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The Impact of Migration on Productivity: Evidence from the United Kingdom

  • Francesco Campo ORCID logo EMAIL logo , Giuseppe Forte and Jonathan Portes
Published/Copyright: April 3, 2024
The B.E. Journal of Economic Analysis & Policy
From the journal The B.E. Journal of Economic Analysis & Policy Volume 24 Issue 2

Abstract

The UK saw a sharp rise in work-related migration, particularly from the EU, in the 2000s and 2010s, with profound impacts on the labour market. We investigate the relationship between migration and productivity in Great Britain between 2002 and 2018, using an instrumental variable approach which follows the commonly used shift-share methodology. Our results, which are robust to a variety of tests, suggest that immigration has a positive and significant impact (in both the statistical sense and more broadly) on productivity, as measured by GVA per job at the Travel-to-Work-Area level. We indeed find that a 1 p.p. increase in the share of migrants is associated with a 0.84 % increase in productivity in 2SLS estimates. We discuss the implications for post-Brexit immigration policy.

Keywords: immigration; productivity; United Kingdom; Brexit
Corresponding author: Francesco Campo, University of Padova, Padova, Italy, E-mail:

The original research underpinning this paper was funded by the Migration Advisory Committee, an independent body that advises the UK government on migration policy. The usual disclaimer applies.

Funding source: Migration Advisory Committee

  1. Research funding: The original research underpinning this paper was funded by the Migration Advisory Committee, an independent body that advises the UK government on migration policy. The usual disclaimer applies.

  2. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose.

  3. Data availability: The datasets generated and analysed for the realization of this paper are publicly available on UK’s Office for National Statistics and NOMIS websites (links to these data are provided in the main text). Codes used to implement the empirical analysis in this paper are available upon request to the authors.

Appendix A
Table A1:

Descriptive statistics on population by country/area of birth – 2002–2018.

Country/area of birth Share of GB pop. Country/area of birth Share of GB pop.
2002 2018 2002 2018
All non UK-born 8.36 14.28 North Africa 0.11 0.17
Afghanistan 0.00 0.11 Caribbean-West Indies 0.08 0.13
Albania 0.02 0.05 Central-Western Africa 0.11 0.27
Australia 0.19 0.21 Eastern Europe 0.04 0.39
Austria 0.03 0.03 European USSR 0.05 0.16
Baltic States 0.02 0.44 Far East 0.18 0.47
Bangladesh 0.33 0.37 Middle East 0.04 0.25
Belgium 0.03 0.05 North America 0.01 0.02
Canada 0.12 0.13 Oceania 0.00 0.01
China 0.22 0.32 South Asia 0.00 0.09
Cyprus 0.13 0.09 South-Eastern Africa 0.32 0.43
Czech Republic 0.03 0.07 Western Europe 0.13 0.12
Democratic Rep. of Congo 0.01 0.03 Pakistan 0.51 0.81
Denmark 0.03 0.04 Poland 0.12 1.27
Finland 0.02 0.03 Portugal 0.10 0.22
Former Yugoslavia 0.01 0.06 Republic of Ireland 0.82 0.56
France 0.16 0.26 Romania 0.02 0.60
Germany 0.44 0.47 Sierra Leone 0.03 0.03
Greece 0.04 0.12 Singapore 0.07 0.08
India 0.77 1.27 Somalia 0.13 0.16
Iran 0.09 0.11 South Africa 0.27 0.37
Iraq 0.06 0.11 South America 0.11 0.31
Italy 0.17 0.38 Spain 0.09 0.23
Jamaica 0.26 0.20 Sri Lanka 0.14 0.20
Japan 0.05 0.07 Sweden 0.03 0.06
Kenya 0.22 0.20 Turkey 0.10 0.15
Luxembourg 0.00 0.00 United Kingdom 91.64 85.72
Malaysia 0.09 0.11 USA 0.23 0.26
Netherlands 0.07 0.12 Western-Central Asia 0.00 0.02
New Zealand 0.09 0.10 Zimbabwe 0.12 0.19

  1. This table shows the share of population by country/area of birth in 2002 and 2018. Source: Annual population survey data.

Table A2:

Balance test of 2001 countries shares on TTWA industries shares.

