Lost in the Mail: A Field Experiment on Corruption

Marco Castillo*Georgia Institute of Technology

Ragan PetrieGeorgia State University

Maximo ToreroInternational Food Policy Research Institute

Angelino VisceizaInternational Food Policy Research Institute

September 2008

Abstract: Efficient markets require the free and safe movement of goodsand services. Corruption in the mail sector, therefore, can hamper the de-velopment of commerce and electronic markets in particular. We present theresults of a field experiment designed to detect corruption in the privately-runPeruvian mail sector and to measure its differential impact across popula-tions. We subtly manipulate the content and information available in severalpieces of mail sent to households across Lima and detect not only high levelsof shirking but of corruption as well. Twenty-one percent of the mail contain-ing money and 15% percent of the mail not containing money never arrivedat its destination. Interestingly, we find that middle-income neighborhoods,rather than the rich or poor neighborhoods, are the most likely to suffer fromcrime. Our study demonstrates two issues. There are barriers to market ex-pansion in developing countries due to corruption borne out of moral hazardthat privatization has been unable to extricate, and those who are the targetsof corruption are not equally distributed across the population.

* We thank David Solis for conducting the follow-up survey and Cesar Ciudad for coordinating the

mail recipients. Seminar participants at Georgia Institute of Technology and the 2007 North American

Economic Science Association Meetings in Tucson gave helpful comments.

1. Introduction

Economic reasoning suggests that neither the participation in illegal ac-

tivities nor the diseconomies caused by criminal activities are expected to

be uniformly distributed across the population (Becker, 1968). Several au-

thors have provided evidence that the social and private benefits and costs

associated with commiting crime are important to determine its incidence

(Erlich, 1973; Levitt, 1997; Duggan and Levitt, 2002; Glaeser, Scheinkman

and Sacerdote, 1996; Jacob and Lefgren, 2003; Di Tella and Schargrodsky,

2003, 2005; Olken, 2007, Fisman and Miguel, 2007, Reinikka and Svensson,

2004; among others). Deterrance, the risk of being caught, and social norms

all seem to be important factors in deciding whether to commit a crime or

not.

Looking at the provision of public goods in developing countries, recent

research shows that corruption is not only widespread but can create impor-

tant inefficiencies and inequities (Bertrand, Djankov, Hanna, Mullainathan,

2006; Reinikka and Svensson, 2004). In an environment where the provision

of public goods is privatized, it is not clear if corruption is lessened or not.

We show that in the presence of moral hazard, even private firms providing

public services can face a significant level of corruption. Importantly, we

show that this has equity consequences. The cost of crime is not equally

shared by all citizens — the middle class suffers from corruption the most.

We use a unique field experiment that allows us to develop a behavioral

measure of crime. The design measures which segments of the population are

more likely to suffer from crime and whether this conforms with economic

rationality broadly understood. Our study concentrates on the delivery of

mail, and we do so for several reasons. First, the existence of a reliable mail

sector is considered to be instrumental in the growth of electronic trade.

Second, mail services are widely used by all segments of the population.

Third, as we will describe, mail delivery is amenable to field experimentation

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with little or no intrusion. Fourth, mail delivery is a highly decentralized

activity likely to suffer from moral hazard problems regardless of ownership.

For instance, sources of lost non-certified mail are nearly impossible to detect.

Finally, crime in the mail sector is expected to be highly correlated with the

expected gains and losses of committing a crime and much less with social

pathologies. That is, corruption in the mail sector is a crime of opportunity

that can allow us to understand the economic motivation behind crime.

We develop a novel and simple empirical strategy to measure the probabil-

ity that a piece of mail arrives at its destination. We send identical envelopes

to different household in Lima, Peru from two American cities and record ar-

rivals. The experiment includes a large population of volunteer households

across neighborhoods of different socio-economic backgrounds. To better un-

derstand the motivation behind the commission of crime, we manipulated

the contents, the sender of mail and the gender of the recipient. In particu-

lar, every household was sent four envelopes over the course of a year. Two

envelopes had a sender with a foreign name and two had the last name of

the sender and recipient matched. Finally, one of each of the two envelopes

contained a small amount of money and the other did not. All these mod-

ifications were as subtle as possible and the order in which each different

envelope was sent was random.

