tax evasion in africa and latin america
TRANSCRIPT
Policy Research Working Paper 8522
Tax Evasion in Africa and Latin America
The Role of Distortionary Infrastructures and Policies
Wilfried A. Kouamé Jonathan Goyette
Africa RegionOffice of the Chief EconomistJuly 2018
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Produced by the Research Support Team
Abstract
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Policy Research Working Paper 8522
This paper examines the impact of the quality of the busi-ness environment as well as the monitoring capacity of the tax agency on firms’ tax evasion and production decisions. First, the paper uses firm-level data for 30 African and Latin American countries to show that tax evasion and distortions stemming from the business environment are positively and significantly correlated, while sales not reported for tax purposes and institutional quality are negatively and sig-nificantly correlated. Second, the paper develops a general equilibrium model where heterogeneous firms make tax evasion decisions based on their assessment of the quality of their business environment as well as the monitoring
capacity of the tax agency. The model simulations for each country in the African and Latin American sample show that the model can explain 35 percent of the variation in tax evasion and more than 49 percent of the dispersion in output per worker across the sample countries. Finally, a series of counterfactual experiments shows that, at the cur-rent level of deterrence, governments could decrease sales not reported for tax purposes by 21 percent, by reducing distortions stemming from the business environment by half. The paper presents empirical supporting evidence consistent with testable predictions of the model.
This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at [email protected].
Tax Evasion in Africa and Latin America: The Role of
Distortionary Infrastructures and Policies
Wilfried A. Kouamé∗ and Jonathan Goyette†
Keywords: Distortions, tax evasion, business environment, infrastructure, institutional quality, Africa and Latin American.
JEL codes: H2; H3; H5; O1
∗Corresponding author: Wilfried A. Kouamé - The World Bank and Economics Department, Universitéde Sherbrooke: [email protected]/[email protected]. Authors are grateful to DiegoRestuccia, Théophile Azomahou, Moussa Blimpo, Jean-François Rouillard, Cesar Calderon, and PunamChulan-Pole for their comments. The paper has also benefited from presentations at Oxford UniversityCSAE 2017 Conference, Canadian Economics Association and Public Economic Theory conferences in 2016.Earlier versions of the paper circulated under the titles "Distortions, Policy Ineffectiveness and Tax Evasion"and "Distortions, Social Infrastructures and Tax Evasion".
†Economics Department, Université de Sherbrooke
I Introduction
A sound tax system and a healthy business environment are crucial for the welfare of an
economy (Easterly and Rebelo, 1993; Aterido et al., 2011). However, many African and
Latin American economies face major challenges on both fronts. Indeed, high levels of tax
evasion are ubiquitous in these countries. On average, 25% of total sales are not reported
for tax purpose in African and Latin American countries compared with only 7% in OECD
countries.1 This shortfall has important social and economic consequences, as evaded taxes
reduce a government’s ability to invest in productive infrastructures and institutional quality,
and to provide public goods and services. Moreover, insufficient government resources result
in an inefficient business environment as various distortions (power outages, corruption, etc.)
hinder firms’ performance and their ability to create jobs (Besley and Burgess, 2004; Aterido
et al., 2011; Buera et al., 2013; Goyette and Gallipoli, 2015). As such, African and Latin
American economies fare poorly in terms of ease of doing business.2
The coexistence of tax evasion and inefficient business environments thus raises the ques-
tion about a potential link between these variables in developing countries. This paper aims
to examine how distortions stemming from the business environment and institutional qual-
ity, i.e., a tax agency’s monitoring capacity, affect firms’ production and tax evasion decisions.
We focus on two specific mechanisms. First, entrepreneurs, knowing that institutional quality
is low, also anticipate low monitoring of taxes and have, therefore, more incentive to evade
their taxes. Second, we argue that a poor business environment generates costs for firms and
that this creates a wedge between firms’ potential and realized profits. As a result, firms
have an incentive to under-report their sales for tax purposes in order to compensate for the
losses incurred due to the distortions in their business environment.
Using firm-level data from the World Bank Enterprise Surveys (WBES) as well as data
from the World Governance Indicators (WGI), we provide supporting evidence consistent1See Table A.1 in Appendix A.2See Appendix A.
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with the mechanisms described above. Tax evasion is proxied by the amount of sales not
declared for tax purposes. Distortions from the business environment are calculated based on
the losses in annual sales due to power outages, corruption, etc. Finally, institutional quality
is based on various variables related to the perceptions of the quality of public services and
the credibility of the government. We find a positive and significant relationship between
losses due to distortions from the business environment and tax evasion. On the contrary,
institutional quality is negatively related to firms’ tax evasion.
However, these relationships are subject to omitted variable bias, potential measurement
error, and reverse causality. As a result, we are not able to identify, using standard econo-
metrics, the mechanisms through which distortions and institutional quality affect firms’ tax
evasion behavior. Instead, we develop a general equilibrium model and use simulations to
match the empirical evidence. Building on Restuccia and Rogerson (2008), the economic
environment consists of (i) one representative household which maximizes his inter-temporal
utility, (ii) a government which balances its budget and, (iii) heterogeneous firms which max-
imize their profits and make tax evasion decisions based on their idiosyncratic productivity
level as well as their idiosyncratic level of distortions stemming from the business environ-
ment. We calibrate the model to the United States and treat that country as an economy
with no distortions as is standard in the literature. This benchmark economy allows nor-
malizing GDP per worker in both the model and the data. The model is then simulated
for each of the 30 African and Latin American countries from the WBES sample, using the
benchmark distribution of productivity and the idiosyncratic distribution of losses due to the
distortions from the business environment of a specific country.
The model explains 35 percent of the variation of tax evasion and 49 percent of the
dispersion of output per worker in the data. The simulation for each region provides similar
findings. As robustness checks, we calibrate the model to the Chilean economy, which exhibits
the lowest combination of distortions and tax evasion in the data. Moreover, the model
is simulated using an alternative measure of the deterrence probability. These additional
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robustness checks show that the findings remain robust, and the model explains at least 20
percent of the variation of tax evasion across countries in all cases.
Having established the ability of the model to replicate some relevant moments in the data
related to tax evasion and output across African and Latin American countries, we conduct
a set of tax neutral counterfactual experiments to examine various policy implications. First,
we examine what happens in Guinea, the country with the highest level of tax evasion in the
data, when distortions are reduced to the average level of the sample. Such improvements
generate a drop between 18.34 and 40.16 percent in tax evasion while output per worker
increases by between one- to six-fold. Second, we examine what happens when governments
could reduce distortions stemming from the business environment by half. Such reduction
could reduce sales not reported for tax purposes by about 21%.
This paper is closely related to Restuccia and Rogerson (2008) and Bah and Fang (2015).
The authors argue in the former paper that a country’s policies and institutions can create
taxes or subsidies (distortions) on establishment output. These distortions reduce aggregate
total factor productivity (TFP) and can explain up to 50% of the cross-country differences
in output, capital accumulation, and TFP. Bah and Fang (2015) introduce distortions as an
idiosyncratic tax on output in the general equilibrium model of Amaral and Quintin (2010).
In addition to a distribution of productivity approximated with firms’ size, Bah and Fang
(2015) use the distribution of distortions from the data and collateral constraints to explain
some of the variations in output in Africa. This paper differs from Restuccia and Rogerson
(2008) and Bah and Fang (2015) by focusing on the effect of distortions and monitoring
capacity on firms’ tax evasion behavior. The paper highlights two mechanisms explaining
the role of distortions stemming from the business environment and institutional quality on
tax evasion in African and Latin American countries.
Well-developed infrastructures and institutions are essential to promoting economic growth
by reducing transaction cost for firms as well as for households. Firms’ performance is af-
fected by the quality of infrastructures such as transport, energy, water, and sanitation as
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those infrastructures and services are used in the production processes and delivery of goods
and services (Bah and Fang, 2015). However, in developing countries, the cost of trans-
portation, logistics, telecommunication, water, electricity, security, and bribes are high, and
firms suffer great losses due to the poor quality of public infrastructures and services (Eifert
et al., 2006). The latter increases transaction costs and makes firms less competitive and
productive than their international counterparts (Bah and Fang, 2015). Similarly, inefficient
institutions in developing countries create barriers to opportunities and increase costs and
risks for microenterprises as well as multinationals (World Bank, 2005; Botero et al., 2004).
Inefficient institutions and policies limit market access and increase the size of the unoffi-
cial economy (Botero et al., 2004; López de Silanes et al., 2002). Also, recent literature on
policy distortions unanimously demonstrates that ineffective public policies lower aggregate
total factor productivity (TFP) and explain an important share of TFP dispersion across
countries (Hseih and Klenow, 2009; Restuccia and Rogerson, 2013; Wu, 2018). The bene-
fits of improving the institutional quality are not limited to developing countries as Prado
(2011) shows on a sample of OECD countries that policies reducing regulation costs have a
significant positive impact on the supply of both private and publicly produced goods, and
effectively reduce the size of the informal sector. We show in this paper that distortions
stemming from the business environment and institutional quality affect firms’ tax evasion
decisions and production.
