Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis


A dynamic panel data (DPD) model is estimated to assess the contribution of the average schooling years, the education expenditure and the inventive coefficient—as an approximation for innovation—to the increased productivity of the Mexican states. The potential difficulties of endogeneity and serial correlation are controlled by adopting system General Method of Moments (GMM) procedures. The findings are compatible with the theory. The importance of the lags is confirmed and the positive and significant impacts on productivity tend to vary according to the income level and the geographical location of the regions. Innovation is an important contributor to northern, central and richer states’ productivity, but education expenditure is important for the poorer states and scholarly attainment stands out in the southern states. The analysis emerging from the model concludes that these regional differences should be seen as a potential opportunity for designing customized policies capable of increasing the productivity and not as a weakness.

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German-Soto, V. and Flores, L. (2015) Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis. Theoretical Economics Letters, 5, 44-55. doi: 10.4236/tel.2015.51008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Becker, G. (1962) Investment in Human Capital: A Theoretical Analysis. Journal of Political Economy, 70, 9-49.
[2] Schultz, T. (1963) Economic Value of Education. Columbia University Press, New York.
[3] Chevalier, A., Harmon, C., Walker, I. and Zhu, Y. (2004) Does Education Raise Productivity, or Just Reflect It? The Economic Journal, 114, F499-F517.
[4] Paus, E.A. (2004) Productivity Growth in Latin America: The Limits of Neoliberal Reforms. World Development, 32, 427-445.
[5] Power, D. and Malmberg, A. (2008) The Contribution of Universities to Innovation and Economic Development: In What Sense a Regional Problem? Cambridge Journal of Regions, Economy and Society, 1, 233-245.
[6] Kaldor, N. and Mirrless, J.A. (1962) A New Model of Economic Growth. The Review of Economic Studies, 29, 174-192.
[7] Crespi, F. and Pianta, M. (2008) Demand and Innovation in Productivity Growth. International Review of Applied Economics, 22, 655-672.
[8] Pencavel, J. (1991) Higher Education, Productivity, and Earnings: A Review. Journal of Economic Education, 22, 331-359.
[9] Mayhew, K. and Neely, A. (2006) Improving Productivity—Opening the Black Box. Oxford Review of Economic Policy, 22, 445-456.
[10] Vieira, E., Neira, I. and Vázquez, E. (2011) Productivity and Innovation Economy: Comparative Analysis of European NUTS-2, 1995-2004. Regional Studies, 45, 1269-1286.
[11] AlAzzawi, S. (2012) Innovation, Productivity and Foreign Direct Investment-Induced R&D Spillovers. The Journal of International Trade & Economic Development, 21, 615-653.
[12] Harris, R., Li, Q.C. and Moffat, J. (2013) The Impact of Higher Institution-Firm Knowledge Links on Establishment-Level Productivity in British Regions. The Manchester School, 81, 143-162.
[13] German-Soto, V., Gutiérrez, L. and Tovar Montiel, S.H. (2009) Factores y relevancia geográfica del proceso de innovación regional en México, 1994-2006. Estudios Económicos, 24, 225-248.
[14] Ríos Bolívar, H. and Marroquín Arreola, J. (2013) Innovación tecnológica como mecanismopara impulsar el crecimiento económico. Evidencia regional para México. Contaduría y Administración, 58, 11-37.
[15] Cabral, R. and González, F.J. (2014) Gasto en investigación y desarrollo y productividad en la industria manufacturera mexicana. Estudios Económicos, 29, 27-55.
[16] Brown, F. and Guzmán, A. (2014) Innovation and Productivity across Mexican Manufacturing Firms. Journal of Technology Management & Innovation, 9, 36-52.
[17] Crespi, G. and Zuniga, P. (2010) Innovation and Productivity: Evidence from Six Latin American Countries. IDB Working Paper Series No. IDB-WP-218.
[18] Mungaray, A. and Ramírez, M. (2007) Human Capital and Productivity in Microenterprises. MPRA Paper No. 4064.
[19] Koyck, L.M. (1954) Distributed Lags and Investment Analysis. North-Holland, Amsterdam.
[20] Baltagi, B.H. (2008) Econometric Analysis of Panel Data. John Wiley & Sons Ltd., Chichester.
[21] Arellano, M. and Bond, S. (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58, 277-297.
[22] Ahn, S.C. and Schmidt, P. (1995) Efficient Estimation of Models for Dynamic Panel Data. Journal of Econometrics, 68, 5-27.
[23] Arellano, M. and Bover, O. (1995) Another Look at the Instrumental Variable Estimation of Error-Component Models. Journal of Econometrics, 68, 29-52.
[24] Blundell, R. and Bond, S. (1998) Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87, 115-143.
[25] Blundell, R. and Bond, S. (2000) GMM Estimation with Persistent Panel Data: An Application to Production Functions. Econometric Reviews, 19, 321-340.
[26] Blundell, R., Bond, S. and Windmeijer, F. (2000) Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator. Advances in Econometrics, 15, 53-91.
[27] Andrews, D.W.K. and Lu, B. (2001) Consistent Model and Moment Selection Procedures for GMM Estimation with Application to Dynamic Panel Data Models. Journal of Econometrics, 101, 123-164.
[28] Arellano, M. and Bond, S. (1998) Dynamic Panel Data Estimation Using DPD98 for Gauss: A Guide for Users.

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