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Lurking in the shadows: Effects of poverty on the shadow economy

Aziz N. Berdiev

Department of Economics, Bryant University, Smithfield, RI 02917, USA

James W. Saunoris

Department of Economics, Eastern Michigan University, Ypsilanti, MI 48197, USA

Friedrich Schneider

Research Institute of Finance and Banking, Johannes Kepler University, A-4040 Linz, Austria

Abstract

While the literature has identified institutional factors such as high taxes and burdensome

regulations among the determinants of the shadow economy, whether and to what extent economic

factors such as the prevalence of poverty drive the shadow economy are less forthcoming. We

contribute to this literature by examining the impact of poverty on the size of the shadow economy

using cross-country panel data for over 100 countries for the period 1991-2015. The results show

that poverty has a positive and significant effect on the size of the shadow economy and these

results withstand a battery of robustness checks. Furthermore, we find that poverty has the largest

effect on the size of the shadow economy when government quality is the lowest and the size of

the government is the largest.

Keywords: Poverty; Shadow Economy; Cross-country data

JEL Classification: I32; E26

January 13 2019

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1. Introduction

Experts have focused their attention towards understanding a large informal, or shadow,

economy that exists across high and low income countries, employing about 2 billion people,

equivalent to 61.2% of the world’s employed population (ILO 2018).1 Indeed, it is estimated that

around 30% of total production takes place in the shadow economy (Medina and Schneider

2017). What drives workers and businesses underground? While the literature has identified

institutional factors such as high taxes and burdensome regulations among the determinants of

the shadow economy (see Schneider (2011), Schneider and Enste (2000), and Gërxhani (2004)

for a review), whether and to what extent economic factors such as the prevalence of poverty

drive the shadow economy are less forthcoming. We contribute to this literature by examining

the impact of poverty on the size of the shadow economy.

According to the most recent estimates from the World Bank, the global poverty rate stands

at 10.9% (28.6%), amounting to approximately 783 million (2.044 billion) people living on less

than $1.90 ($3.20) per day (Atamanov et al. 2018). However, this number masks the remarkable

variance in poverty rates across regions. That is, among lower income regions such as Sub-

Saharan Africa, the $1.90 ($3.20) poverty headcount ratio is 42.3% (67.5%) whereas among

several high income countries the poverty rate is less than 1% (Atamanov et al. 2018).

Interestingly, countries that tend to have the highest poverty rate also tend to have a larger

shadow economy. For example, countries in Sub-Saharan Africa tend to have the largest shadow

economy estimated to be between 39-76% of GDP while among high income countries the

shadow economy tends to be between 8-30% of GDP (Schneider and Enste 2000). It is perhaps

the case that the poverty rate overestimates the extent of poverty as individuals earn unreported

income underground.

The positive relationship between poverty and the shadow economy cursorily observed

motivates the question: does poverty increase the size of the shadow economy? Theoretically, as

we argue in the next section, increases in poverty might promote the development of the informal

economy in several ways. Conceivably, low income households may drive the shadow economy

through stronger demand for goods and services. To the extent that shadow sector goods and

services are cheaper (Schneider and Enste 2013), individuals living in poverty may purchase

goods and services at lower prices in the informal economy. Moreover, the prevalence of poverty

may drive low income individuals to turn to the underground for employment opportunities

(Amuedo-Dorantes 2004). In other words, the informal economy offers refuge for people living

in poverty to earn income. Indeed, individuals receiving government provided welfare may

prefer informal employment to avoid the high implicit tax associated with losing welfare benefits

as a result of taking a formal sector job. Furthermore, because of the high barriers (e.g., taxes and

regulations) to entry in the formal sector, lower income individuals are more likely to start their

informal ventures underground.

While the linkages between income inequality and the informal economy have been well

documented in the extant literature (see, e.g., Rosser et al. 2000; Chong and Gradstein 2007;

Mishra and Ray 2010; Berdiev and Saunoris 2018), we seek to analyze whether and to what

1 Notice that although the terms shadow, informal and underground are employed interchangeably here, they all

denote economic activity that is unregistered in the formal economy.

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extent extreme poverty, measured as people living on less than $1.90 per day, influences the

spread of the shadow economy. Also, whereas empirical studies have examined the relationship

between low income households and informality, they are limited to single country studies (see

Amuedo-Dorantes 2004; Kim 2005; Devicienti et al. 2010; Nazier and Ramadan 2015; Canelas

2015). The results from these studies are therefore country-specific: for example, Amuedo-

Dorantes (2004) finds that poverty is positively associated with shadow sector work using data

for Chile while Nazier and Ramadan (2015) show that poverty has a statistically insignificant

influence on employment in the underground economy using data for Egypt. It is therefore

critical to continue to explore the relationship between poverty and informality to assist

policymakers in developing more effective policies to eradicate both.

Consequently, we contribute to this important literature in several ways. We are the first to

study the impact of poverty on the size of the shadow economy using cross-country panel data

that employs estimates of the shadow economy from Medina and Schneider (2017) covering the

period from 1991 to 2015 for over 100 nations. Our comprehensive empirical analysis accounts

for country, regional and time effects across various specifications, and controls for numerous

institutional factors such as political freedom, bureaucratic quality, government effectiveness,

human capital and regulatory quality and economic factors such as economic growth and income

inequality that have been suggested to drive the shadow economy. Second, we consider three

measures of poverty from the World Bank (2017). In particular, we use the poverty headcount

ratio at $1.90 (2011 Purchasing Power Parity dollars) a day as a percent of the population which

captures extreme poverty that is prevalent particularly in low income nations. We also use two

alternate poverty rates defined by less than $3.20 a day and $5.50 a day which, according to the

World Bank, is generally representative of poverty thresholds in lower-middle and upper-middle

income nations, respectively. Third, given the inherent difficulty in accurately measuring the size

of the shadow economy, we employ two alternate estimates of the shadow economy namely

from Schneider et al. (2010) and Elgin and Öztunali (2012). Fourth, we employ instrumental

variables technique to account for potential endogeneity arising from reverse causality between

poverty and the shadow economy. Finally, we study the interaction effects between poverty and

the quality and quantity of government on the shadow economy: specifically, we analyze

whether and to what extent the impact of poverty on size of the shadow economy is dependent

on government quality and government size. The results show that increases in poverty is

positively associated with the shadow economy, and these results withstand a battery of

robustness checks. Additionally, we find that poverty has the largest effect on the shadow

economy when government quality is the lowest and the size of the government is the largest.

The rest of the paper is organized as follows: Section 2 discusses the relationship between

poverty and the shadow economy. In Section 3, we explain the data and present the empirical

methodology. Section 4 discusses the empirical results and provides various robustness checks.

Our major findings are summarized in the final section.