By manipulating the information made available to the person handling

the mail, our design allows us to test several hypothesis behind the commis-

sion of crime. First, mail can be lost because the cost of delivery is larger

than the cost of being caught shirking. Lost mail might be a reflection of

apathy rather than crime. Therefore, comparing rates of lost mail contain-

ing money with those not containing money permits us to detect if crime is

taking place. Second, if those handling mail behave strategically, one would

expect that they will make use of information on the social distance between

the recipient and the sender and the characteristics of the recipient. There-

fore, comparing similar pieces of mail across subgroups can potentially reveal

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the expectations of those handling the mail.

The experiments show first that the mail service in Peru is highly ineffi-

cient. The overall rate of mail lost is 18%.1 The loss rate, however, hides the

fact that mail containing money is lost 21% of the time while mail containing

no money is lost 15% of the time. That is, we find evidence of shirking as

well as crime. The quality of service is not independent of socio-economic

status. Mail without money is lost 18% of the time when sent to a poor

neighborhood but only 10% of the time when sent to a middle-class neigh-

borhood. We confirm that given the geographical distribution of post offices,

this cannot be attributed to a lack of manpower.

The results contribute to the literature on the economic reasons behind

crime. They show that subtle changes in the information made available

can have large and meaningful economic effects. The observed behavior is

consistent with strategic thinking. Mail is lost whenever it is more likely to

contain valuable contents. But, service is also worse among the poor. The

emerging picture is of an agent that commits a crime whenever it is more

likely to yield benefits and that adjusts service depending on the recipient.

All things constant, mail sent to a woman in a poorer neighborhood is more

likely to be lost than that sent to a man.2

Our approach has several advantages. By randomly assigning treatments

to different populations, it provides the necessary counterfactuals to test the

presence of strategic behavior. The study also avoids potential biases due

to experimenter effects by using an existing service that likely suffers from

moral hazard problems. By using a widely used service and careful selection

of the sample, our study overcomes the criticisms of lack of external validity.

1Compared to the less than 0.5% of mail reported lost in the U.S. or the U.K., this isvery large. Note that loss rates in the U.S. and the U.K. are for reported mail lost. Thiswill underestimate the problem if not all mail lost is reported. Our experimental measureis for all mail lost that should have arrived at a destination.

2Difference in mail loss with or without money in poorer neighborhoods is negligible. Itis unlikely that the gender differences in mail received are due to expectations on containingvaluable contents.

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Finally, our study provides a behavioral measure of crime that avoids common

measurement problems and underreporting.

Our research makes several contributions. First, it speaks to the problems

that developing countries face when trying to solve inefficiencies through pri-

vatization of public services. Private firms suffer the same asymmetric infor-

mation that governments do. The fact that subtle changes in the characteris-

tics of envelopes generate adjustments in behavior suggests that mechanisms

based on random checks (Becker and Stigler, 1974) might be too difficult or

costly to implement. For instance, the presence of electronic devices to mon-

itor behavior might be easy to detect, thus triggering good behavior on the

part of the worker. Second, our research presents new evidence that crime is

not shared equally. The middle class is taxed more heavily by corruption. Fi-

nally, our research shows that market failure due to asymmetric information

looms large in developing countries.

The paper is organized as follows. The next section presents a model of

crime. Section 3 presents the experimental design and Section 4 the results.

In Section 5, we run robustness checks, and Section 6 concludes.

2. Modeling Crime

We present a highly stylized model of interactions between consumers and

mailmen to illuminate our hypotheses on crime. The model abstracts from

the detailed description of individual decision making as presented in Becker

(1968) and Erlich (1973). Instead, it attempts to capture the observed level

of crime as an equilibrium phenomenon.

Table 1 summarizes the decision a consumer of characteristic x needs to

make: send a simple piece of mail with nothing of value or send something

valuable. It also summarizes the decision of a mailman: deliver the mail

or steal/destroy the content of the mail. A simple piece of mail gives a

consumer a utility equivalent to v(x) if it arrives at its destination and a

utility of −l(x) if it gets lost. A piece of mail containing something of value

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gives a consumer a utility equivalent of V (x) if it arrives at its destination

and a utility of −L(x) if it gets lost. A mailman opening a piece of mail willsuffer a, probabilistic, cost of t(x) if he steals and a benefit of P (x) if the

piece of mail has valuable content.

We assume that V (x) > v(x), L(x) > l(x) and P (x) > t(x). These

assumptions imply that players do not have a dominant strategy and that

the unique equilibrium is in mixed strategies. It can be shown that, in

equilibrium, consumers send valuable mail with probability p = t(x)P (x)

and

mailmen steal/destroy mail with probability q = V (x)−v(x)(V (x)−v(x))+(L(x)−l(x)) .

Table 1The Mail GameDeliver (1− q) Cheat (q)

No value (1− p) v(x), 0 −l(x),−t(x)Valuable (p) V (x), 0 −L(x), P (x)− t(x)

The best-response functions of consumers and mailmen are monotone.

The probability of sending valuable mail is decreasing in the probability

that mail is stolen, and the probability of stealing mail is increasing in the

probability of finding valuable mail. The model suggests that across markets,

as x varies, the equilibrium level of valuable mail sent will be proportional

to the mailmen’s cost of being caught stealing and inversely related to how

valuable the content of the mail is to the mailmen. That is, areas where

punishment for crime is high or likely will engender more trust in the mail

sector. Regarding levels of crime, the model predicts that those areas where

the marginal gains from having mail delivered are relatively larger than the

marginal losses associated with lost mail will experience larger levels of crime.

This says that, in equilibrium, areas where there are larger gains from using

standard mail will experience larger levels of crime.

Note that if the incentives (cost and benefits) of mailmen do not vary

across areas (x) we would expect that a random sample of opened mail

would produce similar proportions of mail with valuables. Similarly, if the

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incentives (cost and benefits) of consumers do not vary across areas (x) we

would expect that a random sample of sent mail across areas would produce

similar patterns of lost mail.3 If variable x is observable, we could infer

from the patterns of lost mail the implicit incentives mailmen perceive. For

instance, if mail sent by family members is observed to be more likely to

be lost, this would indicate that mailmen think that family members have a

larger incentive to send valuable mail through standard mail. Across income

groups, if the marginal gain from sending mail of value is large relative to the

perceived loss, we should observe higher levels of crime. So, neighborhoods

with many alternatives for sending valuables are less likely to suffer from

crime.

The model above allows variables in x to contain characteristics of the

sender as well as the piece of mail itself. For instance, a letter sent to a

family member could produce larger returns to the sender than a letter sent

to a non-family member. In equilibrium, these pieces of mail will be more

likely to be opened. Moreover, this effect might vary across areas. Family

members of certain economic backgrounds might have larger potential gains

from sending mail to family members if they lack alternative ways to transfer

valuables. These consumers will experience more delinquency in equilibrium.

3. Experimental Design

The experiment provides a behavioral measure of crime. We send en-

velopes from the United States to Peru through the normal mail services in

both countries (U.S. Postal Service and the Peruvian postal service, respec-

tively). We use a list of residential addresses in metropolitan Lima, Peru that

are geographically representative of poor, middle, and high income neighbor-

hoods. A resident of each address is the recipient of the envelope and reports

3It is important to note that if the best-response of consumers and mailmen aremonotonic, but nonlinear, the incentives of both consumers and mailmen will be reflectedin equilibrium.

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to us if the envelope arrives or not.

The 2 x 2 design we employ allows us to address our research questions

by varying the contents of the envelope and the sender’s name. The contents

of the envelope either contains two $1 bills folded in half or no money. The

sender’s name is either a foreign name (i.e. J. Tucker, M. Scott) or the

same family name as the recipient (i.e. M. Sosa, L. Cordova).4 The design

is outlined in Table 2 and includes the number of envelopes sent in each

treatment.

Table 2Experimental Design

Contents of EnvelopeSender Last Name Money No MoneyForeign n=136 n=131Family n=135 n=139

To get a valid estimate of crime, it is important that the envelope look like

something that would normally be sent in the mail. So, we chose an opaque

solid-colored envelope and card (of the same color). The envelope looks

like one that would be sent for a birthday. Keeping with that idea, on the

inside of each card, we handwrite “Happy Birthday” or “Feliz Cumpleanos” —

depending on the return addressee’s name — and sign Josh or Mike or Marco

or Luis. We do this because if the card is stolen or opened, we want it to

appear, to the mailman, like it was actually sent by the person whose name

appears on the front of the card. Figure 1 gives examples of two of the

envelopes that were sent to the same address in the course of the study. To

preserve confidentiality, we have blacked out the addresses of the sender and

recipient and the recipient’s first name. The first envelope gives an example

of mail sent by a foreigner to a recipient and the second envelope gives an

example of mail from a family member to a recipient.

4In South America, including Peru, everyone has two last names. The first is the lastname from the father and the second is the last name of the mother. We use the first lastname.

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Because the envelope is opaque, the greeting inside the card cannot be

seen. If the card contains money, this also cannot be seen, even if held up

to the light. One can, however, feel that there is something in the envelope

because the folded two $1 bills make a very subtle bump. It is impossible

to determine what exactly is in the envelope.5 But, there is a hint that

the envelope contains something other than the card. We chose this subtle

manipulation so that anyone thinking of stealing the envelope would need

to be looking carefully for signs that the envelope contained something that

might be worth stealing.

5We could very well have placed folded pieces of paper in the envelope instead of money,but we want the envelopes and contents to be realistic, especially in case the envelope gotlost. This is in line with the long-held tradition in experimental economics of avoidingsubject deception.

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Figure 1. Examples of Envelopes Sent(To preserve confidentiality, addresses and first names are blocked)

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All envelopes have handwritten addresses, stamps for postage and an

airmail stamp on the front of the envelope. There are two return addresses

in Atlanta and two inWashington, DC. The envelopes are glued shut, making

it impossible to steam open, reseal and deliver. Envelopes are always mailed

from one of two locations. Envelopes with a return address from Atlanta were

mailed from the main post office in downtown Atlanta, and envelopes with

a return address of Washington DC were mailed from a post office mailbox

in Washington DC. The color of the envelope, the return address and the

handwriting on the envelope are randomized across the four treatments.6

Envelopes were sent in November 2006, June-August 2007, and November

2007.

Mailboxes in Peru are secure and not exposed to theft from people passing

by on the street. Typically there is a mail slot that places the mail inside

the locked residence or in a box inside a locked gate. Mail is not left in post

boxes on the streets, as is the case in the U.S.

To find recipient addresses, we tapped into two networks of people who

engage in research to recruit volunteers willing to receive the cards and report

to us. The two networks include people from a variety of demographic and

income groups. The important design element for us was that the addresses

where the mail was sent were geographically diverse. So, even though the mail

recipients may know one another, the addresses are disperse across locations.

To minimize the number of addresses in the study for any given post office, no

more than three households were within a 1-kilometer radius of each other.

Recipients of the mail reported the arrival or non-arrival of each enve-

lope and kept any money if an envelope with money arrived. They were

instructed to not ask the mailman about the card or go to the post office

to enquire. To compensate recipients for their time and help, at the end of

the experiment, we conducted a lottery with cash prizes for recipients who

6We did this to insure that each envelope sent to a household by a different person wasindeed handwritten by a different person.

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reported. Recipients knew of the lottery before we began sending envelopes.

We conducted a follow-up survey in December 2007 to verify mail receipt

responses and collect more individual data on mail recipients. All previous

responses were confirmed.7

4. Results

We would like to know the patterns of mail loss geographically and across

various demographic characteristics. We first turn to a description of the

sample.

4.1. Sample Selection

Table 3 shows descriptive statistics on the individual and geographical

characteristics of the mail recipients. The sample is split roughly half and

half between male and female recipients. The distribution of residents across

low, middle and high-income neighborhoods is not evenly distribution, with

more people living in middle-income neighborhoods.8 Most recipients have

a university education, are married or living with their partner and have a

family member that lives in the United States. This latter result is important

as it makes receipt of a card from the United States not seem strange and

also attests to the degree of mail that could potentially come from the United

States. Recipients have lived in their current residence for an average of 16.5

years, and the nearest post office is three minutes away.

