The Impact of R&D Expenditures on Firms’ Productivity in the Formal Private Manufacturing Sector in the Eastern Europe and Central Asia Economies and in the Middle East and North Africa Region

Salem Gheit

Abstract


This paper examines - through the lens of theory – the impact of R&D spending on productivity in small and medium enterprises SME’s in the Middle East and North Africa & Eastern Europe and Central Asia nations, using the BEEPS 2013 database. The aim is to contribute to the existing empirical work and drawing the conclusions from the firm level ECA and MENA dataset about the impact of R&D on labour productivity particularly in the manufacturing sector in more than 4000 firms across 28 nations in the ECA region and 3275 firms throughout 9 countries in the MENA region.

To better allow for firm heterogeneity in the analysis, the study adopted two types of matching analysis. The propensity score matching (PSM) and the Mahalanobis distance matching (MDM). The findings of this paper show that there is statistically significant impact of R&D spending (Treatment) on firms performance proxied by output per worker as the (Outcome) variable. 

The novelty of this research is inspired  by the argument of (King and Nielsen, 2016). This is where they suggested that the propensity score matching techniques, could approximate a low-standard experimental design, and could ignore much of the potentially useful information without efficient use, leaving us with higher imbalance, model dependence, and ultimately bias. Thus, these recent developments in the matching methods suggested that the conclusions drawn from PSM analysis are best supported by a second estimator, such as MDM, which has the property of double robustness, and reduces imbalance, model dependence, and bias.

To a great extent, the above (King and Nielsen, 2016) argument is proved to be true. This is where the impact of R&D expenditures on firms’ productivity was found to be statistically significant – as mentioned above – in the outcomes resulted from using both the PSM and MDM, but thedifference is that the imbalance in the confounding covariates, appeared to be extremely lower, and the bias reduction reached 100% in some covariates in the MDM outcomes compared to the PSM results.


Keywords


Productivity;R&D Expenditures;Propensity score matching; Mahalanobis distance matching.

Full Text:

PDF

References


References

Aghion, P. and Howitt, P. (1992) 'A model of growth through creative destruction', Econometrica, 60, pp. 323-351.

Agnew, C. E. and Wise, D. E. 'The Impact of R&D on productivity: A preliminary Report'. 1978.

Aizcorbe, M., Moylan, C. E. and Robbins, C. A. (2009) 'Toward better measurement of innovation and intangibles'.

Austin, P. C. (2009) 'Some Methods of Propensity‐Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulations', Biometrical journal, 51(1), pp. 171-184.

Austin, P. C. (2011) 'An introduction to propensity score methods for reducing the effects of confounding in observational studies', Multivariate behavioral research, 46(3), pp. 399-424.

Bartelsman, E., Haltiwanger, J. and Scarpetta, S. (2009) 'Measuring and analyzing cross-country differences in firm dynamics', Producer dynamics: New evidence from micro data: University of Chicago Press, pp. 15-76.

Beck, T. and Demirguc-Kunt, A. (2006) 'Small and medium-size enterprises: Access to finance as a growth constraint', Journal of Banking & finance, 30(11), pp. 2931-2943.

Blundell, R. and Dias, M. C. (2002) 'Alternative approaches to evaluation in empirical microeconomics', Portuguese economic journal, 1(2), pp. 91-115.

Box, G. E. P. and William, G. Hunger and J. Stuart Hunter. 1978. Statistics for Experimenters. New York: Wiley-Interscience.

Branch, B. (1974) 'Research and development activity and profitability: a distributed lag analysis', Journal of Political Economy, 82(5), pp. 999-1011.

Burgette, L., Griffin, B. A. and McCaffrey, D. (2017) 'Propensity scores for multiple treatments: A tutorial for the mnps function in the twang package', R package. Rand Corporation.

Caliendo, M. and Hujer, R. (2006) '14 The Microeconometric Estimation of Treatment Effects-An Overview', Econometric Analysis, pp. 199.

Chan, L. K. C., Lakonishok, J. and Sougiannis, T. (2001) 'The stock market valuation of research and development expenditures', The Journal of Finance, 56(6), pp. 2431-2456.

Corrado, C., Hulten, C. and Sichel, D. (2009) 'Intangible capital and US economic growth', Review of income and wealth, 55(3), pp. 661-685.

Dole, E. 1989. The Impact of Research and Development on Productivity Growth. In: Norwood, J.L. (ed.). Department of Labor, United States of America.

Evenson, R. E. R. E. (1968) 'The Contribution of agricultural research and extension to agricultural production'.

Fixler, D. 'Accounting for R&D in the National Accounts'. ASSA meetings in San Francisco.

Greevy, R., Lu, B., Silber, J. H. and Rosenbaum, P. (2004) 'Optimal multivariate matching before randomization', Biostatistics, 5(2), pp. 263-275.

Griffith, R. (2000) 'How important is business R&D for economic growth and should the government subsidise it?'.

Griliches, Z. (1973) 'Research expenditures and growth accounting', Science and technology in economic growth: Springer, pp. 59-95.

Griliches, Z. (1992) 'The Search for R&D Spillovers', Scandinavian Journal of Economics, 94, pp. S29-47.

Griliches, Z. and Lichtenberg, F. R. (1984) 'R&D and productivity growth at the industry level: is there still a relationship?', R&D, patents, and productivity: University of Chicago Press, pp. 465-502.

Heckman, J. J., Ichimura, H. and Todd, P. E. (1997) 'Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme', The review of economic studies, 64(4), pp. 605-654.

Heinrich, C., Maffioli, A. and Vazquez, G. (2010) A primer for applying propensity-score matching: Inter-American Development Bank.

