The impact of immigration on output and its components: a sectoral analysis for Italy at regional level

Abstract

This paper studies the impact of immigrant workers on Italian sectors. The analysis is based on the channel output decomposition approach, by means of which the effect of immigration is measured with respect to per capita value added and its components. The empirical investigation is carried out at sector level during the period 2008–2011. The results show that immigrants exert a positive impact on total output which is mainly driven by the effect on total factor productivity. The main finding is that not all sectors benefit from the productivity gains. The sectors that mostly take advantage of immigrant workers are those featured by the predominance of manual tasks not requiring high communication skills.

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Notes

  1. 1.

    This index combines more and less educated workers in a constant elasticity of substitution function where the elasticity of substitution is bounded to be higher than zero.

  2. 2.

    Unioncamere (Italian Union of Chambers of Commerce, Industry, Handicraft and Agriculture) is a public institution that represents the Italian Chamber system.

  3. 3.

    These estimates consider both regular and irregular employment.

  4. 4.

    Italian regions are 20, but ISTAT merges the data of the smallest one, that is Valle D’Aosta, with the neighbouring Piemonte.

  5. 5.

    The empirical model in Eq. (5) could be seen as a linear system of equations. Accordingly, it might be estimated using seemingly unrelated regressions (SUR) in order to gain efficiency from taking account of correlation between error terms across equations. However, it can be demonstrated that when the same regressors appear in the right hand side of each equation (i.e. our case), estimating the model using OLS equation by equation is equivalent to SUR (see for instance Wooldridge 2010).

  6. 6.

    Dropping the interaction terms, the Eq. (5) simplifies to \(lnb_{irt} = d_{i} + d_{r} + d_{t} + \gamma_{b} z_{irt} + \varepsilon_{irt}\).

  7. 7.

    This value is within the range of values (i.e., 0.29 and 0.36) estimated for Italy by Marrocu and Paci (2010).

  8. 8.

    The 2002 is the first year for which data of immigrants by country of origin are available at regional level. Usually the lagged year goes back a couple of decades, however immigration in Italy has experienced some important shocks in both the paths and trends of international immigration between 2002 and 2007 which can justify the adoption of this short lag. The main source of these shocks is the enlargement of the EU to central and eastern European countries.

  9. 9.

    For each region we average the shortest road-distances across all province capital cities. Different road types are considered, not only highways.

  10. 10.

    The number of workers in year t (by region and industry) is constructed as the average of total workers in each quarter. Self employed are not considered.

  11. 11.

    We have used the variable ORELAV which measures the hours worked in a week and multiplied it by 13 (i.e. the average number of weeks in a quarter) to obtain the total number of hours worked in each quarter and then we computed the sum to obtain the total number of hours worked in year t. Hours per worker, for both natives and immigrants in each education cell, is computed as the ratio of total hours worked in year t (by region and industry) to the corresponding total number of workers.

  12. 12.

    The industry weights are constructed with respect to each industry, so that the sum of the weights for each industry over the 19 regions is equal to one.

  13. 13.

    For each quarter we multiply (for each type of worker) the variable RETRIC by COEFF and by three and then take the sum by region and industry. We then sum all the quarters’ pays to obtain the annual pay. The annual pay is then divided by average number of workers in order to obtain the average pay per worker, which is finally divided by the annual hours worked by high (low) skilled worker in region r, industry i and year t.

  14. 14.

    Unfortunately the data do not allow a further disaggregation.

  15. 15.

    The Stock and Yogo (2005) critical values are available only for models including up to two endogenous regressors. For this reason, the statistics has not been computed for the models with the interaction terms.

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Etzo, I., Massidda, C., Mattana, P. et al. The impact of immigration on output and its components: a sectoral analysis for Italy at regional level. Econ Polit 34, 533–564 (2017). https://doi.org/10.1007/s40888-017-0064-4

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Keywords

  • Channel output decomposition approach
  • Immigrant workers
  • Italy
  • Sectors
  • Regions

JEL Classification

  • F22
  • F62
  • J61