by Ashantha Ranasinghe - University of Toronto T-Space...which agents choose between becoming an entrepreneur and a worker, and where a crim inal group (CG) can extort the proceeds

Idiosyncratic Distortions, Misallocation and TFP Losses

by

Ashantha Ranasinghe

A thesis submitted in conformity with the requirementsfor the degree of Doctor of PhilosophyGraduate Department of Economics

University of Toronto

c� Copyright 2013 by Ashantha Ranasinghe

Abstract

Idiosyncratic Distortions, Misallocation and TFP Losses

Ashantha Ranasinghe

Doctor of Philosophy

Graduate Department of Economics

University of Toronto

2013

How resources are allocated across plants is crucial for understanding cross-country out-

put and productivity di↵erences. This thesis contributes to the growing literature on

resource misallocation by studying the particular channels through which misallocation

can arise. In Chapter 1, I examine extortion at the plant-level, its e↵ects on individual

incentives to become an entrepreneur, and how production is a↵ected by the presence

of extortion. I show that extortion is especially burdensome on moderate-ability en-

trepreneurs forcing them to either forgo entry into entrepreneurship or produce at an

ine�ciently small scale. When property rights are weak, the frequency of extortion is

higher producing a society where much of it’s entrepreneurial talent is heavily under-

utilized. In Chapter 2, I study plant-level distortions and it’s e↵ects on incentives to im-

prove productivity. I build a model where plant innovation improves future productivity

so that productivity dynamics are endogenous. Distortions that are tied to productivity

are introduced to the model to examine how plant innovation is a↵ected. All plants lower

innovation resulting in a distribution over productivity that is right-skewed and a distri-

bution over plant size that is left-skewed, consistent with empirical findings in developing

countries. The final Chapter is closely related to Chapter 1 but is more empirically fo-

cused. I study the role of theft as a means to explaining the abundance of small plants

in developing countries and estimate the causal e↵ect of theft on plant capital demand.

I find that plant capital would be significantly higher if theft is eliminated.

ii

Acknowledgements

I am grateful to Diego Restuccia, Margarida Duarte and Geuorgui Kambourov for their

guidance and supervision over the past few years. I would also like to express gratitude

to those who contributed with stimulating discussions and insights at various stages

of this thesis: Pedro Bento, Gustavo Bobonis, Branko Boskovic, Loren Brandt, Elton

Dusha, Yosh Halberstam, Burhan Kuruscu, Josh Lewis, Rob McMillan, Angelo Melino,

Peter Morrow, Carolyn Pitchik, Prashan Ranasinghe, Richard Rogerson, Shouyong Shi,

Aloysius Siow, Trevor Tombe and Xiaodong Zhu.

iii

Contents

1 Property Rights and Extortion 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Facts related to Extortion . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.2 Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.3 Agent and CG Decisions . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.4 Timing and Solution Concept . . . . . . . . . . . . . . . . . . . . 12

1.3.5 Comparative Statics . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3.6 Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.3.7 Special Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.4.1 Model Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.4.2 Aggregation and TFP . . . . . . . . . . . . . . . . . . . . . . . . 19

1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.5.1 Aggregate and TFP E↵ects . . . . . . . . . . . . . . . . . . . . . 20

1.5.2 E↵ects Across Agents . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.6.1 Why is Extortion more than just a tax on capital? . . . . . . . . . 27

1.6.2 Role of Government Policy . . . . . . . . . . . . . . . . . . . . . . 29

1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2 Policy Distortions, Plant-level Innovation and TFP 35

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.2 Plant-level Distortions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.3.1 Incumbents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.3.2 Dynamic Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

iv

2.3.3 Plant Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.3.4 Timing of Events . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.3.5 Law of Motion – Plant Type . . . . . . . . . . . . . . . . . . . . . 44

2.3.6 Household Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.3.7 Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.4.1 Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.4.2 Productivity Shocks . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.5 Policy Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.5.1 Distortions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

2.5.2 Varying the Aggregate Innovation to Output Ratio . . . . . . . . 53

2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.6.1 Endogenous and Exogenous Dynamics . . . . . . . . . . . . . . . 55

2.6.2 Uniform and Correlated Distortions . . . . . . . . . . . . . . . . . 56

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.8 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

2.8.1 Altering the Fraction of Plants that are Taxed . . . . . . . . . . . 60

2.8.2 Taxing Low-productivity Plants . . . . . . . . . . . . . . . . . . . 60

2.8.3 Taxing Moderate-productivity Plants . . . . . . . . . . . . . . . . 61

3 Theft and Capital Misallocation 63

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3.2 Prevalence of Theft in South America . . . . . . . . . . . . . . . . . . . . 65

3.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.3.1 Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.3.2 Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.3.3 Firm and CG Decisions . . . . . . . . . . . . . . . . . . . . . . . . 69

3.3.4 Optimal Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4.1 Enterprise Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4.2 Data on Theft, Protection and Capital . . . . . . . . . . . . . . . 72

3.5 Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.5.1 Estimation Equation . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.5.2 Instrument for Theft and Protection . . . . . . . . . . . . . . . . 74

3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.6.1 OLS Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

v

3.6.2 IV Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Bibliography 79

vi

List of Tables

1.1 Extortion in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Parameter Values and Targets . . . . . . . . . . . . . . . . . . . . . . . . 18

1.3 Model Fit by Capital Distribution . . . . . . . . . . . . . . . . . . . . . . 18

1.4 Property Rights and Losses from Extortion . . . . . . . . . . . . . . . . . 21

1.5 Decomposing changes in TFP and Output . . . . . . . . . . . . . . . . . 22

1.6 Extortion and a tax on Capital . . . . . . . . . . . . . . . . . . . . . . . 28

1.7 Protection Subsidy and a Tax on Profit . . . . . . . . . . . . . . . . . . . 30

2.1 Obstacle to Doing Business – Plant-size . . . . . . . . . . . . . . . . . . . 40

2.2 Obstacle to Doing Business – Plant-productivity . . . . . . . . . . . . . . 41

2.3 Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.4 Losses due to Correlated Distortions . . . . . . . . . . . . . . . . . . . . 51

2.5 Correlated Distortions: High and Low Innovation Economies . . . . . . . 54

2.6 Decomposing TFP Losses . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.7 Uniform and Correlated Distortions . . . . . . . . . . . . . . . . . . . . . 57

2.8 Fraction of productive plants that are taxed . . . . . . . . . . . . . . . . 60

2.9 TFP Losses when Distortions are Negatively-correlated . . . . . . . . . . 61

2.10 Distortions on Moderate-productivity Plants . . . . . . . . . . . . . . . . 62

3.1 Theft as an obstacle to doing business . . . . . . . . . . . . . . . . . . . 66

3.2 Major Obstacles to doing business . . . . . . . . . . . . . . . . . . . . . . 67

3.3 OLS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.4 IV Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.5 Theft as an obstacle to doing business: developed countries . . . . . . . . 79

vii

List of Figures

1.1 Correlation: Property Rights and Extortion . . . . . . . . . . . . . . . . 7

1.2 Correlation: GDP per Capita and Extortion . . . . . . . . . . . . . . . . 8

1.3 Comparative Statics: Extortion and Property Rights . . . . . . . . . . . 14

1.4 E↵ects of extortion across agents when � = 0.7 . . . . . . . . . . . . . . . 23

1.5 E↵ects of extortion across agents when � = 0.53 . . . . . . . . . . . . . . 24

1.6 E↵ects of extortion across agents when � = 0.45. . . . . . . . . . . . . . . . 25

1.7 First-best and distorted entrepreneur capital demand . . . . . . . . . . . 26

1.8 Distribution over plant-size: A missing middle . . . . . . . . . . . . . . . 27

1.9 A Tax on Capital and Capital Demand . . . . . . . . . . . . . . . . . . . . 29

2.1 Plant Size Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.2 Plant Growth and RD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.3 Changes in Plant-level Innovation . . . . . . . . . . . . . . . . . . . . . . 52

2.4 Distribution over Plant-size . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.5 Distribution over Productivity: High-innovation Economy . . . . . . . . . 54

2.6 RD Spending by Plant Size when low-productivity plants are taxed . . . . . . 61

3.1 Correlation: Theft and GDP per capita . . . . . . . . . . . . . . . . . . . 67

3.2 Correlation: Theft-perception and Property Rights . . . . . . . . . . . . 79

3.3 Correlation: GDP per capita and Property Rights . . . . . . . . . . . . . 80

viii

Chapter 1

Property Rights, Extortion and the

Misallocation of Talent

Abstract:Motivated by empirical evidence that there are di↵erences in extortion rates across es-

tablishments, I show that extortion is an important channel for understanding resource

misallocation. I construct a model in which agents choose between entrepreneurship and

working, and where a criminal group can extort entrepreneurial capital. While property

rights are common across agents, extortion rates arise endogenously as an inverted ‘u’

shape in agent ability. In economies characterized by weak property rights, extortion is

prevalent and resource misallocation can generate TFP and output losses of 10 and 30

percent. These losses arise from two channels: selection and misallocation. Extortion

a↵ects selection by altering the ability threshold required for entry into entrepreneurship,

while misallocation occurs because entrepreneurs operate below the optimal scale. As

property rights become weaker, extortion rates are higher and TFP and output losses

from selection and misallocation are magnified.