7We asked the recipient if they received an envelope during a certain period of timeand asked the recipient to report the return address and color of the envelope. Recipientswere able to correctly confirm reports from 4-5 months earlier. This gives us confidencethat the reported data is accurate.

8Low-income neighborhods are ones where the percent of the population living belowthe poverty line ($1/day) is 30% or higher. Middle-income neighborhoods are those wherethe percentage is between 10-30%, and high-income neighborhoods are those where thepercentage is less than 10%. We show later that our results are robust to other definitionsof economic status.

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Table 3Descriptive Statistics of Sample Receipients

Percent Std Dev # ObsMale Recipients 47.5 141Low Income 34.8 49Middle Income 39.7 56High Income 25.5 36Age (mean, years) 37.2 10.1 124University Education 57.4 136Married or Cohabitating 44.1 136Family size (mean, number) 4.1 1.5 124Family in U.S. 47.1 136Time in Residence (mean, years) 16.5 0.5 124Minutes to Post Office (mean) 3.0 8.4 140Note: some variables have missing values because of survey non-response.

An important component of our experimental design, in addition to a di-

verse and representative distribution of individual mail recipient characteris-

tics, is that the distribution of recipient addresses is geographically disperse

across neighborhoods and post offices and is representative of metropolitan

Lima. Figure 1 shows the geographical distribution of residents in our study.

The residents cover the majority of the city. There are fewer residents in

some of the peri-urban areas of the city, but the addresses are nicely dis-

tributed across neighborhoods. This gives us observations across most areas

of Lima and confidence that our results apply to the larger, city-wide mail

sector.

4.2. Results

Turning to loss rates, we see that mail service in Lima is inefficient and

subject to crime. Table 4 shows loss rates overall and by income groups.

Overall, 18% of all envelopes sent through the mail never arrived at their

destination. Envelopes with money were less likely to arrive than envelopes

without money, so it does not appear that mail loss is solely due to bad ser-

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vice. This hints more of criminal activity. Over 21% of envelopes with money

did not arrive, whereas 14.8% of envelopes without money did not arrive.

This 50% increase in loss is statistically significant (one-side p-value=0.023).

Table 4Loss Rates

Overall and by Income Groups (in percent)Percent # Obs

Overall 18.1 98Money 21.4 58No Money 14.8 40Low Income 18.9 49Middle Income 20.4 56High Income 13.5 36

Contents of EnvelopeMoney No Money

Foreign 19.8 16.0Family 23.0 13.7

How was mail lost across our four treatments? The bottom panel of

Table 4 shows loss rates by the contents of the envelope and the sender’s

last name. Again, envelopes with money were more likely to be lost than

those without money, whether or not the sender’s last name was foreign or

a family name. The difference between money and no money envelopes for

envelopes with a foreign sender is not significantly different, but the almost 10

percentage point difference for envelopes with a family last name is (one-sided

p-value=0.023). Indeed, most loss happened for envelopes with money sent

by a family member. Almost one-quarter of those envelopes never arrived.

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Table 5Loss Rates by Income Groups (in percent)

Low Income Middle Income High IncomeOverall 18.9 20.4 13.5Money 19.8 25.7 16.9No Money 18.0 15.3 10.0

Foreign Sender Name 18.9 18.3 16.4Family Sender Name 18.9 22.4 10.3

Male Recipient 14.3 20.0 20.4Female Recipient 21.8 20.8 1.9

Across low, middle and high-income neighborhoods, mail is lost at differ-

ent rates. Table 5 shows residents in middle-income neighborhoods lose mail

at the highest rate, 20.4%, and those in high-income neighborhoods lose mail

at the lowest rate, 13.5%. The difference betweeen low and middle-income in-

come neighborhoods compared to high-income neighborhoods is significantly

different.9

One might wonder if these loss rates can be attributed to the Peruvian

mail service or to the U.S. Postal Service. The results in Table 5 suggest that

lost mail is happening on the Peruvian side. While it may be reasonable to

think that envelopes with money might be lost on the U.S. side, it is highly

unlikely that the significantly different loss rates we see across low, middle

and high-income neighborhoods is due to the U.S. Postal Service. Such loss

rates cannot exist without knowledge of neighborhoods in Lima. The next

section provides further evidence of this.

