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

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  • Péter Elek

    (Eötvös Loránd University (ELTE))

  • János Köllő

    (Hungarian Academy of Sciences (MTA KRTK)
    IZA)

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.

Suggested Citation

  • Péter Elek & János Köllő, 2019. "Eliciting permanent and transitory undeclared work from matched administrative and survey data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(3), pages 547-576, August.
  • Handle: RePEc:kap:empiri:v:46:y:2019:i:3:d:10.1007_s10663-018-9403-0
    DOI: 10.1007/s10663-018-9403-0
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    Cited by:

    1. Bíró, Anikó & Prinz, Dániel & Sándor, László, 2022. "The minimum wage, informal pay, and tax enforcement," Journal of Public Economics, Elsevier, vol. 215(C).
    2. Colin C. Williams, 2023. "A Modern Guide to the Informal Economy," Books, Edward Elgar Publishing, number 18668.
    3. Burgstaller, Lilith & Feld, Lars P. & Pfeil, Katharina, 2022. "Working in the shadow: Survey techniques for measuring and explaining undeclared work," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 661-671.
    4. Gazilas, Emmanouil Taxiarchis, 2024. "An empirical analysis on the impact of labour market regulations on uninsured employment in Greece," MPRA Paper 121311, University Library of Munich, Germany.
    5. Søndergaard, J., 2023. "Undeclared Danish Labor: Using the labor input method with linked individual-level tax data to estimate undeclared work in Denmark," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 708-730.
    6. Arezzo, Maria Felice & Horodnic, Ioana A. & Williams, Colin C. & Guagnano, Giuseppina, 2024. "Measuring participation in undeclared work in Europe using survey data: A method for resolving social desirability bias," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

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    More about this item

    Keywords

    Undeclared work; Labour input method; Matched administrative-survey data; Random-effects panel probit with endogenous selection; Markov chain;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance
    • J46 - Labor and Demographic Economics - - Particular Labor Markets - - - Informal Labor Market

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