2. Theoretical considerations and literature review

The prevalence of poverty might drive the spread of the shadow economy in a number of

ways. In particular, as we discuss below, individuals living in poverty may drive the

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development of the informal economy through higher demand for informal sector goods and

services and by supplying their labor and entrepreneurial aptitude to produce goods and services

in the shadow economy.

First, the spread of the shadow economy from increases in poverty may be driven by higher

demand for shadow sector goods and services. Conceivably, people living in poverty may turn to

the underground economy to purchase goods and services at discounted prices. Indeed, the

average cost of goods and services in the informal sector are, on average, a third to a quarter

cheaper than goods and services of similar quality available in the formal sector (Schneider and

Enste 2013). Accordingly, more individuals with income constraints may contribute to higher

demand for goods and services in the underground sector, thereby promoting informal sector

production (Mishra and Ray 2010). Poverty may therefore drive the spread of the shadow

economy by offering opportunities for underground participants to fulfill demand for these goods

and services.

Next, poverty may also influence the decision to supply labor in the formal versus the

informal sector. In particular, an exogenous increase in poverty may increases the number of

individuals that rely on the shadow economy for employment opportunities (Amuedo-Dorantes

2004). Lower income households may be driven to allocate their labor in the underground as a

result of restricted options that are available in the formal sector due to their relatively lower

skills and education (Berdiev and Saunoris 2018). According to Wade (2004), for instance, pay

rates in manufacturing for entry level and low skilled labor are closely related to the opportunity

cost of time in the informal sector. In this line, the underground economy offers opportunities

for informal employment which tends to be predominately lower skilled labor intensive jobs.2 As

a result, the shadow sector offers an attractive alternative to earn income for individuals who are

prevented from working in the formal sector (Dell’Anno and Solomon 2008).

Amuedo-Dorantes (2004; p. 439) argues that the “inability to cover minimum household

food, clothing, shelter, and fuel requirements, as captured by household poverty, may explain

household heads’ decision to take a job in the informal sector.”3 Therefore, people at the lower

end of the income distribution seek employment in the shadow economy, despite lower wages, in

order to make ends meet. Indeed, to the extent that individuals living in poverty have a lower

reservation wage, they are more inclined to join the shadow sector for employment prospects

(Kim 2005).

Furthermore, poorer households that receive welfare payments may use the shadow economy

because of the high implicit tax rate imposed on them if they were to obtain formal employment.

Indeed, studies show that government distortions such as burdensome taxes drive agents to the

shadow economy (see Schneider and Enste (2000) and Gërxhani (2004) for a review). The

underground economy therefore provides an option for low income households to earn

supplementary income that is undetected.

2 Conceivably, the shadow sector serves as a stepping stone for individuals with low skills to learn new skills that

increases their mobility to the formal sector. 3 Likewise, Devicienti et al. (2010) emphasize that people living in poverty migrate to the shadow sector for

employment in order to support their livelihood.

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It is also possible that individuals who lack sufficient resources to start formal businesses

may choose instead to start their businesses informally. Poverty may thus impact the choice to

operate an entrepreneurial venture formally versus informally. Specifically, because low income

households have limited access to capital, they are less likely to cover the costs to participate in

the formal economy and thus may retreat to the underground to operate their businesses (Mishra

and Ray 2010). For example, Mishra and Ray (2010, p. 23) suggest that participating in the

official economy entails “larger fixed costs and these wealth constrained individuals are forced

to join the informal sector in the absence of well functioning credit markets.” 4 Likewise, Chong

and Gradstein (2007, p. 160) argue that “[p]oor individuals whose endowments are relatively

limited are at a disadvantage in extracting a larger share of the resources, hence, find it beneficial

to move into the informal sector, where although less productive, they are able to fully retain

their production output.” Consequently, due to income constraints, individuals living in poverty

may produce goods and services in the underground.

Overall, empirical literature finds support for the notion that increases in poverty is positively

associated with the development of the shadow economy. However, as we argued earlier, the

empirical analysis is limited to single country studies such as Chile (Amuedo-Dorantes 2004),

Romania (Kim 2005), Argentina (Devicienti et al. 2010), Egypt (Nazier and Ramadan 2015) and

Ecuador (Canelas 2015). A related strand of empirical research analyzes the linkages between

unequal distribution of income and the shadow economy (see, e.g., Rosser et al. 2000; Chong

and Gradstein 2007; Mishra and Ray 2010; Berdiev and Saunoris 2018). Rosser et al. (2000) for

example report a positive relationship between income inequality and the underground economy

using data for 16 transition countries. Similarly, Chong and Gradstein (2007) find that income

inequality is positively associated with the shadow economy using a large sample of developed

and developing countries.

In summary, the above discussion indicates that people living in poverty promote the spread

of the underground economy through stronger demand for shadow sector goods and services and

by supplying their labor and entrepreneurial talents to produce goods and services in the shadow

economy.

This leads us to the following testable hypothesis:

H1: A greater prevalence of poverty is associated with a larger shadow economy, ceteris

paribus.

In what follows, we explain the construction of the variables and present the empirical

approach to test the above hypothesis.

3. Data and methodology

The data set includes a panel of over 100 countries observed from 1991 to 2015. To

overcome obvious measurement issues with both the shadow economy and the poverty rate, we

4 Research shows that the development of financial sector institutions lower the prevalence of the informal economy

(see, e.g., Straub 2005; Capasso and Jappelli 2013; Berdiev and Saunoris 2016).

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use five year averages of the data. The final panel data set includes 114 countries over five time

periods. Data details including variable names, definitions and sources are in Table 1A, and the

countries in the analysis are in Table 2A in the appendix.

Our main variable of interest is a measure of the shadow economy. Measuring the size of the

shadow economy is inherently difficult due to the clandestine nature of shadow activities, thus

researchers have employed a variety of techniques to creatively estimate the size of shadow

economies around the world.5 For instance, the currency demand method is used to estimate the

amount of currency demanded for shadow economies assuming taxes are the main drivers of

shadow activity (Cagan 1958; Tanzi 1983). For example, the difference in currency demanded

when taxes are high and when taxes are low is perceived to be the currency demanded for

underground activity. Alternatively, the physical input approach assumes that electricity

consumption can be used as a measure of overall economic activity, including both formal and

informal activity, therefore, Kaliberda and Kaufmann (1996) subtract the growth in GDP from

the growth in electricity consumption to estimate the growth in the shadow economy. In contrast

to these two methods that rely on one indicator, Medina and Schneider (2017) use the multiple

indicators, multiple causes (MIMIC) method, which extracts covariance information from

observable variables classified as causes or indicators of the latent shadow economy (see

Schneider et al. (2010) for details). The average size of the shadow economy in our sample is

32.3 (% of GDP) with considerable variation across countries ranging from a low of 8.4% in

Switzerland and, in some cases like the Democratic Republic of Congo, almost 50% of total

production is underground.