1

Chapter 1. Property Rights and Extortion 2

1.1 Introduction

A large literature has emphasised that resource misallocation is an important channel

for understanding TFP di↵erences across countries. A central theme to emerge is that

resource misallocation is especially large when distortions are idiosyncratic across estab-

lishments.1 While the literature has examined how specific policies can be distortionary,

little attention has been devoted to the role that institutions can have in creating and per-

petuating distortions that are idiosyncratic across establishments. This Chapter focuses

on a particular class of institutions, namely property rights, their relation to extortion,

and their impact on TFP and aggregate output through resource misallocation.

Though uncommon in the western world, empirical evidence suggests that extortion

is a major hindrance for establishments in developing economies.2 As shown in Table 1.1

(see Section 1.2), extortion payments are an obstacle for doing business, occur frequently

and vary in magnitude across establishments. For example, some establishments do not

face extortion, while for others extortion payments can be as much as 15 percent of

their annual sales. Moreover, there is variation in average extortion payments across

countries and these payments are inversely related to indices of property rights (see

Figure 1.1, Section 1.2). Given the variation in extortion payments, both across and

within countries, how important is extortion for understanding resource misallocation,

and in turn, TFP and output losses?

To see why output and TFP losses can be substantial, consider an environment in

which agents choose between becoming an entrepreneur and a worker, and where a crim-

inal group (CG) can extort the proceeds from entrepreneur production.3 Suppose prop-

erty rights are perfect and there is no opportunity for a CG to extort from entrepreneurs.

Then only the most productive agents become entrepreneurs and production is at op-

timal capacity, yielding the first-best outcome. Now consider the case when property

rights are no longer perfect and where the CG has an opportunity to extort a share of

the proceeds from entrepreneur production. Output and TFP losses can arise from two

potential sources: selection and misallocation. First, forward-looking agents select an

occupation based on their expected post-extortion profit, instead of the first-best profit,

1See, for example, Restuccia and Rogerson (2008), Hsieh and Klenow (2009), and Bartelsman et al.(2012).

2I define extortion as “...a racket in which a local strong man forces merchants to pay tribute inorder to avoid damage – damage the strong man himself threatens to deliver” (Charles Tilly, 1985). Forpapers related to extortion see Gambetta (1996, 2000), Lotspeich (1997) and Volkov (1999).

3Typically, the mafia or criminal organization approaches the potential victim (entrepreneur) anddemands a payment, where failure to comply can result in physical harm or damage to property. I referto such payments as extortion. How successful the extortion is and the magnitude of the extortion feedepend on the property rights (for example policing) and the ability of the victim to pay the fee.


and their outside wage. TFP and output losses from the selection channel are opera-

tive when an agent’s occupational choice is altered as a result of extortion. The second

source of TFP/output losses can arise through misallocation. Consider an entrepreneur

who anticipates that high levels of production can make her susceptible to extortion.

One possibility is that the entrepreneur may reduce production so that she is less of a

target for extortion. Alternatively, the entrepreneur may increase production in order to

recoup her losses from being extorted. In either situation, when the marginal product of

inputs used in production is not set equal to the marginal cost of those inputs, TFP and

output losses due to misallocation will arise.

To examine how extortion a↵ects TFP and output, I build a model to capture the

variation in extortion rates that is empirically documented and then calibrate the model

to match key moments related to extortion in Poland. The model is a variant of Lu-

cas (1978), where agents are heterogeneous in their ability and choose between starting

their own establishment (entrepreneurship) or supplying their labour to existing estab-

lishments. There is a criminal group (CG) with monopoly power that is able to extort a

share of capital that entrepreneurs use in production, the success of which depends on the

strength of property rights in the economy. Entrepreneurs, however, can buy protection

to reduce the likelihood of being extorted. The CG, after observing the entrepreneur’s

choice of capital and protection, chooses the optimal fraction of capital to extort. A

central prediction of the model is that extortion rates are heterogeneous and have an

inverted ‘u’ shape in agent ability despite property rights being common across agents.

High-ability agents are a prime target for extortion but they buy su�cient protection

to reduce the likelihood of being extorted. Low-ability agents buy minimal protection

but use minimal capital in production. Since extortion is costly the CG extorts little

from these agents. Moderate ability agents face the most extortion: they use su�cient

capital to warrant extortion by the CG but cannot a↵ord high levels of protection. The

variation in extortion rates implies there are heterogeneous responses in entrepreneur

production because inputs used in production are chosen to maximize post-extortion

profit. Entrepreneurs who face little extortion produce close to their optimal capacity

but entrepreneurs who face high rates of extortion produce below their optimal capac-

ity. As property rights weaken, extortion rates increase inducing entrepreneurs produce

further below their optimal capacity.

To examine the quantitative e↵ects that extortion can have on output and TFP, I

calibrate the model to match key features in the US economy. The level of property

rights for the US is from the International Property Rights Index (IPRI) 2009. Since the

Enterprise Survey does not contain extortion related data for the US, parameters related


to the cost of extortion and protection are chosen to target establishment-level moments

related to extortion and security spending in Poland. The level of property rights is then

altered and the resulting equilibrium outcome is compared against an economy that has

perfect property rights, the first-best economy.

For Poland, I find that extortion reduces aggregate output and TFP by 8 and 3 per-

cent relative to the first-best economy. Output and TFP losses are due to selection and

misallocation. The fraction of entrepreneurs (FOE) falls from 10 to 9.7 percent, which in-

creases average entrepreneurial productivity in the economy. However, the misallocation

e↵ect dominates because low-ability entrepreneurs use little capital in production, below

their optimal capacity, to reduce the severity in which they are extorted. High-ability

entrepreneurs in contrast buy su�cient protection, face minimal extortion and produce

close to their optimal scale. Overall, TFP and output is lower. As property rights

weaken, output and TFP losses are magnified, both due to selection and misallocation,

as a larger set of entrepreneurs face high rates of extortion.

As property rights begin to fall from the first-best level the FOE in the economy

decreases. However, there is a threshold level, where further deterioration in the level of

property rights increases the FOE. For example, the FOE is about 10 percent in the US

but for countries that have about half the level of property rights as the US, the FOE is

over 20 percent. When property rights are extremely weak, extortion is ubiquitous and

all entrepreneurs reduce capital demand and produce considerably below their optimal

scale. Since labour is complementary in production, the demand for labour falls which

lowers the equilibrium wage. Because the outside option (the wage) is drastically lower,

agents begin to trickle into entrepreneurship. In this regard, the role that extortion

plays is complementary to the ‘entrepreneur out of necessity’ theory.4

This Chapter is related to the literature that examines measured TFP and output

losses that arise from resource misallocation. Previous work has focused on the role that

policy distortions can have in creating resource misallocation.5 A common feature of

policy distortions that are examined is that they are operative only amongst a sub-set

of establishments in the economy. Extortion is distinct from policy distortions because

all establishments can be extorted and to varying degrees. Therefore, extortion a↵ects

all establishments either through altering their production or decision to operate. There

is also a growing literature that studies how financial frictions, channeled through weak

4See for example Poschke (2008; 2010) who notes that low ability agents pursue entrepreneurship,not due to a great business idea but rather due to a lack of suitable employment opportunities.

5For policy distortions related to entry costs see Barseghyan and DiCecio (2011), Moscoso Boedo andMukoyama (2011), related to firing costs see Poschke (2009) and Hopenhayn and Rogerson (1993), andrelated to establishment size see Guner, Ventura and Xu (2008) and Samaniego (2006).


property rights, can generate resource misallocation.6 This Chapter is complementary to

the literature by focusing on the inherent role property rights can have in perpetuating

resource misallocation. It di↵ers, however, by highlighting how important extortion can

be for understanding resource misallocation. In fact, as Johnson et al. (2002) argue, it

is inconsequential whether access to finance is available when property rights are weak

(and extortion high).

