Credit or Crime? The Effects of Online Lending on Crime This draft: May 13, 2018 PRELIMINARY - DO NOT CIRCULATE Abstract Frictions in credit markets impact social welfare. Using bank mergers as a shock to local credit market competitiveness and a matched difference-in-difference methodology, I show that house- holds turn to peer-to-peer (P2P) lending when local credit markets suffer from frictions. In turn, availability of P2P credit offsets between 2% to 26% of the incremental crime incidents due to a bank merger. The moderation in crime growth principally occurs in property-related crimes (e.g., thefts and burglaries) while growth in persons-related and societal crimes are not affected. Overall, the evidence suggests that P2P lending is an important new credit channel which can have real effects. Keywords: Consumer loans, household credit, peer-to-peer, social welfare.
Transcript
Credit or Crime? The Effects of Online Lending on
Crime
This draft: May 13, 2018
PRELIMINARY - DO NOT CIRCULATE
Abstract
Frictions in credit markets impact social welfare. Using bank mergers as a shock to local credit
market competitiveness and a matched difference-in-difference methodology, I show that house-
holds turn to peer-to-peer (P2P) lending when local credit markets suffer from frictions. In turn,
availability of P2P credit offsets between 2% to 26% of the incremental crime incidents due to
a bank merger. The moderation in crime growth principally occurs in property-related crimes
(e.g., thefts and burglaries) while growth in persons-related and societal crimes are not affected.
Overall, the evidence suggests that P2P lending is an important new credit channel which can
have real effects.
Keywords: Consumer loans, household credit, peer-to-peer, social welfare.
1 Introduction
Well-functioning credit markets are important for economic vitality and social welfare. For
instance, consolidation among local banks increases credit market frictions which, in turn, leads
to rising crime (Garmaise and Moskowitz 2006). Yet, a recent resurgence in financial technology
(FinTech) has focused on relieving credit market frictions through the creation of online peer-to-
peer (P2P) markets. While these platforms have expanded the depth of credit markets, it remains
unclear whether they have had real effects. I show that P2P lending impacts social welfare by
moderating crime growth when households’ local credit markets suffer from imperfections due to
bank mergers..
Local banking institutions are traditionally important sources of credit to households due to
information and agency considerations (Petersen and Rajan 1995; Black and Strahan 2002; Han
and Li 2011). For instance, Guiso, Sapienza, and Zingales (2004) show that local financial devel-
opment is an important driver of local economic success even in environments with no frictions
to capital movements. Survey evidence from households and small businesses also indicates that
banking markets are still localized (Kwast, Starr-McCluer, and Wolken 1997; Petersen and Rajan
2002). Therefore, imperfections in households’ local lending channels can have real and social
impacts (Prager and Hannan 1998; Kahn, Pennacchi, and Sopranzetti 2005). Ultimately, the
evidence suggests that there should be benefits to improving credit markets through ameliorating
frictions.
P2P credit markets serve as alternative borrowing channels for households. By using on-
line platforms, these markets facilitate the direct matching of individuals seeking credit with
geographically-dispersed households willing to supply funds.1 The platforms also use technology
to assess and price credit risk. Importantly, individuals are increasingly using these new markets,
which are expected to continue growing, reaching nearly $150 billion per year by 2025 (Pricewa-
terhouseCoopers 2015).2 I posit that P2P loans can serve as an alternative credit source when
individuals’ local credit markets are subject to frictions.
1Institutions are also recently adopting lender roles on P2P platforms.2For context, the Center for Financial Services Innovation estimates that, in 2015, households spent $36.5 billion
on fees and interest for single payment credit products, such as payday loans, and $26.2 billion on fees and interestfor short-term installment loan products, which range from several months to two years in duration.
1
If individuals adopt P2P credit in response to market frictions, there are likely to be real
effects. One channel through which P2P credit can influence welfare is through reducing crime.
For instance, the criminology literature suggests that insufficient cash to meet current expenses
is a primary driver of burglaries (Wright and Decker 1994). While the financial economics of
crime literature is limited, initial evidence suggests that credit market frictions spur crime growth
(Garmaise and Moskowitz 2006) but access to alternative lenders, such as payday lenders, can
relieve financial constraints and reduce crime (Morse 2011).
Overall, I focus on two core hypotheses. First, I posit that households seeking credit will
turn to P2P markets when their local, traditional credit channels suffer from frictions. Second, I
hypothesize that access to P2P credit will impact social welfare by moderating the appreciation
in crime growth that is related to credit market imperfections.
To empirically test my hypotheses, I obtain data from 2007 through 2015 on bank transfor-
mations from the Federal Reserve. I use mergers between two non-failing banks to identify local
credit markets which are likely to be affected by frictions.3 I also obtain loan data from Lending
Club, the largest P2P lending market in the United States, to measure demand for P2P loans
in response to a bank merger.4 Finally, I rely on crime data from the National Incident Based
Reporting System (NIBRS), a reporting system used by law enforcement agencies and under the
jurisdiction of the Federal Bureau of Investigation (FBI), to assess the social welfare implications
of bank mergers and P2P credit.
I test my first conjecture by examining whether P2P loan volume responds to a bank merger.
For this analysis, I estimate panel regressions where the dependent variable is the log of the total
annual P2P loan volume within each three-digit zip code.5 The primary independent variable is
an indicator variable which takes a value of one if a bank merger occurred in the prior year, and
zero otherwise. Consistent with my hypothesis, I find that loan volume significantly increases in
the year after a bank merger, rising by about 28.60% or $353,957. The appreciation in borrowing
3A substantial literature ties bank mergers to less competitive credit markets (Akhavein, Berger, and Humphrey1997; Prager and Hannan 1998). As a result, mergers have been used to study the effects of bank competition ona range of socio-economic outcomes, including small business lending (Berger, Saunders, Scalise, and Udell 1998),loan contracts (Sapienza 2002), and crime (Garmaise and Moskowitz 2006).
4Lending Club loan data report borrowers’ location at the three-digit level. Therefore, references to zip codesare at the three-digit level throughout the paper.
5Lending Club reports borrowers’ locations at the three-digit zip code level. I use the terms three-digit zip Codeand zip code interchangeably.
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is not explained by variation in demographics, aggregate time trends, and unobservable time-
invariant local factors.
Given that households turn to P2P credit in response to bank mergers, I test my second
hypothesis, that crime growth due to a bank merger is moderated by access to P2P credit. To
do so, I employ two approaches. First, I conduct ordinary least squares (OLS) panel regressions
where the dependent variable is annual crime growth. The main independent variables are the
bank merger indicator variable and the log of the total annual P2P loan volume. I also interact
the merger indicator variable with P2P loan volume to capture the effects of P2P credit on crime
growth after a bank merger.
