The Spatial Distribution of Human and Physical Capital in Integrated Economic Systems: Theory and Implications Sascha Sardadvar St. Petersburg, 3 December 2015

2 Presentation outline  Economic geography and neoclassical growth theory  Presentation of two papers Sardadvar, S. (2013): Does the neoclassical growth model predict interregional convergence? On the impact of free factor movement and the implications for the European Union, Economics and Business Letters 2(4), Sardadvar, S. (2016): Regional economic growth and steady states with free factor movement: theory and evidence from Europe, Région et Développement 43  Conclusions and outlook

3 Economic geography “By ‘economic geography’ I mean ‘the location of production in space’; that is, that branch of economics that worries where things happen in relation to one another.” (Krugman 1991, pp. 1) “Economic geography seeks to explain the riddle of unequal spatial development.” (Combes, Mayer and Thisse 2008, pp. xiii) “Economic geography explicitly integrates the mobility of factors (capital and/or labor).” (Combes, Mayer and Thisse 2008, pp. xiv)

4 Core-periphery relations Myrdal (1957):  Investment flows to advanced regions.  Well educated workers migrate from the periphery to the core. Krugman (1991): Economic integration increases or triggers regional disparities.  The location of firms (physical capital) and workers (labour) becomes endogenous.

5 Neoclassical growth theory Assumptions of standard neoclassical models:  Closed economies  Homogeneous labour  No mobility costs  Convergence hypothesis  Convergence between regions is likely due to similarity (Barro and Sala-i-Martin 1995, López-Bazo 2003).  Labour migration accelerates convergence between regions (Barro and Sala-i-Martin 2004).

6 Human capital Plays a paramount importance in accounting for regional differences in development (Gennaioli et al., 2013). Can result in a major spatial reallocation of factors (Faggian and McCann, 2009). A city’s or a region’s stock of human capital is often the main determinant of its economic and social future (Prager and Thisse, 2012).

7 Features of the models Adopting economic geography’s perspectives to a neoclassical setting:  Microeconomic decisions shape macroeconomic outcomes.  The present allocation of physical and human capital is decisive on future allocations.  The mobility of factors is bounded by distance.  In the long run, disparities with respect to factor allocation prevail.

 Model I: two-region growth relationship with investment flows and labour migration (Sardadvar 2013)  Model II: long-run steady state spatial factor allocation for a system of regions (Sardadvar 2016) 8 Contributions to theory

Production functions Q total output K total physical capital stock H total human capital stock L total labour supply a, b, c output elasticities

Model I: Physical capital accumulation k physical capital stock per worker i, j region indexes s K physical capital investment rate (saving rate) r additional investments (subsidies) λ integration parameter (speed of relocations) q output per worker δ depreciation rate Physical capital investments flow to where expected profits are higher:

Human capital accumulation v human capital wage L t otal labour stock h human capital stock per worker s H human capital investment rate (educational spending rate) Human capital suppliers follow wages, not marginal productivity: The compensation for human capital is received by workers in addition to their compensation for raw labour:

Growth under constant returns …expression is negative if: The interplay of factors in both regions determines one region’s growth: 12

Expression depends on interplay of elasticities and factor endowments: Growth under varying returns

Model II: Factor allocation in N regions Evolution of physical capital stocks: Evolution of human capital stocks: w connectivity between regions μ variable of total flows within the system x share of workers who supply human capital

Human capital’s within-region effect Human capital increases within one region affect its growth positively:

Human capital’s neighbourhood effect Human capital increases in neighbouring regions affect its growth unambiguously negatively:

Long-run output steady-states Long-run steady-state levels (as marked by asterisks) of output are similar across neighbouring regions: 17

Empirics Variance of GRP per inhabitant (logs), 250 EU regions 18

Growth regression Spatial lag of X model (LeSage and Pace, 2009): 19 T number of periods α intercept β, γ regression coefficients ι (N,1) identity vector q (N,1) vector of observations on initial output per labour input h (N,1) vector of observations on human capital endowment W (N, N) spatial weight matrix u (N,1) vector of residuals