The results in this Chapter are also related to the work by Hsieh and Klenow (2009),

who find there are large gaps in the marginal product of capital and labour across es-

tablishments in China and India. The presence of extortion is a potential explanation

for this. Entrepreneurs choose capital and labour to maximize post-extortion profit,

and since extortion rates are heterogeneous, the marginal product of capital and labour

di↵er across entrepreneurs. The capital-to-labour ratio also varies by agent ability and

is always lower than that in the first-best economy. As such, economies with weak prop-

erty rights can have aggregate capital-to-output ratios that are 40 percent lower than

in the US. Extortion also o↵ers an alternate explanation for the ‘missing-middle’ that

is documented by Tybout (2000). Since low (high) ability entrepreneurs operate well

below (at or close to) their optimal scale, there is a missing middle in the distribution

over establishment size. Through the lens of the model, a missing middle exists because

extortion induces moderate ability entrepreneurs to operate at an exceptionally small

scale. As property rights increase, entrepreneurs produce close to optimal capacity and

the missing-middle in establishment size gradually disappears.

This work is also related to an earlier literature on extortion/predation.7 A distinc-

tion is that I examine the macroeconomic implications of extortion in a heterogeneous

agent environment where occupational choice and production decisions are endogenous.

The model is tractable and highlights the discriminate impact extortion can have across

agents. Related is Olivella (2010), who examines government expropriation of firm capi-

tal via a contracting problem. She finds that firms who receive high productivity shocks

reduce investment in order to avoid expropriation, while those who receive low shocks

invest at the optimal level. My results di↵er because I consider an environment where

entrepreneurs can buy protection, and as such, the highest ability agents are able to

reduce the severity to which they are extorted.

6See for example Amaral and Quintin (2010), Buera, Kaboski and Shin (2011), Moll (2010) and Green-wood, Sanchez and Wang (2010) who argue that financial frictions can generate TFP losses. Midriganand Xu (2010) is an exception who argue that when a model is fitted to the data, TFP losses fromfinancial frictions are small. Also related is D’Erasmo and Moscoso-Boedo (2011) who examine how aninformal sector might arise when property rights are weak.

7Such papers include Dal Bo and Dal Bo (2006), Choi and Thum (2004), Konrad and Skaperdas(1998), Bliss and Ditella (1997), Grossman and Kim (1995, 1997) and Murphy et al (1993).


The remainder of Chapter 1 is organized as follows. Section 1.2 presents some key facts

related to extortion. Section 1.3 presents the model, Section 1.4 is the calibration of the

model and Section 1.5 presents the numerical results. Section 1.6 contains a discussion of

the unique nature of extortion as means to generating resource misallocation, and what

policies might be implemented to reduce it’s e↵ects. Section 1.7 concludes.

1.2 Facts related to Extortion

I use data from the World Bank Enterprise Survey to examine the severity of extortion on

businesses. The survey is typically conducted via face-to-face interviews with a view to

understand the major obstacles businesses face in their day-to-day operation. The survey

is conducted at the establishment level and contains questions related to extortion for

many post-Communist, European countries. Table 1.1 summarizes some of these results

for a select group of countries.

There are three main facts that stand-out from Table 1.1: extortion occurs frequently,

it is an obstacle to doing business, and there is considerable heterogeneity in the amount

that is extorted from businesses. For example, in Turkey 18 percent of businesses surveyed

reported they experienced extortion in the past year and in Slovakia, 24 percent of

establishments reported as such; in Germany, only 4 percent did. Though extortion

occurs frequently, how severe a constraint is it for business operation? The third column

in Table 1.1 provides such evidence by reporting the percentage of establishments that

indicate extortion is a major or moderate obstacle to the operation of their business.8

In Ukraine, close to a quarter of the establishments surveyed reported that extortion

is an obstacle to business operation, while in Macedonia almost 35 percent did – to

put this in context, in Germany, only seven percent of establishments reported that

extortion is an obstacle to business operation. The last two columns in Table 1.1 report

the average extortion payment for an establishment by country, contingent on having

faced extortion, and the distribution of those payments across establishments. There is

significant variation in extortion payments; many establishments do not face extortion,

while for others, extortion payments can be as much as 15 percent of their annual sales.

As shown in Figure 1.1, there is a negative correlation between the fraction of estab-

lishments that report extortion is an obstacle to doing business and the level of property

8Given the sensitive nature of questions related to extortion, –payments to criminal organizations–the establishment is not directly asked whether payments are made to criminal groups. Instead, theestablishment is asked whether payments to criminal groups are common in the industry, and if yes,roughly how much is paid. Following Johnson et al. (2002), I treat the establishment’s response asreflective of their actual experience with extortion.


Table 1.1: Extortion in Europe (Enterprise Survey 2005, World Bank)

Faced Extortion Obs.to Operate Avg. Extortion Payment 10-90th Percentile(% of firms) (% of firms) (if > 0) (if > 0)

Hungary 5% 8% 33.6 [.51, 104]Macedonia 22% 34% 23 [2.2, 76.8]Poland 15% 25% 26.4 [.48, 133.3]Russia 9% 14% 49.8 [.42, 90]Turkey 18% 28% 102.9 [1.2, 420]Ukraine 9% 24% 13 [.24, 28.7]

rights in a country: countries that have weak property rights have a higher fraction of

establishments that su↵er from extortion. Moreover, since property rights are highly

correlated with GDP per capita, there is a negative correlation between the fraction of

firms that report extortion is an obstacle to doing business and GDP per capita (see

Figure 1.2).

Arm

Bos

BulCze

Est

GerGre Hun

Ire

Kaz

KorLat

Lit

Mac

Mol

Pol

Por

Rom

RusSer

Svk

Slv

Tur

Ukr

0.1

.2.3

.4%

of e

stab

lishm

ents

that

sai

d ye

s

3 4 5 6 7 8International Property Rights Index, 2009

Figure 1.1: Correlation between Property Rights and Extortion as a major or moderate ob-stacle to business operation

To examine how extortion and establishment-level capital are linked, I run the follow-

ing regression at the country-level: extortioni

= �0

+ �1

ki

+ �2

k2i

+ ", where extortioni

is the total value paid to the criminal group by establishment i, and ki

is it’s value of

capital. I find that extortion is increasing in establishment-level capital, but tends to

decrease for further increases in capital; �1

> 0 and �2

< 0.9 It is not surprising that

9The above regression is for countries that have well over 100 establishment-level observations. There


Arm

Bel

Bos

BulCze

Est

Geo

GerGreHun

Ire

Kaz

KorLat

Lit

Mac

Mol

Pol

Por

Rom

RusSer

Svk

Slv

Taj

Tur

Ukr

0.1

.2.3

.4%

of e

stab

lishm

ents

that

sai

d ye

s

6 7 8 9 10 11Log GDP per capita, 2005

Figure 1.2: Correlation between GDP per capita and Extortion as a major or moderate obstacleto business operation

�1

> 0, since there is more capital to extort from larger establishments. To account for

this, I examine how the extortion-to-capital ratio varies by establishment-size (number

of employees), for establishments that experienced extortion. The sample size dwindles

considerably when the data is broken-down by establishment size and includes only those

establishments who experienced extortion. Nonetheless, I find the extortion-to-capital

ratio is increasing in establishment-size and then begins to decrease. Mid-size establish-

ments experience the highest extortion-to-capital ratios.

To summarize, there are several key facts related to extortion that is obtained from

the Enterprise Survey. Extortion occurs frequently, it serves as an obstacle for business

operation and is negatively correlated with property rights. Moreover, there is wide

heterogeneity in the amount that establishments pay to criminal groups (extortion),

both on the extensive and intensive margins. Finally, extortion exhibits a hump-shape

in establishment capital; establishments that pay the most in extortion generally are not

the ones with the highest values of capital.

are 14 countries that meet this threshold. Of these 14, Russia and Ukraine, are the only countries whoset-stats are not significant at the 90th confidence interval.


1.3 Model

1.3.1 Environment

Agents are risk-neutral and heterogeneous with respect to their ability, s 2 S, andchoose between two occupations: being an entrepreneur or earning a wage as a worker.

There exists an exogenous criminal group (CG) with monopoly power that is able to

extort capital from entrepreneurs.10 Entrepreneurs use capital (k) and labour (n) to

produce, and production, which is is decreasing returns to scale, is given by y(s) = sk↵n�.

Entrepreneurs can also purchase protection to lower the likelihood of being extorted

(discussed in the following sub-section).

If an agent with ability s becomes an entrepreneur and is not extorted by the CG,

her profit is given by

⇡(s, k, n) = sk↵n� � wn� (1 + r)k + k

= sk↵n� � wn� rk, (1.1)

where w is the cost of hiring a worker and r is the rental cost of capital. If, however, the

entrepreneur is extorted by the CG, her profit is

⇡e

(s, k, n) = sk↵n� � wn� (1 + r)k + (1� e)k, (1.2)

= sk↵n� � wn� rk � ek,

where e 2 (0, 1) is the fraction of capital that is extorted by the CG.11 e is essentially thepremium entrepreneurs pay for borrowing capital when there is extortion; if e varies by

ability, the e↵ective interest rate entrepreneurs pay for borrowing capital will also vary.

10I assume extortion is on capital instead of output because capital is more easily observed by the CG.However, whether extortion is on output or capital is irrelevant to the predictions of the model becausethere is a direct mapping from capital to output.