In line with my conjecture, the estimates from the baseline panel regressions show that crime
growth spikes after a bank merger, but P2P lending reduces the rise. Specifically, I find that a one
percent increase in loan volume offsets about 0.64 new crimes. The effect is robust to variation
in socio-economic factors, including gender, race, age, education, income, unemployment rates,
home values, and population size, as well as changes in the number of law enforcement agencies
participating in NIBRS. Overall, the collective evidence indicates that P2P credit markets can
reduce a negative externality due to frictions in traditional lending channels.
However, the panel regressions are subject to endogeneity concerns. For instance, expectations
of households’ future incomes may impact present-day decisions by local banks, households’ credit
demands, and crime. Yet, the empirical specification facilitates a counterfactual framework (Ney-
man 1923; Rubin 1974) to solve these econometric problems using a matching and differencing
approach (Heckman, Ichimura, and Todd 1997; Heckman, Ichimura, Smith, and Todd 1998). The
intuition being that difference-in-difference estimation provides unbiased effect estimates if, in the
absence of the treatment, the trend over time would have been the same between the treated
and control groups. A concern with this technique, however, is that the groups may differ in
ways related to their trends over time. Matching is employed to address this type of confounding
factor. This combined procedure has been adopted across a range of disciplines to identify causal
effects, including political science (Liberini, Redoano, and Proto 2017), ecology (Andam, Ferraro,
Pfaff, Sanchez-Azofeifa, and Robalino 2008), and financial economics (Malmendier and Tate 2009;
Morse 2011; Becker and Hvide 2017).
3
Extending this framework to my setting, the underlying modeling challenge is the evaluation of
the causal effects of bank mergers and P2P credit on crime growth. This requires a control zip code
where a bank merger did not occur and P2P credit is unavailable. To construct counterfactuals
for treated communities, those which experienced a bank merger and have access to P2P loans, I
identify zip codes which never experience a bank merger and exploit the restriction of access to
P2P credit in two states, Iowa and West Virginia. Residents of these states could not borrow in
the P2P market due to state regulatory restrictions related to security issuances. This plausibly
exogenous restriction of access allows me to construct counterfactuals for a portion of the treated
zip codes.
For communities which experience a bank merger, using the nearest-neighbor method with
replacement, I select a matched zip code from the pool of non-merger, non-lender communities. I
match based on socio-economic factors in the year prior to a bank merger to remove any economy-
wide fluctuations.6 I am able to match 164 lender communities which experienced a bank merger
to a counterfactual zip code. I then employ a difference-in-difference estimator on the matched
zip codes to isolate the causal effects P2P credit on crime growth.
Evidence from the difference-in-difference tests reinforce the findings from the panel regres-
sions, indicating that access to P2P impacts social welfare by moderating growth in crime following
a bank merger. Specifically, the estimates suggest that, following a merger, there are about 1,037
incremental incidents of crime. However, access to P2P loans mitigates a portion of the spike,
offsetting about 26.44% of the rise. Alternatively, a one percent increase in loan volume inhibits
about 0.52 new crimes.
In light of this primary evidence, I examine if the there are heterogeneous effects across
types of crime. I do so because the economics of crime literature suggests that economic factors
principally impact property-related crimes, as opposed to personal or societal crimes. I exploit
the depth of the NIBRS data to investigate the hypothesis that the moderation in crime growth
due to P2P access is driven by property crimes. To test this conjecture, I examine growth in
6The socio-economic variables underpinning the matching process include, house prices, per capita income, gen-der, the proportion of the residents which are African American, the percentage of residents whom are collegeeducated, the unemployment rate, and the size of the population. These variables are typically important deter-minants of economic conditions and households’ decisions (Campbell 2006), including P2P borrowing (Morse 2015;Bazley 2017) and crime (Freeman 1999).
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thefts, burglaries, and briberies, as these are property-related crimes. I also examine whether
assaults and homicides/manslaughters, both persons-related crimes, and weapon law violations,
a societal crime, are impacted. I re-perform both the panel regressions and matched difference-
in-difference tests using growth in each crime category as the dependent variable. Overall, I find
that growth in property-related crime and assaults increase after a bank merger but access to
P2P lending moderates the appreciation. Homicides/manslaughters and weapon law violations
are not consistently influenced by mergers or P2P credit.
While the findings are robust to known determinants of crime, a natural alternative hypothesis
could be that expected future crime increases may prompt present-day mergers and P2P credit
demand. This explanation implies that, if trending crime risks cause mergers and P2P borrowing,
future crime would be a reflection of the current crime trend. In essence, current crime growth
would spur contemporaneous bank mergers and P2P loan volume. First, I find that present-day
crime growth is significantly and positively correlated with crime growth in the next year. Given
this correlation, positive contemporaneous correlations between crime growth and mergers and
crime growth and P2P borrowing should exist under this alternative hypothesis. However, crime
growth does not significantly predict bank mergers during the same year. Crime growth also does
not significantly explain concurrent P2P loan volume. Overall, the evidence suggests that the
reverse causation theory is unlikely.
These findings contribute to several financial economics literatures, including an emerging
branch focusing on technology and financial innovation (Tufano 2003; Hauswald and Marquez
2003; Balyuk 2017). Peer-to-peer lending markets are a key innovation from the recent FinTech
revolution and their role in the U.S. financial system is subject to ongoing regulatory debate.
Foundational studies have principally focused on identifying determinants of borrowers’ funding
outcomes, including creditworthiness (Iyer, Khwaja, Luttmer, and Shue 2015), trustworthiness
(Duarte, Siegel, and Young 2012), and their local economic conditions (Ramcharan and Crowe
2013). I expand the literature by showing that P2P credit markets impact social welfare through
reducing crime growth in response to capital market frictions.
This paper also speaks to the financial economics of crime and criminology literatures. Build-
ing upon Becker’s (1968) descriptive model of crime, Lochner (2004) constructs a model in which
5
human capital increases the opportunity cost of crime from foregone work and incarceration.
Empirical studies support the link between income and wage earning opportunities and crime
(Patterson 1991; Levitt 2001). Recent evidence also indicates that capital market outcomes im-
pact criminal behavior. For example, Huck (2015) suggests that rising equity returns can spur
crime due to envy and wealth inequality. This study shows that effective credit markets can
alleviate crime through reducing financial frictions.