The main independent variable of interest is the poverty rate collected from the World Bank

(2017). Following Djankov et al. (2018), the poverty rate (Poverty ($1.90)) is measured as the

poverty headcount ratio at $1.90 (2011 Purchasing Power Parity dollars) a day as a percent of the

population. While some countries have virtually nobody living on less than $1.90 per day (e.g.,

Switzerland) some countries like the Democratic Republic of the Congo has a poverty rate of

94.1%. As a robustness check, we also use two alternate measure of the poverty rate defined by

less than $3.20 a day (Poverty ($3.20)) and $5.50 a day (Poverty ($5.50)). According to the

World Bank, Poverty ($1.90), Poverty ($3.20) and Poverty ($5.50) are representative of poverty

thresholds in low income, lower-middle and upper-middle income economies, respectively. The

correlation between the shadow economy and Poverty ($1.90) is 0.41.

To account for confounding factors that may impact the size of the shadow economy, we

follow the literature and include a set of control variables (Schneider and Enste 2000; Gërxhani

2004; Berdiev et al. 2018). From the World Bank (2017), we collect economic growth (Growth),

measured as the growth rate of real GDP per capita (chained PPP in millions of 2005 U.S.

dollars), and GovtSize measured as final government consumption expenditures as a fraction of

final consumption expenditures. Countries that experience robust economic growth raise the

opportunity cost of producing underground by offering more opportunities to earn income

formally; whereas, larger governments translate to less economic freedom that incentivize

individuals to migrate underground except when government resources are used to combat

shadow activities. Alternatively, higher living standards in the formal sector may spillover to the

informal sector in the form of stronger demand and sub-contracting. Next, Democracy from

5 For an extensive review of the methods used to estimate the shadow economy, see Schneider and Enste (2013).

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Freedom House, calculated as the sum of civil liberties and political rights, enables citizens a

mechanism to vote corrupt or ineffective politicians out of office rather than “voting with their

feet” by migrating underground. Similarly, the strength and quality of bureaucracy (BureauQual)

from the International Country Risk Guide, minimizes the need to migrate underground by

limiting the risk associated with changes to policy and disruptions to government services.

To test the impact of poverty on the size of the shadow economy, we estimate the following

equation:

𝑆ℎ𝑎𝑑𝑜𝑤𝑖𝑡 = 𝛽1𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝑖𝑡 + 𝛾𝑖′𝑋𝑖𝑡

𝑘 + 𝛼𝑖 + 𝜏𝑡 + 𝜀𝑖𝑡 (1)

Where i and t index country and time respectively; the dependent variable Shadow is a measure

of the shadow economy as a percent of GDP; Poverty is a measure of the poverty rate as a

percent of the population; X is a vector of control variables described above and includes

Growth, Democracy, BureauQual, and GovtSize; 𝛼𝑖 capture country-specific fixed effects; 𝜏𝑡 capture time-specific effects; and 𝜀𝑖𝑡 is the random error term.

We estimate equation (1) using two-way country and time effects. We alternately control for

other aspects that have shown to impact the size of the shadow economy including regulatory

quality (RegQual), government effectiveness (GovtEffect), tertiary education enrollment

(Education) and income inequality (Inequality). Furthermore, we replace country-specific effects

with regional effects and re-estimate the models. We also check the robustness of these baseline

models to alternate measures of the poverty rate where poverty is based on individuals living on

less than $3.20 and $5.50 per day and two alternate measures of the shadow economy. Finally,

we account for potential endogeneity of poverty using instrumental variables technique and

outliers using robust regression.

4. Results

4.1. Baseline Results

The baseline results after estimating equation (1) are reported in Table 1. The first six models

(Models 1.1-1.6) include both country and time effects and the last six models (Models 1.7-1.12)

include regional and time effects. The R-squared reported at the bottom of Table 1 shows that the

models explain about 58.6% to 69.2% of the variation in the shadow economy.

We begin our analysis with Model 1.1 where the coefficient on Poverty ($1.90) is positive

and statistically significant at the 5% level such that a larger fraction of the population living in

poverty is positively associated with the size of the shadow economy. In Model 1.2, we account

for a set of covariates described in the previous section and the results continue to show that

poverty has a positive and statistically significant effect on shadow operations. More specifically,

a 10% increase in the poverty rate increases the size of the shadow economy by approximately

0.5%. In other words, if the poverty rate increased from a rate consistent with Lithuania to that of

Zimbabwe (one standard deviation) the size of the shadow economy would increase from the

size consistent with Armenia to that of Ukraine. Consistent with our hypothesis, this result

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confirms that low income individuals contribute to the shadow economy by, for example,

increasing demand for shadow sector goods and services and/or by allocating their labor and

entrepreneurial skills to producing goods and services underground.

Turning to the control variables, higher economic growth lowers shadow operations, at the

1% level of significance. This finding is in line with the notion that greater economic growth

increases the opportunity cost of participating in the informal sector by providing more

opportunities in formal sector (Berdiev et al. 2018). Also, it appears that democracy has a limited

statistical impact on the size of the shadow economy at conventional levels. Nevertheless, the

insignificant effect of democracy on the size of the shadow economy is consistent with the extant

literature (see, e.g., Goel and Nelson 2016). Furthermore, we find that a larger government

significantly increases the shadow economy at the 1% level. Larger governments may exemplify

the presence of various government distortions such as onerous taxes which motivate individuals

and businesses to migrate to the underground (see Johnson et al. 1997; Schneider and Enste

2000; Gërxhani 2004). Finally, our results show that institutional quality plays an important role

in mitigating shadow activities: namely, improvements in government bureaucratic quality

reduce the size of the shadow economy, also at the 1% level of significance. This result is

broadly consistent with Dreher et al. (2009).

Next, we account for additional control variables that have been shown to influence shadow

production. First, we control for the quality of regulations (RegQual) from Kauffman et al.

(2010) where burdensome government regulations through for example imposing regulatory

hurdles to commence a venture entices agents to retreat to the informal economy (see Johnson et

al. 1997; Schneider and Enste 2000). Second, we account for government effectiveness

(GovtEffect), also from Kauffman et al. (2010), which increases the benefits of producing

formally (e.g., Dreher and Schneider 2010). Third, we include a variable to capture the level of

human capital, tertiary education enrollment (Education), from the World Bank (2017), which

increases the opportunity cost of operating in the shadow sector (e.g., Loayza et al. 2009;

Gërxhani and Van de Werfhorst 2013; Berdiev et al. 2015; Buehn and Farzanegan 2013). Lastly,

we control for income inequality (Inequality) from Solt (2016) where a more unequal

distribution of income promotes the development of shadow activities (e.g., Rosser et al. 2000;

Chong and Gradstein 2007; Mishra and Ray 2010; Berdiev and Saunoris 2018).

