Volume of the unofficial economy in the Western Balkan region

Figures & data

Table 1. Estimates of the UE in WB economies.

UE in %	Croatia	Serbia	B&H	Montenegro	North Macedonia	Slovenia
Total Eurostat (pilot project 2000–2002)	11.2	14.6	8.5	7.8	17.2	7.6
MIMIC (average 1999–2013)^a	28.7	34.2	36.1	35.3	34.1	26.0
MIMIC (average 1991–2015)^b	28.8		34.2			24.1

Source: Eurostat approach: Nadim (2007) for Croatia, Serbia, B&H, Montenegro, FYR Macedonia; Statistical Office of the Republic of Slovenia (2008) for Slovenia; MIMIC approach.

^a Hassan & Schneider (2016).

^b Medina and Schneider (2018).

Figure 1. General structure of the MIMIC approach.

Source: Schneider and Enste (2000).

Table 2. Parameters estimated by the MIMIC model for 19 NMS and WB economies.

Causes	Model 1	Model 2	Model 3	Medina and Schneider (2018, Table 8. Model 1 for all countries)
Government size	0.119**	0.118***	0.147***	0.102***
GDP per capita (constant 2010 US$)	−0.421***	−0.326***	−0.183***	−0.332***
Unemployment (percentage of labour force)	0.164***	0.093*	0.133	0.051**
Trade openness	0.278***	0.319***	0.307***	−0.086***
Rule of law	−0.147**			−0.049***
Regulatory quality		−0.345***
Government effectiveness			−0.398***
Indicators
Currency				1
GDP per capita growth	−1	−1	−1	−0.521***
Labour force participation	−0.429***	−0.267***	−0.420***	−0.208**
Statistical tests
RMSEA	0.131	0.133	0.126	0.073
χ^2	31.26	32.18	29.43	513.407

Source: Own calculations.

Note: ^***p < 0.01, ^**p < 0.05, ^*p < 0.1.

Table 3. Non-observed economy based on the Eurostat approach.

	Underground, N1 + N6	Illegal, N2	Informal sector, N3 + N4 + N5	Statistical deficiencies	Total NOE	Total, NOE − N2
Czech Republic	6.3	0.4	1.3	0.2	8.1	7.7
Hungary	3.1	0.8	3.1	3.9	10.9	10.1
Poland	12.7	0.9	0	1.8	15.4	14.5
Slovak Republic	12.1	0.5	2.9	0.2	15.6	15.1
Slovenia	3.9	0.3	2.8	3.1	10.2	9.9
Average	7.6	0.6	2.0	1.8	12.1	11.5
Share in total, in %	63.3	4.8	16.8	15.3	100.0

Source: Gyomai and van de Ven (2014).

Figure 2. Estimate of the UE, excluding illegal activities, for WB economies, in the percentage of GDP.

Source: Authors’ calculations.

Table 4. Estimate of the UE in WB economies, a combination of the MIMIC and Eurostat approaches, excluding illegal activities, in percentage of GDP.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	Average (2003–2018)	Difference 2018–2003
B&H	22.3	21.0	20.8	20.4	19.3	18.9	18.8	19.4	19.7	19.6	19.2	19.3	18.9	18.2	17.8	17.5	19.4	−4.8
Croatia	12.7	12.3	11.8	11.6	11.2	10.7	11.2	11.6	11.9	12.3	12.5	12.7	12.6	11.6	11.4	11.2	11.8	−1.5
Kosovo	23.2	21.9	22.0	22.1	21.4	20.6	20.1	20.0	19.7	18.4	18.3	18.6	17.9	16.6	16.8	16.7	19.6	−6.5
North Macedonia	19.3	19.1	19.0	19.6	19.8	20.2	19.0	19.1	19.6	19.6	18.6	18.4	18.3	17.9	18.0	17.9	19.0	−1.4
Montenegro	20.2	21.1	22.2	18.7	18.4	18.7	17.3	17.5	17.4	17.7	16.6	16.2	16.1	16.3	15.7	15.5	17.9	−4.7
Serbia	17.8	18.1	18.2	17.6	17.3	16.6	16.0	16.7	17.0	17.6	17.2	16.7	16.2	16.0	16.1	16.0	16.9	−1.8
Slovenia	7.4	7.4	7.6	7.3	6.5	6.1	6.8	7.7	8.2	8.8	8.9	8.3	8.1	7.6	7.2	7.1	7.6	−0.3
Average	17.6	17.3	17.4	16.8	16.3	16.0	15.6	16.0	16.2	16.3	15.9	15.7	15.4	14.9	14.7	14.6	16.0	−3.0

Source: Authors’ calculations.