The remainder of the paper is structured as follows. Section II describes the data and
examines the relationship between distortions and firms’ tax evasion empirically. Section
III presents the theoretical model. In section IV, we calibrate the model using the United
States as a benchmark economy; we then describe our quantitative analysis and the results
of the counterfactual experiments. Section V assesses the sensitivity of the findings. The
concluding remarks and policy implications are discussed in section VI.
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II Empirical Evidence
This section provides empirical evidence consistent with the testable predictions of the gen-
eral equilibrium model in the next section. We first provide descriptive evidence before using
a multilevel mixed model to examine the impact of distortionary infrastructures and policies
on firms’ tax evasion. The multilevel mixed model allows dealing with the hierarchic structure
of the data and takes into account the potential dependence between firms of a given coun-
try. Moreover, this methodology allows including both microeconomic and macroeconomic
variables while accounting for country fixed effects.
II.1 Data and descriptive statistics
We use the World Bank Enterprise Surveys (WBES) data which are a collection of a firm-
level surveys of a representative sample (random stratified sampling) of firms mainly in
developing countries. Questionnaires cover a wide range of business environment topics
like infrastructure, performance measures, crime, corruption, competition, access to finance.
The surveys are conducted within a framework of common guidelines in the design and
implementation. A module of identical questions included in all questionnaires is used for
assembling the data set, which allows cross-country comparisons. The analysis focuses on
African and Latin American countries having at least 200 establishments as well as tax
evasion and distortions data.3 The distributions of firms used in the structural model below
are representative at the country level. The sample comprises 19,490 firms in 30 African
and Latin American countries during the period 2002-2006.4 The appendix provides a list of3The sample also excludes countries that were involved in armed conflicts over the period of the survey.
We do so to ensure that the measure of institutional quality, as well as the costs stemming from the businessenvironment, are not tainted by armed conflicts. All conclusions of the paper remain the same without theserestrictions and using countries with at least 100 establishments.
4The dataset employed in this paper does not include informal firms due to data issues. However, weacknowledge that informality is pervasive in developing countries and might be connected with low domesticresource mobilization. As discussed by Besley and Persson (2014), the informal sector is inherently hardto tax because transactions are not recorded, and incomes from informal firms are difficult to measure.Moreover, informality is a source of misallocation (D’Erasmo and Boedo, 2012) affecting both productivityand tax collection (Ordóñez, 2014). Consequently, we expect in this paper that the estimated effects ofdistortionary infrastructures and policies provide only lower bounds for tax evasion in African and Latin
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countries.
Tax evasion is captured by the proportion of total sales not reported for tax purposes.5
Distortions are measured as the sum of the losses (in percentage of total sales) due to power
outage or surges from the public grid, insufficient water supply, unavailable main line tele-
phone service, transport failures, crime (loss due to theft, robbery, vandalism or arson) and
gifts or informal payment to public officials to "get things done".6
Institutional quality is proxied using a measure of the effectiveness of public policies from
the World Governance Indicators (WGI). More particularly, institutional quality captures
the perceptions of the quality of public services, the quality of the civil service and the
degree of its independence from political pressures, the quality of policy formulation and
implementation, and the credibility of the government’s commitment to such policies. We
use the percentile distribution of these public policies effectiveness variables.
In the regressions below, we also account for a set of firm individual characteristics. Firms’
access to finance is measured as the share of working capital financed by commercial banks.
Regulatory burden is measured by the percentage of time the senior management spends
dealing with requirements imposed by government regulation. We include the percentage of
the firm owned by foreign interests and the government, firm’s age captured by three cate-
gorical variables: young (1-5 years old), mature (6-15 years old), and older (more than 15
years old), and the percentage of the establishment’s sales exported. Finally, we account for
the size of the firms using four dummy variables capturing firms’ size categories. Microen-
terprises have fewer than 10 permanent employees. Small and medium have between 11 and
American countries, as the paper does not account for the distortions generated by informal firms and theirimpacts on tax collection.
5The question is: "Recognizing the difficulties many enterprises face in fully complying with taxes andregulations, what percentage of total sales would you estimate the typical establishment in your area ofactivity reports for tax purposes?"
6The questions related to the components of distortionary infrastructures are stated as follows. (i) "Pleaseestimate the losses (as a percentage of total sales) of theft, robbery, vandalism or arson against your estab-lishment in the last year." (ii) "What percentage of your total sales value was lost last year due to poweroutages, insufficient water supply, unavailable mainline telephone service, and transport failures?" (iii) "Wehave heard that establishments are sometimes required to make gifts or informal payments to public officialsto "get things done" about customs, taxes, licenses, regulations, services, etc. On average, what percentageof annual sales value would such expenses cost a typical firm like yours?"
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50 or between 51 and 200 permanent employees, respectively. Firms with more than 200
permanent employees are classified as large firms. Older firms and larger firms are used as
the reference categories in the regression analysis.
Table 1 presents the descriptive statistics. On average, about 23.84 percent of total sales
are not reported for tax purposes (tax evasion) and 4.69 percent of annual sales are lost due
to the poor quality of infrastructures, crime, and informal payment to public officials (dis-
tortionary infrastructures). Approximately, 75.77 percent of establishments’ working capital
is financed by commercial banks. On average 12.16 percent of senior management’s time is
spent dealing with requirements imposed by government regulation each week (taxes, cus-
toms, labor regulations, licensing and registration), and only 9.80 percent of establishments’
total sales are exported. Firms are on average 20 years old. Finally, the sample contains 27.2
percent of microenterprises, 43.4 percent of small firms, 20 percent of medium firms and 9.4
percent of large firms.
Figure 1 plots the correlation between tax evasion and distortionary infrastructures across
countries. Each circle represents one country. As it can be seen, there is a positive correlation
between tax evasion and the losses stemming from the business environment. Conversely,
Figure 2 presents the evidence of a negative relationship between institutional quality and
tax evasion. This evidence supports that high distortionary infrastructures and policies are
correlated with the proportions of sales not reported for tax purposes.
Table 1. Summary statistics
Variable Mean Std. Dev. Min. Max.Tax evasion 23.84 32.53 0 100Distortionary infrastructures 4.74 10.53 0 100Access to finance 75.77 31.79 0 100Management time 12.16 18.21 0 100Foreign share 10.33 28.70 0 100Government share 0.52 6.55 0 100Sales exported 11.76 24.02 0 100Age 19.95 17.88 0 196Notes. All variables are expressed in %, except age.
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SLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLV
GTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTM
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Figure 1. Distortionary infrastructures and Tax evasion
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SLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLV
GTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTM
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020
40
60
Ta
x E
va
sio
n (
% o
f to
tal sa
les)
0 .2 .4 .6 .8Policy effectiveness
Figure 2. Distortionary policies and Tax evasion
9
II.2 Methodology: Multilevel mixed model
This section examines the role of distortionary infrastructure and policies on firms’ tax eva-
sion using a multilevel mixed model. This model takes into account the fact that firms in
a given country share similar contextual characteristics such as institutional environment,
macroeconomic framework, and policies which affect their tax evasion behavior. Standard
estimation methods ignore such clustering effects which generate biased standard errors. As
firms in a given country may not be independent, standard errors in standard estimation
methods may be underestimated. The model allows the intercept to vary across countries
(Hox et al., 2010). Moreover, this methodology allows to include both microeconomic and
macroeconomic variables as well as country fixed effects. The approach considers a two-level
model where the highest level is the country, and the lowest level is the firms such that:
Level 1:
Tax_Evasionic = α0c + βDic + ηXic + γIQc + δc + µs + ηt + εic, εic ∼ N(0, σ2) (1)
Level 2:
α0c = α00 + ϑc, ϑc ∼ N(0, σ2), ϑc⊥εic (2)
Combining the two previous equations, the model can be written as follows:
Tax_Evasion_ic = α00 + βDic + γIQc + ηXic + δc + µs + ηt + ϑc + εic (3)
where, ϑc + εic is the error term of the model with ϑc the country-specific error term and εic
the firm-level error term. Tax_Evasion_ic refers to the proportion of sales not reported for
tax purposes of the firm i in the country c. Dic denotes distortions, IQc institutional quality,
and Xic defines firm individual characteristics. All these variables are the same as defined
previously. Finally, δc, µs, and ηt refer to country, sector and year fixed effects respectively.
These fixed effects control for potentially important omitted variables at the country, sector
and year level while accounting for differences in demand conditions, productive structure
and culture of opportunistic behaviors.
10
II.3 Results
Table 2 reports the findings of the estimation of the equation (3). As it can be seen from
the first row, distortions are positively related to firms’ tax evasion. These coefficients are
statistically significant at the 1 percent level and suggest that a 1 percent loss in total sales
due to distortionary infrastructures increases firms’ tax evasion from 0.11 to 0.14 percent.
Conversely, sales not reported for tax purposes decrease with institutional quality (last row).
A one-unit increase in the index of the public policies effectiveness decreases firms’ tax evasion
from 62.79 to 70.71 percentage points.