More broadly, this paper links to the banking (Erel 2011), credit (Agarwal, Chunlin, and
Souleles 2007; Tufano 2009; Zinman 2014; Melzer 2011) and household finance (Gross and Souleles
2002; Campbell 2006; Guiso and Sodini 2013) literatures. The results show that P2P markets
fill a missing rung in the credit ladder. Further, a principal component of the household finance
literature investigates individuals’ adoption of new financial products (Campbell 2006). I deepen
this literature by showing that frictions in traditional financial markets influences households’
engagement with a new capital market.
2 Literature Review
In this section, I review related work examining the relationship between crime and financial
economic factors. I also characterize the emerging literature on peer-to-peer lending. Building
upon these literatures, I develop the primary hypotheses of the study.
2.1 Economics of Crime
The dynamics between economic factors and crime have received considerable attention. A
principal area of focus has been the influence of employment opportunities on crime. While the
literature has found a positive association between the two, the strength of the relationship is not
typically strong (Freeman 1999; Cullen and Levitt 1999). More recently, Levitt (2001) documents
that a 1% increase in the unemployment rate is associated with an increase in property-related
crimes in the range of 1.4 to 2.7%. In line with this evidence, Grogger (1998) and Gould, Weinberg,
and Mustard (2002) show that limited legal wage-earning opportunities spurs criminal activities.
Income is also an important factor in individuals’ decisions to commit crimes. That is, households
turn to crime when facing poverty (Patterson 1991) and when income inequality increases (Kelly
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2000; Fajnzylber, Lederman, and Loayza 2002). However, Dills, Miron, and Summers (2010) point
out that fully understanding the determinants of crime will require significant further study.
The links between crime and capital markets have received less attention. The core of the
literature has centered on capital markets’ and firms’ responses to corporate crimes, including
fraud (Karpoff, Lee, and Martin 2008a; Karpoff, Lee, and Martin 2008b), bribery (Zeume 2017),
and insider trading (Acharya and Johnson 2010). However, Huck (2015) shows that movements in
equity markets impact crime through envy forms of utility (i.e., habit preferences). He finds that
a one standard deviation increase in equity returns is associated with about a 22.8o basis point
increase in crime. An emerging literature shows that developments in credit markets impact crime
rates as well. Garmaise and Moskowitz (2006) provide evidence that frictions in households’ local
credit markets, due to bank mergers, lead to rising crime. However, access to alternative sources
of financing, such as payday loans, can mitigate crime growth when individuals face financial
distress (Morse 2011). Overall, the literature indicates that financial markets impact not only
the economic welfares of households, but also their social welfares. Importantly, the evidence also
suggests that abatement of capital market frictions could have positive externalities.
2.2 Peer-to-Peer Lending
P2P credit markets have experienced significant growth in the recent past and are expected to
continue growing (Fitch Ratings 2014; PricewaterhouseCoopers 2015). Research into the dynamics
of peer-to-peer platforms is also rapidly increasing (Morse 2015). Foundational studies have
focused on determinants of borrowing outcomes. Iyer, Khwaja, Luttmer, and Shue (2015) show
that P2P lenders exhibit sophistication in assessing borrowers’ creditworthiness from financial
data. Yet, social cues and borrowers’ personal characteristics play important roles (Galak, Small,
and Stephen 2011; Duarte, Siegel, and Young 2012; Ravina 2012; Freedman and Jin 2014).
Borrowers’ local economic conditions, such as appreciating home values, also impact lenders’ and
borrowers’ decisions in P2P markets (Ramcharan and Crowe 2013; Bazley 2017).
The implications of P2P markets for traditional credit providers is a growing area of study as
well. For instance, recent evidence suggests that P2P lending generates information spillovers for
banks (Balyuk 2017). On the other hand, whether the rise of P2P markets has had real effects
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remains an open question.
2.3 Hypotheses
In light of the established literature, I construct two core hypotheses. First, it is reasonable
to hypothesize that P2P lending can serve as an alternative channel through which households
can borrow when their local credit markets are impacted by frictions, such as when banks merge.
Specifically, I posit that demand for P2P loans will increase in response to bank mergers. Second,
given the social welfare implications of credit market frictions, I conjecture that access to P2P
credit will offset, at least in part, the deleterious effects of bank mergers. Specifically, I posit that
access to P2P loans will influence social welfare through mitigating rises in crime following bank
mergers.
3 Data and Summary Statistics
I draw data from a variety of sources for this study. I briefly describe the data and provide
summary statistics for the key variables.
3.1 P2P Data
Lending Club is the largest P2P credit market in the U.S. To apply for a loan, an individual
specifies the amount of funds desired, the loan term (three or five years), the reason for borrowing,
and other personal details. Lending Club verifies the information and subsequently assigns an
interest rate to the loan based on a proprietary algorithm. Once the loan application is approved,
it is listed on the online platform and becomes available for investors to fund.7 Once listed on
the platform, loans typically receive full funding.8
Importantly, Lending Club publicly releases its loan data. I use a sample of 197,294 individual
loans with a total volume of approximately $2.950 billion issued between 2007 and year-end 2015.
The data include borrowers’ traditional credit statistics, such as FICO scores, income, and debt-
to-income, as well as their location of residence (at the three-digit zip code level). The data also
7Lending Club restricts applications to individuals with at least a 640 FICO score and a bank account. Thesecond largest P2P lender, Prosper, relies on these same base requirements.
8On average, a listed loan receives over 99% of the requested amount (Bazley 2017).
8
include information on borrowers’ loan details, including the amount, term, interest rate, and
repayments.
I measure annual demand for P2P credit using, P2P Loan Volume, the natural log of the total
loan volume within a zip code area. In Panel A of Table 1, I show that average yearly volume in
a zip code is about $1,237,613 based on about 83 loans. The average interest rate across loans is
about 12.84%. I include definitions of all variables in Table A1.
3.2 Crime Data
I rely on crime data from the National Incident Based Reporting System (NIBRS). The system
is under the jurisdiction of the Federal Bureau of Investigation (FBI) and is a voluntary system
used by U.S. law enforcement agencies for collecting and reporting crimes. NIBRS collects data on
each single incident and arrest within twenty-two offense categories composed of forty-six specific
crimes, termed Group A offenses. Each incident includes information on the type of offense,
victim, offender, location of the offense, date, and time.9 In Table A2, I examine whether law
enforcement agencies’ participation in NIBRS fluctuates over my sample period. Overall, I find
that participation is moderately increasing.
