Chapter 3 Nonlinearity between the Shadow Economy and Economic Development
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International Monetary Fund
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Keywords:
shadow economy size; determinants of the shadow economy; black economy; shadow economy data; size of the shadow economy; Informal economy; Tax incidence; Purchasing power parity; Purchasing power; Income; Global; shadow economy's determinant; Inflation
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Abstract

The Global Informal Workforce is a fresh look at the informal economy around the world and its impact on the macroeconomy. The book covers interactions between the informal economy, labor and product markets, gender equality, fiscal institutions and outcomes, social protection, and financial inclusion. Informality is a widespread and persistent phenomenon that affects how fast economies can grow, develop, and provide decent economic opportunities for their populations. The COVID-19 pandemic has helped to uncover the vulnerabilities of the informal workforce.

Introduction

The shadow economy has been labeled with many names, such as the informal economy, the hidden economy, the black economy, and the underground economy. Although existing studies provide a broad range of definitions or descriptions, most are similar. This chapter follows the definition proposed in Chapter 1 because it uses that chapter’s authors’ estimated data for the size of the shadow economy:

The shadow economy includes all economic activities that are hidden from official authorities for monetary, regulatory, and institutional reasons. Monetary reasons include avoiding paying taxes and all social security contributions; regulatory reasons include avoiding governmental bureaucracy or the burden of regulatory framework; and institutional reasons include corruption law, the quality of political institutions, and weak rule of law. For our study, the shadow economy reflects mostly legal economic and productive activities that, if recorded, would contribute to national GDP.

One of the most intensively studied topics in economics is the cause of the shadow economy around the world. Although our understanding of potential shadow economy drivers has improved substantially in the past two decades, questions regarding its long-term behavioral pattern are still open to debate (Schneider and Enste 2000; La Porta and Shleifer 2008; Feld and Schneider 2010; Williams and Schneider 2016). For example, does the size of the shadow economy converge to a certain level, or does it have a robust long-term linear relationship with its determinants? This chapter aims to fill the gap in the literature by investigating the long-term relationship between the shadow economy and its key determinants.

We identify a U-shaped relationship between the size of the shadow economy1 and the level of economic development, using a panel data set covering 158 countries from 1996 to 2015. We take into account a wide range of the shadow economy’s determinants and adopt various regression specifications to test the robustness of the nonlinearity between the shadow economy and GDP per capita.2 Our results reveal that, after controlling for key economic, policy, and institutional variables, less-developed economies witness a negative relationship between the size of the shadow economy and GDP per capita; however, when GDP per capita exceeds a threshold, the size of the shadow economy increases with per capita income. These findings are consistent with economic intuition that economic development may have two opposite effects on the size of the shadow economy.

On the one hand, economic development, characterized by productivity improvement and technology advance, may support the long-term expansion of the shadow economy because advanced development means a high level of human capital, which helps individuals make a living. When less constrained by financial pressures, some people prefer informal jobs to gain more work flexibility or to reach a better work-life balance, especially if the wage difference between the formal and informal sectors is negligible. In addition, technology innovation can support the shadow economy by providing more convenient jobs and reliable decentralized payment systems.3

On the other hand, economic development can help downsize the shadow economy by offering high-quality public goods and services. Advanced economic development is normally characterized by strong institutional capacity and better social infrastructure, which help absorb firms and individuals from the informal sector or encourage them to stay formal. Two competing forces jointly determine the net effect of economic development on the shadow economy: at a low level, the downsizing effect associated with economic development plays a dominant role—people join or switch to the formal sector to enjoy more benefits of economic growth, thus the shadow economy shrinks. At a high level, more household members obtain enough financial freedom to consider informal jobs to pursue diverse goals. Thus, there is a gradual resurgence of the informal sector.

The U-shaped curve,4 as Figure 3.1 displays, discloses a different development pattern than most studies, which assume or identify a linear relationship between the size of the shadow economy and its determinants. The finding of nonlinearity implies that the shadow economy is able to coexist with different levels of development and that the shadow economy does not disappear in the long term.

Figure 3.1.
Figure 3.1.

Nonlinear Relationship between the Shadow Economy and GDP per Capita

(Average, constant 2011 international dollars)

Sources: Medina and Schneider 2018; and World Bank data.Note: GDP per capita is the average value of constant 2011 international dollars from 1996 to 2015 based on purchasing power parity. Data labels use International Organization for Standardization country codes.