Moreover, as it can be seen in columns (3) to (8) having a higher working capital financed
by commercial banks decreases firms’ tax evasion, suggesting that access to finance reduces
the likelihood that an establishment under-reports its total sales for tax purposes. Financially
constrained establishments seem to use tax evasion as an alternative source of financing or as
a way to survive in a highly distorted environment. Columns (7) and (8) shows that larger
and medium establishments are less likely to underreport sales for tax purposes (large firms
are the omitted category).7 Finally, foreign ownership status is negatively associated with
firms’ tax evasion. The coefficients are statistically significant at the 1 and 5 percent levels.
All estimates include country, sector and year fixed effects.
Although these results corroborate similar findings in the literature, one should treat
them with caution as we do not tackle potential endogeneity issues such as omitted variable
bias, potential measurement error, and reverse causality. Moreover, the empirical approach
limits the possibility to identify the mechanisms through which the losses stemming from
the poor business environment and public policies ineffectiveness affect firms’ tax evasion.
That is why we develop a theoretical model in the next section, which allows examining two
mechanisms relating distortions, institutional quality, and tax evasion behavior.
7The findings remain the same capturing the size and age of the firms by continuous variables.
11
Table2.
Impa
ctsof
distortion
aryinfrastructuresan
dpo
licieson
firm’s
taxevasion
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dep
endent
variab
le:Ta
xevasion
Distortions
0.13
7***
0.13
2***
0.12
7***
0.122*
**0.12
2***
0.12
1***
0.10
9**
0.10
7**
(0.040
6)(0.044
5)(0.044
8)(0.045
5)(0.045
5)(0.045
7)(0.045
7)(0.045
1)Man
agem
entTim
e-0.019
3-0.019
8-0.0201
-0.020
1-0.019
0-0.015
1-0.014
6(0.024
0)(0.024
4)(0.024
3)(0.024
3)(0.024
6)(0.023
7)(0.023
7)Accessto
finan
ce-0.023
0*-0.023
5*-0.023
6*-0.023
0*-0.022
6*-0.022
1*(0.012
8)(0.012
8)(0.012
8)(0.012
9)(0.012
5)(0.012
4)Fo
reignshare
-0.045
7***
-0.0459*
**-0.041
0***
-0.026
7**
-0.027
9**
(0.014
0)(0.014
0)(0.013
0)(0.0121)
(0.012
0)Governm
entshare
-0.033
9-0.034
0-0.003
91-0.002
09(0.037
4)(0.037
1)(0.035
3)(0.035
2)Sa
lesexpo
rted
-0.030
0*-0.007
92-0.009
15(0.017
0)(0.015
7)(0.016
1)Age
-0.038
4-0.019
9(0.025
9)(0.019
3)Microenterprise
8.40
2***
7.91
7***
(1.623
)(1.586
)Sm
all
4.36
9***
4.10
3***
(1.466
)(1.414
)Medium
1.35
11.246
(1.167
)(1.145
)You
ng2.21
9(2.044
)Mature
0.69
6(0.868
)Institutiona
lQ.
-62.79
***
-70.71
***
-69.15
***
-68.11
***
-68.03
***
-69.04
***
-66.46
***
-67.08
***
(16.34
)(14.38
)(14.91
)(15.63
)(15.69
)(16.19
)(16.53
)(16.30)
Observation
s19
,490
17,250
17,100
17,089
17,089
17,068
17,068
17,068
Not
es.The
tablepresents
theestimates
oftheeff
ects
ofdistortion
aryinfrastructuresan
dpo
licieson
firms’
taxevasionusingamultilevelmixed
effects
mod
el.Allregression
sinclud
ecoun
try,
sector,a
ndyear
fixed
effects.Rob
uststan
dard
errors
clusteredat
thecoun
try-sector
levelinpa
rentheses.
***,
**,*
deno
tesign
ificanc
eat
the1,
5,an
d10
percentlevel.
12
III The model
In this section, we develop a general equilibrium model to analyze potential mechanisms
linking distortions and institutional quality to firms’ tax evasion decision. We identify two
mechanisms that can explain the relationship between these variables. First, entrepreneurs,
knowing that institutional quality is low, also anticipate low monitoring of their taxes and
have, therefore, more incentives to evade their taxes. The second mechanism is when dis-
tortionary infrastructures and policies generate losses for firms by creating a wedge between
firms’ potential and realized profits. We assume that firms compensate for some of these
losses by evading their taxes.
The economic environment consists of (i) firms which maximize their profits and are het-
erogeneous along two dimensions: productivity and the level of distortions they face due
to unsound business infrastructures and policies; (ii) one representative household which
maximizes its inter-temporal utility, and (iii) a government which balances its budget.
III.1 Representative household
Consumers are aggregated through a representative household with preferences described by
the following utility function:
∞∑t=0
βtu(ct)
where ct is consumption at date t and β ∈ (0, 1) is the discount factor. The household is
endowed with one unit of productive time in each period and K0 > 0 units of the capital
stock at date 0. We assume that u(ct) satisfies the usual Inada conditions. The representative
household maximizes its lifetime utility subject to the following budget constraint:
∞∑t=0
(ct +Kt+1 + (1− δ)Kt) =∞∑t=0
(rtKt + wtNt + Πt)
13
where, wt and rt are, respectively, the rental price of labor lt and capital Kt at t, and Nt
the total labor supply to the market. We assume that the representative consumer does not
value leisure, i.e. Nt = 1. Finally, Πt is total profits from the operations of all firms.
III.2 Firms
Entrepreneurs are risk-neutral and produce a homogenous consumption good. Each estab-
lishment i makes production decisions to maximize profits based on an idiosyncratic produc-
tivity level zi, which is constant over time and varies across establishments. Values of the
parameter zi, are drawn from a probability density function of g(z). The production function
F (zi, A, ki, li) takes as input capital ki and labor li. We include a variable A capturing insti-
tutional quality. This variable is the same for all the establishments in the same country and
captures the quality of public policies formulation and implementation as well as the quality
of public services. The production function is assumed to exhibit decreasing returns to scale
in both capital and labor, and to satisfy the usual Inada conditions:
F (zi, A, ki, li) = ziAkηi lγi , 0 < η + γ < 1.
There is a proportional tax τ on establishments’ total sales, which is assumed to have two
components, τ c and τ di . The first component τ c represents a tax collected by the government
to finance institutional quality, which can be assimilated public goods and services. The
second component τ di refers to the fraction of output which is lost due to distortionary
infrastructures. To summarize τ = τ di + τ c. We assume that establishments differ in their
idiosyncratic levels of productivity and distortions.
In each period an establishment reports a proportion (1−ϑi) of its total sales for tax purposes
based on its idiosyncratic level of distortions. Hence, each establishment evades a proportion
ϑi of its total sales. We assume there is a probability p to be detected for tax evasion. In
this case, the evading firm must pay a fine. The expected fine is assumed to be a convex
14
function of the establishment’s tax evasion so that the marginal cost of tax evasion is positive
and increasing in tax evasion: B = pϑθiF (zi, A, ki, li), with θ > 2. The expected profit for an
establishment with productivity zi is given by:
π(zi, τi) = (1− τi)(1− ϑi)F (zi, A, ki, li) + ϑiF (zi, A, ki, li)︸ ︷︷ ︸T
−wli − rki − pϑθiF (zi, A, ki, li)︸ ︷︷ ︸B
−cf (4)
where cf is a fixed cost of operation for an incumbent establishment, p is the probability of
detection, w and r are, respectively, the rental prices of labor and capital. T is the amount of
total sales evaded. We assume that this amount is beneficial for the establishment (private
benefit) but is a dead loss at the country level. By rearranging equation (4) we have
π(zi, τi) = [1− (1− ϑi)τi − pϑθi ]F (zi, A, ki, li)− wli − rki − cf (5)
For the sake of simplicity, we abandon the index i; however, it is understood that z, k ,l, τ ,
ϑ are different for each establishment in what follows.
III.3 Government
The government provides public goods and services with institutional quality A by balancing
its budget in each period:
∫ zmax
1
τ c(1− ϑi)F (zi, A, ki, li)d(τ, z) +
∫ zmax
1
pϑθiF (zi, A, ki, li)d(τ, z) ≥ C(A, p)
The government revenues appear on the left hand side (LHS) of this equation. The first
component is the revenues from proportional taxation on establishments’ total sales. The
second component is the revenues from detection activities. The right hand side (RHS) is the
cost associated with the provision of institutional quality A and detection activities p which
is assumed to be a convex cost function in both A and p.
15
III.4 Equilibrium
We consider the steady-state competitive equilibrium of the model in which the decision
problems are described as follows.