11It is assumed that the CG’s threat of extortion is credible; in that it is always optimal for the

entrepreneur to surrender the capital that is demanded by the CG. Dal Bo and Dal Bo (2005) and

Konrad and Skaperdas (1997) consider environments where the illegal organizations extort when their

threats are not credible.


1.3.2 Property Rights

The economy is characterized by an exogenous level of property rights, � 2 [0, 1], thatis provided by the state and common across agents. Higher values of � imply better

legal protection. The CG is successful in extorting capital with a probability 1��; thatis, when property rights are higher it is more di�cult for the CG to extort entrepreneur

capital. Agents can supplement the existing level of property rights by purchasing pro-

tection, which reduces the likelihood of being extorted.12 An agent’s e↵ective rate of

protection (ERP), after buying protection, is given by F (�, z) = �+ �z(s)✓ 2 [0, 1] with✓ > 0 and z(s) 2 [0, z] is the amount of protection bought.13 While � is common acrossagents the ERP can vary across agents based on the amount of protection (z(s)) that is

purchased. The CG extorts capital successfully from an entrepreneur of type s at a rate

of 1 � F (�, z). The success rate is decreasing in both the level of property rights andprotection bought.

1.3.3 Agent and CG Decisions

Agent Decisions

The decision facing all agents (or potential entrepreneurs) is to determine how much

capital and labour inputs to use in production and how much protection to purchase in

order to limit the likelihood of being extorted. Since agents anticipate that the CG will

successfully extort a fraction e 2 [0, 1] of capital, at a rate of 1� F (�, z), agents choosecapital and labour to maximize post-extortion profit (expected profit). The problem of

the potential entrepreneur is given by

e⇡(s) = maxk,n�0

b⇡(s, k, n), (1.3)

where

b⇡(s, k, n) = maxz2[0,z]

F (�, z)⇡(s, k, n) + (1� F (�, z))⇡e

(s, k, n)� bz

, (1.4)

= maxz2[0,z]

⇡(s, k, n)� (1� F (�, z))ek � bz

.

12Protection that is purchased by the entrepreneur is of the legitimate type –legal, private security–and not extortion in disguise. That is, when entrepreneurs buy protection it is not to ‘buy-o↵’ the threatof extortion where the payment ultimately arrives in the hands of the would-be extorter.

13 Since the ERP cannot be greater than one, the maximum amount of protection an agent can buy

is z =�1��

� 1✓ , which is non-binding in equilibrium.


Equation 4 states that with a probability F (·), an entrepreneur of ability level s isnot extorted, earning profit ⇡(s, k, n), and with probability 1� F (·), the entrepreneur isextorted and earns profit ⇡

e

(s, k, n). This expression can be further simplified to imply

the entrepreneur earns her full profit from production less the fraction of capital that is

extorted, e(s) 2 (0, 1), which occurs with a probability 1� F (·). The entrepreneur thenchooses the amount of protection to buy to minimize the likelihood of being extorted

subject to the marginal cost of buying protection ( bz

). Finally, capital and labour are

chosen to maximize b⇡(s, k, n).14 An entrepreneur of type s’s post-extortion profit is givenby e⇡(s). When � = 1, choosing capital and labour to maximize post-extortion profit(b⇡) reduces to maximizing ⇡ as in the first-best case.15 However, when � < 1, therecan be misallocation of capital and labour as entrepreneurs choose inputs to maximize

post-extortion profit. The first best outcome may be infeasible.

The second channel which generates ine�ciency arises through selection – entry into

entrepreneurship. The occupational choice decision for an agent of type s is given by

V (s) = max{e⇡(s), w}. Agent entry into entrepreneurship is based on post-extortionprofit instead of first-best profit. Therefore, ine�ciency through the selection channel

occurs whenever e⇡(s;w) < w ⇡(s;w) –that is, when an agent foregos entry intoentrepreneurship due to the presence of extortion.

CG Decisions

The Criminal Group has monopoly power and complete information about entrepreneur

capital and protection decisions.16 Extortion is costly since the CG has to spend time

initiating contact, monitoring and collecting extortion rents. The fraction of capital that

is extorted from an entrepreneur with ability level s is chosen to maximize

⇧CG

(s) = maxe2[0,1]

(1� F (�, z))k(s)e(s)� ae⇢

⇢. (1.5)

The CG successfully extorts a fraction e(s) of entrepreneur capital with a probability

1 � F (·). Variation in capital and protection choices by the entrepreneur imply thatextortion rates will di↵er across entrepreneurs. The marginal cost of extortion is assumed

to be convex (⇢ > 1).17

14It is equivalent to think of the problem as the entrepreneur choosing capital, labour and protectionsimultaneously.

15� = 1 =) z = 0 and therefore 1� F (�, z) = 0.

16The monopoly power assumption is for modeling convenience. Agents care about the amount thatis being extorted and not which group is doing the extorting.

17Extorting a larger fraction of the entrepreneur’s capital requires more resources on the part of theCG. For example, Gambetta (2000) notes that when large values of profit is extorted by the mafia,


1.3.4 Timing and Solution Concept

The timing of agent and CG decisions is as follows. Agents choose one of two occupations:

entrepreneur or worker.18 Workers supply their labour to hiring entrepreneurs while

entrepreneurs decide how much to produce (using capital and labour) and how much

protection to purchase. The CG has full information on the decisions made by each

entrepreneur and chooses the optimal fraction of capital to extort.

Given the timing sequence, optimal protection, production and extortion choices can

be solved via a two-stage game where the entrepreneur moves first and the CG moves

second. All entrepreneurs therefore choose capital, labour and protection in anticipation

of the CG’s best response for extortion. The CG’s best response function is

ebr(s) =

✓(1� F (�, z(s))k(s)

a

◆ 1⇢�1

,

and can be obtained by solving equation 1.5. Given the CG’s best response each en-

trepreneur chooses an optimal level of protection.

Proposition 1. If ✓ = (⇢�1)⇢

, a closed form solution for the optimal level of protection

exists. This level is dependent on capital and is given by

z⇤(s) =

✓⌦⇢�1�⇢�1(1� �)k(s)⇢

a+ ⌦⇢�1�⇢k(s)⇢

◆ 1✓

, (1.6)

where ⌦ = ✓b

⇢

⇢�1 =

b

. It follows that z⇤(s) < z, 8s. All proofs are in the Appendix.Given z⇤(s), the optimal fraction of capital that is extorted by the CG, for any choice of

capital chosen by the entrepreneur, is given by

e⇤(s) =

✓(1� �)k(s)

a+ ⌦⇢�1�⇢k(s)⇢

◆ 1⇢�1

. (1.7)

Extortion by the CG is decreasing in property rights; as property rights increase the

probability of successfully extorting decreases. Given equation 1.6 and 1.7, the optimal

choice of labour and capital is determined by solving equation 1.3.19 When � < 1,

multiple visits (monitoring) to the entrepreneur are required.18I abstract from an agent’s decision to become an extorter or a protection provider. As long as

the income from being an extorter or protection provider is independent of ability, then in equilibriumthose earnings will equal the wage of a worker; or else all agents who are not entrepreneurs will choosethe occupation that provides them with the highest income. As such, I abstract from these additionaloccupation choices. For papers that do examine the decision to become an extorter, see Acemoglu (1995),Grossman and Kim (1995, 1997).

19There is no closed form solution for the optimal choice of capital or labour and must be solved


extortion and protection are greater than zero and vary by agent ability.

1.3.5 Comparative Statics

I now examine how extortion and protection decisions vary by agent ability and the level

of property rights in the economy.

Proposition 2. Post-extortion profit, e⇡(s), is non-decreasing in agent ability.

Proposition 2 states that high-ability agents earn more from entrepreneurship than

low-ability agents. A high-ability agent can choose the same level of capital and protec-

tion that a low-ability agent chooses and always earn higher post-extortion profit.

Assumption 1: capital is non-decreasing and continuous in agent ability ( @k@s

� 0) and⇢ > 1 and ✓ > 0.

Proposition 3. Given Assumption 1, protection is increasing in agent ability. Moreover,

there is an ability threshold bs, such that agents with ability s bs buy protection at anincreasing rate while agents with ability s > bs buy protection at a decreasing rate.

Proposition 4. Given Assumption 1, there is a critical level of property rights, �c = ⇢�1⇢

,

such that when � � �c, protection and property rights are substitutes. When propertyrights are below this critical level, protection and property rights are complementary for

low ability agents and substitutes for all others.

Proposition 3 and 4 detail agent protection decisions by ability. Protection purchases

are increasing in ability and are of a ‘S’ shape. Property rights have heterogeneous e↵ects

with respect to agent protection decisions. When property rights are above a critical

threshold (�c), protection and property rights are substitutes; that is, further increases

in property rights induce agents to buy less protection. The state provides a su�cient

level of property rights which renders buying additional protection no longer optimal.

