defaults was strongest in
centers with religious homogeneity and borrower concentration within the same lending cohorts, both
28 Similar to the formal test reported earlier, we pooled the actual data and the alternative simulated data, and merged
ZCC and ZCCSimulated2 into a single outcome variable used for a regression model that now also included an
interaction term Post × Actual2 (where Actual2 is an indicator variable set to one for the observations corresponding
to ZCC, and to zero for the observations corresponding to ZCCSimulated2). Consistent with prior analysis, the
coefficient estimate for the interaction term Post × Actual2 represented a difference-in-differences estimate of 12
percentage points, and was statistically significant (p < 0.001).
18
indicative of conditions under which peer relationships are likely to be stronger. Overall, our findings
suggest that, even though a strategy of leveraging community-level peer relationships might benefit
business outcomes under normal conditions of idiosyncratic economic distress, it might also inadvertently
amplify business risks in conditions of systemic economic distress.
Our study contributes to a long tradition of research on how social relationships among a firm’s
stakeholders influences its business outcomes, by showing conditions under which such ties can lead to
negative outcomes. Recent research suggests that firms might intentionally cultivate social capital in order
to improve firm performance, via mechanisms such as motivation (Adler and Kwon 2002), trust (Burt,
2005) and innovation (von Hippel 2005). Microfinance research, in particular, has demonstrated such
positive business effects of social influence within firm-client relationships (Doering 2018) but also client-
client ties (de Quidt et al. 2016). This literature has largely overlooked, however, the oft-demonstrated point
that social ties can also lead to adverse effects for firms (e.g. Roy 1959, Zald and Berger 1978), as well as
for exchange partners themselves (Wacquant 1998). We view our study as helping to clarify environmental
conditions under which an identical pattern of social relations can lead to vastly different patterns of social
influence. Specifically, we show how social relations conventionally understood to facilitate positive
business outcomes (i.e. social capital within the group lending model) (de Quidt et al. 2016) can, under
conditions of systematic economic distress, facilitate negative business outcomes. Furture research might
further clarify such conditions, and thus the business risks of strategically cultivating social capital among
stakeholders.
While we should be cautious in claiming conclusive general implications beyond our empirical
context, we believe that our study offers some general insights. It is also worth noting that different variants
of a business strategy relying on community-mediated exchange are commonly employed across many
BOP settings beyond microfinance. Consider, for instance, Unilever’s “Perfect Village” initiative, launched
in 2013 and as of 2016 already implemented in about 10 percent of Vietnam’s villages. This initiative relies
on community interactions bringing together village residents and local Unilever representatives for the
purposes of market creation, education on product usage, and standardization of these products. Under
normal circumstances, engagement at the community level might shield Unilever against negative
idiosyncratic views or experiences of individuals, with customers initially not convinced about a product
likely to in fact be driven towards adoption by their peers. However, if things start to go wrong, the same
community relationships might hasten a mass rejection of the product. Such nuances of not only positive
but also potentially negative consequences of business strategies relying on community mediation in
different BOP setting might be interesting for future study.
By addressing issues of particular importance to BOP business in emerging markets, our study also
responds to calls for more research in these understudied contexts (Simchi-Levi 2018). Our primary focus
19
has been on examining a potential “dark side” of reliance on peer relationships (Hypotheses 1 and 2), but
our examination of the impact of 2016 Indian demonetization policy can be seen as an empirical
contribution in itself. Our baseline findings are consistent with concerns and anecdotal evidence previously
recorded regarding the policy’s consequences. Further, while our study considers this specific policy shock,
a wide range of political or macroeconomic shocks may produce similar systemic economic distress
(Gartenberg and Pierce 2017, Kuppuswamy and Villalonga 2016). For instance, it seems reasonable to
expect that some of the insights from studying systemic economic distress resulting specifically from the
Indian demonetization should carry over to other settings where a similar distress might instead result from
other kinds of shocks — such as natural disasters like floods and hurricanes. Our research thus provides a
contribution towards broader discussions on how businesses, especially when operating in emerging
markets, might be subject to unique policy risks and political hazards (Henisz 2000, Khanna 2018, Lesmond
2005). Understanding the effects of economic distress on management questions seems particularly
important in the BOP context, as individuals at the BOP are perhaps most vulnerable to economic distress.
And this is not just about customers: To the extent that a business strategy relies on these individuals as
exchange partners in any form — customer, employee, supplier, or other key stakeholder — it is worth
digging further into how economic distress might affect these exchange relationships.