This is contrary to the inference of a linear relationship, which predicts a shrinking trend or the final disappearance of the shadow economy. One question from Figure 3.1 is related to the observation that the average GDP per capita for most countries lies at the downward part of the U-shaped curve, while only a few countries lie on the upslope, including four oil-exporting countries. This chapter examines the robustness of the relationship by using an alternative measure of GDP per capita, which moves the four countries downward on the curve, and by dropping the four countries.

This chapter also seeks to identify factors that could boost GDP per capita. Consistent with the growth literature, we find that educational attainment plays a vital role in improving GDP per capita, especially having a college degree or higher. This result helps shed some light on a possible mechanism of a U-shaped pattern at the micro level. From the individual perspective, people work to make themselves better off. When development is low, education helps build labor productivity; skilled workers with a college education or higher choose to stay in the formal sector to enjoy benefits from high productivity and the social security net. When the economy advances to a new level at which skilled workers’ income becomes high enough and one household member can easily cover a family’s daily expenses, demand for work flexibility or other desirable perks of informal work is likely to increase. Hence, the size of shadow economy reverses its downtrend.

Literature Review

There is a considerable amount of economic research on the shadow economy, with a particular focus on its estimated size and causes.5 The estimation approaches include survey-based methods, observable-variable methods, and model-based methods.6 One of the latest examples is Medina and Schneider (2018), using the multiple indicators, multiple causes (MIMIC) approach to find that the estimated average size of the shadow economy in 158 countries from 1991 to 2015 is 31.9 percent relative to GDP.

The causes of the shadow economy can be categorized into three groups: economic, policy related, and regulatory and institutional.7 Among the key factors are access to financing, political stability, public services provision, tax burden, labor market regulations, and institutional quality. Many papers identify potential determinants of the shadow economy by assuming a simple linear effect, whereas some take advantage of various interactions among the variables to revise or complement early findings. Almost all research, explicitly or implicitly, assumes or agrees that the shadow economy should be expected to shrink with economic growth, upgraded financial and public services, improved institutional quality, and effective regulation.

One related question is whether this shrinking trend is a long-term, irreversible phenomenon. Suppose that all countries keep strengthening their capacity in supervision and regulation, providing efficient public services, and reducing their institutional weaknesses. Is it then reasonable to predict that the shadow economy will continue shrinking until it disappears or becomes negligible?

This is the first attempt to investigate the nonlinear long-term trend of the shadow economy. Our major contribution includes the revelation of a U-shaped relationship between the shadow economy and level of development, using GDP per capita as proxy for development. Although some research already uses GDP per capita, its purpose is to control for the level of development (for example, La Porta and Shleifer 2008). We allow for a U-shaped relationship by including squared GDP per capita. Our main results disclose the significance of the squared GDP per capita term, and the following regressions support its robustness.

Furthermore, this chapter explores possible long-term factors for level of development. It is not surprising to find that educational attainment plays a vital role, especially those who hold college degrees and higher. Even so, our finding regarding this variable contrasts with earlier work. Buehn and Farzanegan (2013) find, by interacting education and institutional quality in the regression model, that higher educational attainment can decrease the shadow economy in a strong institutional environment. This finding suggests that the educational effect on informality depends on institutional quality. When the quality of institutions is high enough, Buehn and Farzanegan (2013) imply, educational achievement contributes to the decline of the shadow economy. The issue with this conclusion is the estimated effect of institutional quality on the shadow economy. As Buehn and Farzanegan (2013) show, the institution has a positive effect on the size of the shadow economy, and the effect declines with education because of the same interaction item, which contradicts economic intuition and is hard to explain.

In addition, our finding is contrary to Elgin and Erturk (2016), who support the negative relationship. The regression in Elgin and Erturk (2016) uses a longer time series on the size of the shadow economy while relying only on fixed-effect dummies to control for all other factors. In addition, Elgin and Erturk (2016) set up a model to capture the underlying mechanism, which assumes the value of total factor productivity (TFP) is constant. Instead the model implies the size of the shadow economy depends on the relative TFP values between the formal and informal sectors. If informal sector productivity catches up to that of the formal sector, the informal tends to grow, which is consistent with our finding.

We focus on the long-term determinants of the size of the shadow economy, whereas Elgin and Birinci (2016) explore the nonlinear effect of the shadow economy on economic growth. One difference is the direction of the effect; Elgin and Birinci (2016) aim to identify one new factor of growth. Furthermore, there is no direct inference between the two studies’ findings. Elgin and Birinci (2016) find an inverted U between the shadow economy and growth of GDP per capita. Given that growth of GDP per capita has no simple monotone relationship with the level of GDP per capita, it is hard to derive from these findings a relationship between the shadow economy and GDP per capita and thus to judge whether this work is consistent with theirs. One key finding of Elgin and Birinci (2016), though, is that the informal sector has positive spillover effects on TFP growth.

Method and Data

In this section, we set up a framework for comprehensive econometric analysis to identify the nonlinear relationship between the size of the informal economy and GDP per capita.

Empirical Method

First, we conduct several regressions with different estimators. The benchmark cross-sectional regression is based on the following setting:

SEi=β0+β1yi+β2yi2+Σk=3nβkxk,i+εi,(1)

where SEi is the percentage ratio of the shadow (or informal) economy relative to GDP of country i; yi stands for GDP per capita for country i; xk,i represents other control variables; and εi denotes the error term. The inclusion of the squared GDP per capita term in the regression is to check for the potential existence of a nonlinear relationship between the size of the informal economy and GDP per capita.

Subsequently, we calculate the cross-sectional regression using variables constructed as 20-year averages. Then we conduct the robustness check with four settings:

  • 1. Dummy variables are used to control for country group effects, complemented by separate regressions on each country group.

  • 2. Regressions on variables of 10-year averages are conducted to further confirm the original findings.

  • 3. The panel regression method with a 5-year average is adopted to continue checking the validity of the empirical results. The method includes regressions with one-period and two-period lagged variables to control for endogeneity.

  • 4. Regressions are conducted to control for other potential economic and institutional factors.

For the panel regression, the equation is set up as follows:

SEit=β0+β1yit+β2yit2+Σk=3nβkxk,it+θi+δt+εit,(2)

where dummies of θi and δt are inserted to reflect the country and time effects. Both the fixed effect and random effect estimators are reported here.

Data

We collect annual cross-country panel data covering 158 countries or regions from 1996 to 2015.8 In our regressions, the variables are 20-, 10-, or 5-year averages. Variables constructed as 20- and 10-year averages are used in the cross-sectional regressions, and the 5-year average variables are fed into the panel data regressions. The size of the shadow economy relative to GDP is borrowed from Medina and Schneider (2018), who revise the standard MIMIC approach by using light intensity instead of GDP as an indicator variable. By limiting GDP to being only a cause (and not also an effect) variable, this revision improves the estimation results. To make the study’s findings reliable, we also use the shadow economy data from Elgin and Oztunali (2012) as a robustness check.9 Elgin and Oztunali (2012) estimate the shadow economy based on a deterministic dynamic general equilibrium model. As the empirical results will show, the two estimated series are highly correlated.

The choice of control variables is based on the existing empirical literature, including GDP per capita, the political stability index, growth of GDP per capita, consumer price index inflation, trade openness, financial depth, tax burden, education-related variables, and capital stock:

  • GDP per capita comes from the World Bank World Development Indicators (WDI) database, and two measurements are used to ensure the robustness of the results: one is constant 2011 international dollars based on purchasing power parity (PPP), and the other is constant 2010 US dollars. This latter series is the main series used to establish this chapter’s major finding.

  • The index of political stability, used to control for institutional differences, is extracted from the database of the World Bank Worldwide Governance Indicators (WGI). Its original values range from -2.5 to 2.5, which we change here into 0 to 100.

  • The noninstitutional variables of GDP per capita growth, consumer price index inflation, trade openness, financial depth, and tax burden are also from the WDI. GDP per capita growth is calculated with national currency, and the lowest GDP per capita growth occurs in Libya in 2011. Inflation is measured with consumer price index data and expressed in percentages. Trade openness is defined as the sum of exports and imports of goods and services as a percentage of GDP. Financial depth is measured as the ratio of domestic credit to private sector credit provided by financial corporations,10 such as through loans, nonequity securities, and trade credit. Tax burden is captured by the ratio of taxes and mandatory contributions payable to commercial profits.