However, when property rights are weak, or below �c, protection and property rights

are complementary for low-ability agents –those below some ability threshold. Since

property rights are weak, a large amount of protection is required to o↵set the threat

of extortion. However, low-ability agents are not su�ciently profitable to warrant such

expenditure on protection. When property rights begin to improve, low-ability agents

numerically. In Section 1.3.7, I consider a special case and show how capital can deviate from thefirst-best level and vary by entrepreneurial ability.


increase protection purchases since protection and property rights are complementary to

an agent’s e↵ective rate of protection (ERP).20

Proposition 5. The e↵ective rate of protection, F (�, z), is increasing in agent ability

and the level of property rights.

Proposition 5 states that the ERP is increasing in agent ability and, holding agent

ability fixed, agents in economies with higher property rights have a higher ERP.

0 100 200 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ability

Exto

rtion

Extortion by Agent Ability

λ=1λ=.8λ=.6λ=.4

0 100 200 3000.4

0.5

0.6

0.7

0.8

0.9

1

Ability

ERP

Effective Rate of Protection by Agent Ability

λ=1λ=.8λ=.6λ=.4

Figure 1.3: Property rights and the variation in protection and extortion across agents when⇢ = = 2.

Proposition 6. Using Assumption 1, there is an ability threshold sx

(�) such that extor-

tion is increasing in agent ability for all s sx

(�) and decreasing otherwise. Moreover,

the ability threshold is decreasing in property rights, that is @sx(�)@�

0.

Proposition 6 states that extortion is an inverted ‘u’ in agent ability. This result

is due to agents being able to mitigate extortion by purchasing protection.21 High-

ability agents, who use the most capital, do not face the highest levels of extortion

because they buy a large amount of protection. Low-ability agents face little extortion

because it is not optimal for the CG to incur costs in order to extort the little capital

20The result holds whenever the functional form for the ERP exhibits Fz, F� > 0 and Fz� > 0.21Extortion would be strictly increasing in agent ability if protection is not available, similar to previous

work that have examined extortion without protection.


that is used in production. Agents of moderate ability face the most extortion because

they are neither su�ciently profitable to warrant large expenditures on protection, nor

unproductive enough to use little capital in production. Moreover, the ability level of the

agent that faces the most extortion is decreasing in the level of property rights. That is,

in economies with lower property rights it is always a higher-ability agent who faces the

most extortion.

Figure 1.3 depicts Proposition 5 and 6 graphically.

1.3.6 Equilibrium

I focus on a sequential game equilibrium where agent decisions precede extortion decisions

by the CG. An equilibrium consists of a series of decision rules for agents given by k(s),

n(s), z(s) and V (s), and extortion decisions by the CG, e(s), such that given prices (w, r)

and a distribution over ability, µ(s):

1. Agents choose their occupation based on V (s) = max{e⇡(s), w}.2. Entrepreneurs choose protection to solve equation 1.4 and capital and labour to solve

equation 1.3.

3. The CG chooses the fraction of capital to extort, for each entrepreneur of type s, to

solve equation 1.5.

4. Markets clear such that,

Z

S

E

2Sn⇤(s)dµ(s) =

Z

S\SE

dµ(s), (1.8)

Z

S

E

2Sk⇤(s)dµ(s) = K, (1.9)

Z

S

E

2Sy⇤(s)dµ(s) = Y, (1.10)

Z

S

E

2Sz⇤(s)dµ(s) = Z, (1.11)

Z

S

E

2S(1� F (�, z(s))e⇤(s)k⇤(s)dµ(s) = E, (1.12)

where SE

denotes the set of entrepreneurs in the economy. Equation 1.8 is the labour

market clearing condition which states that the demand for labour by all entrepreneurs

must equal the supply of labour by all workers. The equilibrium wage is the one that clears


the labour market. Equations 1.9-1.1 relate to aggregate capital, output and protection.

Finally, equation 1.12 is aggregate extortion of capital, which takes into consideration

the probability of successfully extorting across entrepreneur ability.

1.3.7 Special Case

To examine explicitly how misallocation can arise in the model, I consider a special case

where ⇢ = = b = 2 (which simplifies the algebra that follows). When property

rights are less than one, extortion will induce agents to select capital where the marginal

product of capital (MPK) is greater than the cost of capital. Substituting the values for

⇢, and b into equation 1.7 and 1.8 yields

z⇤(s) =�(1� �)k(s)2

a+ �2k(s)2and e⇤(s) =

(1� �)k(s)a+ �2k(s)2

.

By further substituting the optimal values for protection and extortion into equation

1.4, we can examine how entrepreneurs choose capital and labour inputs in production.

Doing so implies

e⇡(s) = maxk,n�0

⇡(s, k, n)� (1� �)2k(s)2

a+ �2k(s)2. (1.13)

Implicitly di↵erentiating equation 1.13 implies that capital is increasing in entrepreneur

ability, @k(s)@s

� 0.

Proposition 7. When � < 1 but not too close to zero, all entrepreneurs, except for the

highest ability ones, choose capital below the optimal level which generates a misallocation

of capital. Furthermore, the deviation from the optimal level is an inverted ‘u’ shape in

agents’ ability.

To see this, di↵erentiating equation 1.13 with respect to capital yields

@⇡(s)

@k(s)� ⇠(�, k) = 0, (1.14)

MPK = r + ⇠(�, k),

where ⇠(�, k) = 2a(1��)2k(s)

(a+�

2k(s)

2)

2 � 0. Misallocation will arise whenever agents do not setthe marginal product of capital equal to the rental rate of capital. From equation 1.14,

when � = 1, ⇠ = 0; all agents choose capital where the MPK = r. When � < 1, the


MPK > r for everyone except the highest ability agents.22 As k ! 1, the MPK ! r,(⇠ ! 0). An implication is that in economies where property rights are not too weak,high-ability entrepreneurs operate close to the optimal scale, while all other entrepreneurs

operate below the optimal scale. For the final part of the proposition, di↵erentiating ⇠

with respect to capital yields an inverted ‘u’ shape in agents’ ability. ⇠ is the premium

for borrowing capital as a result of extortion, which is heterogeneous across agents.

1.4 Calibration

1.4.1 Model Parameters

The model is calibrated to match key features in the US economy taking as given the

level of property rights, �US

. I use the International Property Rights Index (IPRI) score,

as detailed in the IPRI 2009 report, as my measure of property rights.23 Parameters

that require calibration are the elasticities on the production function (↵, �), parameters

related to the cost function of extortion (a, ⇢), parameters related to the cost of protection

(b, ), the complementarity between protection and property rights (✓), the interest rate

(r) and the distribution over agent ability (µ(s)). Once these parameters are determined,

I alter � and examine the equilibrium outcome.

For the decreasing returns to scale production function, I set ↵ and � to 0.283 and

0.567, so that they sum to 0.85 as is standard in the literature. Following Buera and

Shin (2010), I assume that agent ability is Pareto distributed with a smoothing parameter

⌘ = 4.95, in order to target a fraction of entrepreneurs (FOE) in the U.S. economy equal

to 10 percent.24 The interest rate together with the end points in the ability distribution

are chosen to target a capital-to-output ratio of 2.65 (see Quadrini, 2000).

Parameters that remain to be determined are ⇢, , a and b. The Enterprise Survey

does not contain extortion related data for the US. Therefore, I use Polish data, that

22Despite entrepreneurs choosing labour where the MPL = w (see equation 1.13, labour enters onlythrough ⇡(s)), when the MPK > r, labour demand is also misallocated because labour is a function ofcapital.

23The IPRI is a composite score of three variables: legal/political environment, physical propertyrights and intellectual property rights. The score is a cardinal ranking that takes values between 0 and10 with most countries falling in the range of 3 to 8. I divide these values by 10 so that they are between 0and 1 to be comparable with �. The score maps well to the interpretation of � in the model if countriesthat have higher IPRI scores are better at preventing extortion/theft. Alternate mappings between theIPRI score to � (i.e. g(score) ! (0, 1) where g(·) is an increasing function) can be considered withoutsignificant alterations to the results in Section 1.5.

24The FOE in the U.S. can range between 7-16 percent depending on the exact definition one uses todescribe an entrepreneur (see Cagetti and DeNardi, 2006). I target a lower bound of this range so thatmy results are a conservative gauge of output and TFP losses.


has over 600 cross section observations at the firm level in the Enterprise Survey, to pin

down these parameters. Since the IPRI score for Poland is �Pol

= 0.53, I choose ⇢, ,

a and b to minimize the sum-of-squares distance between the predictions of the model

when � = 0.53 against the following four targets: the average extortion to capital ratio,

the average protection to capital ratio, the average extortion to capital ratio in the fifth

decile of the sample relative to the first decile in the sample, and likewise the average

protection to capital ratio in the fifth decile of the sample relative to the first decile in

the sample. I focus on the case where a closed-form solution for protection exists which

implies that ✓ = (⇢�1)⇢

. Table 1.2 summarizes these parameter values and targets.