III.4.1 Consumer’s problem
Using the first order conditions, we find a solution to the consumer’s problem with the rental
price of capital r and the consumption c being constant, that is:
r =1
β− (1− δ) (6)
III.4.2 Incumbent establishment’s problem
Solving for the first order conditions, the optimal tax evasion and factor demands are:
ϑ∗ = [1
θpτ ]
1θ−1 (7)
k∗ = [zA(1− (1 +1− θθ
(1
pθτ)
1θ−1 )τ)]
11−α−γ [
γ
w]
γ1−α−γ [
α
r]
1−γ1−α−γ (8)
l∗ = [zA(1− (1 +1− θθ
(1
pθτ)
1θ−1 )τ)]
11−α−γ [
γ
w]
1−α1−α−γ [
α
r]
α1−α−γ (9)
Given that the establishment-level productivity and tax rate are constant over time, the
discounted present value of an incumbent establishment is given by :
V (z, τ) =π(z, τ)
(1− ρ)(10)
16
where ρ = 1−λ1+r−δ is the discount rate for the establishment and λ the probability of death
which is assumed to be constant. Substituting ρ in equation (10) we have
V (z, τ) =(1− δ) + r
(λ− δ) + rπ(z, τ) (11)
III.4.3 Entry
Establishments’ entry decision is made on the basis of the distribution over potential draws
for the pair (z, τ). Let x̄(z, τ) denote the optimal entry decision with the convention that the
establishment enters and remains in operation if x̄(z, τ) = 1. The actual discounted value of
a potential entrant Ve is given by:
Ve(z, τ) =∑(z,τ)
maxx̄∈[0,1]
[x̄(z, τ)V (z, τ)d(z, τ)− ce]
where, ce is the entry cost paid by a new establishment, and g(z, τ) is the probability density
function. In an equilibrium with entry, the free entry condition is fulfilled for Ve(z, τ) = 0. In
the steady state and according to equations (6) and (11), V (z, τ) is determined only by the
endogenous variable w. Hence, there is a unique value of the wage rate w for which Ve = 0
as in Restuccia and Rogerson (2008).
III.5 Invariant distribution of establishment
Let E and µ(z, τ) denote, respectively, the mass of entrants and the distribution of firms in
period t. Given the decision rule for production of entering establishments x̄(z, τ), the next
period distribution of firms µ′ over the pair (z, τ) satisfies the following condition:
µ′(z, τ) = (1− λ)µ(z, τ) + x̄(z, τ)d(z, τ)E
where (1 − λ)µ(z, τ) refers to the mass of incumbent establishments that have survived,
and x̄(z, τ)d(z, τ)E represents the mass of entering establishments that enter and remain in
17
operation. The unique invariant distribution of establishment µ̂(z, τ) is characterized by a
constant distribution of µ over time and a death rate bounded away from 0. This invariant
distribution of establishments is given by:
µ̂(z, τ) = Ex̄(z, τ)
λd(z, τ)
III.5.1 Labor market clearing
Given values for w and r, the steady state of this model is characterized by the functions
ϑ̄(z, τ), k̄(z, τ), l̄(z, τ), x̄(z, τ) and the associated invariant distribution of establishments
µ̂(z, τ). Using these functions, the aggregate labor demand and the steady-state equilibrium
level of entry are given by:
N(r, w) = E∑(z,τ)
l̄(z, τ)µ̂(z, τ)
E =N(r, w)∑
(z,τ) l̄(z, τ)µ̂(z, τ)
III.5.2 Definition of a competitive equilibrium
The steady-state competitive equilibrium with entry is a set of prices {w∗, r∗}, a set of
decision rules {k∗, l∗, ϑ∗; c∗, K ′∗}, a distribution of establishments µ(z, τ), value functions
π(z, τ), V (z, τ), Ve(z, τ), and a mass of entry E such that :
1. Given prices (w, r) and preferences, the pair {c∗, K ′∗} maximizes the consumer lifetime
utility;
2. Given prices (w, r), the functions π(z, τ), V (z, τ), and Ve(z, τ) solve incumbent and
entering establishment’s problems, with {k∗, l∗, ϑ∗} the optimal policy functions;
3. The free-entry and invariant distribution conditions are satisfied i.e.
Ve = 0,
18
µ(z, τ) = Ex̄(z, τ)
λd(z, τ), ∀ z, τ
4. Market clearing conditions are satisfied :
c+ δK =∑(z,τ)
[f(z, A, k̄, l̄)− cf ]µ(z, τ)− ceE
K =∑(z,τ)
k̄(z, τ)µ(z, τ)
1 =∑(z,τ)
l̄(z, τ)µ(z, τ)
III.6 Theoretical predictions
From the optimal tax evasion equation (7), we can draw two predictions:
Prediction 1: ∂ϑ∗
∂τd> 0, ∀ τ ∈ ]0, 1]. An increase in distortions stemming from busi-
ness environment increases firms’ tax evasion.
Prediction 2: ∂ϑ∗∂p
< 0, ∀ p ∈ ]0, 1]. A higher probability of detection reduces firms’
tax evasion.
19
IV Quantitative model
In this section, we calibrate the model using the United States economy and then simulate
it on a sample of 30 African and Latin American countries.
IV.1 Calibration
The model is calibrated on the United States, which is considered as an economy with no
distortions as is standard in the literature. Several of the parameter values are assigned
following the literature. The period in the model corresponds to one year in the data.
Preferences. The yearly interest rate is targeted to 4% as in Restuccia and Rogerson (2008)
and Bah and Fang (2015). This implies a value of β = 1(1+0.04)
= 0.96.
Technology. We follow the literature by using 0.85 as the returns to scale of the production
function.8 Parameter values η and γ are attributed to match capital and labor shares of
income, respectively, 13and 2
3of the returns to scale of the production function. We choose
δ so that the investment to output ratio is equal to 20%, implying δ = 0.08 as in Restuccia
and Rogerson (2008). The range of employment across establishments in the data determines
the range of establishment-level productivity. According to the United States data from the
Census Bureau,9 the number of employees at the establishment level ranges from 1 to 10,000.
Hence, the minimum and maximum levels of productivity are chosen to obtain the range of
employment, as in the data. Normalizing the lowest firm-level productivity to 1, the highest
level of productivity is chosen to obtain the maximum number of employees of 10,000, as in
the data. We approximate the distribution of establishment-level productivity with 100 grid
points. We choose a log-spaced grid so that the invariant distribution of establishment size
across employment level matches the data. Descriptive statistics about firms highlight one
important stylized fact. Establishments with fewer than 20 employees represent 86.1 percent
of all the establishments. However, these establishments account only for 24.9 percent of the8See for instance Restuccia and Rogerson (2008), Atkeson and Kehoe (2005), and Pavcnik (2002).9The data can be downloaded from the website of the US Census Bureau: http://www.census.gov/
econ/susb/data/susb2007.html in the table "U.S. & states, totals".
20
employment.
Distortions We use the distortions from WBES in the same way as we did in the empir-
ical section as a proxy of distortionary infrastructures. As mentioned previously, we sum
up the percentage of sales lost due to poor infrastructure and services (losses due to power
outage or surges from public grid, insufficient water supply, unavailable mainline telephone
service, transport failures); crime (losses due to theft, robbery, vandalism or arson against
the establishment); and corruption (informal payments to "get things done"). For a given
country, we compute losses for each establishment and use the distribution of distortionary
infrastructures across establishments in the simulation.
Institutional Quality We calibrate institutional quality A using the measure of public
policies effectiveness from the Worldwide Governance Indicators (WGI) as described above.
Recall that this variable is defined as perceptions of the quality of public services, the quality
of the civil service and the degree of its independence from political pressures, the quality of
policy formulation and implementation, and the credibility of the government’s commitment
to such policies. We use the percentile distribution of policy effectiveness variable ranged
from 0 to 1.10 In the calibration, we approximate the detection probability using this in-
dex of public policies effectiveness. We argue that effective government in formulating and
implementing public policies will also be effective in detecting illegal activities such as tax
evasion. In the sensitivity analysis, we relax this assumption using an alternative measure
of detection probability and show that the predictions of the paper remain the same and are
not driven by this assumption.
In the model, the cost of tax evasion for an establishment is assumed to be a convex function,
with a convexity parameter θ > 2. This assumption guarantees that the marginal cost of
tax evasion is positive and increasing with the share of sales not reported for tax purposes.
The convexity parameter θ is calibrated to 3. Sensitivity analysis shows that the findings are10To the best of our knowledge, the proportional tax rate on sales is not publicly available for each country
in the sample. Calibrating institutional quality using the data allows us minimizing errors arising from a notaccurate calibration of the proportional tax rate on sales τ c for each country. The proportional tax rate onsales τ c is therefore normalized to 0 in the quantitative model.
21
robust using alternative values of θ. Table 3 summarizes the parameter values.
Table 3. Benchmark calibration
Parameter Value Description Sourceτ d [0; 1] Distortionary infrastructures WBESA [0; 1] Public policies Effectiveness WGIp [0; 1] Detection probability WGIβ 0.96 Real rate of return Literatureη 0.283 Capital income share Literatureγ 0.567 Labor income share Literatureδ 0.08 Investment to output ratio Literatureλ 0.1 Annual exit rate Literatureθ 3 Evasion cost parameter Assumptionce 1 Entry costs Literaturecf 0 Fixed costs Literaturez [1; 3.98] Distribution of productivity Relative labor demandNotes. See Bah and Fang (2015), Restuccia and Rogerson (2008), Atkeson and Kehoe (2005)
and Pavcnik (2002) for parameter values from the literature.