  • The regression, aiming to explore the determinants of GDP per capita, also uses data on educational attainment from the WDI and total capital stock from the IMF. Three educational variables are constructed: the percentages of people completing primary school only, completing high school only, and completing college and higher.11 The IMF Investment and Capital Stock Dataset includes three measurements of capital stock, namely public capital, private capital, and public-private partnership capital. We calculate total capital stock as the sum of the 20-, 10-, or 5-year time series.

Empirical Results

This section presents empirical findings on the nonlinear interaction between the shadow economy and GDP per capita.

Here endogeneity can come from two main possible sources: (1) the effect of informal activities on the formal sector, through households’ or firms’ decisions, may imply that institution and economic variables on the right side of the regression equation are influenced by the size of the shadow economy;12 and (2) the measurement error embedded in the estimated size of the shadow economy may also lead to the two-way causality between regressors and dependent variables. We adopted various specifications to check the sensitivity of the results and to ensure that endogeneity has been effectively mitigated.

Findings of the Benchmark Model

The results of the benchmark static cross-sectional regression are reported in Table 3.1, which uses each country as one observation by taking a 20-year average on all relevant time series.

Table 3.1.

Nonlinearity between the Shadow Economy and GDP Per Capita, 1996–2015 (Average)

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Sources: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < .10; **p < .05; ***p < .01.

Estimated shadow economy size from Medina and Schneider 2018 and GDP per capita in PPP-based international dollars.

Estimated shadow economy size from Medina and Schneider 2018 and GDP per capita in 2010-based US dollars.

Estimated shadow economy size from Elgin and Oztunali 2012 and GDP per capita in PPP-based international dollars.

The first column reports the regression results using the estimated shadow economy size from Medina and Schneider (2018) and GDP per capita in PPP-based international dollars, while the second column reports the regression on GDP per capita in 2010 US dollars. The third column reports the results of regressing the estimated informality numbers from Elgin and Oztunali (2012) on PPP-based GDP per capita. All three regressions identify, at the 1 percent significance level, the positive coefficient for the squared shadow economy size and thus support the existence of the U-shaped relationship between the shadow economy and GDP per capita. That is, the shadow economy shrinks with the increase of GDP per capita until it reaches a threshold; after that point, the shadow economy and GDP per capita are positively related and the shadow economy size grows with GDP per capita.

The benchmark regression uses the indicator of political stability from the WGI to proxy for institutional factors, which is motivated by Elbahnasawy, Ellis, and Adom (2016). The coefficient for political stability is consistently negative, implying that institutional factors help contain the expansion of the shadow economy.

In addition, the estimated negative coefficient of financial depth reveals that financial development is instrumental in dampening the activities of the shadow economy, which is consistent with the findings of World Bank Enterprise Surveys.13

Further Investigation of the Nonlinear Relationship

Which factors determine the long-term value of GDP per capita and thus indirectly influence the size of the shadow economy? The classic production function implies that physical capital, human capital, and technology are three fundamental variables. In addition, Barro (2013) argues that inflation is negatively related to economic growth. Guided by the existing literature, the regression equation of GDP per capita is set as the following:

yi=β0+β1*collegei+β2*HighSch+β3*PrimSch+β4*inflation+β5*CapStock+εi(3)

The results in Table 3.2 reveal that educational attainment, especially college and graduate degrees, contributes to the increase of GDP per capita. It is not surprising to see that bachelor’s degrees and higher are significant and more important than high school and primary school diplomas in boosting GDP per capita. College and postgraduate education help employees reach higher productivity, and the skill-complementary technology trend in recent decades has created a constant demand for skilled labor, as Acemoglu (2002) shows. In addition, the regression confirms that inflation is detrimental to GDP per capita, supporting existing studies on the long-term negative relationship between inflation and economic growth. Then what is the economic intuition behind the implied long-term relationship between education and the shadow economy? The formal sector is more productive than the informal sector, and firms tend to move out of the informal sector to hire skilled workers when more people become well educated, thus reducing the size of the shadow economy; however, when education reaches a certain level as GDP per capita hits a threshold, further attainment not only pushes up GDP per capita but also reverses the declining trend of the shadow economy. This reversal could be attributed to growing productivity leading to an increase in informal sector salaries. When household revenue exceeds a critical level, financial pressure becomes less intense. Some family members may become more willing to take temporary, unregistered assignments, rather than formal full- or part-time positions, in exchange for flexibility, which leads to the expansion of the shadow economy.

Table 3.2.

Long-Term Determinants of GDP Per Capita, 1996–2015 (Average)

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < .10; **p < .05; ***p < .01.

The R2 value indicates that education, capital stock, and inflation can explain about 30.6 percent of GDP per capita; a large share of GDP per capita remains unexplained in the regression. In addition, capital stock’s effect on GDP per capita is not significantly different from zero in the long term.

Robustness Checks

Besides the initial robustness regressions in Table 3.1, this subsection continues to conduct robustness checks to test the observed U-shaped relationship between the shadow economy and GDP per capita. Specifically, we implement four types of tests:

  • 1. We explore the robustness of the U-shaped relationship with an additional control variable, tax burden, and then within different country groups, which include regressions with country group dummies and separate estimations for each country group.

  • 2. We use 10-year averages to run the benchmark regression to check the results’ consistency.

  • 3. We calculate 5-year averages and run panel regressions with different estimators to check if the same relationship exists.

  • 4. We test the robustness of the result by dropping the four oil-exporting countries on the upward part of the U curve.

Robustness Check Controlling for Tax Burden and for Country Groups

This subsection investigates the robustness of the results by controlling for tax burden and for different country groups. One motivation for firms to remain unregistered and in the informal sector is to avoid taxes.14 A potential question is whether, in the long term and at the national level, the tax burden is a factor affecting a firm’s decision to stay informal or whether it has any implication for long-term nonlinearity. In the regression, we add the ratio of corporate tax to corporate profit as proxy for a firm’s tax burden. The first column of Table 3.3 shows a positive but nonsignificant coefficient for the tax variable, whereas the two GDP per capita variables remain significant.

Table 3.3.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: AEs and Non-AEs

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Source: Authors. Note: Standard errors appear in parentheses. AEs = advanced economies; HICs = high-income countries; LICs = low-income countries; PPP = purchasing power parity. *p < .10; **p < .05; ***p < .01.

To check whether the nonlinear relationship significantly exists within different country groups, the chapter divides the countries with two criteria. We borrow the definition of advanced economy from the October 2017 World Economic Outlook and split the 158 countries into advanced economies and nonadvanced economies. This chapter also follows the World Bank’s country income classification, using 2015 gross national income per capita, to group all countries or regions into three categories: low-income countries with annual income of less than $1,025, middle-income countries with annual income from $1,026 to $12,475, and high-income countries with income of $12,476 or higher.15

The second and third columns of Table 3.3 present the regression results with country group dummies. One advantage of this method is to use all the observations instead of regressing on a subsample of the data. The nonlinear relationship remains significant even with the dummies.

To further investigate the robustness of the U-shaped relationship for each country group, we run separate regressions for country groups of advanced economies, nonadvanced economies, high-income countries, and non–high-income countries. Table 3.4 shows that the squared GDP per capita remains significant for advanced economies, nonadvanced economies, and high-income countries. In contrast, non–high-income countries demonstrate a significant linear relationship between the shadow economy and GDP per capita. The negative linear relationship for non–high-income countries stems from this group lying far from the threshold, where observations suggest a predominantly downward relationship.

Table 3.4.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: AEs and Non-AEs without Dummies

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Source: Authors. Note: Standard errors appear in parentheses. AEs = advanced economies; HICs = high-income countries; LICs = low-income countries; PPP = purchasing power parity. *p < .10; **p < .05; ***p < .01.

That the R2 values for advanced economies and high-income countries are higher than 50 percent while those for nonadvanced economies and non– high-income countries are lower than 50 percent implies that nonlinearity is mainly driven by advanced economies and high-income countries whose GDP per capita lies around the threshold.

Robustness Check with 10-Year Averages

In the previous subsections, all regressions were conducted with variables of 20-year averages to explore the long-term nonlinear relationship. One associated question is whether this nonlinear relationship remains unchanged if empirical analysis concentrates on shorter time horizons, such as a 10- or 5-year average. This subsection conducts analysis based on two 10-year averages, whereas the next one uses four separate 5-year averages.

We run separate regressions for 1996 to 2005 and 2006 to 2015 and summarize the results in Table 3.5. Despite the changes in coefficient values between these two decades, the nonlinear relationship remains as expected, which indicates that the identified nonlinear relationship is stable in the medium term. This finding provides support to the results of the benchmark model.