I utilize data from 2007 through 2015 spread across thirty-five states and 338 three-digit zip
code areas. To assess annual crime growth within each zip code area, I create a variable, Crime
Growth, which is the yearly growth in the total number of incidents across all forty-six Group A
categories. In Panel B of Table 1, I show that there are about 14,632 crimes each year in a zip
code area. Crime growth is also increasing in my sample, with about 42 additional crimes per year
within a zip code. I also separately examine growth across specific crime types, including thefts,
burglaries, incidents of bribery, assaults, homicides/manslaughters, and weapon law violations.
Thefts comprise the largest proportion of total crimes.
9In addition to the Group A offenses, the system includes eleven Group B offense categories for which only arrestdata are reported. Group B offenses include bad checks, loitering, disorderly conduct, driving under the influence,drunkenness, nonviolent family offenses, liquor law violations, peeping, trespassing, and a miscellaneous category.The estimates are not sensitive to including Group B offenses in the analysis.
9
3.3 Bank Data
Building upon Garmaise and Moskowitz (2006), I use bank mergers to identify local credit
markets which are likely impacted frictions. I, therefore, obtain information on all bank trans-
formations from the Federal Reserve. To measure the effect of bank consolidation on P2P credit
demand and crime, I construct an indicator variable, Bank Merger, which takes a value of one if
a merger occurred within a zip code area during the year. I identify bank mergers as mergers be-
tween two non-failing banks where one bank’s FDIC certificate is surrendered. Overall, I identify
389 bank mergers during my sample period.
3.4 Control Data
The criminology literature generally suggests several determinants of crime, including gender,
race, employment, and education (Flowers 1989). To account for these factors in my empirical
tests, I obtain data from a variety of sources. First, I obtain population demographic characteris-
tics for each zip code from the U.S. Census Bureau, including population size, median per capita
income, and the proportion of individuals that are male, African American, and have a graduated
from college. Wage earning opportunities also play a role in households’ decisions to commit
crimes (Grogger 1998; Gould, Weinberg, and Mustard 2002). I, therefore, obtain data on yearly
unemployment rates within zip codes from the Bureau of Labor Statistics. I also obtain annual
house price data from Federal Housing Finance Agency to account for the impacts of housing
market developments on crime (Cui and Walsh 2015).
4 Bank Mergers’ Impact on P2P Credit Demand
To begin, I first examine whether P2P credit serves as a substitute channel through which
individuals can borrow following a bank merger. I posit that as frictions in local credit markets
increase following bank mergers, households seeking credit can turn to the online P2P market.
Ultimately, this will lead to increases in P2P lending volumes.
To test this conjecture, I perform ordinary least squares (OLS) panel regressions which esti-
mate the effects of bank mergers on P2P loan volume in the year following the merger. Specifically,
invariant unobservable variables do not drive the effect since the results are robust to including
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zip code fixed effects (Column 3). Accounting for aggregate time trends (Column 4) also does not
subsume the effect. For instance, the estimates suggest that crime growth increases by about 382
incidents after a merger.
While the estimates for P2P Loan Vol. in Columns (1) and (2) indicate that areas with more
P2P borrowing also have more crime, accounting for unobservable time-invariant factors subsumes
the effect (Column 3). Similarly, the estimate for P2P Loan Vol. from specification (4), which
accounts for aggregate time trends, is not statistically different from zero.
Importantly, following a bank merger, P2P lending is associated with a significant decline in
crime growth. That is, access to P2P credit offsets a portion of the rise in crime associated with
a merger. Specifically, the estimates in Column (4) indicate that, within a zip code area, that a
one percent increase in loan volume reduces crime growth by 0.64 incidents. Based on the average
P2P loan volume response to a bank merger, about a 28.6% increase, P2P borrowing offsets about
18 new crimes.
Overall, the panel regression estimates suggests that the P2P credit market has the potential
to counteract the social costs associated with frictions in traditional capital markets. Specifically,
P2P lending increases following a bank merger which moderates the subsequent rise in crime
growth.
5.3 Matched Difference-in-Difference Methodology
The initial evidence points towards the P2P credit market serving as an alternative credit
channel when lending is restricted at traditional financial institutions. In turn, access to P2P
credit impacts social welfare by offsetting a portion of the growth in crime which stems from a bank
merger. However, the preceding analysis is subject to endogeneity and omitted variable concerns.
For instance, expectations of households’ future incomes may impact present-day decisions by
local banks, households’ credit decisions, and crime.
Yet, Eq.(2) does facilitate a counterfactual framework to solve these econometric problems us-
ing a matching and differencing approach. The counterfactual framework was pioneered in statis-
tics and program evaluation research by Neyman (1923) and Rubin (1974). The design assesses the
effect of a treatment against an estimation of the counterfactual, which would be the outcome had
13
the subject not been exposed to the treatment. Heckman, Ichimura, and Todd (1997) and Heck-
man, Ichimura, Smith, and Todd (1998) expand upon the matching-counterfactual framework by
incorporating difference-in-difference estimation. The intuition being that difference-in-difference
estimation provides unbiased effect estimates if, in the absence of the treatment, the trend over
time would have been the same between the treated and control groups. However, a concern with
this technique is that the groups may differ in ways related to their trends over time. Matching is
employed to address this type of confounding factor. This combined procedure has been adopted
across a range of disciplines to identify causal effects, including political science (Liberini, Re-
doano, and Proto 2017), ecology (Andam, Ferraro, Pfaff, Sanchez-Azofeifa, and Robalino 2008),
and financial economics (Malmendier and Tate 2009; Morse 2011; Becker and Hvide 2017).
Extending this framework to my setting, the underlying modeling challenge is the evaluation of
the causal effects of bank mergers and P2P credit on crime growth. This requires a control zip code
where a bank merger did not occur and P2P credit is unavailable. To construct counterfactuals
for treated communities, those which experienced a bank merger and have access to P2P loans, I
identify zip codes which never experience a bank merger and exploit the restriction of access to
P2P credit in two states, Iowa and West Virginia. Residents of these states could not borrow in
the P2P market due to state regulatory restrictions related to security issuances. This plausibly
exogenous restriction of access allows me to construct counterfactuals for a portion of the treated
zip codes.
For lender communities which experience a bank merger, using the nearest-neighbor method
with replacement, I select a matched community from the pool of non-merger, non-lender com-
munities. I match based on socio-economic factors in the year prior to a bank merger to remove
any economy-wide fluctuations.10 The socio-economic variables underpinning the matching pro-
cess include, house prices, per capita income, gender, the proportion of the residents whom are
African American, the percentage of residents who have graduated college, the unemployment
rate, a vector of age groups, and the size of the population. These variables are typically impor-
tant determinants of economic conditions and households’ decisions (Campbell 2006), including
P2P borrowing (Morse 2015; Bazley 2017) and crime (Freeman 1999).
