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Leandro Medina, Mr. Andrew W Jonelis, and Mehmet Cangul

independent study to calibrate from standardized values to size of informal economy in percent of GDP, and (iii) the estimated coefficients are sensitive to alternative specifications, the country sample and time span chosen. This paper contributes to the literature and addresses the concerns of endogeneity as well as using predictive mean matching as a robustness check for measuring the size of the informal economy in Sub-Saharan African countries. Specifically, by: (a) using a modified version of the standard Multiple Indicator-Multiple Cause (MIMIC) model. This

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.
Leandro Medina, Mr. Andrew W Jonelis, and Mehmet Cangul

Contents Abstract 1. I ntroduction 2. L iterature Review 3. E conometric Strategy 4. V ariables 5. R esults A.MIMIC Estimation Results B.Estimation of the Size of the Informal Economy 6. Robustness Tests A. Estimating the Size of the Informal Economy Using Predictive Mean Matching B. Estimating the Size of the Informal Economy Using Traditional MIMIC Approach (a la Schneider, 2010) C. Comparison with Countries’ National Accounts Statistics 7. C onclusions 8. R eferences

Céline Allard

found to be inversely related to the size of the informal economy ( Figure 3.5 ). 5 Figure 3.5. Informality and Governance Quality, Average over 2006–14 Sources: World Bank, World Governance Indicators; and IMF staff calculations. How Reliable are Estimates of the Size of the Informal Economy? The robustness of the MIMIC estimates was crosschecked using two approaches. First, an alternative and fully independent approach, the Predictive Mean Matching method (PMM) was used ( Rubin 1987 ). This alternative method treats informality as a missing data

Leandro Medina and Mr. Friedrich Schneider
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.
International Monetary Fund

for 158 countries from 1991 to 2015 while addressing early criticism. In particular, when using the MIMIC approach, GDP per capita, growth rate of GDP, or first differences in GDP are often used as cause as well as indicator variables. Instead of GDP, we use a light intensity approach as an indicator variable, then run a variety of robustness tests to further assess the validity of our results. 2 We, in addition to MIMIC, use a fully independent method, the predictive mean matching (PMM) method by Rubin (1987) , which overcomes these calibration problems. This is

Leandro Medina and Mr. Friedrich Schneider

economy (compare Hashimzade and Heady (2016) , Feige (2016a) , Schneider (2016) and Breusch (2016) ). In this paper, we additionally use a fully independent method, the Predictive Mean Matching Method (PMM) by Rubin (1987) , which overcomes these problems. To our knowledge this is one of the first attempts to include both the light intensity approach as an indicator variable within MIMIC and to use a full alternative methodology, as PMM 3 . (3) To compare the results of the different estimation methods, showing the strengths and weaknesses of these methods, and