• Media type: E-Book
  • Title: Quantifying Informal Employment From Irregular Migration Shocks
  • Contributor: Gries, Timm [Author]; Trapani, Lorenzo [Author]; Valente, Marica [Author]
  • Published: [S.l.]: SSRN, 2022
  • Extent: 1 Online-Ressource (65 p)
  • Language: English
  • DOI: 10.2139/ssrn.4221232
  • Identifier:
  • Keywords: Informal employment ; Migration shocks ; Farm labor ; Machine learning
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 16, 2022 erstellt
  • Description: Using machine learning methods in a difference-in-differences setting, we quantify the population of informal workers generated by an irregular migration shock. We exploit the exogenous variation from the Arab Spring wave on southern Italian coasts, and a rich data on Italian vineyards. We identify informal employment caused by the shock from abnormal increases in reported (vs. predicted) labor productivity. We find unexplained increases in labor productivity for farms exposed to the shock, which translate into about one undeclared worker every three farms on average, or, equivalently, 23,000 workers over 2011-12. We also find that informal employment raises farm profits through lower labor costs, though it does not affect sales, grape prices, or wages of formal workers. We estimate tax evasion for about 75 million euros due to unpaid labor contributions
  • Access State: Open Access