10However, I retain year fixed to address any residual concerns about time effects.
14
I match 164 lender communities which experienced a bank merger to a counterfactual zip code.
Table 4 presents the results of the matching. For each variable, I report differences in the means
and t-statistics for tests of the hypothesis that the difference between zip codes with access to the
P2P market, in the year prior to a bank merger, and those that are restricted from borrowing,
in the same year, are zero. Among the ten variables, none have a difference in means that is
significantly different from zero. This suggests that the matched pairs are similar on observable
dimensions. I also examine whether crime growth, in the year prior to a bank merger, varies
significantly between the treated community its matched-control zip code and find no significant
difference. Given the reasonable matching results, I employ a difference-in-difference estimator to
isolate the causal effects of bank mergers and P2P credit on crime growth.
5.4 Matched Difference-in-Difference Results
I report estimates from the difference-in-difference analysis in Table 5. Column (1) shows
that a community which experienced a bank merger has higher crime growth in the next year
relative to similar community which did not have a merger. On the other hand, the estimate for
P2P Area, an indicator variable which takes a value of one if the zip code has access to the P2P
credit market, is not statistically significant. This suggests that the availability of P2P loans is
not associated with higher crime growth. Importantly, the estimate for the interaction between
Bank Merger and P2P Area is negative and statistically significant, indicating that P2P market
access reduces crime growth following a merger.
While the matching results indicate that the aligned zip code pairs are similar, in Column
(2), I include control variables to adjust for any residual bias and increase efficiency. Including
the socio-economic controls does not materially impact the estimates. I find that a merger is
associated with about 1,037 incremental crime incidents while the net effect of access to P2P
lending inhibits about 275 incidents. This is a reduction of about 26.54% of the new incremental
crimes due to a merger.
To be consistent with the baseline tests, in Columns (3) and (4), I report estimates using
P2P Loan Vol. instead of the indicator variable, P2P Area. The principal conclusions remain the
same when using P2P Loan Vol. Crime growth increases following a merger, but P2P lending
15
moderates the rise. That is, the cumulative effect of a one percent increase in volume reduces
crime growth by about 0.52% after a merger.
The collective evidence from the panel regressions and the difference-in-difference tests indi-
cates that bank mergers incite growth in crime. However, access to P2P credit has an offsetting
effect. That is, the P2P credit market impacts social welfare by moderating the growth in crime
due to frictions in traditional lending channels.
6 Additional Evidence
The evidence from the primary tests indicates that access to P2P credit markets have social
welfare implications. In light of the findings, several additional points warrant investigation.
6.1 Crime Types
The criminology literature suggests that there is heterogeneity in the determinants of crimes
across crime types. For instance, economic variables are generally linked to property crimes, such
as theft, as opposed to violent crimes, like homicides (Levitt 2001; Raphael and Winter-Ebmer
2001). In addition, Wright and Decker (1994) note that many burglars identify insufficient cash to
meet their current expenses, i.e., insufficient liquidity, as a principal driver of their crimes. In light
of this, I examine how property, personal, and societal crimes respond following a bank merger
and whether P2P lending has heterogeneous impacts across crime types. I posit that property
crimes will increase following a bank merger, but that access to P2P credit will moderate the
rise. Conversely, I expect personal and societal crimes to be little influenced by mergers and P2P
lending.
To test these conjectures, I examine growth across several types of crimes. I identify property-
related, personal, and societal crimes according to their Uniform (UCR) Offense Codes through
NIBRS.11 To measure property-related crimes, I construct three variables. First, Theft Growth
is the yearly growth in the number of pocket-picking, purse-snatching, and shoplifting crimes.
11Specifically, NIBRS classifies crimes into three categories: (i) property, (ii) persons, and (iii) societal. Accordingto NIBRS, crimes against persons are those whose victims are always individuals. The objective of crimes againstproperty is to obtain money, property, or some other benefit. Crimes against society represent society’s prohibitionagainst engaging in certain types of activity.
16
Second, Burglary Growth is the yearly growth in the number of burglary and breaking and entering
crimes. Finally, Bribery Growth is the annual growth in bribery crimes.
I also create three variables related to crimes against persons and society. Specifically, Assault
Growth is the annual growth in aggravated and simple assaults and intimidation crimes. Next,
Homicide/Manslaughter Growth is the yearly increase in murder/non-negligent manslaughter,
negligent manslaughter, and justifiable homicide crimes.12 Lastly, I calculate the annual growth
in weapon law violations as a measure of societal crimes.
I re-perform both the OLS panel regressions and the matched difference-in-difference tests
using the aforementioned crime growth variables as dependent variables. I report estimates for
property crimes in Panel A of Table 6. The principal conclusion from the estimates is that
property-related crime growth increases after a bank merger, but access to P2P lending markets
moderates the rise. Specifically, the estimates from both the OLS panel regressions and difference-
in-difference tests show that growth in thefts (Columns 1 and 2) increases in the year following a
bank merger. That is, the difference-in-difference estimate for Bank Merger in Column (2) shows
that about 129 additional thefts occur in the year after a bank merger.
Importantly, the estimate for the interaction term, Bank Merger × P2P Loan Vol., is negative
and statistically significant, indicating that rising P2P borrowing counteracts the spike and offsets
a portion of the growth in thefts. Based on the average increase in borrowing after a merger, the
estimates imply that P2P lending offsets about 1.98% of the incremental thefts. I find similar
effects for growth in burglaries, where about 1.59% of the new crimes due a merger are offset.
Growth in briberies is also reduced but the effect is economically small.
In Panel B, I report estimates related to growth in personal and social crimes. Assaults signif-
icantly increase after a bank merger, with about 254 incremental incidents (Column 2). However,
about 1.52% of the new assaults are offset based on the average increase in P2P borrowing. This
finding is interesting in light of the limited evidence linking economic factors and personal crime.
However, given that assaults typically occur at home between individuals who know each other,
access to credit may moderate stress and other visceral factors (e.g., Card and Dahl (2011)),
ultimately, reducing assaults. On the other hand, the estimates for homicides/manslaughters and
12The results are not sensitive to excluding justifiable homicides.
17
weapon law violations indicate that bank mergers and P2P borrowing have limited impact on
growth in these types of crimes.
