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Interregional Migration ‘Wage Premia’: The Case of Creative and Science and Technology Graduates in the UK

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Applied Regional Growth and Innovation Models

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

Since the seminal contribution by Sjaastad (1962), the so-called ‘human capital migration theory’ has become extremely popular among economists, especially regional economists. The basic idea is that migration itself can be viewed as an investment in human capital. A rational individual would use relocation as a means to maximize long-term utility and would move if the future discounted benefits of relocating outweigh the costs associated with the move.

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Notes

  1. 1.

    See Greenwood (1975) for a review focused on the US case and Jayet (1996) for a review of studies outside the US.

  2. 2.

    We excluded students who studied part time because they are generally much older and less likely to migrate because of family ties, or because their degree is an integrated part of their employment. Secondly the response rates of part time students (around 60 %) are lower than those of full time students.

  3. 3.

    Note that medicine and dentistry students were omitted, given they are salary outliers and the majority do not have a degree classification.

  4. 4.

    Since we have more than one category (four migration strategies) we could have employed a multinomial logit to estimate the migration probabilities but following Lechner (2002) we estimate a series of probits for each pair of migration strategy and non-migration. Using a series of probits instead of a multinomial model in our case leads to better matches in terms of lower standardised bias measures.

  5. 5.

    Traditionally UK degrees are measured using the following degree classifications: first, upper second, lower second, third, pass and fail, with a first or a second class degree classified as a ‘good degree’.

  6. 6.

    We use a caliper of 0.01 for the nearest neighbour and radius matching and a bandwidth of 0.06 for the kernel matching.

  7. 7.

    See http://www.lv.com/media_centre/press_releases/university-ghost-towns

  8. 8.

    JACS is the Joint Academic Coding System used by HESA to classify subjects see http://www.hesa.ac.uk/dox/jacs/JACS_sg.pdf for these codes.

  9. 9.

    A complete list of these subjects can be found in Comunian et al. (2010).

  10. 10.

    Note that medicine and dentistry students were omitted, given they are salary outliers and the majority do not have a degree classification.

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Author information

Authors and Affiliations

  1. Department of Economics, University of Reading, Whiteknights, Reading, RG6 6AA, UK

    Sarah Jewell

  2. AED Economics Department, The Ohio State University, 2120 Fyffe Road, Columbus, OH, 43210, USA

    Alessandra Faggian

Authors
  1. Sarah Jewell
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  2. Alessandra Faggian
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Correspondence to Sarah Jewell .

Editor information

Editors and Affiliations

  1. Department of Spatial Economics, VU University, Amsterdam, The Netherlands

    Karima Kourtit

  2. Department of Spatial Economics, VU University, Amsterdam, The Netherlands

    Peter Nijkamp

  3. Building and Planning Level 5 Architecture Building, University of Melbourne Faculty of Architecture,, Parkville, VIC, Australia

    Robert Stimson

Appendix

Appendix

1.1 Subject Definitions

The definition of creative subjects was based on the definition used by Faggian et al. (2013). Consistent with previous definitions (Comunian et al. 2010; Abreu et al. 2012; Faggian et al. 2013) creative subjects included all JACS,Footnote 8 HESA’s subject coding system, codes starting with W (creative arts and design) and P (mass communication and documentation) plus architecture and landscape designFootnote 9 (K1, K3, K9). However, the results of Comunian et al. (2014) suggest that multi-media computer science; software engineering and design students are better classified as STEM students rather than creative students, and they are therefore classified under STEM students rather than creative students.

STEM subjects were defined using a definition similar to that of several government reports (BICS 2011; Oxford Economics 2009; UKCES 2011) consisting of:

  • Medicine and Dentistry – JACS codes beginning with A

  • Veterinary Sciences, Agriculture and Related Subjects – JACS codes beginning with D

  • Subjects Allied to Medicine (excluding Nursing) – JACS codes beginning with B (excluding B7)

  • Biological Sciences (including Psychology) – JACS codes beginning with C

  • Physical Sciences– JACS codes beginning with F

  • Technologies – JACS codes beginning with J

  • Engineering – JACS codes beginning with H

  • Mathematical and Computer Sciences – JACS codes beginning with G

  • Built Environment (excluding Planning subjects). – JACS codes beginning with K (excluding K4 and K1, K3, K9 classified as creative subjects)

All subjects not covered by the STEM or creative subject definitions were grouped under ‘other’ subjects.Footnote 10

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Jewell, S., Faggian, A. (2014). Interregional Migration ‘Wage Premia’: The Case of Creative and Science and Technology Graduates in the UK. In: Kourtit, K., Nijkamp, P., Stimson, R. (eds) Applied Regional Growth and Innovation Models. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37819-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-37819-5_9

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