Conditioning on the contents of the envelopes, mail with money is signif-

icantly more likely to be lost than without money in middle-income neigh-

borhoods. In middle-income neighborhoods the loss rate of envelopes with

money is over 10 percentage points larger than for envelopes without money.9One-sided t-tests yield p-values of 0.098 comparing low to high-income loss rates and

0.045 comparing middle to high-income loss rates.

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The loss rate in poor neighborhoods is around 18% but does not change much

for envelopes with money. High-income neighborhoods have an almost 7 per-

centage point increase for envelopes with money, but this is not significantly

different. This pattern of loss is consistent with our equilibrium model of

crime where the poor are not expected to be sending valuables by mail. The

loss rates in poor neighborhoods seem to be more a reflection of poor service.

However, loss rates in middle-income neighborhoods are consistent with a

population that would have valuable items sent through the mail and do

not have alternatives. And, indeed, the loss of valuable mail is significantly

higher. High-income neighborhoods do experience loss, but search is lower

as the rich probably have alternatives for sending valuables.

The pattern of loss across neighborhoods is primarily driven by envelopes

where the sender’s last name is the same as the family. Table 5 illustrates

this. This result is important because it suggests that those handling the mail

attribute a similar probability of being caught when disposing of mail sent

by non-family members. Since other factors do not seem to be as important

when the mail is sent by a foreigner, this comparison gives a measure of the

effect of adding money to the envelope.

Across the gender of the recipient, women suffer larger losses in poor

neighborhoods and men suffer larger losses in rich neighborhoods. The loss

rate for women in poor neighborhoods is 50% higher than that for men, and

the loss rate for men in high-income neighborhoods is ten times higher than

that for women. It is important to note that there are more women in poor

neighborhoods than men and more men in high income neighborhoods than

women. Since we do not find other significant treatment effects that could

explain the differential treatment of men and women in poor neighborhoods,

we conclude that those handling the mail perceive that mishandling women’s

mail in poor neighborhoods carries less risk.

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Table 6Loss Rates

by Money and Income Groups (in percent)Envelope with MoneyLow Income Middle Income High Income

Foreign Sender Name 21.7 21.1 17.1Family Sender Name 18.1 30.2 13.9

Male Recipient 17.6 27.1 22.7Female Recipient 21.4 24.6 3.7

Envelopes with No MoneyLow Income Middle Income High Income

Foreign Sender Name 14.6 14.9 16.7Family Sender Name 17.1 14.5 3.3

Male Recipient 12.1 12.8 16.7Female Recipient 18.4 16.4 0.0

Table 6 allows us to calculate a difference-in-difference estimate of the

effect of income on crime. The presence of money in envelopes sent by a

family member increases the rate of mail lost by 15.7 percentage points in

middle-income neighborhoods but by only 1 percentage point in poor neigh-

borhoods. In other words, people are 14.7 percentage points more likely to

keep envelopes sent to middle-income neighborhoods when there is suspicion

of valuable content. A comparison of the richer neighborhoods and poorer

neighborhoods give a smaller estimate (9.3). This reduction can be explained

by either the perceived existence of safe alternatives in richer neighborhoods

or a perceived larger risk of being caught. The first reason would imply that

those in richer neighborhoods are less likely to send valuables in the mail,

all things constant, and the second implies that those delivering mail will

perceive a smaller return of committing a crime. Note that the nonlinear

effect of income on the loss rate of envelopes with money holds across several

populations.

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5. Robustness Checks

This section presents regression analysis of mail loss rates to test the

robustness of the results to omitted variables and specification assumptions.