Overall, the evidence points towards P2P lending principally offsetting growth in crimes related
to property. That is, P2P credit alleviates local capital market frictions and reduces growth in
crimes where the objective is to obtain money, property, or some other benefit. Conversely, access
to P2P credit has limited impact on severe personal and societal crimes.
7 Robustness
In this section, I perform additional tests to investigate the robustness of the primary findings.
7.1 Reverse Causality
I focus on mergers between non-failing banks to alleviate reverse causality concerns related to
crime, bank mergers, and P2P credit demand. Nevertheless, a hypothesis could be that expected
future crime increases may prompt present-day mergers and P2P credit demand. An implication
of this conjecture is that, if trending crime risks cause mergers and P2P borrowing, future crime
would have to be a reflection of the current crime trend. In sum, current crime growth would
spur contemporaneous bank mergers and P2P loan volume. First, I note that crime growth is
positively correlated with crime growth in the next year (estimate = 0.205; p-value < 0.001).
In light of this correlation, I should find positive contemporaneous correlations between crime
growth and mergers and crime growth and P2P borrowing under this alternative hypothesis.
In Table 7, I directly address whether contemporaneous crime growth impacts merger activity
and P2P borrowing by regressing Bank Merger (probit regressions) and P2P Loan Vol. on the
concurrent change in crime. In Columns (1) and (2), crime growth does not significantly predict
a bank merger during the same year. In Columns (3) and (4), crime growth also does not
significantly explain concurrent P2P loan volume. Collectively, the evidence suggests that the
reverse causation theory is unlikely.
18
8 Conclusion
Imperfect credit markets impact households’ welfares. The recent rise in P2P lending markets
have the potential to further complete credit markets and alleviate traditional frictions. However,
the real effects of P2P markets remains unclear. I show that P2P lending impacts social welfare
by moderating crime growth when households’ local credit markets suffer from imperfections.
Using a matched difference-in-difference empirical methodology and mergers between non-
failing banks to identify local credit markets which are likely to be affected by frictions, my tests
indicate that households turn to P2P loans in response to a merger. More importantly, P2P lend-
ing offsets about 25% of the spike in crime growth which follows a merger. I also I find heteroge-
neous effects across types of crimes. Growth in property-related crime and assaults increase after
a bank merger but access to P2P lending moderates the appreciation. Homicides/manslaughters
and weapon law violations are not consistently influenced by mergers or P2P credit.
Overall, this paper contributes to a range of financial economics literatures, including an
emerging branch focusing on technology and financial innovation. I also shed light on the dy-
namics between financial markets and crime. Finally, the findings in this study have important
implications for the ongoing debate about the role of P2P financial markets in the economy.
19
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Table 1: Descriptive Statistics
The table presents summary statistics for the primary data sets used in the study. Panel A reports statistics forcrimes. Panel B presents statistics related to P2P loans. P2P Loan Volume is the natural log of the total loan volume withina zip code area in a year. Number P2P Loans is the natural log of the number of P2P loans within a zip code area in ayear. Panel C presents statistics describing the demographics and economic characteristics for the three-digit zip code areas.Definitions for all variables used in the analysis are included in Appendix I.
Panel A: P2P Credit Statistics
Mean St. Dev. Min. Median Max. N
Total P2P Loan Volume 1,237,613 3,541,534 0.00 57,900 73,347,104 2,383
P2P Loan Vol. 8.26 6.36 0.00 10.97 18.11 2,383
Total Number P2P Loans 82.79 204.49 0.00 6.00 4,309 2,383
Number P2P Loans 2.28 2.19 0.00 1.95 8.37 2,383
Interest Rate 12.84 1.84 0.00 12.978 23.63 2,383
Panel B: Crime Statistics
Mean St. Deviation Min Median Max N
Total Crimes 14,632 22,811 2.00 7,355 254,718 2,383
Total Crime Growth 41.55 2,892 -24,676 -39.00 77,989 2,383
Weapons Law Violations 116.42 230.29 0.00 44.00 2,888 2,383
Weapons Law Violations Growth 2.38 43.02 -345.00 0.00 864.00 2,383
Number Law Agencies 179.79 172.20 1.00 132.00 1,248 2,383
Panel C: Socio-economic Statistics
Mean St. Dev. Min. Median Max. N
African American 8.43 10.91 0.00 3.87 70.84 2,383
Age 15 - 24 13.64 2.40 9.53 13.18 27.40 2,383
Age 25 - 44 24.54 2.83 17.48 24.42 45.43 2,383
Age 45 - 64 27.70 2.45 19.18 27.17 37.01 2,383
College Educated 28.52 10.27 7.34 27.37 74.30 2,383
House Prices 130.58 21.72 62.26 128.65 238.83 2,383
Income 26,930 5,915 12,961 25,940 64,424 2,383
Male 49.25 1.05 46.23 49.14 55.02 2,383
Population 179.85 244.73 2.81 95.43 2,799 2,383
Unemployment Rate 7.32 2.78 1.27 6.86 21.50 2,383
24
Table 2: Effects of Bank Mergers on P2P Credit Demand
The table presents estimates from panel regressions of the effects of bank mergers on peer-to-peer credit demand.The dependent variable in the regressions is P2P Loan Vol., the natural log of the P2P loan volume within the zip codeduring the year. The main explanatory variable is Bank Merger, which takes a value of one during the year following a bankmerger, and zero otherwise. Other variables are defined in Appendix I. The estimates for Income have been multiplied by1,000 and the estimates for Population have been multiplied by 10,000. Standard errors are clustered by at the zip codelevel and t-statistics are presented in parentheses. Significance at the 10%, 5%, and 1% levels are denoted by *, **, and ***,respectively.