We also acknowledge multiple limitations of our study. First, demonetization was an extraordinary
case in its magnitude and complexity. While episodes of systemic economic distress are generally likely to
have these qualities, results should be interpreted with caution. Whether the patterns we observe here would
have been observed following a smaller-intensity shock is a question for future research. Second, our
findings here might, to some extent, be specific to the microfinance firm whose data we analyzed, and the
particular features of its borrower population and methods of community building and management. While
the practices employed by this firm are typical of community-mediated exchange in the microfinance
industry, there may inevitably exist some unobserved differences in how this firm builds and cultivates
these communities. Third, the structure of our data and the simultaneous nature of the shock, though
inherent to our research question, prevent us from directly analyzing causal vectors of peer-to-peer
influence. Fourth and finally, while we demonstrate that demonetization increased the extent to which
individual defaults were clustered in a community-based arrangement, we are unable to evaluate the net
effect of community-mediated exchange strategies on the individual likelihood of default, as our partner
firm, like most Indian microlenders, engaged only in community-mediated lending.
In closing, we wish to highlight implications of our study for managers and entrepreneurs, for
whom community-mediated exchange often presents a tradeoff between efficiency and control. Such
strategies have significant potential benefits but can be a double-edged sword. Under normal conditions,
the capacity of peer relationships for normative pressure and coordination may serve firm interests. But one
20
should remain aware that, in systemically difficult times, the same social mechanism can have unexpected,
adverse consequences for firm. This calls for caution in pursuing such strategies. We urge future research
to more fully consider the complex, contingent consequences of such community-mediated strategies at the
BOP.
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24
Table 1 Variables and Summary Statistics for Individual-Level Analysis
Notes. The summary statistics are based on 15,018,922 observations at the borrower-month level. The observation window is four months pre-demonetization and
four months post-demonetization, thus covering the 8-month period from July 2016 to February 2017. The panel is unbalanced because the repayment schedule
for some of the 2,036,108 borrowers either started after the first month or ended before the last month in this observation window. Means of some of the variables
appear as 0.00 as we only report two significant digits after the decimal.
Variable Description Mean SD Min Max
Missed Payment Binary variable. Takes the value of 1 for loan default (i.e., borrower failed to pay the scheduled repayment). 0.24 0.42 0 1
Post Binary variable. Takes the value of 1 for the period post demonetization (i.e., November 2016 and later). 0.52 0.50 0 1
NonBrickHouse Binary variable. Takes the value of 1 for the poorest borrowers (i.e., those who live in nonbrick houses). 0.66 0.47 0 1
Hindu Binary variable. Takes the value of 1 if the borrower is a Hindu (i.e., the majority religion in India). 0.72 0.45 0 1
Muslim Binary variable. Takes the value of 1 if the borrower is a Muslim (i.e., the largest minority religion in India). 0.17 0.37 0 1
Sikh Binary variable. Takes the value of 1 if the borrower is a Sikh (one of the religious minority groups in India). 0.11 0.30 0 1
Christian Binary variable. Takes the value of 1 if the borrower is a Christian (one of the religious minority groups in India). 0.00 0.02 0 1
LoanCycle1 Binary variable. Takes the value of 1 if the borrower was in loan cycle 1 at the time of demonetization. 0.68 0.46 0 1
LoanCycle2 Binary variable. Takes the value of 1 if the borrower was in loan cycle 2 at the time of demonetization. 0.19 0.39 0 1
LoanCycle3 Binary variable. Takes the value of 1 if the borrower was in loan cycle 3 at the time of demonetization. 0.08 0.27 0 1
LoanCycle4 Binary variable. Takes the value of 1 if the borrower was in loan cycle 4 at the time of demonetization. 0.03 0.16 0 1
LoanCycle5 Binary variable. Takes the value of 1 if the borrower was in loan cycle 5 at the time of demonetization. 0.01 0.11 0 1
LoanCycle6 Binary variable. Takes the value of 1 if the borrower was in loan cycle 6 at the time of demonetization. 0.01 0.07 0 1
LoanCycle7 Binary variable. Takes the value of 1 if the borrower was in loan cycle 7 at the time of demonetization. 0.00 0.02 0 1
LoanCycle8 Binary variable. Takes the value of 1 if the borrower was in loan cycle 8 at the time of demonetization. 0.00 0.00 0 1
LoanCycle9 Binary variable. Takes the value of 1 if the borrower was in loan cycle 9 at the time of demonetization. 0.00 0.00 0 1
Age Denotes the age of the borrower in years at the time of demonetization. 36.9 8.9 17 62
25
Table 2 Analyzing the Likelihood of a Missed Payment (Baseline Prediction)
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the borrower-month panel described in Table 1, and employs a linear
probability model. Robust standard errors clustered at loan officer level are reported in parentheses. The month
immediately before demonetization (October 2016) is taken as the reference month in Columns 2-4 (making Pre1 the
omitted category).