Table 3.5.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < 0.10; **p < 0.05; ***p < 0.01.

Robustness Check with 5-Year Averages

This subsection of robustness checks employs panel data regressions with 5-year averages. These results are shown in Tables 3.6 through 3.8. Following the growth literature, the 5-year averages are used to smooth the cyclical elements contained in the time series. Both the fixed effect and the random effect estimators are listed in Table 3.6, although the Hausman test suggests that the random effect estimator may be inconsistent.

Table 3.6.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: Panel Regression

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < 0.10; **p < 0.05; ***p < 0.01.

One concern with the panel regression is endogeneity. To check the effect of endogeneity, panel data regressions are conducted with one- and two-period lags (Tables 3.7 and 3.8). All panel regression results support the U-shaped relationship identified previously. These checks clearly demonstrate that all results are robust after undertaking the usual tests.

Table 3.7.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: Panel Regression with One-Period Lags

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < .10; ***p < .01.
Table 3.8.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: Panel Regression with Two-Period Lags

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. ***p < .01.

Robustness Check Controlling for the Four Oil-Exporting Countries

This subsection aims to isolate the effect of some outliers and check the robustness of the U-shaped curve. As Figure 3.1 suggests, one valid concern is that the four oil-exporting countries lying on the upward part of the curve, namely Brunei Darus-salam, Kuwait, Qatar, and United Arab Emirates, may play a dis proportionately large role in determining the nonlinear relationship. One way to check is to switch to another measurement of GDP per capita. With GDP per capita measured by constant 2010 US dollars, these four countries move to the downward part of the U curve in Figure 3.2. When this measurement of level of development is used, the regression outcome in Table 3.9 supports the U-shaped relationship.

Figure 3.2.
Figure 3.2.

Nonlinear Relationship between the Shadow Economy and GDP Per Capita, 1996–2015

(Average, constant 2010 US dollars)

Sources: Medina and Schneider 2018; and World Bank.Note: GDP per capita is the average value of constant 2011 international dollars from 1996 to 2015 based on purchasing power parity. Data labels use International Organization for Standardization country codes.
Table 3.9.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: Alternate Measure of GDP Per Capita, 1996–2015

(Average)

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < 0.10; **p < 0.05; ***p < 0.01.

Another way to check the effect of these four oil-exporting countries, which may be convincing, is to do the regression with the same measurement while dropping the outliers. The results are summarized in Table 3.9 and the same U curve is still identified, although at a less significant level than before.

Labor Market and Policy Implications

One view on the shadow economy is that labor market rigidity makes job search and matching lengthy and costly for firms. Firms may thus be reluctant to register newly hired workers with the authorities or to stay formal. This, in turn, may cause workers, especially migrant workers, to be less interested in formal jobs. Thus, it is important to control for the institutional effect of labor market rigidities when examining the determinants of the shadow economy. In response, we use the indicator of labor market flexibility from the World Economic Forum Global Competitiveness Index to control for the institutional factors of the labor market. The indicator is on a scale of 1 to 7, with high values meaning more flexibility. The results are summarized in Table 3.10. Labor market flexibility helps reduce the size of the shadow economy in the long term, although not significantly. Meanwhile, the coefficient of squared GDP per capita remains significant to support the U-shaped curve.

Table 3.10.

Robustness Check for Nonlinear Relationship between the Shadow Economy and GDP: Labor Market Indicator, 1996–2015

(Average)

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Source: Authors. Note: Standard errors appear in parentheses. PPP = purchasing power parity. *p < 0.10; **p < 0.05; ***p < 0.01.

It is arguable that not only institutional factors but also the labor force composition of the market can influence the size of the shadow economy. For example, Goldin (1994) finds a U-shaped curve between female labor participation and economic development. Female participation declines initially with economic development and then picks up after a turning point. If the estimated trend in Goldin (1994) is true, it is reasonable to predict that more women tend to work informally with economic development. Differences between men’s and women’s involvement in the shadow economy is an interesting topic for further investigation with data availability.