We add each of these control variables separately to our baseline model. The results from the

inclusion of the additional control variables are displayed in Models 1.3-1.6 of Table 1. Across

all models the results continue to show that the coefficient on poverty is positive and statistically

significant at least at the 5% level, thereby suggesting that a greater prevalence of poverty is

associated with a larger shadow economy. Overall, the results for the additional control variables

are in line with the extant literature discussed above. For example, as expected, we find that

higher quality of regulations (Model 1.3) and more effective government (Model 1.4)

significantly lower shadow operations. Additionally, a more educated populace (Model 1.5),

while negative, is insignificant in its effect on the shadow economy. Finally, a more unequal

distribution of income (Model 1.6) also encourages shadow activity given by the positive and

statistically significant coefficient on income inequality. Thus, more individuals living in poverty

as well as a more unequal distribution of income both contribute to a larger shadow economy.

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The rest of the control variables (Growth, Democracy, BureauQual, and GovtSize) are consistent

with the baseline model.

As an additional test, we re-estimate Models 1.1-1.6 by including regional and time effects.

The regression estimates are presented in Models 1.7-1.12 of Table 1. As can be seen, the results

across all models confirm our earlier finding that more individuals living in poverty is positively

associated with the size of the shadow economy. The results for all the control variables are in

line with our earlier findings.

In summary, the evidence supports our hypothesis that a greater prevalence of poverty is

associated with a larger shadow economy. Moreover, our results are broadly in line with single

country studies that reported a positive relationship between poverty and informality (see, e.g.,

Amuedo-Dorantes 2004; Kim 2005; Devicienti et al. 2010; Canelas 2015). Our findings

therefore support the notion that low income individuals living in poverty may promote the

development of the shadow economy through for example increasing demand for goods and

services offered at lower prices in the underground thereby increasing aggregate demand of the

shadow sector and/or by supplying their labor to produce goods and services in the underground

which increases shadow production.

4.2. Robustness Checks

In the next several sub-sections, we further check the validity of our baseline results after

accounting for (i) two additional measures of poverty (Table 2), (ii) two alternate measures of the

shadow economy (Table 3), (iii) potential outliers (Table 4, columns 1-5) and (iv) potential

simultaneity between poverty and the shadow economy (Table 4, columns 6-10).

4.2.1. Alternate measures of the poverty rate

To check the robustness of the baseline results, we first replace our main poverty rate

measure with two alternate measures based on individuals living on less than $3.20 per day

(Poverty ($3.20) and $5.50 per day (Poverty ($5.50)). These results are reported in Table 2. The

coefficients on Poverty ($3.20) and Poverty ($5.50) are positive and statistically significant at

conventional levels in all equations, thus even at a higher threshold the results continue to show

that poverty is positively associated with the size of the shadow economy. The results pertaining

to the control variables tell the same story as the baseline results such that economic growth and

bureaucratic quality reduce shadow operations, and larger governments increase shadow activity.

However, the alternate control variables vary somewhat from the baseline models. More

specifically, regulatory quality and income inequality are statistically insignificant in Models 2.9

and 2.12, respectively.

4.2.2. Alternate measures of the shadow economy

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Due to the difficulty in measuring underground activity, we next employ two alternate

measures of the shadow economy. First, we use a widely used measure of the shadow economy

from Schneider et al. (2010) that is estimated using the MIMIC models. Additionally, we

consider a measure of the shadow economy from Elgin and Öztunali (2012) that estimates the

shadow using a two sector (formal and informal) general equilibrium model. Covering a

somewhat different time period the three measures of the shadow economy are highly correlated

– i.e., correlation coefficient exceeds 0.97. The results after replacing Shadow (MS) with Shadow

(SBM) and Shadow (EO) are reported in Table 3 as Models 3.1-.3.5 and Models 3.6-3.10,

respectively. While the coefficient on Poverty ($1.90) are slightly smaller than the baseline

model the sign and statistical significance are consistent with the baseline results. Ultimately this

suggests that poverty’s positive effect on the size of the shadow economy is robust to alternate

measures of the shadow economy.

In contrast, the control variables show some differences. For instance, the effect of economic

growth is insignificant when Shadow (SBM) is the dependent variable and is positive and

significant when Shadow (EO) is the dependent variable. The size of government is also

insignificant (except in Model 3.10). Furthermore, inequality is insignificant in both cases and

education is negative and significant when Shadow (SBM) is the dependent variable. Despite the

contrasting results across control variables, poverty remains positive and significant thus

confirming the baseline results.

4.2.3. Accounting for outliers

Because outliers can skew the results, as an additional robustness check we account for the

effects of outliers by re-estimating the baseline models using robust regression controlling for

country and time effects.6 Robust regression is an iterative process by first eliminating outliers

with a Cook’s distance greater than one and then Huber iterations are performed followed by

biweight iterations (Li 1985). Models 4.1-4.5 of Table 4 include the set of results using robust

regression. Consistent with the baseline models, the coefficient on poverty is positive (albeit

slightly smaller in magnitude) and significant. Furthermore, with the exception of the positive

and significant effect of democracy in Model 4.1, the effect of the control variables are similar to

the baseline models. These results confirm that the baseline model results are immune to the

effects of outliers.

4.2.4. Accounting for simultaneity

As one final robustness check, we confront the issue of the possibility that the shadow

economy may impact poverty, and thus poverty is endogenous. For example, a larger shadow

economy may attract impoverish people that have been shutout of the formal sector. As

impoverish people seek employment in the informal sector, this could result in a poverty trap

preventing them from seeking formal employment due to, for example, lack of formal

6 Moreover, we remove potential outliers by winsorizing the shadow economy measure (Barnett and Lewis 1994)

and re-estimate the baseline regressions. The results, not reported here to conserve space but available upon request,

continue to show that poverty is positively associated with the size of the shadow economy.

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employment history. Alternatively, the informal sector may also provide an avenue to learn new

skills or start a new business that may increase income mobility among impoverished people. To

account for this inherent simultaneity bias, we employ instrumental variables and estimate

equation (1) using two-stage least squares (2SLS). To instrument poverty, we use the gross

saving rate as a percent of GDP (lagged one period) from the World Bank (2017). Countries with

a higher saving rate are likely to have less poverty due to a lower marginal propensity to

consume, and the lagged saving rate likely has no direct influence on the shadow economy.

The results, reported in Models 4.6-4.10 of Table 4, show that the coefficient on Poverty

($1.90) is positive and highly significant consistent with more poverty increasing the size of the

shadow economy. In fact, the coefficient is considerably greater than in the baseline models. In

terms of elasticity, a 10% increase in poverty increases the shadow economy by 2.9% (Model 4.6

results). The results of the control variables are in line with the baseline model results except that

the coefficient on education is negative and statistically significant. The significance of the

Kleibergen-Paap (2006) rk LM and the Kleibergen-Paap (2006) rk Wald F test statistics confirm

the relevancy of the instrument (for details see Baum et al. (2007)).