Table 1.2: Parameter Values and Targets

Parameter Value Target Models 2 S [1.5, 4] Pareto Dist.⌘ 4.95 FOE, 10% 10%↵, � 0.283, 0.567 Prod. fcnr 0.24 K

Y

= 2.65 2.65a 2.28

�E

K

�avg.

= 0.015 0.015

b 9.08�Z

K

�avg.

= 0.014 0.0134

⇢ 1.26( EK

)d5,avg.

( EK

)d1,,avg.

= 0.9 0.95

1.42( ZK

)d5,avg.

( ZK

)d1,,avg.

= 1.83 1.57

Table 1.3 presents the results for the model fit in relation to extortion and protection

data in Poland, broken down by quartiles in firm-level capital. The model does quite

well in fitting the data despite not being targeted to fit such moments.

Table 1.3: Model Fit by Capital Distribution

�Z

K

�avg.

�E

K

�avg.

Data Model Data Model1st Quartile 0.011 0.014 0.025 0.0262nd Quartile 0.014 0.018 0.018 0.0253rd Quartile 0.018 0.014 0.008 0.0074th Quartile 0.012 0.008 0.009 0.002

Concerning the property rights in a given country, I have used the IPRI 2009 Report

as a measure for �. Because the IPRI score is a cardinal ranking it maps reasonably

well to the interpretation of � in the model. For example, if country A and country B


have a score of 4 and 8, it is to be interpreted that country B has twice the level of

property rights that country A has. In the context of the model, country B is twice

as likely to prevent the CG from extorting entrepreneur capital than country A. While

the IPRI score may not be the ideal counterpart to � in the model, finding an exact

mapping from the data to � is problematic. Nonetheless, as a robustness check I re-run

the calibration exercise described above for Poland but include � in the sum-of-squares

minimization. I find that the parameters that minimize the distance between the model

and data are similar to those listed in Table 1.2 with an implied � of 0.56. This is very

close to �Pol

= 0.53 found in the IPRI Report, which suggests that, at least for Poland,

the IPRI score may be an adequate mapping to � in the model.

1.4.2 Aggregation and TFP

Given the closed form solution for z⇤(s) and e⇤(s) from equation 1.6 and 1.7, then the

optimal choice for capital and labour is determined by equation 1.3. As there is no closed

form solution for capital or labour, the optimal choices must be determined through a

grid search over all combinations of capital and labour. A more convenient approach is

to substitute the optimal choice of labour into the production function and re-write it

only in terms of capital.25 Doing so implies

⇡(s) = As1

1�� k↵

1�� rk,

where A = (1 � �)��

w

� �1�� . The grid search is only over capital and the optimal labour

input can be backed-out from the first-order condition for labour.

Aggregating the output of every entrepreneur in the economy, when � = 1, generates

the following aggregate production function

Y = ��=1

⇣K↵

1�� , (1.15)

where ⇣ = AN1�↵��1�� , N =

Rs2S

E

dµ(s), and ��=1

⌘⇣

1

N

Rs2S

E

s1

1�↵�� dµ(s)⌘ 1�↵��

1��is mea-

sured TFP or the average productivity of entrepreneurs in the first-best economy. When

� < 1, there is no closed-form solution for capital and therefore, expressing the aggregate

production function and TFP analytically is not possible. However, given the structure

of equation 1.15, I can computationally solve TFP as total output divided by total in-

25Because e⇤(s) and z⇤(s) are independent of n(s), optimal labour is dependent only on capital –

solving equation 1.14 implies n⇤(s) =⇣�sk↵

w

⌘ 11��

. Re-writing the production function only in terms of

capital does not alter the entrepreneur or CG’s problem.


puts used in production –denoted by TFPfb

and TFP�

for when � = 1 and � < 1. TFP

losses due to selection (�TFPsel

) and misallocation (�TFPmis

) can be backed-out in

the following way. By holding production at the optimal level, for a given equilibrium

wage in the economy, I can measure changes in TFP that arise from the selection channel

directly from ��


TFP losses.26 When property rights are weak, only the highest ability entrepreneurs can

a↵ord to purchase su�cient protection to avoid being extorted. All other entrepreneurs,

therefore, intentionally choose to operate below their optimal scale which generates TFP

and output losses. For example, when � = 0.45, aggregate output and TFP losses are

over 25 and 10 percent, respectively.

Table 1.4: Property Rights and Losses from Extortion

�fb

= 1 � = 0.7 �Pol

= 0.53 � = 0.45 � = 0.35rel Y 1 0.996 0.92 0.74 0.62

rel TFP 1 1 0.97 0.89 0.94rel K

Y

1 0.995 0.85 0.57 0.36FOE 10% 9.9% 9.7% 16.3% 21.6%

rel. wage 1 0.995 0.92 0.79 0.73

As this is a model that studies the extensive and intensive margins of entrepreneurship,

I decompose changes in TFP and output that arise due to selection (extensive margin)

and misallocation (intensive margin). TFPsel

and Ysel

denote the TFP and aggregate

output level in the distorted economy if production is at the optimal level (MPK = r),

but where the composition of entrepreneurs is at the distorted economy’s level - an

economy in which there is no misallocation but where the selection channel is operative.

As shown in Table 1.5, TFPselTFP

fb

� TFP�TFP

fb

for all values of �, which implies that extortion

induces many entrepreneurs to operate below the optimal capacity. �TFPmis

is the

change in TFP that is due to misallocation, between the first-best and distorted economy

(�TFPsel

= 1 ��TFPmis

). When � = 0.5, �TFPmis

= 97% and �TFPsel

= 3%; the

fraction (and the composition) of entrepreneurs in the economy is close to the first-

best level and hence, there is little e↵ect along the selection channel. When � = 0.3,

�TFPmis

= 77% and �TFPsel

= 23%. The selection channel becomes prominent in

generating TFP losses because the entry threshold for entrepreneurship is lower (see

Section 1.5.2); the FOE is higher and the average productivity of entrepreneurs in the

economy is lower. Turning to output, when � = 0.4, Y�Y

fb

= 0.65 but YselY

fb

= 4.16 . If

entrepreneurs operated at their optimal scale aggregate output would be 4 times higher

than the first-best economy. There is a larger FOE in the distorted economy and the

equilibrium wage is lower, hence, output should be significantly higher. The fact thatY

�

Y

fb

= 0.65 implies that entrepreneurs operate significantly below their optimal scale.

26TFP is higher in the economy with � = 0.35 than � = 0.45. This occurs only when property rightsare extremely weak and is due to the decreasing returns to scale feature of the production technology–when all entrepreneurs use minimal capital in production, TFP increases. Nonetheless, TFP is well


Table 1.5: Decomposing changes in TFP and Output

� = 0.6 � = 0.5 � = 0.4 � = 0.3TFP

�

TFP

fb

t1 0.94 0.93 0.94TFP

sel

TFP

fb

t1 0.998 0.987 0.986�TFP

mis

- 97% 76% 77%Y

�

Y

fb

0.98 0.88 0.65 0.6Y

sel

Y

fb

1.07 1.68 4.16 5.36

1.5.2 E↵ects Across Agents

Figures 1.4, 1.5 and 1.6 present the results when � = 0.7 (tUS economy), � = 0.53(Polish economy) and when � = 0.45, respectively. In the US economy, misallocation

is minimal, capital demand is indistinguishable from the first-best level, and the ability

threshold for entry into entrepreneurship is marginally higher (selection e↵ect). The

e↵ective rate of extortion, (e⇤(s)(1 � F )), is an inverted ‘u’ in agent ability but strictlydecreasing in entrepreneur ability because only the most productive agents enter into

entrepreneurship. Since property rights are high in the US, buying minimal protection

is su�cient for entrepreneurs to reduce the likelihood of being extorted –they have an

e↵ective rate of protection, F (�, z) t 1, and operate close to the optimal level. Thecapital-to-labour (CL) ratio is below the first-best level for low ability agents, although

they do not select into entrepreneurship, and marginally below the first-best level for

those who are entrepreneurs.