Figure 3. Distribution of answers on personal experiences with purchasing products delivered by the UDW sector.

Source: Survey data.

Table 5. Persons declaring experience in purchasing products delivered by the UDW sector by socioeconomic groups, in percentage of the total number of respondents per country.

	Croatia	Slovenia	B&H	Serbia	Montenegro	North Macedonia	Kosovo	WB
WB	8.4	11.2	6.0	9.0	21.9	6.3	3.8	9.5
Gender
Male	10.7	14.8	6.5	7.9	22.1	5.3	3.6	10.2
Female	6.3	7.7	5.5	10.0	21.7	7.2	3.8	8.8
Age
18–24	8.4	9.8	5.3	6.9	32.7	5.4	11.1	11.7
25–34	10.4	14.5	5.6	14.7	27.6	9.6	1.3	11.3
35–44	10.2	15.5	7.0	9.8	22.5	7.9	5.5	10.7
45–54	8.3	10.1	7.2	9.2	21.1	5.7	2.6	9.0
55–64	7.8	9.4	5.1	6.5	15.0	4.9	2.6	7.8
65+	6.1	8.3	5.2	6.8	17.5	2.9	0.3	7.3
Education
Elementary school or less	4.4	6.2	2.2	3.4	4.7	3.8	0.0	2.1
High school	8.3	9.9	5.6	8.4	22.7	5.8	7.2	10.0
College, university or higher	9.5	13.2	8.6	12.5	23.0	8.8	4.6	12.4
Income level
Below average	6.9	8.7	2.5	8.9	18.5	6.2	5.8	7.7
Average	7.9	10.8	5.1	9.2	25.0	8.2	2.4	11.4
Above average	13.4	19.1	10.4	12.0	20.8	15.8	5.3	14.9
Settlement size
Up to 2000	7.1	9.8	6.5	6.4	22.2	6.1	3.3	8.6
2001–10,000	8.8	11.5	7.1	8.8	10.6	4.6	1.4	8.3
10,001–100,000	8.4	9.8	7.1	9.7	17.9	5.5	1.7	8.7
More than 100,000	10.0	15.3	1.0	10.8	31.2	8.9	14.6	13.5

Source: Survey data.

Figure 4. Percentage of respondents receiving underreported income.

Source: Survey data.

Figure 5. Percentage of respondents receiving compensation in money or a kind of undeclared secondary activity.

Source: Survey data.

Figure 6. Total undeclared income estimated by the survey, in the percentage of GDP.

Source: Authors’ calculation based on the survey.

Table 6. Persons declaring experience on undeclared secondary employment by socioeconomic groups, in percentage of the total number of respondents per country.

	Croatia	Slovenia	B&H	Serbia	Montenegro	North Macedonia	Kosovo	WB
WB	4.4	5.0	4.7	3.6	2.3	1.4	0.9	3.2
Gender
Male	5.7	6.5	5.7	3.5	3.0	1.3	0.8	3.8
Female	3.2	3.5	3.7	3.8	1.5	1.4	1.0	2.6
Age
18–24	9.7	10.9	4.2	3.9	3.6	2.7	1.1	4.8
25–34	6.2	10.6	5.8	7.7	5.0	2.5	2.2	5.4
35–44	6.7	6.6	5.4	4.1	2.9	1.1	0.6	3.7
45–54	3.3	2.4	5.5	3.7	2.5	1.1	0.4	2.7
55–64	3.0	3.2	4.3	2.1	0.7	0.4	0.4	2.1
65+	0.8	1.6	2.6	1.3	0.2	0.4	0.0	1.0
Education
Elementary school or less	2.7	2.0	1.8	3.1	0.7	1.5	0.0	1.1
High school	4.9	5.0	4.7	3.1	2.5	1.1	1.7	3.3
College, university or higher	3.9	5.4	5.9	5.2	1.8	1.9	1.6	4.0
Income level
Below average	3.9	5.1	1.6	4.5	1.8	1.3	1.6	2.9
Average	4.2	4.5	4.7	3.3	2.5	1.5	0.4	3.2
Above average	6.8	6.5	6.9	3.9	2.3	2.5	0.0	5.1
Settlement size
Up to 2000	2.9	4.8	4.1	2.0	2.7	1.4	1.3	2.8
2001–10,000	4.9	4.5	6.8	3.7	1.8	1.2	0.0	3.6
10,001–100,000	5.7	5.8	7.4	3.9	1.2	1.4	0.1	3.3
More than 100,000	5.2	5.8	1.0	4.8	2.9	1.4	0.0	3.4

Source: Survey data.

Table 7. Total income generated by UDW, in EUR million.

	Croatia	Slovenia	B&H	Serbia	Montenegro	North Macedonia	Kosovo	WB
Net envelope wages. regular employees	476.3	364.6	446.7	1161.6	91.4	265.0	104.4	2910.2
Unreported secondary employment	569.9	495.5	301.6	1194.3	192.2	191.7	215.3	3160.6
Income related to evasion of income taxes and contributions	322.7	377.9	207.8	752.1	125.8	100.3	41.4	1928.1
Income related to evasion of taxes on products	219.1	134.7	155.5	552.4	64.7	74.4	83.0	1283.8
Total UDW income	1588.1	1372.7	1111.7	3660.5	474.1	631.5	444.1	9282.7
UDW income in percentage of GVA	3.9	3.7	8.6	12.1	14.5	7.2	8.6	6.7
UDW income in percentage of GDP	3.2	3.2	7.3	9.9	12.0	6.3	6.9	5.6

Source: Authors’ calculations based on the survey.

Note: UDW income estimated by the survey method includes only N1 and N6 type of non-observed economy (underground producers and deliberate misspecification of payment).

Figure 7. Comparison of undeclared income based on survey results and underground economy (N1 + N6) estimated by the MIMIC approach, in the percentage of GDP.

Source: Authors’ calculations.

Table 8. Main advantages and limitations of essential UE measurement methods.

Method	Advantages	Limitation
Survey method	socioeconomic characteristic of UDW participants information on various types of the UE results could be used for the formulation of methods to combat UDW	results depend on the willingness of respondents to reveal participation in the UE (provide a lower limit of the estimate) require significant financial resources results depend on the skills and experience of persons who conduct the survey cover only the period when the study is conducted
MIMIC	systematically relates the size of the UE to the potential causes and indicators does not require additional costly resources result in estimates for a longer period	results are available in index form only need another independent benchmark estimate implicitly assumes that causes are related to the indicators only through the hidden economy channel results sensitive to sample size, period and model specification
The exhaustiveness of national account	systematically classify and separate total non-observed economy in different types based on many various data sources results available by industry level and tested for consistency in the context of supply and use tables quality audits assure comparability of results of experts delegated by Eurostat and EU GNI Committee	requires significant human resources of statistical personal results are rarely available for public estimates depend on the skills and efforts of stuff in national statistical offices

Source: Authors’ systematisation.

Nadim, A. (2007). Measuring the non-observed economy in WB countries: Practical lessons for transition economies [Paper presentation]. In Experiences and challenges in measuring national income and wealth in transition economies. International conference organized by the International Association for Research in Income and Wealth (IARIW) and the National Bureau of Statistics (NBS) of China, Beijing, China, September 18–21.

 Google Scholar

Medina, L., & Schneider, F. (2018). Shadow economies around the world: What did we learn over the past 20 years? (IMF Working Paper No. 18/17). International Monetary Fund. https://doi.org/https://doi.org/10.5089/9781484338636.001[]

 Google Scholar

Schneider, F., & Enste, D. (2000). Shadow economies: Size, causes and consequences. Journal of Economic Literature, 38(1), 77–114. https://doi.org/https://doi.org/10.1257/jel.38.1.77

 Web of Science ®Google Scholar

Gyomai, G., & van de Ven, P. (2014). The non‐observed economy in the system of national accounts (OECD Statistics Brief No. 18). https://www.oecd.org/std/na/Statistics%20Brief%2018.pdf

 Google Scholar