4.3. The effect of poverty on the shadow economy condition on the quantity and quality of

government

Lastly, we examine the government’s role in conditioning the effects of poverty on the size

of the shadow economy. Good governance is a potentially important “missing link” in the

relationship between poverty and the shadow economy. For instance, low quality governments

may enable corrupt bureaucrats to siphon off foreign assistance that was meant to reduce

poverty, thereby forcing poor individuals to rely on the shadow sector for their livelihood.

Moreover, growth in government bureaucracy as a result of poverty reduction programs may

raise taxes and increase government reach into the market economy that promotes the

development of the underground economy. To account for the influence of governance in the

effect of poverty on the shadow economy, we consider two dimensions: (1) the quantity of

government and (2) the quality of government.

Accordingly, government quantity and government quality may amplify or mitigate

poverty’s effect on the shadow economy. Nations with higher quality governments, measured by

bureaucratic quality (BureauQual), have less drastic changes in government policy with minimal

interruption of government services, and are relatively immune from political pressure. In these

nations, lower income individuals are less likely to be forced to rely on the shadow sector.

Alternatively, nations with governments that control a larger part of the economy, measured by

GovtSize, reduce opportunities for income mobility of lower income individuals as a result of

burdensome taxation and regulations that limit growth potential; however, larger governments

may make more resources available to impoverished individuals through income redistribution

thereby reducing the benefits of participating in the shadow sector.

This leads us to two additional testable hypotheses:

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H2: Poverty’s effect on the shadow economy is amplified with low quality governments, ceteris

paribus.

H3: Poverty’s effect on the shadow economy is amplified with larger government, ceteris

paribus.

To test hypothesis 2, we augment equation (1) by interacting poverty with the quality of

government as follows:

𝑆ℎ𝑎𝑑𝑜𝑤𝑖𝑡 = 𝛽1𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝑖𝑡 + 𝛽2𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝑖𝑡 ∗ 𝐵𝑢𝑟𝑒𝑎𝑢𝑄𝑢𝑎𝑙𝑖𝑡 + 𝛾𝑖′𝑋𝑖𝑡

𝑘 + 𝛼𝑖 + 𝜏𝑡 + 𝜀𝑖𝑡 (2)

The marginal effect of poverty on the shadow economy conditional on the quality of

government is given by:

𝜕𝑆ℎ𝑎𝑑𝑜𝑤

𝜕𝑃𝑜𝑣𝑒𝑟𝑡𝑦= 𝛽1 + 𝛽2 ∗ 𝐵𝑢𝑟𝑒𝑎𝑢𝑄𝑢𝑎𝑙

(3)

where BureauQual is evaluated at the 10%, 25%, 50%, 75%, and 90% level.

To test hypothesis 3, we augment equation (1) by interacting poverty with the size of

government as follows:

𝑆ℎ𝑎𝑑𝑜𝑤𝑖𝑡 = 𝛽1𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝑖𝑡 + 𝛽2𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝑖𝑡 ∗ 𝐺𝑜𝑣𝑡𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛾𝑖′𝑋𝑖𝑡

𝑘 + 𝛼𝑖 + 𝜏𝑡 + 𝜀𝑖𝑡 (4)

The marginal effect of poverty on the shadow economy conditional on the size of

government is given by:

𝜕𝑆ℎ𝑎𝑑𝑜𝑤

𝜕𝑃𝑜𝑣𝑒𝑟𝑡𝑦= 𝛽1 + 𝛽2 ∗ 𝐺𝑜𝑣𝑡𝑆𝑖𝑧𝑒

(5)

where we evaluate GovtSize at the 10%, 25%, 50%, 75%, and 90% level.

The regression estimates are reported in Models 5.1-5.10 of Table 5. Specifically, Models

5.1-5.5 display the interaction effect between poverty and government quality (BureauQual)

whereas Models 5.6-5.10 report the interaction effect between poverty and government size

(GovtSize). We report the marginal effects of poverty on the size of the shadow economy

conditional on the levels of government quality and government size respectively at the bottom

of Table 5.7 To facilitate interpretation, we plot the marginal effects (solid line) and the upper

and lower bounds of the 95% confidence intervals (dashed lines) from Models 5.1 and 5.6 in

Figures 1 and 2 respectively.

As can be seen from Figure 1, the results illustrate that poverty has the largest effect on the

size of the shadow economy when government quality is the lowest. Indeed, the effect of poverty

on the shadow economy turns statistically insignificant at higher levels of government quality. In

7 Notice that the results for the rest of the control variables in Table 5 are consistent with our earlier findings.

13

other words, countries with higher quality governments play an important role in mitigating

poverty’s effect on the size of the shadow economy. Moreover, the results in Figure 2 show that

poverty has the largest impact on shadow operations when the size of the government is the

largest. Countries with larger governments therefore amplify poverty’s effect on underground

operations.

5. Conclusion

In this paper, we study the effect of poverty on the size of the shadow economy. Using cross-

country panel data, we find that extreme poverty, measured as the poverty headcount ratio at

$1.90 a day as a percent of the population, is positively associated with the size of the shadow

economy. These results withstand various robustness checks including to alternate measures of

the shadow economy, accounting for endogeneity of poverty, inclusion of additional covariates,

and correcting for outliers. Employing two alternate measures of poverty rates defined by less

than $3.20 a day and $5.50 a day continue to show that the prevalence of poverty is associated

with a larger shadow economy.

Our findings therefore suggest that the prevalence of poverty is an important determinant of

underground activities. Income-constrained individuals may seek out ways to save money by

demanding goods and services offered at cheaper prices in the shadow economy, thus boosting

aggregate demand of the underground. Furthermore, individuals that are shutout of the formal

sector for various reasons may seek employment in the shadow economy thereby increasing

production underground. Accordingly, if people living in poverty are employed underground, it

seems plausible that poverty rates might be overstated if they ignore the income earned in the

shadow economy. A summary of the results I reported in Table 6.

Therefore, policy coordination to combat poverty and the shadow economy would seem

appropriate to account for these spillovers. For instance, policies that attempt to eradicate the

shadow economy may have the unintended consequence of injuring impoverished individuals

that rely on the underground for purposes of sustainability. Nevertheless, nations aiming to abate

the development of the shadow economy would benefit from policies that combat the prevalence

of poverty through such things as promoting freedom from regulation (see, e.g., Djankov et al.

2018).

