Human Capital Composition and Economic Growth at a Regional Level.

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Research Institute of Applied Economics 2009 Working Papers 2009/13, 44 pages Human Capital Composition and Economic Growth at a Regional Level. By Fabio Manca 1 1 UNIVERSIDAD DE BARCELONA, Dept. Econometría, Estadística y Economía Española, Facultad de Economía y Empresa AQR-IREA, Avenida Diagonal, 690, 08034 Barcelona. Email: fabio.manca@ub.edu, tel: +34 934035886 Abstract: With this paper we build a two-region model where both innovation and imitation are performed. In particular imitation takes the form of technological spillovers that lagging regions may exploit given certain human capital conditions. We show how the high skill content of each region s workforce (rather than the average human capital stock) is crucial to determine convergence towards the income level of the leader region and to exploit the technological spillovers coming from the frontier. The same applies to bureaucratic/institutional quality which are conductive to higher growth in the long run. We test successfully our theoretical result over Spanish regions for the period between 1960 and 1997. We exploit system GMM estimators which allow us to correctly deal with endogeneity problems and small sample bias. -Very very preliminary, do not quote... Key words: Human Capital, Growth, Catch-Up. JEL codes: O38, O33. 1

Table 1 Dependent Variable: Regional GDP gap Pooled OLS Pooled OLS Pooled OLS Log Initial GDP.932 (.023)*** (i) (ii) (iii).916 (.023)***.907 (.023)*** HK32 higher education, second -.017 (.019) HK3 higher education, first.029 (.023) HK22 upper secondary.007 (.031) HK21 lower secondary -.044 (.020)** HK1 Primary -.052 (.019)** C.084 (.028)**.023 (.010)** -.044 (.011)**.028 (.010)**.031 (.014)** -.044 (.017)**.021 (.010)** R2 0.97 0.90 0.97 n. Obs 119 119 119 ***, ** Statistically significant respectively at 1%, 5% Standard errors are corrected for heteroskedasticity and reported in parenthesis.

Table 2 Dependent Variable: Regional GDP gap System GMM one-step Log Initial GDP.779 (.077)*** System GMM one-step System GMM two-step windmeijer robust (i) (ii) (iii).692 (. 056 )***.701 (.200)*** HK32-higher education, second.022 (.006)***.025 (. 004)***.007 (.035)** HK31-higher education, first -.094 (.077)*** -.075 (.014)*** -.024 (.009)** HK22-upper secondary.016 (.002)***.014 (.003)***.006 (.002)*** Hk21-lower secondary -.004 (.004) -.002 (.003).005 (.008) HK1-primary -.003 (.002) -.004 (.001)*** -.001 (.001) C -2.41 (.271)*** -2.35 (.212)*** -2.44 (.381)*** Arellano-bond test AR(2) p-values 0.129 0.207 0.155 Hansen test for Over-ID 0.816 1.00 0.219 n. Instruments 25 42 19 Time dummies No No Yes n. Obs 119 119 119 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finite-sample covariance matrix.

Table 3 Dependent Variable: Regional GDP gap System GMM one-step Log Initial GDP.118 (.090) System GMM one-step System GMM two-step windmeijer robust (i) (ii) (iii).791 (. 047 )***.777 (.045)*** HK32+HK31 higher education, (second+first).012 (.005)**.005 (. 001)***.005 (.001)** HK22+HK21+HK1 Upper+Lower secondary + primary.003 (.005) -.000 (.000).000 (.000) C -0.861 (.416)** -2.56 (.153)*** -2.51 (.137)*** Arellano-bond test AR(2) p-values 0.054 0.338 0.319 Hansen test for Over-ID 0.297 0.783 0.783 n. Instruments 18 13 13 Time dummies No Yes Yes n. Obs 119 119 119 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finite-sample covariance matrix.