Ho, D. E., Imai, K., King, G. and Stuart, E. A. (2007) 'Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference', Political analysis, 15(3), pp. 199-236.

Iacus, S. M., King, G. and Porro, G. (2011) 'Multivariate matching methods that are monotonic imbalance bounding', Journal of the American Statistical Association, 106(493), pp. 345-361.

Imai, K., King, G. and Nall, C. (2009) 'The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation', Statistical Science, 24(1), pp. 29-53.

Imai, K., King, G. and Stuart, E. A. (2008) 'Misunderstandings between experimentalists and observationalists about causal inference', Journal of the royal statistical society: series A (statistics in society), 171(2), pp. 481-502.

Jones, C. I. (1995) 'R & D-based models of economic growth', Journal of political Economy, 103(4), pp. 759-784.

King, G. and Nielsen, R. (2016) 'Why propensity scores should not be used for matching', Copy at http://j. mp/1sexgVw Download Citation BibTex Tagged XML Download Paper, 378.

King, G., Nielsen, R., Coberley, C., Pope, J. E. and Wells, A. (2011) 'Comparative effectiveness of matching methods for causal inference', Unpublished manuscript, 15.

Kortum, S. S. (1997) 'Research, patenting, and technological change', Econometrica: Journal of the Econometric Society, pp. 1389-1419.

Lantz, J.-S. and Sahut, J.-M. (2005) 'R&D investment and the financial performance of technological firms', International Journal of Business, 10(3), pp. 251.

Link, A. N. (1978) 'Rates of induced technology from Investments in research and development', Southern Economic Journal, pp. 370-379.

Mansfield, E. (1972) Research and innovation in the modern corporation. Springer.

Manual, O. F. 2003. Proposed Standard Practice for Surveys on Research and Experimental Development. OECD Publishing.

McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R. and Burgette, L. F. (2013) 'A tutorial on propensity score estimation for multiple treatments using generalized boosted models', Statistics in medicine, 32(19), pp. 3388-3414.

Morgan, S. L. and Winship, C. (2014) Counterfactuals and causal inference. Cambridge University Press.

Muehler, G., Beckmann, M. and Schauenberg, B. (2007) 'The returns to continuous training in Germany: new evidence from propensity score matching estimators', Review of Managerial Science, 1(3), pp. 209-235.

Nadiri, M. I. and Bitros, G. C. (1980) 'Research and development expenditures and labor productivity at the firm level: A dynamic model', New Developments in Productivity Measurement: University of Chicago Press, pp. 387-418.

Pakes, A. and Griliches, Z. (1984a) 'Estimating distributed lags in short panels with an application to the specification of depreciation patterns and capital stock constructs', The Review of Economic Studies, 51(2), pp. 243-262.

Pakes, A. and Griliches, Z. (1984b) 'Patents and R&D at the firm level: a first look', R&D, patents, and productivity: University of Chicago Press, pp. 55-72.

Parham, D. 'Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement'. 2006, 16-18.

Parham, D. 'Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement'. 2006, 16-18.

Piesse, J. and Thirtle, C. (2000) 'A stochastic frontier approach to firm level efficiency, technological change, and productivity during the early transition in Hungary', Journal of comparative economics, 28(3), pp. 473-501.

Ravenscraft, D. and Scherer, F. M. (1982) 'The lag structure of returns to research and development', Applied economics, 14(6), pp. 603-620.

Rosenbaum, P. R. and Rubin, D. B. (1985) 'Constructing a control group using multivariate matched sampling methods that incorporate the propensity score', The American Statistician, 39(1), pp. 33-38.

Rubin, D. B. (1973) 'Matching to remove bias in observational studies', Biometrics, pp. 159-183.

Rubin, D. B. (1976) 'Inference and missing data', Biometrika, 63(3), pp. 581-592.

Rubin, D. B. and Stuart, E. A. (2006) 'Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions', The Annals of Statistics, pp. 1814-1826.

Rubin, D. B. and Thomas, N. (2000) 'Combining propensity score matching with additional adjustments for prognostic covariates', Journal of the American Statistical Association, 95(450), pp. 573-585.

Scherer, F. M. (1981) Research and Development, Patenting and the Micro-structure of Productivity Growth. University of Northwestern.

Scherer, F. M. (1982) 'Inter-industry technology flows and productivity growth', The review of economics and statistics, pp. 627-634.

Schmookler, J. (1965) 'Technological change and economic theory', The American Economic Review, 55(1/2), pp. 333-341.

Stuart, E. A. (2010) 'Matching methods for causal inference: A review and a look forward', Statistical science: a review journal of the Institute of Mathematical Statistics, 25(1), pp. 1.

Stuart, E. A. and Rubin, D. B. (2008) 'Best practices in quasi-experimental designs', Best practices in quantitative methods, pp. 155-176.

Sveikauskas, L. (1981) 'Technological inputs and multifactor productivity growth', The Review of Economics and Statistics, pp. 275-282.

Terleckyj, N. E. (1974) Effects of R&D on the productivity growth of industries: an exploratory study. National Planning Association.

Terleckyj, N. E. (1982) 'R&D and US Industrial Productivity in the 1970s in The Transfer and Utilization of Technical Knowledge edited by D', Sahal. Lexington, MA: Lexington Books.

Zhu, Z. and Huang, F. (2012) 'The Effect of R&D Investment on Firms’ Financial Performance: Evidence from the Chinese Listed IT Firms', Modern Economy, 3(08), pp. 915.


Refbacks

  • There are currently no refbacks.