Empirica

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Eliciting permanent and transitory undeclared work from matched administrative and survey data

Original Paper
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Abstract

We study the undeclared work patterns of Hungarian employees in relatively stable jobs, using a panel dataset that matches individual-level self-reported Labour Force Survey data with administrative records of the Pension Directorate for 2001–2006. We estimate the determinants of undeclared work using Heckman-type random-effects panel probit models, and develop a two-regime model to separate permanent and transitory undeclared work, where the latter follows a Markov chain. We find that about 6–7% of workers went permanently unreported for six consecutive years, and a further 4% were transitorily unreported in any given year. The models show lower reporting rates—especially in the permanent segment—among males, high-school graduates, those in agriculture and transport, small firms and various forms of atypical employment. Transitory non-reporting may be partly explained by administrative records missing for technical reasons. The results suggest that (1) the “aggregate labour input method” widely used in Europe can indeed be a simple yet reliable tool to estimate the size of informal employment, although it slightly overestimates the true magnitude of black work and (2) the long-term pension consequences of undeclared work may be substantial because of the high share of permanent non-reporting.

Keywords

Undeclared work Labour input method Matched administrative-survey data Random-effects panel probit with endogenous selection Markov chain 

JEL Classification

C23 C25 H26 J46 

Notes

Acknowledgements

The authors would like to thank Anikó Bíró, Márton Csillag and Gábor Kézdi for useful comments on an earlier version of the paper. Péter Elek was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and later by the ÚNKP-17-4 New National Excellence Program of the Ministry of Human Capacities in Hungary.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EconomicsEötvös Loránd University (ELTE)BudapestHungary
  2. 2.Institute of EconomicsHungarian Academy of Sciences (MTA KRTK)BudapestHungary
  3. 3.IZABonnGermany

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