Table 4 Dependent Variable: Regional GDP gap System GMM one-step (i) Log Initial GDP.538 (.071)*** System GMM two-step windmeijer robust (iii).468 (.109)*** HK32 higher education.019 (.007)**.022 (.009)** HK31+HK22 Intermediate education.003 (.005).006 (.007) HK21 Lower secondary education -.001 (.005) HK1 Basic education.006 (.002)** C -2.17 (.182)** -.001 (.003).007 (.002)** -2.51 (.189)*** Arellano-bond test AR(2) p-values 0.108 0.504 Hansen test for Over-ID 0.839 0.839 n. Instruments 26 26 Time dummies No No n. Obs 119 119 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finitesample covariance matrix.

Table 5 Dependent Variable: Provinces GDP gap System GMM one-step Log Initial GDP System GMM two-step windmeijer robust System GMM one-step (i) (ii) (iii) (iv).568 (.047)***.544 (.059)***.592 (.049)*** System GMM two-step windmeijer robust.567 (.050)*** HK5 higher, secondary.298 (.047)***.298 (.058)***.227 (.064)***.252 (.070)*** HK4 vocational training -.336 (.051)*** -.340 (.061)*** -.379 (.103)*** -.403 (.118)*** HK3_secundary -.072 (.023)*** -.064 (.028)***.004 (.057).006 (.052) HK2-primary.161 (.022)***.154 (.023)***.186 (.033)***.187 (.034)*** Social Capital.001 (.000)***.001 (.000)*** C -4.39 (.306)*** -4.22 (.362)*** -4.82 (.457)*** -4.65 (.426)*** Arellano-bond test AR(2) p-values 0.393 0.447 0.566 0.789 Hansen test for Over-ID 0.133 0.133 0.202 0.202 n. Instruments 40 40 44 44 Time dummies No No No No n. Obs 400 400 250 250 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finite-sample covariance matrix.

Table 6 Dependent Variable: Provinces GDP gap System GMM two-step windmeijer robust System GMM two-step windmeijer robust Difference GMM two-step windmeijer robust Difference GMM two-step windmeijer robust (i) (ii) (iii) (iv) Log Initial GDP.600 (.051)***.777 (.055)***.266 (.025)***.131 (.062)** HK5 Higher education.101 (.034)**.183 (.051)***.068 (.023)***.214 (.053)*** HK3+HK4 Intermediate education -.432 (.080)*** -.698 (.123)*** -.475 (.072)*** -.531 (.185)*** HK2 Basic education.411 (.051)***.550 (.079)***.069 (.079)**.241 (.097)*** Social Capital.001 (.000)***.000 (.000) C -4.19 (.438)*** -4.19 (.438)*** - - Arellano-bond test AR(2) 0.129 0.155 0.311 0.380 p-values Hansen test for Over-ID 0.270 0.254 0.405 0.862 n. Instruments 47 44 50 19 Time dummies No No No No n. Obs 400 250 350 200 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finite-sample covariance matrix.

Table 7 Dependent Variable: Provinces GDP gap Log Initial GDP System GMM one-step System GMM two-step windmeijer robust Difference GMM one-step Difference GMM two-step windmeijer robust System GMM two-step windmeijer robust (i) (ii) (iii) (iv) (v) (vi).92 (. 022 )***.925 (.022)***.271 (.026)***.269 (. 029 )***.111 (.047)*** Difference GMM two-step windmeijer robust.007 (.029) HK4+HK5 Higher education, (second+first).034 (. 028).034 (.023).035 (.021)**.036 (. 025).049 (.024)**.129 (.025)*** HK2+HK3 secondary+ primary -.045 (.027)* -.047 (.024)** -.392 (.079)*** -.047 (.009)***.062 (.051) -.390 (.151)** Social capital.001 (.007) -.006 (.009) C -6.52 (.182)*** -6.50 (.188)*** - - -1.56 (.368)*** - Arellanobond test AR(2) 0.926 0.909 0.215 0.222 0.660 0.419 p-values Hansen test for Over-ID 0.672 0.672 0.112 0.112 0.038 0.531 n. Instruments 42 42 37 37 28 14 Time dummies Yes Yes No No No No n. Obs 400 400 400 400 250 200 Note: ***, **,* Statistically significant respectively at 1%, 5% and 10%. Robust standard error estimates are consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels standard errors and are reported in parenthesis. Two-step System GMM are corrected as in Windmeijer (2005) for finite-sample covariance matrix.

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