To set out appropriate and effective measures to tackle the ramifications of the shadow economy, it is necessary first to have a comprehensive, current view of how the shadow economy affects economic growth and social welfare.16 First, the existence of the shadow economy poses a severe threat to fiscal revenue collection and thus undermines the government’s ability to provide adequate public goods and services. Second, empirical evidence shows that firms in the shadow economy are smaller and less productive than those in the formal sector. Third, authorities have limited access to information related to the shadow economy, which weakens their efforts to implement economic monitoring and management. Fourth, the shadow economy keeps evolving and adapts to new developments; it is thus crucial for policymakers to regularly update their knowledge. Even so, the shadow economy does play a positive role by improving some workers’ welfare. For example, the informal sector provides temporary low-paid jobs when the economy does not have a well-established social safety net. Also, the informal sector helps the economy maintain an untapped reservoir of labor supply. When a positive shock to demand emerges, the economy can quickly increase production by making use of the extra labor supply in the shadow economy.

Views are mixed on the shadow economy, and this chapter’s finding provides another reason for policymakers to be cautious. If the long-term trend of the shadow economy can be reversed with economic development, then it is key to be aware of the current economic state and remain alert to policy effectiveness. If an economy is less developed or experiences a catch-up phase, its shadow economy is expected to downsize. In this period, authorities can attract more firms and workers out of the shadow economy by promoting financial development, containing inflation, stabilizing the political situation, and expanding educational spending. By contrast, when the economy has reached the threshold GDP per capita and starts to show the positive relationship between GDP per capita and the shadow economy, authorities should make working in the formal sector more beneficial by, for example, reducing labor market rigidities to improve market efficiency and simplifying tax-compliance procedures with recent technology innovations.

Our results, which show the importance of the level of economic development, indicate that taking harsh measures to dramatically reduce or even to eradicate the shadow economy is not a first-best solution. The appropriateness of policy depends on the level of economic development. Authorities might consider the following policy recommendations:

  • 1. Strengthen capacity in data collecting and processing to assess the current relationship between the shadow economy and level of development. Exploiting all available data sources is crucial, especially given the rapid growth of the digital economy, and relevant government agencies must work collectively to cross-check data quality. The authorities should be able to measure the effect of policy accurately and thus keep adjusting to achieve the better outcomes.

  • 2. Streamline administrative procedures to reduce firms’ and households’ compliance costs and make public goods and services more accessible by taking advantage of technology innovations. The growing digital economy makes it advantageous and convenient for workers to stay in the informal sector. In response, the associated government agencies should revise their policy measures to create a business-friendly environment and ensure that firms and workers can obtain these advantages and stay in or move to the formal sector.

  • 3. Expand education, not only to improve human capital but also to teach workers about the role of the shadow economy and promote healthy social norms that will positively influence economic behavior. It is important that all firms and households be advised on the merits and risks of the shadow economy, take a positive attitude toward formal jobs, and understand the importance of transparency for economic monitoring and policy design.

Conclusion

This chapter reveals a long-term U-shaped relationship between GDP per capita and the size of the shadow economy using a data set of 158 countries. Furthermore, the chapter examines the possible long-term determinants of GDP per capita and finds that the share of the population with a bachelor’s degree or higher promotes average productivity, which is consistent with existing literature. The U-shaped pattern between the shadow economy and GDP per capita is worth further investigation. One possible direction is whether the nonmonotonic relationship before and after the threshold is symmetric. Although a long-term nonlinearity in the shadow economy is identified here using the quadratic regression equation, the relationship between GDP per capita and the size of the shadow economy may be asymmetric. One possible scenario is that a shadow economy may accelerate in productivity when the country’s development exceeds a certain stage, resulting from industrial advancement in the formal sector and technological innovation.

Annex 3.1. Supplementary Tables

Table 3.1.1.

Economy Names and ISO Codes

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Source: International Organization for Standardization. Note: ISO = International Organization for Standardization.
Table 3.1.2.

Correlations of Variables, 1996–2015

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Sources: Elgin and Oztunali 2012; Medina and Schneider 2018; IMF, Investment and Capital Stock Dataset; World Bank, World Development Indicators; World Bank, Worldwide Governance Indicators; and World Economic Forum, Global Competitiveness Index. Note: The correlation matrix is calculated with 958 annual observations; educational variables are not included because of their small numbers of observations; CPI = consumer price index; PPP = purchasing power parity; WGI = World Bank Worldwide Governance Indicators. *p < .10; **p < .05; ***p < .01.
Table 3.1.3.