Another important result is that poverty has the largest impact on underground operations

when government quality is the lowest. Consequently, these findings suggest that government

quality mitigates the poverty’s effect on the shadow economy. In fact, we find that the

prevalence of poverty has no statistical impact on shadow participation in countries with higher

levels of government quality. Alternatively, the results show that poverty has the largest

influence on the underground economy when the size of the government is the largest, thereby

suggesting that nations with larger governments amplify poverty’s effect on shadow operations.

Our findings thus demonstrate the importance of government quality and government size in

conditioning the influence of poverty on the size of the shadow economy.

14

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18

Table 1: The effect of poverty on the shadow economy: Baseline results

(1.1) (1.2) (1.3) (1.4) (1.5) (1.6) (1.7) (1.8) (1.9) (1.10) (1.11) (1.12)

Poverty($1.90) 0.093*** 0.087*** 0.104*** 0.096*** 0.061** 0.091*** 0.087*** 0.073** 0.086*** 0.077** 0.052* 0.074**

(0.033) (0.031) (0.034) (0.032) (0.028) (0.033) (0.033) (0.031) (0.033) (0.031) (0.028) (0.033)

Growth -0.127*** -0.157*** -0.138** -0.148*** -0.109** -0.133*** -0.158*** -0.137** -0.152*** -0.121**

(0.047) (0.055) (0.054) (0.045) (0.050) (0.046) (0.055) (0.054) (0.045) (0.051)

Democracy 0.082 -0.156 -0.154 0.117 0.042 0.204 -0.058 -0.061 0.203 0.179

(0.208) (0.246) (0.254) (0.205) (0.181) (0.197) (0.224) (0.227) (0.193) (0.176)

BureauQual -1.063*** -3.353*** -2.684*** -1.110*** -1.098*** -1.389*** -3.341*** -2.466*** -1.443*** -1.437***

(0.368) (0.790) (0.880) (0.392) (0.370) (0.365) (0.731) (0.794) (0.391) (0.380)

GovtSize 0.301*** 0.273*** 0.234** 0.296*** 0.351*** 0.227*** 0.212** 0.188** 0.227*** 0.255***

(0.088) (0.103) (0.106) (0.088) (0.102) (0.081) (0.093) (0.094) (0.082) (0.092)

RegQual -2.597** -2.903***

(1.210) (1.122)

GovtEffect -3.879*** -4.374***

(1.162) (1.040)

Education -0.015 -0.019

(0.024) (0.022)

Inequality 0.194* 0.179*

(0.105) (0.101)

Time Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country Effects Yes Yes Yes Yes Yes Yes No No No No No No

Regional Effects No No No No No No Yes Yes Yes Yes Yes Yes

R-squared 0.588 0.638 0.659 0.667 0.692 0.666 0.588 0.635 0.657 0.665 0.689 0.662

Observations 398 398 344 344 354 379 398 398 344 344 354 379

Countries 114 114 113 113 111 113 114 114 113 113 111 113 Notes: See Table 1A for variable details. Constant is included but not reported. Robust standard errors are in parentheses. Asterisks denote significance at the

following levels: *** p<0.01, ** p<0.05, * p<0.1.

19

Table 2: The effect of poverty on the shadow economy: Robustness check 1 – Alternate measures of the poverty rate

(2.1) (2.2) (2.3) (2.4) (2.5) (2.6) (2.7) (2.8) (2.9) (2.10) (2.11) (2.12)

Poverty($3.20) 0.083** 0.073** 0.081** 0.073** 0.044* 0.073** 0.096***

(0.032) (0.030) (0.032) (0.031) (0.025) (0.030) (0.028)

Poverty($5.50) 0.087*** 0.098*** 0.093*** 0.066*** 0.086***

(0.025) (0.028) (0.027) (0.024) (0.025)

Growth -0.114** -0.132** -0.117** -0.141*** -0.103** -0.099** -0.101* -0.088* -0.125*** -0.095**

(0.044) (0.055) (0.053) (0.045) (0.048) (0.043) (0.055) (0.051) (0.044) (0.045)

Democracy 0.170 -0.070 -0.076 0.189 0.150 0.256 0.015 -0.001 0.244 0.245

(0.214) (0.249) (0.257) (0.219) (0.192) (0.205) (0.235) (0.243) (0.217) (0.186)

BureauQual -1.116*** -3.487*** -2.847*** -1.162*** -1.162*** -1.172*** -3.546*** -2.983*** -1.165*** -1.216***

(0.359) (0.838) (0.921) (0.375) (0.374) (0.350) (0.841) (0.925) (0.361) (0.372)

GovtSize 0.299*** 0.254** 0.219** 0.295*** 0.356*** 0.281*** 0.222** 0.193* 0.283*** 0.344***

(0.088) (0.106) (0.110) (0.090) (0.104) (0.084) (0.100) (0.101) (0.084) (0.098)

RegQual -2.308* -1.891

(1.233) (1.214)

GovtEffect -3.611*** -3.171***

(1.178) (1.158)

Education -0.002 0.015

(0.024) (0.023)

Inequality 0.190* 0.159

(0.104) (0.103)

Time Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.588 0.637 0.653 0.662 0.688 0.667 0.606 0.651 0.674 0.681 0.701 0.683

Observations 398 398 344 344 354 379 398 398 344 344 354 379

Countries 114 114 113 113 111 113 114 114 113 113 111 113 Notes: See Table 1A for variable details. Constant is included but not reported. Robust standard errors are in parentheses. Asterisks denote significance at the

following levels: *** p<0.01, ** p<0.05, * p<0.1.

20

Table 3: The effect of poverty on the shadow economy: Robustness check 2 – Alternate measures of the shadow economy

Dep. Variable: Shadow (SBM) Shadow (EO)

(3.1) (3.2) (3.3) (3.4) (3.5) (3.6) (3.7) (3.8) (3.9) (3.10)

Poverty($1.90) 0.051*** 0.052*** 0.048*** 0.051*** 0.047*** 0.068*** 0.070*** 0.068*** 0.060** 0.048**

(0.016) (0.016) (0.016) (0.017) (0.018) (0.023) (0.026) (0.026) (0.024) (0.024)

Growth -0.043 -0.042 -0.036 -0.033 -0.041 0.136*** 0.129*** 0.127*** 0.137*** 0.128***

(0.033) (0.031) (0.032) (0.034) (0.033) (0.034) (0.034) (0.035) (0.043) (0.033)

Democracy -0.083 -0.107 -0.108 -0.070 -0.065 0.020 -0.177 -0.132 0.038 0.065

(0.108) (0.108) (0.108) (0.113) (0.105) (0.164) (0.155) (0.152) (0.190) (0.155)

BureauQual -1.452** -1.429** -1.154** -1.570** -1.559** -0.995** -1.581*** -1.615*** -0.854* -1.062***

(0.619) (0.577) (0.566) (0.667) (0.603) (0.381) (0.490) (0.495) (0.498) (0.376)

GovtSize -0.013 -0.017 -0.028 -0.027 -0.002 -0.078 -0.131 -0.125 -0.058 -0.143*

(0.060) (0.059) (0.058) (0.061) (0.063) (0.099) (0.080) (0.084) (0.104) (0.078)

RegQual -0.776* -1.499**

(0.439) (0.704)

GovtEffect -1.452** -0.050

(0.690) (0.969)

Education -0.038** 0.011

(0.015) (0.020)

Inequality 0.070 -0.052

(0.047) (0.051)

Time Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.689 0.697 0.707 0.701 0.696 0.561 0.554 0.529 0.544 0.567

Observations 256 256 256 224 253 312 257 257 274 309

Countries 111 111 111 103 111 113 112 112 108 113 Notes: See Table 1A for variable details. Constant is included but not reported. Robust standard errors are in parentheses. Asterisks denote significance at the

following levels: *** p<0.01, ** p<0.05, * p<0.1.