For the economy with � = 0.53 and 0.45 (see Figure 1.5 and 1.6), there is a disconti-

nuity in capital demand across agents, which coincides with the discontinuity in extortion

and protection choices agents encounter/make.27 There is a critical threshold level of

ability, sx

(�), where agents with ability s sx

(�) choose capital below the first-best level

and agents with ability above sx

(�) choose capital above the first-best level. The point

of discontinuity is decreasing in property rights, @sx(�)@�

0; that is, as property rightsdecrease a larger set of entrepreneurs choose capital below the first-best level, similar to

Proposition 6 in Section 1.3.5. As entrepreneurs choose capital below the first-best level

the distribution over establishment size is a↵ected. As shown in Figure 1.8, extortion re-

sults in a dis-proportionally higher number of small and large establishments while there

are fewer mid-size establishments. For the economy with � = 0.45, the distribution over

below the first-best level.27Protection acts as fixed cost leading to a discontinuity in capital across agents. e⇤(s) and z⇤(s) are

discontinuous because they are functions of k⇤(s).


0 100 200 300 400 5000

20

40

60

80

Cap

ital

λ=1λ=0.7

0 100 200 300 400 5000

5

10

15x 10−4

Eff.

Exto

rtion

0 100 200 300 400 500

0.8

1

ERP

0 100 200 300 400 5000

5

10

Prof

it

150 200 250 300 3500

0.5

1

Ability

Entry

0 100 200 300 400 5002.7

2.8

2.9

3

Ability

CL

Rat

io

Figure 1.4: E↵ect of extortion across agents when � = 0.7. Note that the axis on the plot forentry (bottom left hand corner) is adjusted.

establishment size exhibits a missing middle. The majority of establishments operate at

a very small scale. The few, high-ability entrepreneurs that can a↵ord to buy su�cient

protection operate at the optimal level, and given the low equilibrium wage they operate

as mega-size establishments.

The intuition behind an agent’s capital choice is as follows: low ability agents, those

with s sx

(�), buy minimal protection as they are not su�ciently productive (low abil-

ity) to recoup the cost of large protection expenditures. Failure to spend on protection


0 100 200 300 400 5000

50

100

Cap

ital

λ=1λ=0.53

0 100 200 300 400 5000

0.01

0.02

0.03

Eff.

Exto

rtion

0 100 200 300 400 5000.4

0.6

0.8

1

ERP

0 100 200 300 400 5000

5

10

Prof

it

150 200 250 300 3500

0.5

1

Ability

Entry

0 100 200 300 400 5001.5

2

2.5

3

Ability

CL

Rat

io

Figure 1.5: E↵ects of extortion across agents when � = 0.53. Note that the axis on the plotfor entry (bottom left hand corner) is adjusted.

implies that the CG can extort with a high probability of success (since property rights

are weak). Foreseeing this, low ability agents operate at a smaller scale. There are TFP

losses due to misallocation because capital is below the optimal level (the MPK > r).

Conversely, high ability agents, those with s > sx

(�), buy a large amount of protection,

have an ERP close to one and therefore, face e↵ective extortion rates of close to zero.

These agents operate near the optimal scale, choosing capital where the MPK t r. Thehighest-ability agents in the distorted economy are larger, in terms of capital and labour


0 100 200 300 400 5000

50

100

150

200

Cap

ital

λ=1λ=0.45

0 100 200 300 400 5000

0.05

0.1

Eff.

Exto

rtion

0 100 200 300 400 5000.4

0.6

0.8

1

ERP

0 100 200 300 400 5000

5

10

Prof

it

0 100 200 300 400 5000

0.5

1

Ability

Entry

0 100 200 300 400 5000

1

2

3

Ability

CL

Rat

io

Figure 1.6: E↵ects of extortion across agents when � = 0.45.

demand, than agents of similar ability in the first-best economy. The equilibrium wage in

the distorted economy is lower resulting in higher capital demand by high-ability agents

(See Figure 1.7).28 Nonetheless, the CL ratio for agents in the distorted economy is

below the CL ratio for agents of similar ability in the first-best economy. Moreover, the

CL ratio is inversely related to the e↵ective extortion rate: agents that face the highest

rates of extortion have the lowest CL ratios in production. This is especially important

28This implies, because they face lower wages, all entrepreneurs in the distorted economy should belarger than entrepreneurs of similar ability in the first-best economy.


for understanding TFP losses given that @sx(�)@�

0; in economies with weaker propertyrights, a larger set of entrepreneurs, both in terms of number and ability, use minimal

capital in production and produce below the optimal scale exacerbating TFP losses.

As agent choices of capital are altered as a result of extortion, so in turn are the occu-

pational choice decisions they make. For economies with � 2 [0.6, 1), extortion increasesthe ability threshold required for entry into entrepreneurship. For agents near the entry

threshold, being extorted, or the costs associated with buying protection, lowers their

post-extortion profit below the outside, equilibrium wage. These are agents that should

operate as entrepreneurs in the economy –e⇡(s;w) < w < ⇡(s;w)– however, anticipatedlosses from extortion induce them to forgo entrepreneurship. Measured TFP, or the av-

erage productivity of entrepreneurs in the economy is larger because there is a higher

threshold for entry into entrepreneurship.

0 100 200 300 400 5000

10

20

30

40

50

60

70

80

90

100

Ability

Cap

ital

kλ=1

kλ=0.53

k*w=wλ

Figure 1.7: This Figure shows capital demand for agents who are entrepreneurs. The dashedline is capital demand in the first-best economy. The thin, solid line is capital demand byentrepreneurs when� = 0.53. The thick, solid line is what capital demand should be ( MPK =r) given the prevailing wage for the economy when � = 0.53. Di↵erences between the thin,solidand thick-solid line are �TFP

mis

.

As property rights decrease from the first-best level, entry into entrepreneurship falls

as extortion becomes more prevalent. However, as property rights decrease further, entry

into entrepreneurship begins to increase. For example, the FOE is 9.9 percent when � =

0.7 but over 16 percent when � = 0.45 (see Table 1.3). When property rights are weak,

extortion rates are so high such that entrepreneurs use minimal capital in production.

Since capital and labour are complimentary in production, the demand for labour falls,

which in turn lowers the equilibrium wage. More agents select into entrepreneurship due


0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1x 10−3

Establishment Size

Mea

sure

− µ

λ=0.7λ=0.53

0 10 20 30 40 50 60 70 800

0.2

0.4

0.6

0.8

1

1.2

1.4x 10−3

Establishment Size

Mea

sure

− µ

λ=0.7λ=0.45

Figure 1.8: Distorted economies have a dis-proportionally larger amount of small establish-ments, as measured by labour demand. For the economy with � = 0.45, there is a missingmiddle in the distribution over establishment size.

to a weak outside option, similar to the ‘entrepreneur out of necessity’ theory.29

1.6 Discussion

Extortion distorts entrepreneur behaviour by either lowering production or inducing

agents to forgo entrepreneurship altogether. In this Section, I examine two questions

related to extortion and resource misallocation. First, how is extortion di↵erent from a

standard, proportional tax on capital? And second, since extortion reduces output and

TFP, are there simple tax policies the government could adopt to lessen the impact on

output and TFP?

1.6.1 Why is Extortion more than just a tax on capital?

To determine if extortion is di↵erent from a tax on capital, I examine a policy where

capital is taxed but where there is no extortion. The problem of the firm is given by,

⇡(s) = maxk

As1

1�� k↵

1�� (r + ⌧k

(s))k,

where ⌧k

(s) is the tax rate imposed on an entrepreneur of type s. I examine two tax

policies: the first, is a tax on the cost of renting capital that is set equal to the e↵ective

rate of extortion the entrepreneur would have encountered in the distorted economy with

extortion – ⌧k

(s) = x(s) where x(s) = e⇤(s)[1 � F (�, z⇤(s))]; in the second policy, the

29The ‘entrepreneur out of necessity’ theory is also one in which entry into entrepreneurship is non-monotonic in ability. My model does not capture this particular feature.


tax rate is homogeneous across entrepreneurs and equal to the highest, e↵ective rate of

extortion faced by an entrepreneur – ⌧k

(s) = max{x(s1

), ..., x(sn

)} 8s.Table 1.6 presents the results. All values are presented relative to the first-best

economy –an economy without extortion or taxes on capital. The first column, is the

extortion economy for Poland, where � = 0.53, but where there are no taxes on capital,

⌧k

(s) = 0, 8s. The middle column, is the economy where the e↵ective rate of extortionis replaced by an equivalent tax on capital, ⌧

k

(s) = x(s), while the last column is for

the economy where the tax is set equal to the highest, e↵ective extortion rate, ⌧k

(s) =

max{x(s1

), ..., x(sn

)}, 8s. From Table 1.6, it is clear that extortion produces larger lossesin aggregate output, aggregate capital and TFP than either of the tax policies examined.

The reason for this is, a tax on capital is static –the entrepreneur observes the tax rate and

chooses capital to maximize profit. In the economy with extortion, the amount of capital

is determined strategically by the inter-play between the entrepreneur and the CG. The

entrepreneur is choosing over combinations of capital and protection, anticipating the

amount of extortion she would face for each of those choices. The entrepreneur chooses

capital to maximize over post-extortion profit, which includes the cost of protection

required to deter extortion.