(1) (2) (3) (4)
Sample NonBrickHouse BrickHouse
Variables
Missed
Payment
Missed
Payment
Missed
Payment
Missed
Payment
Post 0.43***
(0.006)
Post1 0.37*** 0.40*** 0.32***
(0.005) (0.006) (0.004)
Post2 0.44*** 0.47*** 0.38***
(0.006) (0.008) (0.006)
Post3 0.42*** 0.46*** 0.36***
(0.006) (0.008) (0.006)
Post4 0.42*** 0.45*** 0.36***
(0.006) (0.007) (0.006)
Pre2 -0.01*** -0.01*** -0.01***
(0.001) (0.001) (0.001)
Pre3 -0.02*** -0.02*** -0.02***
(0.001) (0.002) (0.001)
Pre4 -0.03*** -0.03*** -0.03***
(0.001) (0.002) (0.002)
Observations 15,018,922 15,018,922 9,924,421 5,094,501
R-squared 0.560 0.562 0.579 0.527
Number of borrowers 2,036,108 2,036,108 1,347,923 688,185
Borrower fixed effects Yes Yes Yes Yes
Full
26
Table 3 Variables and Summary Statistics for Center-Level Analysis
Notes. The summary statistics are based on 985,004 observations at the center-month level. The observation window is the same as in Table 1, i.e., from July 2016
to February 2017. The panel is again unbalanced because the repayment schedule for all borrowers in some of the 130,980 centers either started after the first month
or ended before the last month in this observation window. Means of some of the variables appear as 0.00 as we only report two significant digits after the decimal.
Variable Description Mean SD Min Max
ZCCBinary variable. Takes the value of 1 for zero collection center (i.e., if every borrower at the center failed
to pay the scheduled repayment).0.12 0.33 0 1
Post Binary variable. Takes the value of 1 for the period post demonetization (i.e., November 2016 and later). 0.52 0.50 0 1
Proportion of NonBrickHouseDenotes the proportion of poorest borrowers at the center (i.e, those who live in nonbrick houses as a
proportion of total borrowers at the center) at the time of demonetization.0.65 0.38 0 1
Proportion of Hindus Denotes the proportion of Hindu borrowers at the center at the time of demonetization. 0.73 0.38 0 1
Proportion of Muslims Denotes the proportion of Muslim borrowers at the center at the time of demonetization. 0.17 0.31 0 1
Proportion of Sikhs Denotes the proportion of Sikh borrowers at the center at the time of demonetization. 0.09 0.28 0 1
Proportion of Christians Denotes the proportion of Christian borrowers at the center at the time of demonetization. 0.00 0.01 0 1
Diversity by Religion
Denotes the borrower diversity by religion at the center measured by Blau Index of Heterogeneity and
standardized by dividing the value of Blau Index by theoretically maximum possible value of (k-1)/k, where
k is the number of categories (for four religions, k = 4).
0.13 0.21 0 0.95
Proportion of LoanCycle1 Denotes the proportion of LoanCycle1 borrowers at the center at the time of demonetization. 0.71 0.34 0 1
Proportion of LoanCycle2 Denotes the proportion of LoanCycle2 borrowers at the center at the time of demonetization. 0.18 0.25 0 1
Proportion of LoanCycle3 Denotes the proportion of LoanCycle3 borrowers at the center at the time of demonetization. 0.07 0.15 0 1
Proportion of LoanCycle4 Denotes the proportion of LoanCycle4 borrowers at the center at the time of demonetization. 0.02 0.08 0 1
Proportion of LoanCycle5 Denotes the proportion of LoanCycle5 borrowers at the center at the time of demonetization. 0.01 0.06 0 1
Proportion of LoanCycle6 Denotes the proportion of LoanCycle6 borrowers at the center at the time of demonetization. 0.00 0.03 0 1
Proportion of LoanCycle7 Denotes the proportion of LoanCycle7 borrowers at the center at the time of demonetization. 0.00 0.01 0 0.58
Proportion of LoanCycle8 Denotes the proportion of LoanCycle8 borrowers at the center at the time of demonetization. 0.00 0.00 0 0.15
Proportion of LoanCycle9 Denotes the proportion of LoanCycle9 borrowers at the center at the time of demonetization. 0.00 0.00 0 0.03
Diversity by Cohort
Denotes the borrower diversity by loan cycle at the center measured by Blau Index of Heterogeneity and
standardized by dividing the value of Blau Index by theoretically maximum possible value of (k-1)/k, where
k is the number of categories (for nine loan cycles, k = 9).