Table 7 presents probit regression marginal effects for a dummy variable that

equals 1 if the mail did not arrive at its destination and 0 otherwise. Each

regression presents the effect of covariates on different subpopulations.10

The first column in Table 7 confirms our first basic result. Envelopes con-

taining money are 6.4% more likely to get lost and lost rates have a nonlinear

relationship with neighborhood income (percent classified as poor). The sec-

ond column shows that the effect of neighborhood income is stronger for the

envelopes with money. The regressions dividing the population receiving en-

velopes from family and non-family members confirm that it is the envelopes

coming from family members that are more likely to get lost. The last two

columns of Table 7 show that men are more likely to be targeted if envelopes

contain money. Finally, note that the patterns of lost mail do not seem to

respond to the proximity of post offices. The number of minutes it takes to

get to the closest post office is not significant.11

We collected detailed socio-economic information on most of the mail re-

cipients. We did this because mailmen might be aware of the characteristics

of the mail recipients and respond accordingly. Table 8 reproduces the re-

sults of Table 7 but adds covariate information of receipients. The loss in

observations is due to survey nonresponse.

Table 8 shows that the basic results hold after controlling for receipient

covariate information. The regressions show that neighborhood income is

nonlinearly related to loss rates and that the content and information of the

envelopes matter. These regressions, however, further confirm the strategic

10The results are robust to autocorrelation and other specifications.11The number of minutes to the closest post office is based on an accessibility model

which calculates the least cost path surface (based on time) from any place, using GIS. Theaccessibility measure uses three different levels of roads with different speeds of movement.

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reasoning behind corruption. While holding weakly, envelopes with money

are lost more frequently the further the post offices is from the receipient’s

house. Households with larger families and with families in the US are also

less likely to be affected by loss. Finally, the likelihood of being a victim

of crime falls with the time in residence. All this confirms that lost mail is

likely due to strategic reasons and unlikely to originate in the U.S.

Table 7Probability of Mail Loss

Probit Regressions - Marginal Effects

All Money No Money Family Foreign Man WomanMoney 0.064* 0.100** 0.029 0.118** 0.018

(0.065) (0.043) (0.550) (0.022) (0.693)Family 0.004 0.032 -0.022 -0.026 0.028

(0.894) (0.523) (0.606) (0.586) (0.518)Man 0.030 0.073 -0.013 0.002 0.058

(0.379) (0.151) (0.772) (0.973) (0.225)Percent Poor 0.011** 0.013* 0.008 0.011 0.012 0.010 0.022***

(0.035) (0.076) (0.257) (0.130) (0.110) (0.233) (0.004)Percent Poor -0.000* -0.000* -0.000 -0.000 -0.000 -0.000 -0.000**Squared (0.053) (0.069) (0.395) (0.196) (0.115) (0.165) (0.011)

Minutes to 0.001 0.004 -0.001 0.000 0.003 0.006 0.001Post Office (0.482) (0.206) (0.729) (0.971) (0.323) (0.509) (0.598)

Mailing number 0.001 -0.005 0.006 0.011 -0.009 0.016 -0.013(1-4) (0.959) (0.817) (0.761) (0.606) (0.679) (0.481) (0.530)

N 537 269 268 272 265 258 279Log Likelihood -246.15 -134.63 -109.44 -124.90 -119.98 -119.79 -120.38

Note: p-value in parentheses. *p-value < 0.10, **p-value < 0.05, ***p-value < 0.01.Variables: Money=1 if envelope contained money, Family=1 if sender’s last name was thesame as recipient’s, Man=1 if recipient was a man, Percent Poor=percent of population inneighborhood below poverty line ($1/day), Minutes to post office=number of minutes fromresidence to closest post office, Mailing number=1 if first mailing, =2 if second mailing,etc.

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Table 8Probability of Mail Loss

Probit Regressions - Marginal Effects

All Money No Money Family Foreign Man WomanMoney 0.049 0.100 0.019 0.107** -0.002

(0.170) (0.104) (0.706) (0.047) (0.962)Family 0.010 0.038 -0.015 -0.019 0.038

(0.757) (0.444) (0.743) (0.697) (0.377)Man 0.052 0.105** -0.009 0.020 0.086*

(0.147) (0.049) (0.855) (0.693) (0.088)Percent Poor 0.017*** 0.026*** 0.010 0.017** 0.018** 0.008 0.032***

