Determinants of Undeclared Work in the EU Member States

Popescu, Mădălina Ecaterina; Cristescu, Amalia; Stanila, Larisa; Vasilescu, Maria Denisa

doi:10.1016/s2212-5671(16)30294-5

Cited by 3 publications

(2 citation statements)

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“…Things get more interesting when going deeper into the tree. In most cases, the third-best choice for filtering wrongdoers was their age, which exerts a substantial negative effect on the propensity to participate in the undeclared sphere (as already evidenced by, e.g., Arendt et al 2020 ; Hofmann et al 2017 ; Popescu et al 2016 ). The only exception was individuals with low tax morale who have an undeclared worker in their surroundings (the rightmost rectangle in layer 3).…”

Section: Findings and Discussionmentioning

confidence: 86%

“…; 9: business proprietor; 10: employed professional (doctor, etc. ); 11: general management; 12: middle management; 13: employed position, et desk; 14: employed position, travelling; 15: employed position, service job; 16: supervisor; 17: skilled manual worker; 18: unskilled manual worker 0 Franic and Cichocki ( 2021 ), Gregorio and Giordano ( 2016 ), Kayaoglu and Williams ( 2017 ), Strielkowski and Čábelková ( 2015 ), Williams et al ( 2015a ), Windebank and Horodnic ( 2017 ) Size of the company Categorical 0: not working or self-employed without workers; 1: 1–4 employees; 2: 5–9 employees; 3: 10–19 employees; 4: 20–49 employees; 5: 50–99 employees; 6: 100–499 employees; 7: 500 + employees 772 Elek and Köllő ( 2019 ), Franic and Cichocki ( 2021 ), Popescu et al ( 2016 ), Vallanti and Gianfreda ( 2020 ), Williams et al ( 2015a ) Financial difficulties Categorical 1: most of the time; 2: from time to time; 3: almost never/never 388 Arendt et al ( 2020 ), Boone et al ( 2013 ), Hofmann et al ( 2017 ), Popescu et al ( 2016 ), Williams and Efendic ( 2021 ), Williams and Horodnic ( 2015a , b ), Williams et al ( 2015a , b ) Social class (self-assessed) Categorical 1: working class; 2: lower middle class; 3: middle class; 4: upper-middle class; 5: higher class 868 Williams et al ( 2015a , b ), Williams and Horodnic ( 2015a ), Williams and Horodnic ( 2017b ) Perceptions and attitudes Perceived detection risk Categorical 1: very high; 2: fairly high; 3...…”

Section: Methodsmentioning

confidence: 99%

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What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning

Franić

2022

AI & Soc

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It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle . To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic.

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Section: Findings and Discussionmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning

Franić

2022

AI & Soc

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County-Level Patterns of Undeclared Work: An Empirical Analysis of a Highly Diversified Region in the European Union

2019

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Muhasebe Meslek Mensuplarinin Kayit Dişi İsti̇hdama Yöneli̇k Bakiş Açilarinin Değerlendi̇ri̇lmesi̇

Şenel¹

2021

Sosyal Guvence

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Undeclared work is described as a situation of failing in declaration of employees to public authorities and/or incomplete declaration in terms of number of workdays and/or wage information. Undeclared work might result in number of socioeconomic issues such as income unjustness, poverty, public finance deficit, and lack of social security. In this regard, accountants could be regarded as the most intimate witnesses of undeclared work cases. The objective of the present study is to search for the ways to decrease undeclared work proportion by evaluating the opinions of professional accountants towards the issue. To that end, Micro Data Set of Household Labor Survey conducted by the Turkish Statistical Institute was studied through quantitative analysis methods while the qualitative aspect of the study was founded on the interview study maintained with the 14 professional accountants from Denizli City. The quantitative findings reveal that professional accountants were highly educated and 85% of them are employed declared-based. The qualitative findings suggest that the most prominent motivator of undeclared work was relatively high tax and premiums collected by the government on the top of the employee wages. According to the respondents, employees are pushed to work undeclared based because of difficulty with finding regular employment opportunity and they suffer poverty. Based on our findings, proportion of undeclared work could be reduced through structuring efficient auditing mechanisms and deterring punitive measures.

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Determinants of Undeclared Work in the EU Member States

Cited by 3 publications

References 4 publications

What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning

What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning

County-Level Patterns of Undeclared Work: An Empirical Analysis of a Highly Diversified Region in the European Union

Muhasebe Meslek Mensuplarinin Kayit Dişi İsti̇hdama Yöneli̇k Bakiş Açilarinin Değerlendi̇ri̇lmesi̇

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