0.28 0.32 0 0.96
Mean Age Denotes the mean borrower age at the center at the time of demonetization. 36.9 3.29 18 59
Center Size Denotes the number of borrowers at the center at the time of demonetization. 15.2 5.7 1 50
27
Table 4 Analyzing the Likelihood of a Center Receiving Zero Collections (Hypothesis 1)
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the center-month panel described in Table 3, and employs a linear probability model. Robust standard errors clustered
at loan officer level are reported in parentheses. The month immediately before demonetization (October 2016) is taken as the reference month in Columns 3-4
(making Pre1 the omitted category).
(1) (2) (3) (4)
Variables ZCC ZCCSimulated ZCC ZCCSimulated
Post 0.22*** 0.04***
(0.004) (0.002)
Post1 0.18*** 0.01***
(0.003) (0.001)
Post2 0.24*** 0.05***
(0.005) (0.003)
Post3 0.22*** 0.04***
(0.005) (0.002)
Post4 0.21*** 0.04***
(0.005) (0.002)
Pre2 -0.00*** -0.00*
(0.000) (0.000)
Pre3 -0.00*** -0.00
(0.001) (0.000)
Pre4 -0.01*** -0.00
(0.001) (0.000)
Observations 985,004 985,004 985,004 985,004
R-squared 0.477 0.261 0.479 0.267
Number of centers 130,980 130,980 130,980 130,980
Center fixed effects Yes Yes Yes Yes
28
Table 5 Effect of Diversity on Likelihood of a Center Receiving Zero Collections (Hypothesis 2)
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the center-month panel described in Table 3, and employs a linear
probability model. Robust standard errors clustered at loan officer level are reported in parentheses. As additional
controls not reported due to space constraints, we also included the proportion of borrowers in a center that belonged
to each of the religions or loan cycles (using corresponding variables from Table 3, with Proportion of Hindus and
Proportion of LoanCycle1 omitted to prevent multicollinearity). We also included interaction of each of these indicator
variables with Post.
(1) (2) (3)
Period Pre Post Full
Variables ZCC ZCC ZCC
Post x Diversity by Religion -0.15***
(0.007)
Post x Diversity by Cohort -0.04**
(0.011)
Post x Proportion of NonBrickHouse 0.08***
(0.007)
Post x Mean Age -0.01***
(0.001)
Post x Center Size (log) -0.03***
(0.003)
Post 0.39***
(0.021)
Diversity by Religion -0.01*** -0.09*** 0.03***
(0.001) (0.006) (0.003)
Diversity by Cohort -0.03*** -0.09*** -0.05***
(0.003) (0.007) (0.006)
Proportion of NonBrickHouse 0.00*** 0.00 -0.04***
(0.001) (0.003) (0.003)
Mean Age -0.00 -0.00*** 0.00***
(0.000) (0.000) (0.000)
Center Size (log) -0.03*** -0.08*** -0.03***
(0.002) (0.003) (0.002)
Observations 469,536 515,415 985,004
R-squared 0.078 0.281 0.266
Number of loan officers 2,990 3,031 3,037
Loan Officer fixed effects Yes Yes Yes
29
Table 6 Further Analysis Accounting for Joint Liability at Group Level
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the center-month panel described in Table 3, and employs a linear probability model. Robust standard errors clustered
at loan officer level are reported in parentheses. The month immediately before demonetization (October 2016) is taken as the reference month in Columns 3-4
(making Pre1 the omitted category).