Table 1.6: Extortion and a tax on Capital

Extortion Economy Policy 1 Policy 2⌧k

(s) = 0 ⌧k

(s) = x(s) ⌧k

(s) = 0.03rel Y 0.92 0.98 0.96

rel TFP 0.97 t1 1rel K 0.78 0.93 0.85

With regard to aggregate output, capital and TFP, taxes are less distortionary than

extortion. However, not all entrepreneurs are made ‘better-o↵’ if extortion is permanently

replaced by a tax. As shown in Figure 1.9, high-ability entrepreneurs produce more in

the extortion environment than in one where capital is taxed; the converse is true for low-

ability agents. In the extortion economy, high-ability agents can buy su�cient protection

to eliminate extortion, and therefore do not face a distortion in production. Because

the cost of buying protection serves a fixed-cost, they operate close to the first-best

level. A tax on capital, however, forces high-ability entrepreneurs to reduce production

because they are un-able to avoid the distortion. Low-ability entrepreneurs prefer a

tax on capital to extortion. These entrepreneurs cannot a↵ord adequate protection to

deter extortion –they are not su�ciently productive/profitable, to incur the protection


expenditure required prevent extortion– and operate at a far smaller scale than if they

are to face a tax on capital.

0 100 200 300 400 5000

10

20

30

40

50

60

70

80

90

100

Ability

Cap

ital

extortionPolicy 1Policy 2

Figure 1.9: A Tax on Capital and Capital Demand

1.6.2 Role of Government Policy

Extortion induces (high) low-ability entrepreneurs to produce (more) less than entrepreneurs

of similar ability operating in the first-best economy. Since e�ciency is maximized when

production is at the first-best level, I examine whether a re-distributive tax policy across

entrepreneurs, imposed in the extortion economy, can steer production towards the first-

best level. I consider two separate, revenue-neutral tax policies. The first is a tax/subsidy

policy on entrepreneur profit: to increase the production of low-ability entrepreneurs, a

proportional subsidy on profits provided, while the profits of high-ability entrepreneurs

are taxed, in order to lower production. In the second policy, a proportional tax is im-

posed on the profits of all entrepreneurs, while a proportional subsidy is provided towards

the cost of buying protection.30

The first policy, a re-distributive tax/subsidy, moves the economy further away from

the first-best level; TFP and output are lower in the extortion economy. Under such a

30The problem for the entrepreneur becomes,

e⇡(s) = maxk,n

b⇡(s, k, n),

b⇡(s, k, n) = maxz2[0,z]

(1� ⌧⇡)⇡(s, k, n)� (1� F (�, z))ek � (1� ⌧s)bz

.


policy, capital demand is no longer monotonic in entrepreneurial ability; a sub-set of high-

ability entrepreneurs use less capital in production than several low-ability entrepreneurs.

The tax induces the sub-set of high-ability entrepreneurs to reduce protection expendi-

ture, which increases their exposure to extortion, in turn, drastically lowering capital

used production. The subsidy to low-ability entrepreneurs, enables them to increase

protection expenditure, and in turn, increase capital demand slightly. Again, protec-

tion expenditure, which acts like a fixed-cost to operation, is critical for the tax/subsidy

generating these results and reducing TFP and aggregate output.

The second policy, in which protection is subsidized and profits are taxed, is welfare

improving. As shown in Table 1.7, – where ⌧s

is the subsidy provided to protection, and ⌧⇡

is the tax on profit required for budget balance – TFP, aggregate output, aggregate capital

and the average productivity of entrepreneurs in the economy increase. Subsidizing

protection can eliminate the distortion created by extortion, while the tax on profit is

non-distortionary to entrepreneur capital demand – this is a static model without capital

accumulation.31 Mid-ability entrepreneurs gain the most from such a policy as they are

able to buy su�cient protection to eliminate the threat of extortion and produce close

to the first-best level. Despite the gains in production and TFP, profit is lower for the

majority of entrepreneurs – except for mid-ability entrepreneurs – due to the imposition

of the tax.

Table 1.7: Protection Subsidy and a Tax on Profit

⌧s

= 0 ⌧s

= 10% ⌧s

= 30% ⌧s

= 50%rel Y 0.92 0.93 0.94 0.96

rel TFP 0.97 0.98 0.99 t1rel K 0.78 0.80 0.84 0.88FOE 9.9% 9.5% 8.5% 7.4%⌧⇡

0% 2.5% 9.6% 18.3%

1.7 Conclusion

In this Chapter, I have argued that when property rights are weak, extortion is a key

driver of resource misallocation. A key prediction of the model is that while property

rights are common across agents, extortion rates follow an inverted ‘u’ shape in agent

31In the first policy, the tax on profit distorts capital indirectly through protection expenditure. Thetax on profit reduces the quantity of protection an entrepreneur can buy, which forces the entrepreneurto reduce capital in order to avoid extortion.


ability. The inverted ‘u’ shape arises because agent ability dictates how much protection

they can a↵ord. Given the varying rates of extortion, how agents respond to extortion

will di↵er. Low-ability entrepreneurs may select out of entrepreneurship due to the high

extortion rates they anticipate, while moderate ability entrepreneurs may continue to

operate but at a smaller scale. High ability entrepreneurs buy significant protection,

face little extortion and operate close to the optimal scale. Aggregate output and TFP

losses due to extortion can be upwards of 30 and 10 percent, respectively, and are due to

both selection and misallocation.

A large literature has emphasised that developing countries have an abundance of

small establishments. Typical explanations involve policy distortions or financial fric-

tions that limit the ability of establishments to expand. Extortion provides another

explanation as to why developing countries may have so many small establishments. In

an environment without financial frictions or policy distortions, the presence of extortion

is su�cient to induce many establishments to remain small. This is similar to the em-

pirical results in Johnson et al. (2002), who find that weak property rights, channelled

through low investment, is more important for establishments remaining small than lim-

ited access to finance. While access to finance is crucial for growth, a necessary condition

is that property rights are secure. Entrepreneurs will seek financing for projects only if

they are confident they can keep the entire returns from production.

This work highlights how important property rights are for understanding resource

misallocation. While property rights are exogenous in the model, they tend to be persis-

tent over time, evolve gradually and require resources to sustain or improve their quality.

From the perspective of a benevolent government, taxes need to be imposed in order

to fund the maintenance or improvement of property rights. A benevolent government,

therefore, from an e�ciency perspective, faces a dilemma: raising taxes to fund prop-

erty rights can lead to TFP and output losses as the taxes on establishments generate

misallocation and selection e↵ects; however, by sustaining or improving property rights,

TFP and output losses that arise due to extortion can be mitigated, as I have argued

throughout this Chapter. It naturally follows to ask, from the view point of the govern-

ment, whether there is an optimal tax policy that funds property rights which is welfare

improving. While an in-depth analysis of such a question is beyond the scope of this

Chapter, I o↵er several insights concerning the way such a tax policy might a↵ect agent

incentives, based on the model outlined in Section 1.3.

Consider a proportional tax on profits/wages imposed on all agents in an economy

where high (low) ability entrepreneurs face low (high) extortion rates, similar to the

Polish economy in Figure 1.5. While sustained or improved property rights benefit all


agents, the varying extortion rates entrepreneurs face imply that a proportional tax

will benefit some agents more than others. For example, high-ability entrepreneurs buy

su�cient protection and face low extortion. A tax on profit can lower the likelihood that

this entrepreneur can buy high levels of protection, thereby leaving the entrepreneur

more prone to extortion. Conversely, low-ability entrepreneurs buy little protection and

face high extortion. A proportional tax on profit is likely to, therefore, have little e↵ect

on their protection spending. However, the improvement in property rights, from the

imposed taxes, will lower extortion and benefit them. As such, low-ability entrepreneurs

are likely to gain from a policy that aims to improve property rights via a tax, while

high-ability entrepreneurs are likely to be made worse-o↵.

Alternatively, one can consider a lump-sum tax that standard economic theory sug-

gests is non-distortionary. However, in an environment in which extortion and protection

decisions are present, lump-sum taxes can be distortionary. This is because the en-

trepreneur’s protection expenditure is essentially a fixed-cost –the more (less) protection

that is bought the higher (lower) is the fixed-cost. Therefore, a lump-sum tax will reduce

the ability of the entrepreneur to buy protection. As a result, either the entrepreneur

will operate at a lower scale to avoid excessive extortion or exit entrepreneurship and

become a worker.

The e↵ect a tax can have on an economy depends on the existing level of property

rights and whether the government takes a short-run or long-run view of improving

welfare. For countries with strong property rights, a tax can be welfare improving if it

is o↵set by lower protection expenditure due to increased property rights. In coun

Documents

by Ashantha Ranasinghe - University of Toronto T-Space...which agents choose between becoming an entrepreneur and a worker, and where a crim inal group (CG) can extort the proceeds