21

Table 4: The effect of poverty on the shadow economy: Robustness checks 3 and 4 – Accounting for outliers and endogeneity

(4.1) (4.2) (4.3) (4.4) (4.5) (4.6) (4.7) (4.8) (4.9) (4.10)

Poverty($1.90) 0.073*** 0.079*** 0.086*** 0.055*** 0.086*** 0.287*** 0.259*** 0.265*** 0.228** 0.480***

(0.017) (0.021) (0.020) (0.018) (0.020) (0.100) (0.098) (0.094) (0.091) (0.133) Growth -0.060** -0.097*** -0.070** -0.011 -0.027 -0.174** -0.164** -0.153* -0.156* -0.049

(0.029) (0.036) (0.034) (0.032) (0.033) (0.086) (0.078) (0.079) (0.083) (0.096)

Democracy 0.213* -0.144 -0.015 0.105 0.182 -0.226 -0.293 -0.277 -0.217 -0.549

(0.121) (0.154) (0.144) (0.128) (0.123) (0.311) (0.302) (0.305) (0.289) (0.343)

BureauQual -0.859*** -2.590*** -1.206* -0.871** -0.736** -4.510*** -4.558*** -3.990*** -3.759*** -5.683***

(0.307) (0.658) (0.639) (0.339) (0.309) (1.384) (1.311) (1.382) (1.192) (1.868)

GovtSize 0.171*** 0.253*** 0.114* 0.232*** 0.168*** 0.400*** 0.388*** 0.359*** 0.384*** 0.497***

(0.058) (0.071) (0.068) (0.062) (0.064) (0.106) (0.099) (0.104) (0.100) (0.140)

RegQual -2.874*** -2.856***

(0.709) (0.970)

GovtEffect -5.140*** -3.433***

(0.803) (1.251)

Education -0.005 -0.056**

(0.016) (0.028)

Inequality 0.134*** 0.363***

(0.047) (0.109)

Time Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.984 0.985 0.987 0.984 0.986 0.575 0.619 0.613 0.619 0.509

Observations 395 341 341 349 374 316 316 316 280 295

Countries 100 100 100 93 94

Estimation Robust

OLS

Robust

OLS

Robust

OLS

Robust

OLS

Robust

OLS

2SLS 2SLS 2SLS 2SLS 2SLS

Kleibergen-Paap rk Wald F

statistic

36.92 36.22 35.63 30.30 14.59

Kleibergen-Paap rk LM

statistic

13.29***

[0.000]

13.69***

[0.000]

12.90***

[0.000]

11.24***

[0.000]

12.35***

[0.000]

Notes: Constant is included but not reported. Robust standard errors are in parentheses and probability values are in brackets. Asterisks denote significance at the

following levels: *** p<0.01, ** p<0.05, * p<0.1. In Models 4.6-4.10 Poverty ($1.90) is instrumented using the one period lag of SavingRate. The critical values

for the Kleibergen-Paap rk Wald F statistic are in Stock and Yogo (2005).

22

Table 5: The effect of poverty on the shadow economy condition on the quantity and quality of government

Government Quality Government Quantity

(5.1) (5.2) (5.3) (5.4) (5.5) (5.6) (5.7) (5.8) (5.9) (5.10)

Poverty($1.90) 0.104*** 0.151** 0.164** 0.093** 0.088** 0.061 0.089 0.086 0.065 0.078

(0.038) (0.065) (0.063) (0.041) (0.042) (0.047) (0.055) (0.054) (0.051) (0.066)

Interaction -0.012 -0.030 -0.043 -0.021 0.002 0.002 0.001 0.001 -0.000 0.001

(0.019) (0.037) (0.037) (0.019) (0.020) (0.003) (0.003) (0.003) (0.003) (0.003)

Growth -0.128*** -0.155*** -0.134** -0.147*** -0.108** -0.126*** -0.155*** -0.137** -0.148*** -0.109**

(0.046) (0.055) (0.054) (0.045) (0.051) (0.047) (0.055) (0.054) (0.046) (0.051)

Democracy 0.106 -0.123 -0.113 0.161 0.038 0.090 -0.150 -0.149 0.116 0.048

(0.217) (0.245) (0.250) (0.208) (0.190) (0.203) (0.245) (0.252) (0.203) (0.175)

BureauQual -0.803 -2.745** -1.780 -0.645 -1.140* -1.111*** -3.370*** -2.694*** -1.103*** -1.123***

(0.569) (1.170) (1.235) (0.562) (0.586) (0.357) (0.799) (0.885) (0.388) (0.371)

GovtSize 0.307*** 0.286*** 0.251** 0.304*** 0.351*** 0.257** 0.244* 0.215 0.302*** 0.335***

(0.087) (0.103) (0.105) (0.086) (0.102) (0.107) (0.124) (0.131) (0.109) (0.109)

RegQual -2.514** -2.629**

(1.207) (1.196)

GovtEffect -3.995*** -3.886***

(1.144) (1.166)

Education -0.016 -0.015

(0.024) (0.024)

Inequality 0.195* 0.194*

(0.110) (0.105)

𝜕𝑆ℎ𝑎𝑑𝑜𝑤

𝜕𝑃𝑜𝑣𝑒𝑟𝑡𝑦= 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 + 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 ∗ 𝐵𝑢𝑟𝑒𝑎𝑢𝑄𝑢𝑎𝑙

𝜕𝑆ℎ𝑎𝑑𝑜𝑤

𝜕𝑃𝑜𝑣𝑒𝑟𝑡𝑦= 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 + 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 ∗ 𝐺𝑜𝑣𝑡𝑆𝑖𝑧𝑒

10% 0.092*** 0.121*** 0.121*** 0.072** 0.090*** 0.079** 0.100*** 0.094*** 0.063* 0.086**

(0.030) (0.038) (0.035) (0.029) (0.033) (0.032) (0.037) (0.034) (0.032) (0.040)