Growth regression, 250 EU regions 20 without dummyincluding NMS dummy α0.275 (0.000)0.428 (0.000) (0.817)0.195 (0.000)0.370 (0.000) (0.018) β1β (0.000) (0.000) (0.000) (0.000) (0.000) (0.420) γ1γ (0.000) (0.000)0.019 (0.000)0.020 (0.001)0.020 (0.000) β2β (0.000)0.018 (0.000)0.020 (0.000)0.013 (0.000)0.017 (0.000)0.018 (0.000) γ2γ (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) NMS―――0.018 (0.015)0.013 (0.306)0.019 (0.002) σ2σ R²R² LIK AIC-1, , , , , ,273.81

Steady state regression Spatial Durbin model (LeSage and Pace, 2009): 21 ρ spatial auto-correlation coefficient μ standard regression coefficient α intercept ι (N,1) identity vector q (N,1) vector of observations on initial output per labour input h (N,1) vector of observations on human capital endowment W (N, N) spatial weight matrix u (N,1) vector of residuals

Steady state regression, 250 EU regions α (0.000) (0.872) (0.788) (0.935) (0.858) (0.747) (0.748) (0.286) (0.095) (0.104) (0.116) (0.127) (0.199) (0.146) μ1μ (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) μ1μ (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Direct (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Indirect (0.019) (0.030) (0.118) (0.127) (0.063) (0.211) (0.167) (0.435) (0.960) (0.941) (0.834) (0.727) (0.913) (0.752) Total (0.000) (0.000) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) (0.005) (0.016) (0.011) (0.019) (0.008) (0.016) ρ (0.003) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) σ2σ LIK BP (0.003) (0.011) (0.026) (0.105) (0.176) (0.413) (0.439) (0.409) (0.170) (0.162) (0.162) (0.299) (0.257) (0.835) Wald (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

 Human capital determines a region’s attractiveness for mobile factors, which includes human capital.  Regions with initially high factor endowments benefit from economic integration.  Instruments to support convergence: altering the level of economic integration, compensating disadvantaged regions by subsidies, benefitting from increasing returns (e.g. metropolitan regions), increasing investments and educational spending. 23 Summary of results

24 Conclusions and outlook  The spatial distribution of human capital is both cause and effect of factor relocations.  Under free market forces, factor relocations lead to spatial inequality of factor distribution.  Without state intervention, disparities will prevail in the long run.

25 References Barro, R.J., Mankiw, G., Sala-i-Martin, X.X. (1995): Capital mobility in neoclassical models of growth, American Economic Review 85(1), Barro, R.J., Sala-i-Martin, X.X. (2004): Economic Growth [2nd edition]. New York, McGraw-Hill Combes, P.-P., Mayer, T., Thisse, J.-F. (2008): Economic Geography – The Integration of Regions and Nations. Princeton, Princeton University Press Faggian, A., McCann, P. (2009): Human capital and regional development, in Capello, R., Nijkamp, P. (eds.): Handbook of Regional Growth and Development Theories. Cheltenham and Northampton [MA], Edward Elgar, Gennaioli, N., La Porta, R., Lopez-de-Silanes, F., Shleifer, A. (2013): Human capital and regional development, The Quarterly Journal of Economics 128(1), Krugman, P. (1991): Geography and Trade [reprint 1992]. Leuven and Cambridge [MA], Leuven University Press LeSage, J., Pace, R.K., (2009): Introduction to Spatial Econometrics. Boca Raton, London and New York, CRC Press López-Bazo, E. (2003): Growth and convergence across economies: the experience of the European regions, in Fingleton, B., Eraydin, A., Paci, R. (eds.): Regional Economic Growth, SMEs and the Wider Europe. Aldershot and Burlington, Ashgate, Myrdal, G. (1957): Economic Theory and Under-Developed Regions [German edition 1974]. Frankfurt/Main, Fischer Taschenbuch Verlag Prager, J.C., Thisse, J.F. (2012): Economic Geography and the Unequal Development of Regions. Abingdon and New York, Routledge Sardadvar, S. (2013): Does the neoclassical growth model predict interregional convergence? On the impact of free factor movement and the implications for the European Union, Economics and Business Letters 2(4), Sardadvar, S. (2016): Regional economic growth and steady states with free factor movement: theory and evidence from Europe, Région et Développement 43

Model simulation regions: A, B, …, L, a = 0.3, b = 0.2, δ = 0.05, s K = 0.25, s H = 0.15, λ = 0.1

Long-run human capital distribution Human capital (logs) Periods 27 Regions:

Simulation: output distribution Output (logs) Periods Regions: 28

Simulation: physical capital distribution Physical capital (logs) Periods regions: 29