night lights (that is, light intensity) as a proxy for the size of an economy, and discuss additional robustness tests. We also cover the econometric results of the MIMIC estimations of the size of the shadow economy for 158 countries and critically evaluate them. Later on we compare the MIMICresults with micro survey results and SNA discrepancy method results before summarizing our findings and providing a conclusion.
Individuals are rational calculators who weigh costs and benefits when considering breaking the law. Their
ational A ccounts D iscrepancy M ethod
A. MIMICResults Versus National Accounts – Discrepancy Method Results
B. MIMIC Versus Micro Survey Methods Results
C. Macro Versus Micro Methods – Newer Results
5. S ummary and C oncluding R emarks
B. What Types of Conclusions Can We Draw From These Results?
C. Open Research Questions
6. R eferences
7. T ables
8. A ppendix
Leandro Medina, Mr. Andrew W Jonelis, and Mehmet Cangul
Mean Matching (MIPMM), developed by Rubin D.B. The MIMICresults are found to be robust when cross-checked with MIPMM results. This alternative method treats informality as a missing data issue. The objective is to match the countries where data exist to the those where data are missing by using characteristics that would be relevant to the size of the informal economy. 2 When using this procedure, countries are ordered in groups based on the size of the informal economy. These groups are broadly aligned with our estimates from the MIMIC.
The robustness of MIMIC
Leandro Medina, Mr. Andrew W Jonelis, and Mehmet Cangul
The multiple indicator-multiple cause (MIMIC) method is a well-established tool for measuring informal economic activity. However, it has been criticized because GDP is used both as a cause and indicator variable. To address this issue, this paper applies for the first time the light intensity approach (instead of GDP). It also uses the Predictive Mean Matching (PMM) method to estimate the size of the informal economy for Sub-Saharan African countries over 24 years. Results suggest that informal economy in Sub-Saharan Africa remains among the largest in the world, although this share has been very gradually declining. It also finds significant heterogeneity, with informality ranging from a low of 20 to 25 percent in Mauritius, South Africa and Namibia to a high of 50 to 65 percent in Benin, Tanzania and Nigeria.
: Author’s calculation based on Model 1 MIMICresults.
Note: As detailed in Appendix I , in order to calculate the absolute value of the informal economy, extra information regarding the size of the informal economy of a particular country is required. According to the results of a comprehensive study conducted by the Inter-American Development Bank ( De la Roca et al. 2002 ), the informal economy in Jamaica accounted for about 35 percent of the total GDP in 2000-01, and is the benchmark study used in this chapter.
71. The size of the informal economy is found
based on Model 1 MIMICresults.
Table 3. Estimated Size of the Informal Economy: Standardized and Absolute Values, early 2000s
Absolute value (% of GDP)
St. Kitts and Nevis
Trinidad and Tobago
Antigua and Barbuda
. Section 3 presents results on the size of the shadow economy of the 158 countries. In section 4 a comparison of the MIMICresults with micro survey results and National Discrepancy Method results is undertaken. Section 5 summarizes and concludes.
2. Theoretical Considerations
Individuals are rational calculators who weigh up costs and benefits when considering breaking the law. Their decision to partially or completely participate in the shadow economy is a choice overshadowed by uncertainty, as it involves a trade-off between gains, if their activities are
We undertake an extended discussion of the latest developments about the existing and new estimation methods of the shadow economy. New results on the shadow economy for 158 countries all over the world are presented over 1991 to 2015. Strengths and weaknesses of these methods are assessed and a critical comparison and evaluation of the methods is carried out. The average size of the shadow economy of the 158 countries over 1991 to 2015 is 31.9 percent. The largest ones are Zimbabwe with 60.6 percent, and Bolivia with 62.3 percent of GDP. The lowest ones are Austria with 8.9 percent, and Switzerland with 7.2 percent. The new methods, especially the new macro method, Currency Demand Approach (CDA) and Multiple Indicators Multiple Causes (MIMIC) in a structured hybrid-model based estimation procedure, are promising approaches from an econometric standpoint, alongside some new micro estimates. These estimations come quite close to others used by statistical offices or based on surveys.
This paper estimates the size of the informal economy for 32 mainly Latin American and Caribbean countries in the early 2000s. Using a structural equation modeling approach, we find that a stringent tax system and regulatory environment, higher inflation, and dominance of the agriculture sector are key factors in determining the size of the informal economy. The results also confirm that a higher degree of informality reduces labor unionization, the number of contributors to social security schemes, and enrollment rates in education.
estimates with the estimates of statistical agencies of the eight sub-Saharan African countries that publish their estimates of the size of the informal economy ( Table 3.1 ). The rank correlation is high (86 percent) between the MIMICresults and these estimates. While the estimates of statistical agencies are useful, their applicability is limited for cross-country comparisons. First, not all countries publish the information. Second, methodologies and sampling methods may affect the comparability of cross-country estimates. Finally, estimates may be rooted in