(1) (2) (3) (4)
Bank Merger 0.321** 0.315*** 0.302*** 0.286***
(2.34) (4.17) (4.12) (4.60)
African American 0.014*** 0.014*** 0.019***
(3.27) (3.29) (7.74)
Age 15 - 24 0.005 0.007 0.015
(0.17) (0.22) (0.91)
Age 25 - 44 0.019 0.025 0.042***
(0.65) (0.82) (2.63)
Age 45 - 64 -0.046 -0.042 -0.010
(-1.20) (-1.08) (-0.45)
College Educated -0.006 -0.006 -0.009*
(-0.84) (-0.86) (-1.90)
House Prices -0.007*** -0.006** -0.007***
(-2.67) (-2.37) (-5.62)
Income 0.001*** 0.069*** 0.077***
(4.51) (4.30) (8.14)
Male 0.063* 0.058* 0.040*
(1.78) (1.67) (1.80)
Population 0.000*** 0.010*** 0.008***
(5.96) (6.31) (8.04)
Unemployment Rate 0.018 0.016 0.010
(0.88) (0.74) (0.79)
Year FE N N Y Y
Zip Code FE N N N Y
N 2,383 2,383 2,383 2,383
Adj. R-sq. 0.008 0.135 0.688 0.691
25
Table 3: Effects of P2P Credit on Crime After a Bank Merger: Ordinary Least Squares
The table presents estimates from panel regressions of the effects of peer-to-peer credit on crime growth. The de-pendent variable in the regressions is the growth in total crimes. The main explanatory variable is Bank Merger × P2PLoan Vol., the natural log of the P2P loan volume within the zip code during the year following a bank merger. The otherindependent variables include Bank Merger, which takes a value of one during the year following a bank merger, and zerootherwise, and P2P Loan Vol., the natural log of the P2P loan volume within the zip code during the year. Other variablesare defined in Appendix I. Standard errors are clustered by at the zip code level and t-statistics are presented in parentheses.Significance at the 10%, 5%, and 1% levels are denoted by *, **, and ***, respectively.
(1) (2) (3) (4)
Bank Merger 187.713 324.360* 434.512* 381.983*
(1.00) (1.70) (1.95) (1.72)
P2P Loan Vol. 25.232** 36.790*** 20.042 28.100
(2.33) (2.78) (1.50) (1.65)
Bank Merger × P2P Loan Vol. -50.616** -58.006*** -74.915*** -63.755**
(-2.46) (-2.75) (-2.91) (-2.50)
African American -19.530** -14.853 -21.439
(-2.52) (-1.05) (-1.63)
Age 15 - 24 24.293 147.446 265.053
(1.19) (1.10) (1.23)
Age 25 - 44 39.424 237.689* 347.131
(1.33) (1.84) (1.54)
Age 45 - 64 32.331 189.197 337.363
(0.90) (1.07) (1.13)
College Educated -9.722 -20.108* -10.662
(-1.20) (-1.83) (-0.87)
House Prices 8.496** 6.420 -5.943
(2.51) (0.74) (-0.49)
Income 0.004 0.009 0.001
(0.25) (0.34) (0.05)
Male -49.744 -160.564** -140.692**
(-1.06) (-2.13) (-2.01)
Law Agencies 0.056 9.158 9.216*
(0.08) (1.24) (1.82)
Population -0.207 0.019 0.049
(-0.85) (0.07) (0.20)
Unemployment Rate -7.115 -46.258* -31.659
(-0.27) (-1.77) (-1.08)
ZIP Code FE N N Y Y
Year FE N N N Y
N 2,383 2,383 2,383 2,383
Adj. R-sq. 0.003 0.007 0.097 0.146
26
Table 4: Matching Results
The table presents results from the matching of lender communities which experienced a bank merger (treatment)with non-lender zip codes which did not experience a merger (control). Communities are matched in the year prior to amerger. Column (1) reports the difference in means between matched treatment and control communities. Column (2)presents t-statistics for difference in means tests between treatment and control communities. Significance at the 10%, 5%,and 1% levels are denoted by *, **, and ***, respectively. Definitions of all variables are presented in Appendix I.
(1) Difference (2) T -statistic N
African American 4.234 1.65 328
Age 15 - 24 -0.934 -1.16 328
Age 25 - 44 3.053 1.44 328
Age 45 - 64 0.087 0.17 328
College Educated -3.119 -1.18 328
House Prices 2.817 1.01 328
Income -30.043 -0.07 328
Male -0.247 -0.76 328
Population 5.010 1.42 328
Unemployment Rate 1.070 1.40 328
Crime Growth 86.525 0.28 328
27
Table 5: Effects of P2P Credit on Crime After a Bank Merger: Matched Difference-in-Difference
The table presents estimates from matched difference-in-difference regressions of the effects of peer-to-peer credit oncrime growth. The dependent variable in the regressions is Crime Growth, the annual growth in crime. Bank Merger isequal to one if a bank merger occurred within the zip code area in the prior year, and zero otherwise. In columns (1) and(2), the main explanatory variable is Bank Merger × P2P Area, which indicates the effect on crime growth of having accessto P2P credit in the year after a banker merger. Specifically, P2P Area takes a value of one if P2P loans are available inthe zip code, and zero otherwise. In columns (3) and (4), the main explanatory variable is Bank Merger × P2P Loan Vol..P2P Loan Vol. is the natural log of the P2P loan volume within the zip code during the year. Columns (2) and (4) reportestimates from regressions which include the control variables used in Table 3. All regressions include year fixed effects andmatched pair-group fixed effects. Pair-group fixed effects are dummy variables specific to each matched set of communities.Standard errors are clustered by at the zip code level and t-statistics are presented in parentheses. Significance at the 10%,5%, and 1% levels are denoted by *, **, and ***, respectively.
(1) (2) (3) (4)
Bank Merger 1089.837*** 1037.159*** 1342.575*** 1326.055***
(4.74) (3.82) (4.34) (3.48)
P2P Area 92.127 299.124
(0.44) (1.11)
Bank Merger × P2P Area -642.756** -574.392*
(-2.02) (-1.89)
P2P Loan Vol. 29.772 29.233
(0.94) (0.93)
Bank Merger × P2P Loan Vol. -88.646** -81.287**
(-2.18) (-2.07)
Controls N Y N Y
Pair FE Y Y Y Y
Year FE Y Y Y Y
N 644 644 644 644
Adj. R-sq. 0.252 0.285 0.266 0.297
28
Table 6: Effects of P2P Credit on Crime After a Bank Merger: Crime Types
The table presents regression estimates of the effects of P2P credit on growth in crimes across crime types. Thedependent variables in the regressions are the growth in crimes by type. The main explanatory variable is Bank Merger× P2P Loan Vol., the natural log of the P2P loan volume within the zip code during the year following a bank merger.The other independent variables include Bank Merger, which takes a value of one during the year following a bank merger,and zero otherwise, and P2P Loan Vol., the natural log of the P2P loan volume within the zip code during the year.The regressions also include the socio-economic controls used in Table 3. Columns (1), (3), and (5) report estimatesfrom OLS regressions which include year and zip code fixed effects. Columns (2), (4), and (6) report estimates fromdifference-in-difference regressions which include year and pair-group fixed effects. Standard errors are clustered by at thezip code level and t-statistics are presented in parentheses. Significance at the 10%, 5%, and 1% levels are denoted by *, **,and ***, respectively.