Dependent variable: s cl,2001 – 2001 country share
Baltic States Czech Republic India Nigeria Other East. Europe Poland Romania
(1) (2) (3) (4) (5) (6) (7)
2001 share of workers in
Agriculture, hunting, forestry 0.00100 0.0209 −0.0131 0.0997 0.0109 0.000420 0.0158
(0.0610) (0.0643) (0.0692) (0.136) (0.0730) (0.0653) (0.0715)
Fishing −0.265 0.0339 −0.321 0.150 −0.118 −0.323 0.0292
(1.023) (1.233) (1.068) (2.473) (1.344) (1.137) (1.332)
Mining and quarrying 0.244 0.287 0.118 0.764 0.354 0.273 0.368
(0.399) (0.428) (0.432) (0.898) (0.487) (0.432) (0.474)
Manufacturing −0.0185 −0.0327 0.000684 −0.0438 −0.0320 −0.0205 −0.0342
(0.0232) (0.0247) (0.0263) (0.0508) (0.0277) (0.0245) (0.0273)
Electricity, gas and water supply −0.267 −0.377 −0.217 −0.669 −0.397 −0.265 −0.406
(0.351) (0.374) (0.382) (0.775) (0.420) (0.366) (0.412)
Construction −0.350 −0.292 −0.396 −0.614 −0.336 −0.367 −0.319
(0.267) (0.274) (0.293) (0.583) (0.312) (0.277) (0.307)
Wholesale, retail and repair of motor vehicles −0.0672 −0.151 −0.0831 −0.261 −0.142 −0.0946 −0.135
(0.119) (0.124) (0.131) (0.263) (0.141) (0.125) (0.138)
Hotels and restaurants 0.0678 0.0746 0.114 0.170 0.0877 0.0968 0.0770
(0.0953) (0.102) (0.105) (0.209) (0.113) (0.0999) (0.110)
Transport storage and communications 0.0561 0.116 0.0812 0.149 0.0927 0.0770 0.0750
(0.0836) (0.0914) (0.0937) (0.190) (0.102) (0.0915) (0.100)
Financial Intermediation 0.146 0.134 0.144 0.290 0.130 0.139 0.139
(0.145) (0.146) (0.157) (0.317) (0.168) (0.150) (0.166)
Real estate, renting and business activities 0.247 0.332 0.265 0.550 0.353 0.281 0.332
(0.233) (0.242) (0.253) (0.519) (0.275) (0.244) (0.271)
Public administration and defence −0.0515 −0.0534 −0.0624 −0.0847 −0.0588 −0.0584 −0.0595
(0.0429) (0.0431) (0.0507) (0.0896) (0.0495) (0.0458) (0.0484)
Education −0.159 −0.256 −0.160 −0.543 −0.268 −0.203 −0.248
(0.248) (0.258) (0.275) (0.552) (0.293) (0.260) (0.288)
Health and social work 0.201 0.265 0.186 0.540 0.282 0.227 0.273
(0.231) (0.241) (0.254) (0.515) (0.274) (0.243) (0.270)

  1. We here consider the group of top-5 Rotemberg weights for both shift-share IVs (All-SSIV and EUA-SSIV). Each column reports the regression of 2001 country c’s share (s cl,2001) on 2001 TTWA NACE industries share. Source: 2001 census data. Excluded category: “other industries”. White-robust standard errors in parenthesis (*** p < 0.01, ** p < 0.05, * p < 0.1).

Table A3:

Balance test of 2001 countries shares on TTWA occupations shares.

Dependent variable: s cl,2001 – 2001 country share
Baltic States Czech Republic India Nigeria Other East. Europe Poland Romania
(1) (2) (3) (4) (5) (6) (7)
2001 share of workers in
Professional occupations 0.0399 0.0329 0.0351 −0.00314 0.0446 0.0454 0.0459
(0.0610) (0.0706) (0.0707) (0.143) (0.0787) (0.0671) (0.0732)
Associate professional and technical occupations 0.161 0.220 0.155 0.460 0.244 0.186 0.230
(0.231) (0.255) (0.244) (0.532) (0.286) (0.245) (0.279)
Administrative and secretarial occupations 0.163 0.173 0.238* 0.209 0.154 0.152 0.139
(0.108) (0.115) (0.124) (0.235) (0.130) (0.114) (0.125)
Skilled trades occupations −0.137 −0.131 −0.113 −0.208 −0.146 −0.135 −0.138
(0.0889) (0.0934) (0.0971) (0.194) (0.105) (0.0937) (0.103)
Personal service occupations −0.140 −0.113 −0.172 −0.246 −0.153 −0.163 −0.146
(0.129) (0.139) (0.140) (0.282) (0.153) (0.134) (0.151)
Sales and customer −0.147 −0.152 −0.167 −0.289 −0.152 −0.119 −0.147
Service occupations (0.141) (0.150) (0.155) (0.310) (0.168) (0.148) (0.165)
Process, plant and machine operatives 0.0236 −0.00807 0.100** 0.00549 0.00885 0.0255 −0.00325
(0.0331) (0.0356) (0.0473) (0.0608) (0.0372) (0.0330) (0.0363)
Elementary occupations −0.0137 −0.0726 −0.123 −0.0818 −0.0587 −0.0431 −0.0458
(0.0669) (0.0721) (0.0782) (0.135) (0.0772) (0.0686) (0.0760)

