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Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach

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  • Fingleton, Bernard
  • Szumilo, Nikodem

Abstract

This paper estimates the impact of a multi-billion pound investment in Britain's rail transport infrastructure, in the form of high-speed rail links, on wage levels across each of 347 districts of England and Wales. The impacts are based on a dynamic spatial panel model adaptation of standard urban economics based on employment density and commuting patterns. This allows estimation of these global impacts operating via improved commuting times. We demonstrate that while estimates of a traditional market potential approach with fixed effects are to some extent qualitatively and quantitatively similar to our predictions, our predictions allow more heterogeneous effects and give more accurate forecasts. The study finds that on average wages increase by around 2% as employment centres gain improved access to more skilled workers and as spillover effects become spatially more extensive. While most areas see modest positive effects, some locations are negatively affected, in the extreme case by as much as 7%.

Suggested Citation

  • Fingleton, Bernard & Szumilo, Nikodem, 2019. "Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 148-164.
    • Handle: RePEc:eee:regeco:v:75:y:2019:i:c:p:148-164
    DOI: 10.1016/j.regsciurbeco.2018.12.004
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    3. Fan, Wei & Yan, Ling & Chen, Boyang & Ding, Wangwang & Wang, Ping, 2022. "Environmental governance effects of local environmental protection expenditure in China," Resources Policy, Elsevier, vol. 77(C).
    4. Sanchis-Guarner, Rosa & Szumilo, Nikodem & Vernet, Antoine, 2024. "Startup stations: the impact of rail access on entrepreneurship (self-employment) in England and Wales," LSE Research Online Documents on Economics 124680, London School of Economics and Political Science, LSE Library.
    5. Feng, Qiyun & Chen, Zhenhua & Cheng, Chuanchao & Chang, Haiqing, 2023. "Impact of high-speed rail on high-skilled labor mobility in China," Transport Policy, Elsevier, vol. 133(C), pages 64-74.
    6. Bernard Fingleton, 2020. "Exploring Brexit with dynamic spatial panel models: some possible outcomes for employment across the EU regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 455-491, April.
    7. Wang, Ke-Liang & Jiang, Wei & Miao, Zhuang, 2023. "Impact of high-speed railway on urban resilience in China: Does urban innovation matter?," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    8. Bernard Fingleton & Daniel Olner & Gwilym Pryce, 2020. "Estimating the local employment impacts of immigration: A dynamic spatial panel model," Urban Studies, Urban Studies Journal Limited, vol. 57(13), pages 2646-2662, October.
    9. Victor Medeiros & Rafael Saulo Marques Ribeiro & Pedro Vasconscelos Maia do Amaral, 2022. "Infrastructure and income inequality: An application to the Brazilian case using hierarchical spatial autoregressive models," Journal of Regional Science, Wiley Blackwell, vol. 62(5), pages 1467-1486, November.
    10. Rosa Sanchis-Guarner & Nikodem Szumilo & Antoine Vernet, 2024. "Startup Stations: The Impact of Rail Access on Entrepreneurship (Self-Employment) in England Wales," CESifo Working Paper Series 11227, CESifo.
    11. Marinos, Theocharis & Belegri-Roboli, Athena & Michaelides, Panayotis G. & Konstantakis, Konstantinos Ν., 2022. "The spatial spillover effect of transport infrastructures in the Greek economy (2000–2013): A panel data analysis," Research in Transportation Economics, Elsevier, vol. 94(C).
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    13. Egger, Peter H. & Kaynak, Pinar & Zoller-Rydzek, Benedikt, 2020. "Indirect effects of trade shocks on Turkish local labor markets," Regional Science and Urban Economics, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    Dynamic spatial panel model; Simulation; Prediction; Transport infrastructure; Commuting; Wages; Regional development;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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