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Immigration and manufacturing in Italy: evidence from the 2000s

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Abstract

This paper tests for the effect of an increase in the migration rate on manufacturing firms’ performance at the local level. The model is estimated for the Italian economy during the recent years of rapid and varied migration. We construct measures for both a representative province-sector firm and a representative province firm and estimate the impact of migrants on high- and low-tech sectors by also considering migrants heterogeneity (in terms of the characteristics of origin nationalities) in order to approximate the effect of high- and low-skill migrants. Migrants’ presence positively affects firm’s performance: a doubling of the migration ratio to provincial population raises sales per worker by 8–9 % on average. However, this increase is unevenly distributed and favors low-tech versus high-tech sectors. On the labor supply side, low-skill (primary-educated) migrants have a higher effect on firms’ performance than high-skill (tertiary-educated) migrants.

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Notes

  1. Among the many articles on the subject, Borjas (2003) and Borjas and Katz (2007) found a significative effect on US wages, whereas Card (2007) does not confirm this finding. Ottaviano and Peri (2012) challenge the traditional approach by pointing at the imperfect substitution between natives and migrants in the detailed and fine segments (or cells) of the labor market.

  2. See D’Amuri and Peri (2014). They considered all segments of the European labor markets and not only the low-educated one, as in many other studies on the US.

  3. They relate their results to the debate on skill-biased technological change in the US, or imported skill-biased technological change in the case of Israel.

  4. The excellent survey in Lewis (2013) shows the mathematical conditions required in the production function to marginalize the effect on capital and concentrate on the ratio low- to high-skilled labor as emphasized in the recent literature, e.g. Ottaviano and Peri (2012).

  5. We define as non native resident in Italy an individual residing in Italy, but not holding the Italian citizenship. The term non native resident is used hereafter interchangeably with migrant.

  6. The amnesties came together with new migration laws: (a) a first amnesty occurred in 1992 after the first relevant change in the immigration law (citizenship requirements were also extended to 10 years of residence from the original 5 years); around 250 thousands regularized migrants; (b) the second amnesty occurred in 1998 with the introduction of a new migration law (so called Turco-Napolitano law); migrant’s residence permits were not strictly linked to labor contracts and expulsions with deportation of the illegal migrants back to their origin country were excluded (unless there were bilateral agreements, as in the case of Albania); around 200 thousand regularized migrants; (c) the third amnesty occurred in 2002 together with a stricter law (the Bossi-Fini law), which required the pre-existence of a labor contract to enter and stay in the country; around 640 thousand regularized migrants.

  7. See, for instance, Jayet et al. (2010).

  8. The years 2005 and 2006 are the first two years for which these statistics are available for Italy. The figures reported on the histograms are 2005–2006 averages.

  9. It is worth noting that plant-level balance-sheet data are not available, focusing only on pre-consolidation data we are at least partially able to control for multi-plant firms, whose incidence is relatively small in Italy.

  10. Results available from the authors upon requests.

  11. The classification is taken from the Statistics on high-tech industry and knowledge-intensive services for a detailed description see http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/en/htec_esms.htm.

  12. We have also used \(\ln (production/workers)\) for a robustness check and results, available upon request, are very similar.

  13. We do not include the constant to avoid dropping one of the dummies. The excluded reference group in form size is the first quintile, i.e. bottom 20 %

  14. Sector dummies \(\gamma _{s}\) are referred to 2-digit Ateco2007 (NACE rev 2).

  15. Results are also confirmed using non-weighted OLS with either province clustering or bootstrap for the standard errors. They are available from the authors upon request.

  16. There is no significant difference when considering Prod/W. Results are available from the authors upon request.

  17. Results are also confirmed using non-weighted OLS with either province clustering or bootstrap for the standard errors. They are available from the authors upon request.

  18. In the baseline regression Bratti et al. (2014) found an elasticity of exports to migrants of 0.128.

  19. The role of language proximity and ability in acquiring the host-country language has been investigated in the literature and may involve human capital considerations that are however beyond the scope of this paper.

  20. Specifically, the stock (and rates) of migration inflows for each OECD country are provided by the level of schooling and gender for 195 source countries in 1990 and 2000.

  21. Results with Prod/W as a dependent variable are very similar and are available upon request.

  22. Since identification is province by year we consider only those cells with at least 10 firms in both high- and low-tech industries. Note that in the AIDA database firms do not change both sectors and province overtime; hence, although firm-specific the dummy \(\gamma _{is^{Low}}\) does not contain the province \(j\) and the time \(t\) subscript.

  23. The analysis has been carried out also for the other dependent variable relative production per worker. Results are very similar and available upon request.

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Acknowledgments

We would like to thank Carlo Altomonte, Giulia Bettin, Paolo Giordani, Hubert Jayet, Fabio Mariani, Peter Neary, Gianmarco Ottaviano, Diego Puga, Cristina Tealdi and participants at the workshop “Production, R&D and Knowledge Offshoring: Economic Analyses and Policy Implications” and at various seminars.

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Authors and Affiliations

  1. Dipartimento di Scienze Sociali ed Economiche, Sapienza University of Rome, Rome, Italy

    Giuseppe De Arcangelis

  2. DISES and CSEF, University of Naples Federico II, Naples, Italy

    Edoardo Di Porto

  3. Institute of Economics, Scuola Superiore Sant’Anna, Pisa, Italy

    Gianluca Santoni

Authors
  1. Giuseppe De Arcangelis
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  2. Edoardo Di Porto
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  3. Gianluca Santoni
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Corresponding author

Correspondence to Giuseppe De Arcangelis.

Additional information

We kindly acknowledge financial support from Sapienza University of Rome (Ateneo Grant). The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. The usual disclaimers apply.

Appendix A: Other Results

Appendix A: Other Results

In this Section Table 10 reports the results of the first-stage regression for the general model and considering different dependent variables – \(\ln (Sales/Workers)\) and \(\ln (Production/Workers)\).

Table 10 First-Stage Regression for Equation (2); Different Dependent Variables
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De Arcangelis, G., Di Porto, E. & Santoni, G. Immigration and manufacturing in Italy: evidence from the 2000s. Econ Polit Ind 42, 163–187 (2015). https://doi.org/10.1007/s40812-014-0005-y

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  • DOI: https://doi.org/10.1007/s40812-014-0005-y

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