22
IV.2 Quantitative analysis
The quantitative analysis consists in validating the model and conducting counterfactual ex-
periments to assess the implications of changes in the distortions stemming from the business
environment and the detection probability.
IV.2.1 Validation of the model
We simulate the model for 30 African and Latin American countries using the calibrated
parameters described above. For each country, the model predicts the level of tax evasion
and output per worker. Figure 3 plots GDP per worker from the model against GDP per
worker from the World Bank’s World Development Indicators (WDI). The reported value of
the GDP per worker is normalized by the United States levels in both the model and the
data. Each circle represents one country and corresponds to the correlation of output per
worker between the simulated data and the WDI data. The straight line is obtained from
an OLS regression between the model and the data. As it can be seen, the predicted values
of output per worker in the model are positively correlated with the data. The coefficient
is statistically significant at the 1% level, and the regression coefficient is 4.58. Similarly,
Figure 4 plots the level of tax evasion from the model and the data. The predicted values of
tax evasion are highly correlated with the WBES data. The regression coefficient is 0.92 and
is statistically significant at the 1% level. Focusing on the R-squared, the model explains
49% of the variation of the GDP per worker and 35% of the dispersion of tax evasion among
African and Latin American countries.
23
ARG
BOL
BRA
CHL
COL CRI
ECU
EGY
SLVGTM
HND
KEN
MDG
MEX
MAR
NIC
PAN
PRY
PER
ZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZAUGA
0.1
.2.3
.4.5
Mo
de
l
0 .02 .04 .06 .08Data
R−squared=.4931813103211073
Figure 3. GDP per worker - data vs the model predictions
ARG
BOL
BRA
CHL
COL
CRIECU
EGYSLV
GTM
HNDKEN
MDG
MEX
MAR
NIC
PAN
PRY
PERZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZA UGA
0.2
.4.6
Mo
de
l
.1 .2 .3 .4 .5Data
R−squared=.3505665524954171
Figure 4. Tax evasion - data vs the model predictions
24
IV.2.2 Counterfactual experiments
Having established the ability of the model to replicate some of the variations of tax evasion
and output per worker across African and Latin American countries, we conduct in this sec-
tion three tax neutral counterfactual experiments to examine the implications of changes in
the distortions stemming from the business environment and the detection probability. Tax
neutral experiments guarantee that the tax revenues remain the same as in the benchmark.
In those experiments, the tax revenues from the decrease in tax evasion are used as a subsidy
to production, i.e., the price of the final good is now a function of this subsidy.
The counterfactual experiments focus on Guinea, the country with the highest level of tax
evasion in the data. We first examine what happens regarding tax evasion, ceteris paribus,
when the losses stemming from the business environment in Guinea are reduced to the aver-
age level observed in the sample. In particular, we replace the distribution of losses stemming
from business environment across size in Guinea by the average distribution of losses across
firms’ size from the sample. All the parameters of the model remain the same as previously,
except the losses stemming from the business environment. In the second experiment, we
explore the second mechanism by examining the impact of a change in Guinea’s deterrence
probability. In particular, we increase the deterrence probability by 50 percent in Guinea. All
other parameters remain the same. Finally, we combine in a third experiment both changes,
i.e., a distribution of distortions similar to the average level of the sample and a detection
probability 50% larger. Table 5 reports the change on key variables relative to the baseline
findings on Guinea.
Experiment 1. In the first scenario, we conduct a tax neutral experiment analysis and
set the distribution of losses stemming from business environment to the average level by size
category in the sample. As described in Table 4, the distribution of losses by size in Guinea
is between 14.97 and 20.60 percent of sales, whereas in the sample the latter is between 5.18
and 7.36 percent of their sales. On average, counterfactual experiment 1 corresponds to a
25
65.25 percent drop in the losses stemming from the business environment in Guinea.
Table 4. Distribution of losses in Guinea vs. the sample
Establishment size Average losses Average losses(number of employees) in Guinea in the sample< 10 19.59 7.3610 to 19 19.24 7.0020 to 49 14.97 6.3950 to 99 20.60 5.49≥ 100 17.00 5.18
The results show that a drop to the average level by size category generates a 26.72 percent
drop in sales not reported for tax purposes in Guinea. Guinean firms’ sales not reported
for tax purposes decline from 53.74 percent to 39.38 percent. As discussed above, losses
stemming from the business environment are a proportional tax on firms’ sales and create
therefore a wedge between potential and realized profits. In such environment, a drop in
additional costs stemming from business environment decreases sales not reported for tax
purposes as witnessed in experiment 1. Using the proceeds from increased tax revenues to
subsidize production, a drop in distortionary infrastructures in Guinea to the average level
of the sample generates a 1.06-fold increase in output per worker. The aggregate TFP is
one of the channels through which the output per worker is stimulated. As Table 5 shows,
the aggregate TFP increases slightly by 0.16 percent. Also, a drop in distortions stemming
from the business environment stimulates the entry of firms in the market and the aggregate
capital as both increase by 22 percent.
This experiment seems to benefit more to medium and large firms, which represent firms
having between 20-100 and more than 100 employees. The share of firms in both groups
increases by about 8.23 and 2.12 percent respectively while the share of small firms in the
distribution of establishments decreases by 0.98 percent. Moreover, this experiment gener-
ates a scale effect as the average size of firms increases by 21.52 percent. This is a direct
consequence of the increase in the share of medium and large firms in the distribution of
establishments. Finally, this experiment implies a reallocation of the labor force towards
26
large firms. The share of total employment in large firms grows by around 3 percent, while
both medium and small firms face a drop in their employment share.
Experiment 2. In the second counterfactual experiment, we examine what happens in
Guinea when the detection probability can be improved by 50 percent while the distribution
of losses stemming from the business environment is left untouched. When the detection
probability is increased from 0.0634 to 0.0951, sales not reported for tax purposes decline
from 53.73 percent to 43.88 percent, corresponding to a 18.34 percent drop in firms’ tax eva-
sion in Guinea. The improvement of the institutional quality raises the expected costs related
to tax evasion activities as monitoring and auditing increase. Consequently, the incentive for
firms to underreport their sales for tax purposes declines.
In this tax neutral experiment, the output per worker is nearly 6.33-fold as large as the
initial output per worker of the country.11 Compared to the baseline findings on Guinea,
a 50 percent improvement in the institutional quality causes the aggregate TFP to rise by
208 percent and boosts the entry of firms by 173 percent. Similarly, the aggregate capital
increases by 173 percent. These important changes in aggregate TFP and the entry of firms
might explain the substantive growth in output per worker. This experiment facilitates the
emergence of medium firms as their share in the distribution of firms increases by 8.71 percent
while the share of small firms decreases by 0.98 percent for a constant share of large firms.
The evidence is consistent with the literature on the missing middle in the size distribution
of firms in developing countries, where the quality of the business environment is identified as11Although the change in the output per worker might appear high, they are in line with similar evidence in
the literature. IMF (2003) shows for instance that improving the quality of institutions in Cameroon (-0.72)to the average level of institutions in the sample (0.13) generates almost a 5-fold increase in income per capitain Cameroon. Rodrik et al. (2004) find that one standard deviation increase in institutional quality generatesa 6.4-fold difference in income per capita. Similarly, Acemoglu et al. (2001) show that improving the qualityof institutions in Nigeria to the level of Chile could lead to a 7-fold increase in Nigeria’s income. Also, Halland Jones (1999) show in a cross-country regression that difference in institutions between Niger and theUnited States which is only 3.78-fold is more than enough to explain the 35-fold difference in output perworker. The contribution of Hall and Jones (1999), one of the most important in the literature, highlightshow a small change in institutions can generate an exponential variation in output per worker.
27
a critical factor explaining the limited number of medium-sized firms.12 The counterfactual
experiment shows that improving the quality of business environment favors the distribution
of firms toward medium-sized firms. Moreover, the experiment shows no scale effect. The
average size of firms decreases slightly by 4.69 percent. Finally, the improvement in the
institutional quality generates a reallocation of the labor force towards medium firms, which
account for an 8.86 percent increase in employment share.
Experiment 3. Finally, we combine both changes, i.e., a reduction in distortions by 65.25%
on average and an increase in detection by 50% and observe a decrease in tax evasion by
40.16 percent, i.e., sales not reported for tax purposes declines from 53.74 percent to 32.16
percent, while the output per worker is now nearly 6.26-fold larger than its initial level. As
it can be seen, the combination of a drop in losses stemming from the business environment
with an improvement of the institutional quality generates a more substantive decline in
sales not reported for tax purposes relative to previous experiments. In this experiment,
both changes contribute to improving the overall business environment. Qualitatively the
channels are similar to those described above: as distortions in infrastructures drop and in-
stitutional quality increases, the aggregate TFP, the mass of firms entering on the market,
and the aggregate capital increase by 185.86 and 200.22 percent respectively. Moreover, we
observe a reallocation of firms towards medium and large firms and a scale effect. As Table 5
shows, the shares of medium and large firms increase 8.23 and 2.12 percent respectively. The
average size of firms grows by 20.99 percent suggesting thereby a scale effect. Finally, this
experiment generates a reallocation of the labor force towards large firms. The total employ-
ment share of large firms increases by about 3 percent, while the employment shares of small
and medium firms decrease by 16.43 and 6.53 percent respectively.