  1. We here consider the group of top-5 Rotemberg weights for both shift-share IVs (All-SSIV and EUA-SSIV). Each column reports the regression of 2001 country c’s share (s cl,2001) on 2001 TTWA SOC occupations share. Source: 2001 census data. Excluded category: “manager and senior officials”. White-robust standard errors in parenthesis (*** p < 0.01, ** p < 0.05, * p < 0.1).

Table A4:

Adao, Kolesár, and Morales (2019) standard errors.

Coefficient Std. error p-value Lower CI Upper CI
(A) All countries shift-share IV
1st stage 0.3026 0.0000 0.0000 0.3026 0.3026
2nd stage 0.0084 0.0000 0.0000 0.0084 0.0084
(B) EUA countries shift-share IV
1st stage 0.8655 0.0000 0.0000 0.8655 0.8655
2nd stage 0.0078 0.0000 0.0000 0.0078 0.0078

  1. Notes: This table reports the output from the STATA package ivreg_ss which computes the standard errors according to the methodology, developed by Adao, Kolesár, and Morales (2019), which account for correlation in regression residuals across TTWA with similar initial countries shares used for the construction of the shift-share instrumental variable.

Table A5:

Immigration and productivity – local authority estimates – 2002–2018.

Dependent variable: (log) GVA per filled job
OLS 2SLS: shift-share IV
All countries EUA countries
(1) (2) (3)
Share of migrants 0.00335*** 0.00799*** 0.00810***
(0.000999) (0.00252) (0.00288)
Observations 5746 5746 5746
R 2 0.976
Local authority FE Yes Yes Yes
Year FE Yes Yes Yes
MOP F-stat 27.97 37.41

  1. Notes: the outcome and explanatory variable of interest in all estimates are, respectively, the natural logarithm of gross value added per filled job, and the migrants’ share, m lt , i.e. the fraction, out of resident population in local authority l in year t, of non UK-born residents. All regressions consider yearly data for each year between 2002 and 2018, and include local authority and year fixed effects. Column 1 shows OLS estimates, while Columns 2 and 3 2SLS results with shift-share instrumental variable which are defined according to a methodology similar to Card (2001) and described in detail in Section 3.1. 2SLS regression in Column 2 uses a shift-share IV which considers the whole set of countries/areas of origin, while in Column 3 we select the group of countries which gained access to EU after 2004. Bottom row reports Montiel Olea and Pflueger (2013) F-statistic for weak instrument test of corresponding first stage. Regressions results are weighted by employment size in 2001. Standard errors clustered at the local authority level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

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Received: 2023-06-01
Accepted: 2024-02-16
Published Online: 2024-04-03

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bejeap-2023-0179
Keywords for this article
immigration; productivity; United Kingdom; Brexit

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Tradeoffs in the Power of Regulatory Regimes
  4. Pay as You Throw Threshold Tariff: Evidence on the Incentive to Recycle
  5. Industrial Technology Boundary, Product Quality Choice, and Market Segmentation
  6. Human Capital Investments and Family Size in Italy: IV Estimates Using Twin Births as an Instrument
  7. Maternal Labour Supply and School Enrolment Laws: Empirical Evidence from Brazilian Primary School Reforms
  8. Pricing Personalised Drugs: Comparing Indication Value Based Prices with Performance Based Schemes
  9. The Impact of Migration on Productivity: Evidence from the United Kingdom
  10. In the Eye of the Storm: The Disrupted Career Paths of Young People in the Wake of COVID-19
  11. Separating the Accountability and Competence Effects of Mayors on Municipal Spending
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  14. Keeping Mobile Firms at Home: The Role of Public Enterprise
  15. The Trouble with Take-Up
  16. Sex Ratio and Terrorist Group Survival
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