Shadow economies all around the world: Model-based estimates

Ceyhun Elgin, Oguz Oztunali, 10 May 2012

a

A

Even though informality is a widespread phenomenon and poses serious social, economic, cultural, and political challenges across the world, many issues about its nature and consequences still remain largely under-explored or unresolved. For example, the evidence presented in the existing literature has failed to generate a consensus among researchers around the measurement of the informal sector. There are also many other open questions regarding the determinants and/or effects of informality, including even such basic ones such as whether the informal sector size is larger in low-income or high-income nations (see Dreher and Schneider 2010); whether taxes are positively correlated with informal sector size or not (see Schneider and Enste 2000, Friedman et al 2000, and Elgin 2010 among others) or whether the shadow economy and corruption are substitutes or complements (Dreher and Schneider 2010).

As the number of papers in the growing literature on informality indicates, there is an increasing focus on the economic analysis of the shadow economy. Yet one particular setback that, despite the development of various methods, still persists in the literature is the lack of significantly large datasets that would make informality subject to robust (applied) policy analysis. Even though there are various methodologies suggested for its measurement, this issue mostly arises due to the fact that the size of the shadow economy, by definition, is hard to measure and to subject to empirical analysis. Most of the suggested methodologies (see the next section for a longer review) are usually used for a particular country or even a region and could not be generalised to cross-country panel frameworks. One particular exception is the dataset presented by Schneider et al (2010), which reports the shadow economy size (as a percent of GDP) for 162 countries in an annual basis for the nine years between 1999 and 2007. In this study, however, the authors rely on the MIMIC (Multiple Indicators and Multiple Causes) approach to estimate the size of the shadow economy which, according the Breusch (2005), is largely unfit for purpose.

New research on shadow economies

In recent research (Elgin and Oztunali 2012), we aim to address two issues:

  • First, using a two-sector dynamic general equilibrium, we present a new approach to estimate the size of the shadow economy. We believe that this approach has various advantages over the existing methodologies.
  • Second, we use this new methodology to construct a new unbalanced 161-country panel dataset over the period 1950 to 2009. This aims to be the largest dataset in the literature, particularly with its time-series dimension. Among many possible advantages regarding its use, the construction of such a dataset would allow for various policy analyses that require a significantly large time dimension.

Our procedure allows us to have a dataset with 7,395 observations for 161 countries in an unbalanced panel framework running from 1950 to 2009. The complete dataset is reported on a country-by-country and year-by-year basis in Elgin and Oztunali (2012). Nevertheless, we report various descriptive statistics and present illustrative figures here.

In order to observe the variation of the shadow economy size in different group of countries over time, we divided the world into six different groups. These are OECD-EU, Latin American and Caribbean, post-Socialist (Transition), Middle East and North African, sub-Saharan African, and Asian-Oceanian countries.

As looking at unweighted series may be a misleading way of calculating the shadow economy size in a group, in Table 1 we report the descriptive statistics of GDP-weighted series in different groups of countries. Note that these are regional descriptive statistics for the whole 1950-2009 period.

Table 1. GDP-weighted shadow economy as percentage of GDP

Region Mean Median Minimum Maximum Std. dev.
OECD-EU 17.84 17.82 13.97 21.99 2.64
Latin 41.98 38.44 35.54 55.51 6.29
Post-Socialist 37.37 34.98 28.18 55.67 7.83
MENA 31.50 25.61 22.61 86.79 12.30
Sub-Saharan 43.06 39.90 36.84 55.25 5.64
Asia 32.84 33.06 18.05 51.83 9.40
World
22.67 21.84 18.54 27.74 2.98

 

Investigating Table 1 reveals two crucial points:

  • First, judging from the standard deviations, the size of the shadow economy experienced a significant variation both across groups and within groups.
  • Second, Latin American and sub-Saharan economies have significantly larger shadow economies than the other groups of countries, while the OECD-EU group has a significantly smaller shadow economy.

Next, in Table 2, we report the evolution of the shadow economy size in different groups over time in approximately 10-year intervals. In line with Table 2, Figure 1 presents the GDP-weighted shadow economy size on an annual basis. For almost all country groups (except for the post-Socialist one), we observe a declining trend over time. However, the pace of the reduction seems to lose some momntum in the last decade. Somewhat more interestingly, we observe a spike staring in 2007. Considering the emergence of the global economic crisis, this could give further support for the hypothesis that the size of the shadow economy is countercyclical, as suggested by Roca et al (2001) and Elgin (2012).

Table 2. Regional trends over time

Region 1960-1970 1971-1980 1981-1990 1991-2000 2001-2009
OECD-EU 20.32 17.89 16.51 15.939 14.56
Latin 47.50 40.86 36.88 36.59 36.19
Post-Socialist - - - 34.13 35.95
MENA 34.58 27.00 24.77 23.93 23.51
Sub-Saharan 48.71 41.74 37.44 38.68 39.00
Asia 39.40 34.39 29.63 23.97 19.85
World
25.75 22.56 20.76 20.02 21.67

 

Figure 1. GDP-weighted shadow economy size (as % of GDP) over time

In Figure 2, we group countries with respect to GDP per-capita and then report the average GDP-weighted shadow economy size in each group. Here, we divide the countries into five categories – poorest, second, third, fourth and the richest 20%. Not surprisingly, richer countries tend to have a smaller shadow economy; however, Figure 2 shows that this relationship is not exactly linear, especially in a cross-country sense. Even though further research is required, this might be considered as a support for informality dimension of the Kuznets Curve hypothesis.

Figure 2. Evolution of the shadow economy in different income groups

References

Breusch, T (2005), “Estimating the Underground economy using MIMIC models”, Econometrics 0507003, Econ WPA.

Dreher, A and F Schneider (2010), “Corruption and the Shadow Economy: An Empirical Analysis”, Public Choice, 144:215-238.

Friedman, E, S Johnson, D Kaufman, and P Zoldo-Lobaton (2000), “Dodging the Grabbing Hand: The Determinants of Unofficial Activity in 69 Countries”, Journal of Public Economics, 76(3):459-493.

Elgin, C (2010), “Political Turnover, Taxes, and the Shadow Economy”, Working Papers 2010/08, Bogazici University.

Elgin, C (2012), “Cyclicality of the Informal Economy”, Working Papers 2012/02, Bogazici University.

Elgin, C and O Oztunali (2012), “Shadow Economies around the World: Model Based Estimates”, Working Papers 2012/05, Bogazici University.

Roca, JCC, CD Moreno, and JEG Sanchez (2001), “Underground economy and aggregate fluctuations”, Spanish Economic Review, 31:41-53.

Schneider F and DH Enste (2000), “Shadow Economies: Sizes, Causes and Consequences”, Journal of Economic Perspectives, 38:77-114.

Schneider, F, A Buehn, and CE Montenegro (2010), “Shadow Economies all over the World”, World Bank Policy Research Working Paper, 5356.

 

Topics: Frontiers of economic research, Global economy
Tags: black market, informal sector, shadow economy

Assistant Professor of Economics, Bogazici University
Department of Economics, Bogazici University