Table 7: Contemporaneous Crime Growth’s Impact on Mergers and P2P Volume
The table presents estimates of the effects of contemporaneous crime growth on bank mergers and P2P borrowing.Columns (1) and (2) report marginal effects from probit regressions where the dependent variable is Bank Merger. BankMerger is equal to one if a bank merger occurred within the zip code area during the year, and zero otherwise. Columns (3)and (4) present estimates from OLS panel regressions where the dependent variable is P2P Loan Vol., the natural log of theP2P loan volume within the zip code during the year. The primary independent variable in the regressions is Crime Growth,the yearly growth in total crimes during the year. The estimates for Crime Growth have been multiplied by 10,000. Thecontrol variables used in the regressions are the same as those in Table 3. Standard errors are clustered by at the zip codelevel and z -statistics or t-statistics are presented in parentheses. Significance at the 10%, 5%, and 1% levels are denoted by*, **, and ***, respectively.
(1) Bank Merger (2) Bank Merger (3) P2P Loan Vol. (4) P2P Loan Vol.
Crime growth 0.007 0.018 0.260 0.271
(0.26) (0.69) (1.28) (1.34)
Controls N Y N Y
Year FE N N Y Y
Zip Code FE N N Y Y
N 2,383 2,383 2,383 2,383
Pseudo/Adj. R-sq. 0.007 0.036 0.779 0.780
30
Table 8: Effects of P2P Credit on House Prices After a Bank Merger - MAY NOT USE
The table presents regression estimates of the effects of P2P credit on growth in crimes across crime types. Thedependent variables in the regressions are the growth in crimes by type. The main explanatory variable is Bank Merger× P2P Loan Vol., the natural log of the P2P loan volume within the zip code during the year following a bank merger.The other independent variables include Bank Merger, which takes a value of one during the year following a bank merger,and zero otherwise, and P2P Loan Vol., the natural log of the P2P loan volume within the zip code during the year.The regressions also include the socio-economic controls used in Table 3. Columns (1), (3), and (5) report estimatesfrom OLS regressions which include year and zip code fixed effects. Columns (2), (4), and (6) report estimates fromdifference-in-difference regressions which include year and pair-group fixed effects. Standard errors are clustered by at thezip code level and t-statistics are presented in parentheses. Significance at the 10%, 5%, and 1% levels are denoted by *, **,and ***, respectively.
House Prices House Price Growth
(1) OLS (2) D-i-D (3) OLS (4) D-i-D
Bank Merger -1.542 -1.918 -1.471** -0.913
(-1.03) (-0.51) (-2.54) (-1.14)
P2P Loan Vol. 0.103 0.235 -0.009 -0.117
(0.94) (1.05) (-0.21) (-1.63)
Bank Merger × P2P Loan Vol. 0.080 0.028 0.097** 0.192***
(0.66) (0.11) (2.02) (2.65)
Controls Y Y Y Y
Year FE Y Y Y Y
Zip Code/Pair FE Y Y Y Y
N 2383 644 2383 644
Adj. R-sq. 0.866 0.394 0.515 0.488
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Appendix I: Variable Definitions
This table describes the variables used in the empirical analysis.
Primary Dependent Variables Definition
Crime Growth The growth in the total number of crimes within the three-digit zip code
area during the year. The data are from the National Incident-based Re-
porting System.
Assault Growth The annual growth in aggravated assaults, simple assaults, and intimi-
dation crimes within a three-digit zip code area. The UCR codes are
131, 132, and 133, respectively.
Burglary Growth The yearly growth in burglary and breaking and entering crimes. The
UCR code is 220.
Bribery Growth The annual growth in bribery crimes. The UCR is 510.
Homicide/Manslaughter Growth The yearly increase in murder/non-negligent manslaughter, negligent man-
slaughter, and justifiable homicides. The UCR codes are 91, 92, and 93,
respectively.
Theft Growth The annual growth in the number of pocket-picking, purse-snatching, and
shoplifting crimes. The UCR codes are 231, 232, and 233, respectively.
Weapon Law Violation Growth The yearly growth in weapon law violations which are violations of laws
or ordinances prohibiting the manufacture, sale, purchase, transportation,
possession, concealment, or use of firearms, cutting instruments, explosives,
incendiary devices, or other deadly weapons. The UCR code is 520.
Key Explanatory Variables Definition
Bank Merger An indicator variable which equals one if a bank merger occurred within
the three-digit zip code area during the year, and zero otherwise.
Number P2P Loans The natural log of the number of loans issued within the three-digit zip
code during the year.
P2P Loan Volume The natural log of the total amount of loans issued within the three-digit
zip code during the year.
Control Variables Definition
African American The annual proportion of the three-digit zip code population that is Af-
rican American. Data are from the U.S. Census Bureau.
Age 15 - 24 The percentage of the three-digit zip code population that is between 15
and 24 years old. Data are from the U.S. Census Bureau.
Age 25 - 44 The percentage of the three-digit zip code population that is between 25
and 44 years old. Data are from the U.S. Census Bureau.
Age 45 - 64 The percentage of the three-digit zip code population that is between 45
and 64 years old. Data are from the U.S. Census Bureau.
College Educated The annual proportion of the three-digit zip code population that has
graduated from college. Data are from the U.S. Census Bureau.
House Prices The annual three-digit zip code-level All-transactions House Price Index-
es. The data are from the Federal Housing Finance Agency.
Income Median per capita income among households within the three-digit zip
code during the year. Data are from the U.S. Census Bureau.
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Variable Definitions – Continued
Control Variables Definition
Male The annual proportion of the three-digit zip code population that is male.
Data are from the U.S. Census Bureau.
Number Law Agencies The number of law enforcement agencies within each zip code area that
participate in NIBRS during the year.
Population The annual, three-digit zip code-level population. The data are from the
U.S. Census Bureau and scaled by 1,000.
Unemployment Rate The annual, three-digit zip code-level unemployment rate. The data are
from the Bureau of Labor Statistics.
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Table A1: NIBRS Participation
The table presents reports statistics for law enforcement agencies participation in NIBRS during my sample period.Number Zip Codes is the annual mean number of three-digit zip codes, within the corresponding state, during mysample period. Law Agencies is the annual mean number of law enforcement agencies participating in NIBRS withinthe corresponding state. Law Agencies Growth is the yearly average change in the number of law enforcement agenciesparticipating in NIBRS.
State Num. Zip Codes Law Agencies Law Agencies Growth