(1) (2) (3) (4)
Variables ZCC ZCCSimulated2 ZCC ZCCSimulated2
Post 0.22*** 0.09***
(0.004) (0.003)
Post1 0.18*** 0.05***
(0.003) (0.002)
Post2 0.24*** 0.12***
(0.005) (0.004)
Post3 0.22*** 0.10***
(0.005) (0.004)
Post4 0.21*** 0.10***
(0.005) (0.003)
Pre2 -0.00*** 0.00
(0.000) (0.000)
Pre3 -0.00*** 0.00**
(0.001) (0.001)
Pre4 -0.01*** 0.00***
(0.001) (0.001)
Observations 985,004 985,004 985,004 985,004
R-squared 0.477 0.285 0.479 0.291
Number of centers 130,980 130,980 130,980 130,980
Center fixed effects Yes Yes Yes Yes
30
Figure 1 Organization Structure of the Microfinance Firm
Notes. At the time of demonetization, the microfinance firm operated 536 branches across 15 states in India. The geography served by each branch was divided
among an average of about six loan officers, each of who managed an area that covered a set of microfinance centers located in close proximity. Borrowers
belonging to a center met periodically to make their scheduled repayments together, with each center typically comprising about 15 borrowers belonging to the
same village or neighboring villages. The borrowers at each center were divided into 3-5 groups with formal joint liability.
HQ
15 States
536 Branches
3,037 Loan Officer Areas
130,980 Microfinance Centers
429,575 Joint Liability Groups
2,036,108 Individual Borrowers
31
Figure 2 Incidence of Loan Default Immediately Before vs. After Demonetization
Notes. This figure shows the incidence of loan defaults in the month immediately before versus after demonetization by state, with the unshaded parts of the map
representing states where the microfinance firm did not operate. All states where the firm operated had a loan default rate of under 10 percent pre-demonetization.
Most of these states showed a remarkable increase in the loan defaults rate post-demonetization. (Maps generated from https://mapchart.net.)
32
Figure 3 Temporal Trend in Loan Default Pre vs. Post Demonetization
A
B
Notes. These plots show the monthly incidence of loan default rates for the months just before and after demonetization
for the microfinance borrower sample described in Table 1. X-axis denotes the end of respective month.
33
Figure 4 Applying the “Dartboard Approach” to Detect Center-Level Clustering of Default Behavior
Notes. This hypothetical example illustrates the construction of the variables capturing the incidence of zero-collection
centers for the actual loan default data (ZCC) versus the simulated data (ZCCSimulated). The diagram shows just one
loan officer, who serves 30 borrowers through two microfinance centers (each with 15 borrowers). Further, Center 1
sees all 15 borrowers in the center default (so ZCC=1 for Center 1), while only 5 of the 15 borrowers default for Center
2 (so ZCC=0 for Center 2). In construction of the simulated baseline, we randomly reassign the 20 loan defaults
observed (in total) across the two centers. ZCCSimulated for the two centers is thus calculated using the simulated
scenario where borrowers missing their payments are randomly reassigned among the centers served by the same loan
officer (with a possible outcome resulting in a symmetric split between the centers shown here, one way of realizing
ZCCSimulated=0 for both). Doing such reassignment only across (close by) centers within each loan officer helps
account for unobserved heterogeneity across different regions and loan officers.
34
Figure 5 Distribution of Centers in Terms of the Fraction of Borrowers Missing Payments
Notes. This figure depicts the fraction of centers that fall into each of six different categories in terms of the proportion
of borrowers with defaults for the month immediately before vs. after demonetization, repeating the exercise for both
actual and simulated data. We refer to the extreme case of all borrowers in a center defaulting (d=100%) as a “zero
collection center” or ZCC, and the other extreme case of no borrower defaulting (d=0%) as a “full collection center”.
It is worth noting that the post-demonetization distribution of actual loan defaults at the center level shows fat tails:
A center is very likely to have either no borrower defaulting or all borrowers defaulting. In contrast, post-
demonetization distribution of simulated defaults does not show such fat tails. This difference between actual and
simulated data on defaults is indicative of collective behavior driven by peer relationships.
35
Figure 6 Temporal Frequency of Incidence of “Zero Collection Centers”
Notes. This chart shows the fraction of microfinance centers witnessing a loan default by all borrowers belonging to the center for the months immediately before
vs. after demonetization, repeating the exercise for both actual data (ZCC) and simulated data (ZCCSimulated).
Supplementary Material (Page 1)
SUPPLEMENTARY MATERIAL
Table S1 Regression Models Predicting Total Arrears Pre vs. Post Demonetization
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the borrower-month panel described in Table 1, and employs an ordinary
least squares model. Robust standard errors clustered at loan officer level are reported in parentheses. The month
immediately before demonetization (October 2016) is taken as the reference month in Columns 2-4 (making Pre1 the
omitted category).