12See for instance Sleuwaegen and Goedhuys (2002) and Tybout (2000) for extensive discussion on themissing middle in developing countries.
28
Table 5. Effects from counterfactual experiments
Counterfactual experiments
Variables Experiment 1 Experiment 2 Experiment 3Tax evasion -26.72 -18.34 -40.16Output per worker 1.06 6.33 6.26Aggregate TFP 0.16 208.07 184.86Aggregate capital 22.92 173.24 200.22Mass of entry 22.92 173.24 200.22Share of small firms -0.98 -0.98 -0.98Share of medium firms 8.23 8.71 8.23Share of large firms 2.12 0 2.12Employment share - Small -16.75 - 0.09 -16.43Employment share - Medium -6.94 8.86 -6.53Employment share - Large 3.04 -1.04 2.95Average size of firms 21.52 -4.69 20.99Notes. Except for the change in output per worker that is reported in fold, all effects arereported in percentage change. Here small, medium and large firms refer to firms having
between 1-19, 20-99, and 100 and more employees.
In summary, the counterfactual experiments show that a drop in losses stemming from the
business environment along with an improvement in detection probability in Guinea could
decrease sales not reported for tax purposes by between 17.78 and 32.86 percent. The drop in
tax evasion combined with the improvement in institutional quality generate between a 1.06-
and 6.33-fold increase output per worker in Guinea through their effects on the aggregate
TFP, the mass entering on the market, and the reallocation of firms and employment.
29
V Sensitivity analysis
This section assesses the sensitivity of the findings simulating the model for each region,
employing alternative detection probability and a recalibrating the model on the Chilean
economy.
V.1 Simulations for each region
So far, the model shows the role of distortionary infrastructures and policies in explaining the
variation in tax evasion and output per worker across African and Latin American countries.
In this section, we simulate the model for the two groups of countries separately. The intuition
of this exercise is twofold. First, we evaluate how the model fares in explaining the variation
of tax evasion and GDP per worker within each region. Second, we show that compositional
effects across the continents do not drive the findings of the paper. The simulation procedure
is the same as previously, except that we are now focusing on each region. Figure 5 plots
tax evasion and output per worker from the model against the data for African countries.
As previously, each circle represents one country and the straight line is obtained from the
OLS regression between the model and the data. The simulated data are highly correlated
with the actual data, with a regression coefficient of around 0.88 for tax evasion and 5.58
for GDP per worker. Based on R-squared values, the model explains 31% of the variation
of tax evasion and 43% of the dispersion in GDP per worker across African countries. Sim-
ilarly, Figure 6 plots tax evasion and output per worker from the model against the data
for Latin American countries. For this group of countries, the regression coefficient is 0.77
for tax evasion and 3.98 for GDP per worker. Using the R-squared, the model explains 23%
of the dispersion of tax evasion and 71% of the variation of the output per worker in Latin
American countries. All regression coefficients are statistically significant. These findings are
comparable to those obtained in the overall sample.
30
EGY
KEN
MDGMAR
ZAF
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZA UGA
0.2
.4.6
Model
.1 .2 .3 .4 .5Data
R−squared=.3079162075672508
(a) Tax evasion
EGY
KEN
MDG
MAR
ZAF
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZAUGA
0.1
.2.3
.4M
odel
0 .01 .02 .03 .04Data
R−squared=.4296512555503219
(b) GDP per worker
Figure 5. Tax evasion and GDP per worker - data vs the model predictions across Africancountries
ARG
BOL
BRA
CHL
COL
CRI
ECU
SLV
GTM
HND
MEX
NIC
PAN
PRY
PER
URY
.1.2
.3.4
Model
.05 .1 .15 .2 .25Data
R−squared=.2279314032419751
(a) Tax evasion
ARG
BOL
BRA
CHL
COLCRI
ECU SLVGTM
HND
MEX
NIC
PAN
PRY
PER
URY
VEN
.1.2
.3.4
.5M
odel
0 .02 .04 .06 .08Data
R−squared=.4735910912144733
(b) GDP per worker
Figure 6. Tax evasion and GDP per worker - data vs the model predictions across LatinAmerican countries
31
V.2 Alternative detection probability
In the baseline calibration, we use the same variable to capture both institutional quality and
the detection probability. As argued previously, the level of institutional quality may send an
equivalent signal of the level of monitoring and auditing, i.e., the detection probability. How-
ever, this assumption can raise questions as the latter may drive our findings. In this section,
we check the sensitivity of the results using the perception of the control of corruption as an
alternative measure of the detection probability. The latter is from the World Governance
Indicators’ database and is defined as the perceptions of the extent to which public power
is exercised for private gain, including both petty and grand forms of corruption, as well as
"capture" of the state by elites and private interests. We use the percentile ranking of the
index which is ranged between 0 and 1. The simulation procedure is the same as previously,
and the model is calibrated to the United States economy. Figure 7 plots tax evasion and
output per worker from the model against the data for African and Latin American countries.
As previously, the model’s prediction is strongly correlated with the data. The model still
explains around 20% of the variation in tax evasion and 49% of the dispersion of GDP per
worker across African and Latin American countries.
32
ARG
BOL
BRA
CHL
COL
CRIECU
EGYSLV
GTM
HNDKEN
MDG
MEX
MAR
NIC
PAN
PRY
PERZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZA UGA
0.2
.4.6
Mo
de
l
.1 .2 .3 .4Data
R−squared=.1977976014267405
(a) Tax evasion
ARG
BOL
BRA
CHL
COL CRI
ECU
EGY
SLVGTM
HND
KEN
MDG
MEX
MAR
NIC
PAN
PRY
PER
ZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZAUGA
0.1
.2.3
.4.5
Mo
de
l
0 .02 .04 .06 .08Data
R−squared=.4941246242786512
(b) GDP per worker
Figure 7. Tax evasion and GDP per worker - Data vs the model predictions - Alternativedetection probability
Counterfactual experiments. With the calibrated model, we first conduct three tax
neutral counterfactual experiments on Guinea as in the quantitative analysis before exploring
two additional counterfactual analyses. In the first round of experiments, we examine what
happens in Guinea regarding tax evasion if the level of distortionary infrastructures and
deterrence probability can be improved to the average level of the sample and by 50 percent
respectively. These changes lead to a decline in sales not reported for tax purposes by
26.72 percent and 18.34 percent if the level of distortionary infrastructures and deterrence
probability can be improved to the average level of the sample and by 50 percent respectively.
Regarding the output per worker, it grows by 1.22- and 6.46-fold respectively. The third
experiment combines both changes in the level of distortionary infrastructures and deterrence
probability and leads to a 40.16 percent decline in sales not reported for tax purposes, while
the output per worker grows by 6.62-fold. These counterfactual experiments provide the same
conclusion as in the quantitative analysis, where an improvement in the level of distortionary
33
infrastructures and institutional quality leads to a decline in tax evasion between 18.34 and
40.16 percent.
We also conduct two additional counterfactual experiments that are consistent with the
previous findings. Firstly, using the current level of control of corruption as a proxy of the
detection probability, we conduct a counterfactual experiment that examines what happens
in Africa and Latin America if the losses stemming from the business environment are cut
down by half. The deterrence probability is proxied by the index of the control of corruption,
while the other parameters remain the same, except the level of distortionary infrastruc-
tures. The latter is assumed to be cut down by half in each country. This counterfactual
experiment shows that cutting down losses stemming from the poor business environment by
half, governments could reduce firms’ tax evasion by 20.62% on average in African and Latin
American countries. Firms’ tax evasion declines from 17.84% to 14.16%.
Secondly, we use so far the difference in both the detection probability and the level of distor-
tionary infrastructures to explain 35% of the variation of tax evasion across African and Latin
American countries. Using such approach, we are unable to distinguish between the effects of
deterrence policies and distortionary infrastructures on tax evasion, while the distinction can
be highly relevant regarding policy implications. Alternatively, we conduct a counterfactual
experiment consisting of setting the same level of detection probability for all the countries.
The latter corresponds to the average level of the detection probability in the sample. We
further examine how much the difference in losses stemming from the business environment
can explain the variation of tax evasion across African and Latin American countries. The
calibration and simulation procedures are the same as previously, except that the detection
probability is the same for all the countries. Figure 8 plots tax evasion and output per worker
from the model against the data for African and Latin America countries. As it can be seen,
the model’s predicted value remains highly correlated with the data, and the model explains
14% of the variation of tax evasion across African and Latin America countries. Also, the
model explains 50% of the variation of GDP per worker across countries.
