Testing for Causality between Productivity and Agglomeration Economies

The productivity effects of agglomeration economies are often treated as endogenous in empirical work due to the potential for reverse causality. The extent to which these relationships are actually simultaneously determined, however, remains largely unobserved. This paper estimates panel data vector autoregressions for different sectors of the economy to test for bidirectional causality between productivity and both localization and urbanization economies. The aim is to address some key questions that will help to identify the extent of the endogeneity problem. Can we actually observe bidirectionality in the data? Does it feature more for some industries than for others? Is it more prevalent for localization or urbanization economies? The results show that agglomeration economies are not strictly unidirectional and that higher levels of productivity can induce growth in the scale of local urban and industrial environments. The paper discusses the difficulties that these issues pose for the estimation of agglomeration economies.

[1]  Paolo Veneri,et al.  Spatial Structure and Productivity in Italian NUTS-3 Regions , 2011 .

[2]  Daniel J. Graham,et al.  Estimating the agglomeration benefits of transport investments: some tests for stability , 2011 .

[3]  D. Graham.  Identifying urbanisation and localisation externalities in manufacturing and service industries , 2009 .

[4]  S. Rosenthal,et al.  The attenuation of human capital spillovers , 2008 .

[5]  Laurent Gobillon,et al.  Estimating Agglomeration Economies with History, Geology, and Worker Effects , 2008 .

[6]  Marius Brülhart,et al.  Sectoral Agglomeration Economies in a Panel of European Regions , 2007 .

[7]  Eleonora Patacchini,et al.  Wages and the City - Evidence from Italy , 2007 .

[8]  Paolo Naticchioni,et al.  Urbanization Externalities, Market Potential and Spatial Sorting of Skills and Firms , 2005 .

[9]  B. Fingleton.  The new economic geography versus urban economics : an evaluation using local wage rates in Great Britain , 2006 .

[10]  Kari Hämäläinen,et al.  Regional Labor Market Dynamics, Housing, and Migration , 2004 .

[11]  A. Venables,et al.  Spatial Determinants of Productivity: Analysis for the Regions of Great Britain , 2004 .

[12]  P. Combes,et al.  Spatial Wage Disparities: Sorting Matters! , 2004 .

[13]  S. Rosenthal,et al.  Evidence on the nature and sources of agglomeration economies , 2004 .

[14]  B. Fingleton.  Increasing returns: evidence from local wage rates in great britain , 2003 .

[15]  G. Duranton,et al.  Micro-Foundations of Urban Agglomeration Economies , 2003 .

[16]  Jerry A. Hausman,et al.  Weak Instruments: Diagnosis and Cures in Empirical Econometrics , 2003 .

[17]  Giordano Mion,et al.  Spatial externalities and empirical analysis: the case of Italy , 2004 .

[18]  Clive G. Bowsher.  On testing overidentifying restrictions in dynamic panel data models , 2002 .

[19]  Jacques-François Thisse,et al.  Economics of Agglomeration: Cities, Industrial Location, and Regional Growth , 2002 .

[20]  J. Henderson,et al.  Marshall's scale economies , 2001 .

[21]  Gaetano Carmeci,et al.  Fixed investment and economic growth: new results on causality , 2001 .

[22]  Stephen Bond,et al.  Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator , 2000 .

[23]  A. Ciccone,et al.  Agglomeration-Effects in Europe , 1999 .

[24]  Jan P. A. M. Jacobs,et al.  Output effects of infrastructure investment in the Netherlands, 1853-1913 , 1999 .

[25]  Stephen Bond,et al.  GMM Estimation with persistent panel data: an application to production functions , 1999 .

[26]  R. Eberts,et al.  Agglomeration Economies and Urban Public Infrastructure , 1999 .

[27]  Gordon H. Hanson.  Market Potential, Increasing Returns, and Geographic Concentration , 1998 .

[28]  Bent E. Sørensen,et al.  GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study , 1996 .

[29]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .

[30]  M. Arellano,et al.  Another look at the instrumental variable estimation of error-components models , 1995 .

[31]  Helmut Seitz,et al.  The productivity and supply of urban infrastructures , 1995 .

[32]  David A. Jaeger,et al.  Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak , 1995 .

[33]  J. Stock,et al.  Instrumental Variables Regression with Weak Instruments , 1994 .

[34]  J. Tatom.  Is an Infrastructure Crisis Lowering the Nation's Productivity? , 1993 .

[35]  Helmut Seitz,et al.  A dual economic analysis of the benefits of the public road networks , 1993 .

[36]  R. Hall,et al.  Productivity and the Density of Economic Activity , 1993 .

[37]  M. Arellano,et al.  Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations , 1991 .

[38]  W. Newey,et al.  Estimating vector autoregressions with panel data , 1988 .

[39]  J. Henderson,et al.  Efficiency of resource usage and city size , 1986 .

[40]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .