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International Tax and Public Finance

08.05.2024 | Policy Watch

A review of the driving forces of the informal economy and policy measures for mitigation: an analysis of six EU countries

verfasst von: Alban Asllani, Friedrich Schneider

Erschienen in: International Tax and Public Finance

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Abstract

This study presents detailed estimates of the shadow economy's size and development in all European Union (EU) countries with particular emphasis on six specific countries (Germany, Austria, Denmark, Greece, Italy, and Romania) from 2003 to 2022. It focuses on understanding the key factors that motivate economic agents to engage in shadow economy activities within EU and particularly these countries. The estimates presented show a significant reduction in the shadow economy's size from 22.6% of GDP in 2003 to 17.3% in 2022, highlighting the effectiveness of various policy measures implemented in most EU countries with particular focus on these six countries. Despite a slight increase in the shadow economy across most EU nations due to the Coronavirus pandemic after 2020, our research identifies the main determinants of economic informality in most EU countries. Our analysis expose that weak institutional quality, ineffective government institutions, complex and burdensome tax and regulatory systems, the lack of strong legal systems, and pervasive corruption are the main determinants of economic informality in most countries of the EU. The study thoroughly reviews the driving forces behind the shadow economy and discusses the specific policy measures these six countries part of this policy analysis paper have adopted to mitigate and reduce its presence.

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Fußnoten
1
Compare here Schneider (2023), which provides extensive discussion about the definition of SE and its wording.
 
2
Here we provide the shadow economy estimates in accordance with Schneider (2022). These findings, as reported in Schneider (2022), build upon the research conducted by Medina and Schneider (2021) and serve as an extension focused on OECD countries.
 
3
These EU countries were selected to include both small and large countries and to provide a somewhat balanced distribution between North and South as well as East to West.
 
4
Countries part of this study are: Germany, Austria, Romania, Denmark, Italy and Greece.
 
5
Several studies provide thorough discussion on the main methods used to measure or estimate the size of the shadow economy. See, for example, Feld and Schneider (2010), Medina and Schneider (2018, 2021) and Schneider (2023).
 
6
Shadow Economy values from 2003 to 2021 are taken from Schneider (2022). These estimations employ the methodology established by Buehn and Schneider (2012, 2013).
 
7
Compare a similar interesting study by Williams and Kayaoglu (2020).
 
8
Compare e.g Oviedo (2009), Alm and Embaye (2013), Schneider (2021, 2023), Williams et al. (2017, 2018, 2020), and Zhanabekov (2022).
 
9
Calculation of the size and development of the shadow economy is done with the MIMIC (Multiple Indicators and Multiple Causes) estimation procedure. The MIMIC estimation procedure provides only relative values and one needs other methods, like the currency demand approach or the income discrepancy method, to calibrate MIMIC values into absolute values. For a detailed explanation of these calculation methods, see Schneider (2011, 2021), Schneider and Williams (2013), and Williams and Schneider (2016), as well as Medina and Schneider (2018, 2021). More recent literature on the application of the MIMIC approach to estimate the shadow economy is given by Dybka et al. (2019) and Dell’Anno (2022, 2023).
 
10
The estimates on Shadow Economy provided here are the values taken from Schneider (2022). Shadow Economy values prior to 2003 (from 1999) are taken from Medina and Schneider (2021). Their estimates and calculations are based on the MIMIC model approach to calculate the shadow economy as a latent variable. Their methodology mainly follows that of Buehn and Schneider (2012, 2013) in estimating the values of the shadow economy using the MIMIC approach, while taking into account limitations of such method discussed in various literature, such as that from (2016) and Feige (2016a, 2016b).
 
11
Compare here Gaspareniene et.al. (2022).
 
12
Compare here Davidescu (2014, 2016 and 2017) and Williams and Horodnic (2017).
 
13
Social Fraud Act – SozBeG (2004).
 
14
For more details, see Williams and Renooy (2008).
 
15
General Social Security Act (Allgemeines Sozialversicherungsgesetz, ASVG).
 
16
More details are provided in the databases of Eurofound (2023) and the report of Eurofound (2013).
 
17
For more details see ILO, 2010, Labour Inspection in Europe: Undeclared Work, Migration, Trafficking, Labour Administration and Inspection Programme p.10.
 
18
See Sustainable Governance Indicators for Austria (n.d.).
 
19
See Stimulus packages – Government and institution measures in response to COVID-19 from KPMG (2020).
 
20
See Sustainable Governance Indicators for Denmark (n.d.).
 
21
See the report on Denmark's National Reform Programme 2022 from the Ministry of Finance of Denmark (2022)
 
22
Ibid.
 
23
AML-framework – Anti money laundering framework.
 
24
For more details see ILO (2010, p.22).
 
25
For more details see Renooy, et al., (2004, p.35).
 
26
For further information refer to Sustainable Governance Indicators for Germany (n.d.).
 
27
SGI Data for Greece (2017).
 
28
Sustainable Governance Indicators (SGI) Data for Greece (2022).
 
29
Sustainable Governance Indicators (SGI) Data for Greece (2017).
 
30
Eurobarometer Data (n.d.). Please note that the data presented in this study for the EU aggregate relate to EU-28 (i.e. current EU 27 + the UK), since the UK was an EU Member before end of January 2020.
 
31
European Commission – Greece’s recovery and resilient plan, European Commission (2021a)
 
32
See Italy’s recovery and resilience plan from European Commission (2021b)
 
33
Ibid.
 
34
Government of Romania (2019).
 
35
The Labour Inspectorate is organised in accordance with Law no.108/1999 (republished last as of 2012, Romanian Official Journal/Monitorul Oficial al Romaniei no.290/03.05.2012.
 
36
Sustainable Governance Indicators (SGI) Data on Romania (2022).
 
37
There is evidence from the questionnaires questionnaires from Germany and Austria for this assumption. See Schneider (2022) for more details.
 
38
It must be noted here that since 2000, several European National Institutes of Statistics have revised their official statistics to account for the non-observed economy. From 2016, these adjustments have been standardized across all EU member states, utilizing the Tabular Approach of Non-Exhaustiveness. This information is crucial as it impacts the outcomes of the MIMIC (Multiple Indicators Multiple Causes) model, which relies on the method of official GDP calculation. For instance, GDP growth or per capita figures, often used as a cause or indicator of the latent variable in such models, incorporate the official GDP as a key component. Specifically, the official GDP forms the denominator in the ratio defining the unobserved variable.
 
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Metadaten
Titel
A review of the driving forces of the informal economy and policy measures for mitigation: an analysis of six EU countries
verfasst von
Alban Asllani
Friedrich Schneider
Publikationsdatum
08.05.2024
Verlag
Springer US
Erschienen in
International Tax and Public Finance
Print ISSN: 0927-5940
Elektronische ISSN: 1573-6970
DOI
https://doi.org/10.1007/s10797-024-09842-z