34
ARG
BOL
BRA
CHL
COL
CRIECU
EGYSLV
GTM
HNDKEN
MDG
MEX
MAR
NIC
PAN
PRY
PERZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZA UGA
0.2
.4.6
Mod
el
.1 .15 .2 .25Data
R−squared=.1407139537033489
(a) Tax evasion
ARG
BOL
BRA
CHL
COL CRI
ECU
EGY
SLVGTM
HND
KEN
MDG
MEX
MAR
NIC
PAN
PRY
PER
ZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZAUGA
0.1
.2.3
.4.5
Mod
el
0 .02 .04 .06 .08Data
R−squared=.4995535949815552
(b) GDP per worker
Figure 8. Tax evasion and GDP per worker - data vs the model predictions using the samedetection probability for all the countries
V.3 Alternative calibration
The analysis so far relied on the model that is calibrated to the United States’ economy. How-
ever, the latter can raise criticism the parameters for the United States and those for African
and Latina American economies might be different. As a sensitivity check, we recalibrate the
model to the Chilean economy, the country with the lowest combination of distortions and
tax evasion in our data and one of the largest sample size.
The parameter value for the labor share in Chile is set to the average share of labor income
in the GDP from the ILOSTAT.13 We set labor share to be 0.414 which is the average labor’s
share in the GDP.14 As in the baseline calibration, we assume a decreasing return in the
firm-level production function. The extent of the returns is obtained under the assumption13The ILOSTAT is the world leading data source on labor statistics from the International Labour Orga-
nization (ILO). The data can be downloaded from the following website: http://www.ilo.org/ilostat.14The average labor’s share of the GDP is available for 2009, 2011, and 2013.
35
that the average share of labor income in the GDP corresponds to 23of the returns to scale.
The returns to scale obtained is 0.62, very close to the calibration in Gourio and Kashyap
(2007) of the returns to scale for Chile.15 We set the interest rate to be 17.6 percent which
is the average discount rate for African and Latin American economies from the IMF Inter-
national Financial Statistics (IFS) dataset. Using this information, the parameter β is set to
0.850. We choose the depreciation rate of capital to be δ = 0.06 which is the calibrated value
from Gourio and Kashyap (2007). Since there are no studies on the exit rate parameter in
developing countries, we set the annual exit rate to the same value as previously, i.e., λ = 0.1.
The other parameters of the model remain the same as previously. Table 6 below summarizes
the parameters in the alternative calibration.
Table 6. Alternative calibration
Parameter Value Description Sourceτ d [0; 1] Distortionary infrastructures WBESA [0; 1] Public policies effectiveness WGIp [0; 1] Detection probability WGIβ 0.850 Real rate of return IMF International
Financial Statistics (IFS)η 0.207 Capital income share ILOSTAT and Literatureγ 0.414 Labor income share ILOSTATδ 0.06 Investment to output ratio Gourio and Kashyap (2007)λ 0.1 Annual exit rate Literatureθ 3 Evasion cost parameter Assumptionce 1 Entry costs Literaturecf 0 Fixed costs Literaturez [1; 3.98] Distribution of productivity Relative labor demand
Notes. See Restuccia and Rogerson (2008) for parameter values from the literature.
15Gourio and Kashyap (2007) calibrated the returns to scale to 0.6 in Chile. Our findings remain the sameusing this returns to scale and 1
3 and 23 as the share of capital and labor respectively.
36
Figure 9 plots tax evasion and output per worker from the model against the data for
African and Latin American countries. Here, the output per worker is normalized by the
output per worker of Chile. As previously, the data on GDP per worker are from the World
Bank’s World Development Indicators. The reported value of the GDP per worker is nor-
malized by the levels in Chile in both the model and the data. As previously, each circle
represents one country and corresponds to a combination of the output per worker from both
the model and the data. As it can be seen, the model’s predicted values are highly corre-
lated with the data, with a regression coefficient of around 0.92 for tax evasion and 0.20 for
GDP per worker. These coefficients are statistically significant at the 1 and 10 percent level
respectively. Using Chile as a benchmark economy, the model explains 35% of the variation
of tax evasion and around 10% of the dispersion in GDP per worker across African and Latin
American countries. These findings are comparable to those obtained from the calibration
to the United-States, where the model explains 35% of the variation of tax evasion and 49%
of the variation of GDP per worker across African and Latin American countries.
With the calibrated model on the Chile economy, we conduct three tax neutral counterfac-
tual experiments using Guinea as in the quantitative analysis. Especially, we examine what
happens in terms of tax evasion if both the level of distortionary infrastructures and policies
can be improved to the average level of the sample and by 50 percent respectively. The
approach consists of examining the effects of the change separately in experiments 1 and 2
before combining them in experiment 3. Experiments 1 and 2 lead respectively to a decline
in sales not reported for tax purposes by 26.72 and 18.34 percent. In these experiments, the
output per worker increases up to 13.20-fold respectively. The third experiment leads to a
decrease in firms’ tax evasion by 40.16 percent, while the output per worker is nearly 5-fold
as large as in the benchmark situation of the country. The counterfactual experiments are
in line with the findings from the calibration to the U.S. economy.
37
ARG
BOL
BRA
CHL
COL
CRIECU
EGYSLV
GTM
HNDKEN
MDG
MEX
MAR
NIC
PAN
PRY
PERZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZA UGA
0.2
.4.6
Mo
de
l
.1 .2 .3 .4 .5Data
R−squared=.3505665524954171
(a) Tax evasion
ARG
BOL
BRA
CHL
COL CRI
ECU
EGY
SLVGTM
HND
KEN
MDG
MEX
MAR
NIC
PAN
PRY
PER
ZAF
URY
AGO
BWA
GIN
MRT
NAM
RWA
SWZ
TZAUGA
0.2
.4.6
.81
Mo
de
l
0 .5 1 1.5 2Data
R−squared=.1007588272129979
(b) GDP per worker
Figure 9. Tax evasion and GDP per worker - Data vs the model predictions across - Alter-native calibration
38
VI Conclusion
Firms in developing countries face considerable constraints in their development due to dis-
tortionary infrastructures and policies, and weak institutions. In parallel, tax evasion is
widespread and pervasive in these countries, generating a substantial negative effect on do-
mestic resource mobilization as well as on government’s ability to provide infrastructure and
public goods. African and Latin American countries rank poorly in terms of ease of doing
business, with around one-quarter of annual sales not reported for tax purposes. As a novelty,
this paper examines empirically and theoretically the impacts of distortionary infrastructures
and institutional quality on firms’ tax evasion in African and Latin American countries.
The paper sheds light on two potential mechanisms driving firms’ tax evasion which have
not been given a lot of attention in the literature. First, entrepreneurs, knowing that govern-
ment effectiveness is weak, also anticipate low monitoring and have, therefore, more incentive
to evade their taxes. In the second mechanism, distortions and poor institutional quality gen-
erate losses for firms by creating a wedge between potential and realized profits. Tax evasion
is a way for firms to counterbalance the negative effects of distortionary infrastructures and
policies. This paper provides supporting evidence of the negative impact of distortionary
infrastructures and policies on firms’ tax evasion. In particular, using WBES data, we show
that losses stemming from the business environment are positively correlated with firms’ tax
evasion. Using WGI data, we show that institutional quality is negatively related to the
proportion of sales not reported for tax purposes.
To understand the mechanisms through which distortionary infrastructures and policies
affect firms’ tax evasion, we develop a general equilibrium model with heterogeneous firms.
The model is calibrated using the United States as a benchmark economy. We simulate tax
evasion and output data for firms in a sample of 30 African and Latin American countries.
The simulated output per worker and tax evasion are strongly correlated with the data, as
the model explains 35 percent of the variation in tax evasion and 49 percent of the dispersion
in output per worker across African and Latin American countries.
39
Having established the ability of the model to replicate some of the variations of tax eva-
sion across countries, we conduct counterfactual experiments to examine the implications of
changes in distortionary infrastructures and policies. The counterfactual experiments show
that African and Latin American countries could reduce tax evasion by about 21 percent by
cutting down the losses stemming from the business environment by half. Finally, setting
the same detection probability for all the countries, the last counterfactual experiment shows
that distortionary infrastructures explain 14 percent of the variation of tax evasion across
African and Latin American countries.
The paper highlights that distortionary infrastructures and policies can generate a vicious
circle of underdevelopment in which tax evasion and distortions interact positively. Firms’
tax evasion shrinks public revenues and this, in turn, reduces the government’s capacity
to curtail tax evasion, invest in productive infrastructures, and thereby reduce distortions
in the business environment. Hence, improving the quality of the business environment in
Africa and Latin America would generate a double-dividend. On the one hand, it would
boost firms’ productivity through better public infrastructure, better access to finance, and
a lighter regulatory environment. On the other hand, improving the quality of the business
environment could reduce firms’ tax evasion. African and Latin American countries could
generate additional domestic revenues and boost the aggregate productivity simultaneously
by pushing the agenda of reforms that help to improve the quality of infrastructures and
policies related to the business environment. The impacts of distortionary infrastructures
and policies examined in this paper might represent lower bounds for tax evasion at the
country level as the standardized dataset employed does not include informal firms. Those
firms are widespread in African and Latin American countries, are inherently hard to tax,
and might represent an additional source of distortion and resource misallocation.