(0.002) (0.002) (0.173) (0.032) (0.020) (0.373) (0.000)Percent Poor -0.000*** -0.001*** -0.000 -0.000* -0.000** -0.000 -0.000***Squared (0.006) (0.002) (0.356) (0.066) (0.026) (0.425) (0.001)

Minutes to 0.002 0.005 -0.001 -0.000 0.004 0.009 -0.000Post Office (0.369) (0.116) (0.759) (0.983) (0.221) (0.708) (0.685)

Mailing number 0.004 0.008 0.001 0.012 -0.000 0.010 -0.002(1-4) (0.820) (0.726) (0.951) (0.590) (0.989) (0.677) (0.927)

Family Size -0.019 -0.017 -0.021 -0.021 -0.015 -0.061*** 0.023(0.139) (0.391) (0.218) (0.256) (0.417) (0.003) (0.207)

Married -0.063* -0.054 -0.066 -0.058 -0.066 -0.025 -0.068(0.077) (0.296) (0.159) (0.244) (0.183) (0.646) (0.134)

Time in Residence -0.004*** -0.006*** -0.002 -0.002 -0.005** -0.002 -0.005***(0.008) (0.006) (0.328) (0.251) (0.010) (0.319) (0.007)

Family in U.S. -0.065* -0.072 -0.061 -0.077 -0.057 -0.038 -0.070(0.061) (0.164) (0.184) (0.122) (0.245) (0.482) (0.117)

N 492 246 246 251 241 240 252Log Likelihood -216.28 -110.92 -100.03 -112.28 -101.58 -10.43 -98.07

Note: p-value in parentheses. *p-value < 0.10, **p-value < 0.05, ***p-value < 0.01.Variables: Money=1 if envelope contained money, Family=1 if sender’s last name was thesame as recipient’s, Man=1 if recipient was a man, Percent Poor=percent of populationin neighborhood below poverty line ($1/day), Minutes to post office=number of minutesfrom residence to closest post office, Mailing number=1 if first mailing, =2 if secondmailing, etc., Family size=number of members in recipient’s family, Married=1 if marriedor cohabitating, Time in Residence=number of years lived at recipient’s address, Familyin US=1 if recipient’s household has a family member living in the U.S.

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6. Conclusions

Using a novel behavioral measure of crime, we examine corruption in the

mail sector in Lima, Peru. We hypothesize that the very nature of mail

delivery gives an opportunity to those who handle the mail to “lose” mail if

it is beneficial to do so. Our design allows us to differentiate poor service

from targeted crime and to investigate what information is pertinent in crime

and who suffers the most from it.

We have several key findings. First, loss rates are very high. Over 18%

of all mail sent never arrived at its destination. Second, this high loss rate is

partially explained by poor service but not completely. Envelopes containing

money were 50% more likely to be lost than those without money. So, lost

mail is not random. Third, when the sender’s last name matched the recipi-

ent’s last name, the mail was almost twice as likely to be lost if it contained

money. Clearly, those who handle the mail are looking for clues that might

suggest that an envelope holds something of value. Fourth, middle-income

neighborhoods suffer the highest loss rates and high-income neighborhoods

suffer the lowest. This result (and the previous) lends support for the crime

occurring in Peru rather than the U.S. since it would require the U.S. Postal

Service to know which neighborhoods were rich or poor. Finally, women

in low-income neighborhoods lose more mail than men, and men in high-

income neighborhoods lose more mail than women. This result suggests that

the poor handling of mail for women in poor neighborhoods carries little risk

for the mail carrier.

Put together our results suggest a model of crime where those who handle

the mail are looking for items of value to steal. Moreover, crime is not

independent of the characteristics of the receipients.

While our study cannot speak to the presence of large inefficiencies in all

the sectors dealing with the transaction of goods and services, it highlights

the large barriers to market development that developing economies face.

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Our study therefore shows that private firms providing public services face

incentives problems due to moral hazard in the same way the government

does. The nature of the good seems to be as important as the nature of

ownership. The sophistication in criminal activity found in our study suggest

that inexpensive, reliable alternatives and affordable monitoring might be

difficult to obtain. This suggests that incentives problems prevent market

development as well as inefficiencies in governance.

21

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Figure 1: Distribution of Addresses Across Lima, Peru

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