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Immigration and Offshoring: two forces of globalisation and their impact on employment and the bargaining power of occupational groups

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Abstract

This paper estimates conditional demand models to examine the impact of immigration and different measures of offshoring on the labour demand and demand elasticities of native workers in four different types of occupational groups: managers/professionals, clerical workers, craft (skilled) workers and manual workers. The analysis is conducted for the period 2008–2017 for four economies Austria, Belgium, France and Spain. Our results point to important and occupation-specific direct and indirect effects: both offshoring – particularly services offshoring – and immigration have negative direct employment effects on all occupations, but native clerks and manual workers are affected the most, and native managers/professionals the least. Our results also identify an important elasticity-channel of immigration and offshoring and show that some groups of native workers can also gain from globalisation through an improvement in their wage-bargaining position. Overall, our results indicate a deterioration in the bargaining power of native manual workers arising from both immigration and offshoring and an improvement in the bargaining position of native craft workers.

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Change history

  • 02 June 2022

    The layout has been corrected in the original version.

Notes

  1. For a recent assessment of this literature, see Dustmann et al., 2016; see also the earlier meta-study by Longhi et al., 2010.

  2. The interaction term in general can be interpreted as how a percentage increase in the migrant share (of offshoring) affects employment of natives at a given wage rate. Centring refers to setting the variables always in relation to the average values (of wage rates, of migrant shares, of offshoring).

  3. In this rather broad group managers represent the minority, only accounting for between 30% and 40%, on average.

  4. See, for example, Landesmann et al., (2015) for an overview.

  5. All variables stem from either Eurostat or OECD. A variety of labour market programmes were tried, all of them designed to impact labour supply.

  6. This paper was published in a somewhat modified form as Wright (2014).

  7. EU-LFS statistics provides information on country of birth at a relatively aggregate level; however, the advantage of LFS statistics was that we could compile the composition of migrants at the industry level and occupational level.

  8. The population growth figures of migrants and the overall population were obtained from Eurostat population statistics, complemented with data from national sources and the OECD International Migration Database.

  9. The construction of this variable used access to three data bases: WIOD release 2016, plus the upcoming WIOD release available to the authors regarding imported intermediate inputs (at the industry level) and output growth, while hours worked was taken from EU-LFS statistics.

  10. NTMs were taken from a special wiiw database (see wiiw NTM Data) and AVEs from Adarov & Ghodsi (2021).

  11. We did not include Luxembourg, whose migration numbers and patterns are too different from the other ‘old’ EU member states.

  12. A more extensive descriptive analysis is available from the accompanying working paper (Landesmann & Leitner, 2020).

  13. See Table S1 in the online annex for the underlying industry classification.

  14. Results for 2-year and 4-year differences are available in Tables S2 and S3 in the online annex. Following one referee’s suggestion, we also tested for the interrelationship between effects from immigration and the extent to which industries are open to trade in intermediates. We ran estimates with triple interaction terms between wages, our offshoring measures and the migration shares. With regard to industry differentiation, our estimates hardly gave any significant results. Given the lack of statistically significant results, we do not report the results in this paper.

  15. Results for 2-year and 4-year differences are available in Tables S6 and S7 in the online annex. Following one referee’s suggestion, we also tested for the interrelationship between effects from immigration and the extent to which industries are open to trade in intermediates. We ran estimates with triple interaction terms between wages, our offshoring measures and the migration shares. With regard to industry differentiation, our estimates hardly gave any significant results. Given the lack of statistically significant results, we do not report the result tables in this paper.

  16. Specifically, we included interactions between three variables, namely wages, our measure(s) of offshoring and the ‘South’ dummy to capture differences with respect to offshoring and between wages, the migrant share (total and by occupational category) and the ‘South’ dummy to capture differences with respect to migration.

  17. Results for all remaining estimations (i.e. when the total offshoring measure is used or when total offshoring is further differentiated in terms of narrow and broad offshoring) are available from the authors upon request.

  18. Results for the remaining specifications are not reported here but are available from the authors upon request.

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Authors and Affiliations

  1. Johannes Kepler University, Altenberger Straße 69, A-4040, Linz, Austria

    Michael Landesmann

  2. The Vienna Institute for International Economic Studies (wiiw), Rahlgasse 3, A-1060, Vienna, Austria

    Michael Landesmann & Sandra M. Leitner

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  1. Michael Landesmann
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  2. Sandra M. Leitner
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Correspondence to Sandra M. Leitner.

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Landesmann, M., Leitner, S.M. Immigration and Offshoring: two forces of globalisation and their impact on employment and the bargaining power of occupational groups. Rev World Econ 159, 361–397 (2023). https://doi.org/10.1007/s10290-022-00470-5

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