Summary Statistics of Variables, 1996–2015 (Average)

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Sources: Elgin and Oztunali 2012; Medina and Schneider 2018; IMF, Investment and Capital Stock Dataset; World Bank, World Development Indicators; World Bank, Worldwide Governance Indicators; and World Economic Forum, Global Competitiveness Index. Note: CPI = consumer price index; PPP = purchasing power parity; WGI = World Bank Worldwide Governance Indicators.
Table 3.1.4.

Analytical Categorization of the Global Economy

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Sources: IMF, October 2017 World Economic Outlook; and World Bank data. Note: Income economy groups are based on the World Bank definition. The group of advanced economies is consistent with the IMF’s October 2017 World Economic Outlook.

References

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1

Here the size of the shadow economy is expressed as the percentage ratio of the shadow economy to official GDP.

2

GDP per capita is used here as proxy for economic development.

3

Going forward, new waves of the digital economy, data sharing, and the gig economy are expected to boost the shadow economy. Also, blockchain technology and cryptocurrencies can be used to pay workers in the informal sector.

4

The following sections examine the nonlinearity with rigorous econometric methods. In addition, the nonlinear relationship may not necessarily be symmetric around the turning point. Investigation of the asymmetric nonlinearity is left to future research.

5

A recent survey is by Elgin and Erturk 2019.

6

For a detailed discussion, see Medina and Schneider 2018; see also Gerxhani 2003; Kirchgaessner 2016; and Adair 2017. See Medina and Schneider (2018) for a detailed discussion, as well as Gerxhani (2003); Adair (2017); and Kirchgaessner (2016).

7

For details, see La Porta and Shleifer 2008, 2014; Schneider 2014; and Williams and Schneider 2016. Regarding the effect of the shadow economy on economic development, see the dual view proposed by Lewis 1954 and advocated by La Porta and Shleifer 2008, 2014; Feld and Larsen 2009; and Feld and Schneider 2010.

8

Most of the key time series are from 1990 to 2015, except for the World Bank Worldwide Governance Index, which starts from 1996. Therefore, our regressions change the sample period to 1996 to 2015.

9

The estimated size of the shadow economy from Elgin and Oztunali (2012) spans 1950 to 2014.

10

Financial corporations include monetary authorities, deposit money banks, and other financial corporations, such as finance and leasing companies and insurance corporations.

11

For most countries, data on completion of high school or higher are available only after 2012. This is why the number of observations for high-school-or-higher educational attainment is so small compared with that of primary school attainment.

12

See Annex Table 3.1.2 for the correlation matrix.

13

La Porta and Shleifer (2014) highlight the role of financial access, one important aspect of financial development. The study compares perceived obstacles to doing business reported by informal and formal entrepreneurs and lists access to financing as the top factor for firm owners to decide whether to stay formal.

14

Recent research provides empirical and quantitative evidence to support the negative correlation between taxes and the informal sector and attributes it to high-quality institutional factors (Friedman and others 2000) or public turnover and public trust in government (Elgin and Solis-Garcia 2012). The appearance of the negative correlation is likely attributable to the failure of the analysis to identify or control for other determinants.

15

See Annex Table 3.1.4 for the country list of each group.

16

La Porta and Shleifer (2008, 2014) comprehensively summarize views of the role of the shadow economy.

Within Same Series

Other IMF Content

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Cover The Global Informal Workforce
The Global Informal Workforce
Priorities for Inclusive Growth
Author:
International Monetary Fund
ISBN:
9781513575919
Publication Date:
23 Jul 2021
Publisher:
International Monetary Fund
DOI:
https://doi.org/10.5089/9781513575919.071
Pages:
412
  • <sup><xref ref-type="fig" rid="Rch003fig01">Figure 3.1.</xref></sup>
    View in gallery
    Figure 3.1.

    Nonlinear Relationship between the Shadow Economy and GDP per Capita

    (Average, constant 2011 international dollars)

  • <sup><xref ref-type="fig" rid="Rch003fig02">Figure 3.2.</xref></sup>
    View in gallery
    Figure 3.2.

    Nonlinear Relationship between the Shadow Economy and GDP Per Capita, 1996–2015

    (Average, constant 2010 US dollars)

Figure 3.1.

Nonlinear Relationship between the Shadow Economy and GDP per Capita

(Average, constant 2011 international dollars)
Figure 3.2.

Nonlinear Relationship between the Shadow Economy and GDP Per Capita, 1996–2015

(Average, constant 2010 US dollars)