25% 0.089*** 0.113*** 0.110*** 0.066** 0.090*** 0.087*** 0.105*** 0.097*** 0.062** 0.090***

(0.030) (0.034) (0.031) (0.028) (0.033) (0.031) (0.034) (0.032) (0.029) (0.034)

50% 0.080** 0.092** 0.078** 0.051* 0.092** 0.095*** 0.110*** 0.100*** 0.060* 0.094**

(0.034) (0.037) (0.035) (0.028) (0.036) (0.035) (0.036) (0.035) (0.030) (0.034)

75% 0.069 0.062 0.035 0.030 0.094* 0.106** 0.117** 0.104** 0.059 0.100**

(0.047) (0.063) (0.063) (0.038) (0.048) (0.046) (0.045) (0.046) (0.040) (0.044)

23

90% 0.057 0.032 -0.008 0.009 0.096 0.116** 0.123** 0.109* 0.057 0.105*

(0.063) (0.097) (0.097) (0.053) (0.063) (0.058) (0.057) (0.060) (0.053) (0.059)

Time Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.638 0.661 0.672 0.694 0.666 0.639 0.659 0.668 0.692 0.666

Observations 398 344 344 354 379 398 344 344 354 379

Countries 114 113 113 111 113 114 113 113 111 113 Notes: See Table 1A for variable details. Constant is included but not reported. Robust standard errors are in parentheses. Asterisks denote significance at the

following levels: *** p<0.01, ** p<0.05, * p<0.1.

24

Table 6: Summary of results

Hypothesis Description Hypothesis Confirmed

H1 A greater prevalence of poverty is associated with a larger shadow

economy, ceteris paribus.

Yes

H2 Poverty’s effect on the shadow economy is amplified with low

quality governments, ceteris paribus.

Yes

H3 Poverty’s effect on the shadow economy is amplified with larger

government, ceteris paribus.

Yes

25

Figure 1: The effect of poverty on the shadow economy conditional on the quality of government

Notes: The solid line represents the marginal effect of poverty on the size of the shadow economy conditional on the

levels of government quality whereas the dashed lines denote the upper and lower bounds of the 95% confidence

intervals.

-0.1

-0.05

0

0.05

0.1

0.15

0.2

10% 25% 50% 75% 90%

Mar

gin

al E

ffec

t

Percentile

26

Figure 2: The effect of poverty on the shadow economy conditional on the quantity of government

Notes: The solid line represents the marginal effect of poverty on the size of the shadow economy conditional on the

levels of government quantity whereas the dashed lines denote the upper and lower bounds of the 95% confidence

intervals.

0

0.05

0.1

0.15

0.2

0.25

10% 25% 50% 75% 90%

Mar

gin

al E

ffec

t

Percentile

27

Appendix

Table 1A: Variable definitions, sources and summary statistics

Variable Description [observations; mean; standard deviation] Source

Shadow (MS) The size of the shadow economy measured as a percent of

GDP from 1991-2015. Based on the MIMIC method. [790;

32.28; 12.43]

Medina and

Schneider

(2017)

Shadow (SBM) The size of the shadow economy measured as a percent of

GDP from 1997-2007. Based on the MIMIC method. [481;

32.97; 12.73]

Schneider et al.

(2010)

Shadow (EO) The size of the shadow economy measured as a percent of

GDP from 1950-2009. Based on a two-sector dynamic

general equilibrium model. [643; 33.04; 12.98]

Elgin and

Öztunali (2012)

Poverty($1.90) The poverty rate measured as the percent of the population

that lives at $1.90 a day. [532; 18.67; 23.51]

World Bank

(2017)

Poverty($3.20) The poverty rate measured as the percent of the population

that lives at $3.20 a day. [532; 32.82; 31.24]

World Bank

(2017)

Poverty($5.50) The poverty rate measured as the percent of the population

that lives at $5.50 a day. [532; 49.00; 35.07]

World Bank

(2017)

Growth Economic growth measured as the log difference of

expenditures-side real GDP (chained PPPS in millions of

2005 dollars). [830; 3.73; 5.68]

Penn World

Tables

Democracy An index measuring the degree of political freedom based

on the sum of civil liberties and political rights. [959; 6.83;

3.93]

Freedom House

BureauQual An index measuring the strength and quality of

bureaucracy on a scale of 0 to 4 with higher numbers

denoting better outcomes. [686; 2.19; 1.13]

International

Country

Risk Guide

GovtSize Total government final consumption expenditures as a

percent of total final consumption expenditures. [859;

21.06; 9.10]

World Bank

(2017)

RegQual An index from -2.5 to 2.5 measuring the quality of

regulations. [823; 0.001; 0.99]

Kaufmann et al.

(2010)

GovtEffect An index from -2.5 to 2.5 measuring the effectiveness of

government. [823; 0.002; 0.99]

Kaufmann et al.

(2010)

Education Gross enrollment ratio (%) in tertiary education. [764;

27.78; 24.26]

World Bank

(2017)

Inequality Inequality measured by the Gini index based on income net

of taxes and transfers on a scale from 0 to 100 where

higher numbers denote more inequality. [680; 38.11; 8.79]

Solt (2016) and

SWIID

SavingRate The gross saving rate as a percent of GDP. [750; 20.80;

16.47]

World Bank

(2017) Notes: Summary statistics includes all available data from 1991 to 2015.

28

Table 2A: List of countries in the analysis

Albania Ecuador Korea, Rep. Senegal

Angola Egypt Latvia Sierra Leone

Argentina El Salvador Lebanon Slovak Republic

Armenia Estonia Liberia Slovenia

Australia Ethiopia Lithuania South Africa

Austria Finland Luxembourg Spain

Azerbaijan France Madagascar Sri Lanka

Bangladesh Gabon Malawi Suriname

Belarus Gambia Malaysia Sweden

Belgium Germany Mali Switzerland

Bolivia Ghana Malta Syrian Arab Republic

Botswana Greece Mexico Tanzania

Brazil Guatemala Moldova Thailand

Bulgaria Guinea Mongolia Togo

Burkina Faso Guinea-Bissau Morocco Trinidad and Tobago

Cameroon Honduras Mozambique Tunisia

Canada Hungary Namibia Turkey

Chile Iceland Netherlands Uganda

China India Niger Ukraine

Colombia Indonesia Nigeria United Kingdom

Congo, Dem. Rep. Iran, Islamic Rep. Norway United States

Congo, Rep. Ireland Pakistan Uruguay

Costa Rica Israel Paraguay Venezuela

Cote d'Ivoire Italy Peru Vietnam

Croatia Jamaica Philippines Yemen, Rep.

Cyprus Japan Poland Zambia

Czech Republic Jordan Portugal Zimbabwe

Denmark Kazakhstan Romania Dominican Republic Kenya Russia

Notes: N=114