(1) (2) (3) (4)
Sample NonBrickHouse BrickHouse
Variables
Total Arrears
(Log)
Total Arrears
(Log)
Total Arrears
(Log)
Total Arrears
(Log)
Post 3.15***
(0.046)
Post1 2.60*** 2.80*** 2.21***
(0.033) (0.039) (0.030)
Post2 3.18*** 3.44*** 2.66***
(0.048) (0.057) (0.045)
Post3 3.22*** 3.48*** 2.71***
(0.051) (0.059) (0.049)
Post4 3.22*** 3.47*** 2.72***
(0.051) (0.059) (0.050)
Pre2 -0.07*** -0.08*** -0.07***
(0.005) (0.007) (0.006)
Pre3 -0.15*** -0.17*** -0.13***
(0.010) (0.012) (0.010)
Pre4 -0.24*** -0.26*** -0.19***
(0.011) (0.013) (0.013)
Observations 15,018,922 15,018,922 9,924,421 5,094,501
R-squared 0.577 0.581 0.599 0.545
Number of borrowers 2,036,108 2,036,108 1,347,923 688,185
Borrower fixed effects Yes Yes Yes Yes
Full
Supplementary Material (Page 2)
Table S2 Regression Models with Interaction Terms
*** p < 0.001; ** p < 0.01; * p < 0.05
Notes. This regression analysis is based on the borrower-month panel described in Table 1. Column 1 employs a linear
probability model, and Column 2 employs an ordinary least squares model. Robust standard errors clustered at loan
officer level are reported in parentheses. The month immediately before demonetization (October 2016) is taken as
the reference month in both columns (making Pre1 the omitted category). Interactions of NonBrickHouse with Pre2,
Pre3, and Pre4 were also included in the regression models but those coefficients (very small) are not shown for
brevity.
(1) (2)
Variables
Missed
Payment
Total Arrears
(Log)
NonBrickHouse x Post1 0.08*** 0.59***
(0.005) (0.035)
NonBrickHouse x Post2 0.10*** 0.77***
(0.007) (0.051)
NonBrickHouse x Post3 0.09*** 0.77***
(0.007) (0.054)
NonBrickHouse x Post4 0.09*** 0.76***
(0.007) (0.055)
Post1 0.32*** 2.21***
(0.004) (0.030)
Post2 0.38*** 2.66***
(0.006) (0.045)
Post3 0.36*** 2.71***
(0.006) (0.049)
Post4 0.36*** 2.72***
(0.006) (0.050)
Pre2 -0.01*** -0.07***
(0.001) (0.006)
Pre3 -0.02*** -0.13***
(0.001) (0.010)
Pre4 -0.03*** -0.19***
(0.002) (0.013)
Observations 15,018,922 15,018,922
R-squared 0.565 0.584
Number of borrowers 2,036,108 2,036,108
Borrower fixed effects Yes Yes
Supplementary Material (Page 3)
Figure S1 Temporal Frequency of Incidence of “Full Collection Centers”
Notes. This chart shows the fraction of microfinance centers witnessing no loan default by any borrower belonging to the center for the months immediately before
vs. after demonetization, repeating the exercise for both actual data (FCC) and simulated data (FCCSimulated).
Supplementary Material (Page 4)
Figure S2 Moderating Role of Diversity on the Effect of Demonetization on ZCC
Notes. This figure shows the marginal effect of demonetization on the probability of zero collections at a center for
the full range of scores of the moderator variables (Diversity by Religion and Diversity by Cohort). The two outer lines
(depicted as grey dash lines) show the 95 percent confidence interval for the interaction line (depicted as a solid black
line). Small circles represent all observations for the respective moderator variables in the sample.
Supplementary Material (Page 5)
Figure S3 Temporal Frequency of Incidence of “Zero Collection Centers”: ZCC versus ZCCSimulated2
Notes. This chart shows the fraction of microfinance centers witnessing a loan default by all borrowers belonging to the center for the months immediately before
vs. after demonetization, repeating the exercise for both actual data (ZCC) and alternative simulated data (ZCCSimulated2). ZCCSimulated2 refers to the estimate
of zero collection center that would have arisen if the groups with zero collections within a loan officer in a given month were kept equal in number but were
randomly reassigned across centers within the same loan officer. This counterfactual scenario estimates the zero collection center that would be expected if the
borrowers across groups were making independent decisions but the borrowers within a group were not assumed to be making independent decisions.