40
References
Acemoglu, D., Johnson, S., and Robinson, J. (2001). The colonial origins of comparative
development: An empirical investigation. American Economic Review, 91(5):1369–1401.
Amaral, P. and Quintin, E. (2010). Limited enforcement, financial intermediation, and
economic development: A quantitative assessment. International Economic Review,
51(3):785–811.
Aterido, R., Hallward-Driemeier, M., and Pagés, C. (2011). Big constraints to small firms’
growth? business environment and employment growth across firms. Economic Develop-
ment and Cultural Change, 59(3):609–647.
Atkeson, A. and Kehoe, P. J. (2005). Modeling and measuring organization capital. Journal
of Political Economy, 113(5):1026–1053.
Bah, E.-H. and Fang, L. (2015). Impact of the business environment on output and produc-
tivity in africa. Journal of Development Economics, 114(C):159–171.
Besley, T. and Burgess, R. (2004). Can labor regulation hinder economic performance?
evidence from india. The Quarterly Journal of Economics, 119(1):91–134.
Besley, T. and Persson, T. (2014). Why do developing countries tax so little? Journal of
Economic Perspectives, 28(4):99–120.
Botero, J. C., Djankov, S., La-Porta, R., de Silanes, F. L., and Shleifer, A. (2004). The
regulation of labor. Quarterly Journal of Economics, 119(4):1339–1382.
Buera, F. J., Moll, B., and Shin, Y. (2013). Financial frictions and the persistence of history:
A quantitative exploration. Journal of Political Economy, 121(2):221–272.
D’Erasmo, P. and Boedo, H. (2012). Financial structure, informality and development.
Journal of Monetary Economics, 59(3):286–302.
41
Easterly, W. and Rebelo, S. (1993). Fiscal policy and economic growth. Journal of Monetary
Economics, 32(3):417–458.
Eifert, B., Gelb, A., and Ramachandran, V. (2006). Business environment and comparative
advantage in africa: Evidence from the investment climate data. In Annual World Bank
Conference on Development Economics (ABCDE) 2006: Growth and Integration, pages
195–233. The World Bank.
Gourio, F. and Kashyap, A. (2007). Investment spikes: New facts and a general equilibrium
exploration. Journal of Monetary Economics, 54(1):1–22.
Goyette, J. and Gallipoli, G. (2015). Distortions, efficiency and the size distribution of firms.
Journal of Macroeconomics, 45:202–221.
Hall, R. E. and Jones, C. I. (1999). Why do some countries produce so much more output
per worker than others? The Quarterly Journal of Economics, 114(1):83–116.
Hox, J. J., Moerbeek, M., and van de Schoot, R. (2010). Multilevel analysis: Techniques and
Applications. Routledge.
Hseih, C.-T. and Klenow, P. (2009). Misallocation and manufacturing tfp in china and india.
The Quarterly Journal of Economics, 124(4):1403–1448.
IMF (2003). World Economic Outlook, April 2003 : Growth and Institutions. IMF.
López de Silanes, F., Djankov, S., La Porta, R., and Shleifer, A. (2002). The regulation of
entry. Quarterly Journal of Economics, 117(1):1–37.
Ordóñez, J. (2014). Tax collection, the informal sector, and productivity. Review of Economic
Dynamics, 17(2):262–286.
Pavcnik, N. (2002). Trade liberalization, exit, and productivity improvements: Evidence
from chilean plants. The Review of Economic Studies, 69(1):245–276.
42
Prado, M. (2011). Government policy in the formal and informal sectors. European Economic
Review, 55(8):1120–1136.
Restuccia, D. and Rogerson, R. (2008). Policy distortions and aggregate productivity with
heterogeneous establishments. Review of Economic Dynamics, 11(4):707–720.
Restuccia, D. and Rogerson, R. (2013). Misallocation and productivity. Review of Economic
Dynamics, 16(1):1–10.
Rodrik, D., Subramanian, A., and Trebbi, F. (2004). Institutions rule: the primacy of
institutions over geography and integration in economic development. Journal of Economic
Growth, 9(2):131–165.
Sleuwaegen, L. and Goedhuys, M. (2002). Growth of firms in developing countries, evidence
from cote d’ivoire. Journal of development Economics, 68(1):117–135.
Tybout, J. (2000). Manufacturing firms in developing countries: How well do they do, and
why? Journal of Economic Literature, 38(1):11–44.
World Bank (2005). World Development Report 2005 : A better Investment Climate for
Everyone. Oxford University Press.
Wu, G. L. (2018). Capital misallocation in china: Financial frictions or policy distortions?
Journal of Development Economics, 130(1):203–223.
43
Appendices
A Investment climate indicators
The World Bank’s Doing Business index measures aspects of regulation that enable or pre-
vent private sector businesses from starting, operating and growing. These regulations are
measured using 11 indicator sets: opening a business, dealing with construction permits,
getting electricity, registering property, getting credit, protecting minority investors, paying
taxes, trading across borders, enforcing contracts, resolving insolvency and labor market reg-
ulation. The annual report provides a ranking at the country level in term of the ease of
doing business. In the last Doing Business Report in 2017, the average regional rankings of
African and Latin American countries were 143 and 107,16 respectively, constraining firms’
creation and the long-term performance of incumbent firms.
Moreover, comparing different indicators of investment climate variables from the World
Bank Enterprise Surveys (WBES) for the high-income countries of the Organisation for Eco-
nomic Co-operation and Development (OECD) and African and Latin American countries,
it can be seen that firms in these countries face a poor investment climate compared to their
counterparts in OECD countries (cf. Table A.1 below). For example, respectively, 22.4 per-
cent and 19.3 percent of firms in African and Latin American countries identify corruption
as a major or severe obstacle to business, while the corresponding number is 7.2 percent in
the OECD countries. Similarly, the average percentile rank of policy effectiveness is 45 in
African and Latin American countries against 85 in the OECD sample. The high-income
OECD countries include Germany, Greece, Ireland, Portugal, The Republic of South Korea
and Spain.
16See the 2017 Doing Business Report: http://www.doingbusiness.org/~/media/WBG/DoingBusiness/Documents/Annual-Reports/English/DB17-Report.pdf
44
Table A.1. African and Latin American countries vs OECD counterparts
AFR LAC OECDElectricity as an obstacle to business (% of firms) 17 9.3 5.2Costs of power outages (%) 7 4 2.3Corruption as an obstacle to business (% of firms) 22.4 19.3 7.2Costs of corruption (% of sales) 2.7 1.9 0.27Crime as an obstacle to business (% of firms) 14 17 6Costs of crime (% of sales) 1.5 1 0.42Regulation (% of time spent) 8.60 13.9 1.69Tax evasion (%) 28.29 22.51 6.80Institutional Quality 39 48 85Notes. An investment climate variable is considered as an obstacle to businessif a firm states that the latter is a major or a very severe obstacle to business.
45
B List of countries
Table B.1. Business environment in Africa and Latin American countries
Country Year Sample size Tax evasion (%) Distortions (%) InstitutionalQuality
Angola 2006 409 50.95 7.10 0.06Argentina 2006 913 17.21 1.76 0.55Bolivia 2006 544 20.34 3.72 0.29Botswana 2006 309 47.80 3.14 0.70Brazil 2003 1506 32.70 2.82 0.61Chile 2004 894 2.97 2.20 0.88
2006 925 13.20 2.13 0.83Colombia 2006 918 17.10 2.47 0.53Costa Rica 2005 284 28.49 7.04 0.59Ecuador 2003 356 20.47 8.71 0.20
2006 603 26.24 3.31 0.17Egypt, Arab Rep. 2004 946 16.50 7.36 0.48El Salvador 2003 409 23.21 3.93 0.43
2006 546 18.93 3.90 0.50Guatemala 2003 437 22.59 6.49 0.39
2006 491 26.83 4.76 0.31Guinea 2006 213 64.25 19.70 0.06Honduras 2003 333 31.66 5.97 0.33
2006 355 15.85 4.69 0.30Kenya 2003 218 13.32 11.94 0.31Madagascar 2005 271 6.41 11.07 0.42Mauritania 2006 225 47.13 6.22 0.26Mexico 2006 1295 23.67 2.02 0.60Morocco 2004 833 3.91 0.49 0.56Namibia 2006 301 24.68 2.29 0.59Nicaragua 2003 346 32.58 9.03 0.25
2006 415 40.85 10.51 0.21Panama 2006 545 36.99 5.50 0.57Paraguay 2006 452 19.34 6.02 0.20Peru 2006 607 10.53 1.00 0.32Rwanda 2006 209 19.00 8.89 0.45South Africa 2003 560 9.16 1.43 0.74Swaziland 2006 290 58.13 4.04 0.21Tanzania 2003 229 30.06 9.95 0.41
2006 416 47.03 12.53 0.43Uganda 2006 546 46.50 13.59 0.37Uruguay 2006 341 14.85 0.83 0.66
46