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Small Business Economics

Erschienen in:

01.04.2010

Economic performance of Brazilian manufacturing firms: a counterfactual analysis of innovation impacts

verfasst von: Sérgio Kannebley Jr., Júlia Valeri Sekkel, Bruno C. Araújo

Erschienen in: Small Business Economics | Ausgabe 3/2010

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Abstract

This article assesses if innovators outperform non-innovators in Brazilian manufacturing during 1996–2002. To do so, we begin with a simple theoretical model and test the impacts of technological innovation (treatment) on innovating firms (treated) by employing propensity score matching techniques. Correcting for the survivorship bias in the period, it was verified that, on an average, the accomplishment of technological innovations produces positive and significant impacts on the employment, the net revenue, the labor productivity, the capital productivity, and market share of the firms. However, this result was not observed for the mark-up. Especially, the net revenue reflects more robustly the impacts of the innovations. Quantitatively speaking, innovating firms experienced a 10.8–12.5 percentage points (p.p. henceforth) higher growth on employment, a 18.1–21.7 p.p. higher growth on the net revenue, a 10.8–11.9 p.p. higher growth on labor productivity, a 11.8–12.0 p.p. higher growth on capital productivity, and a 19.9–24.3 p.p. higher growth on their market share, relative to the average of the non-innovating firms in the control group. It was also observed that the conjunction of product and process innovations, relative to other forms of innovation, presents the stronger impacts on the performance of Brazilian firms.

Vorheriger Artikel Patterns of ethnic self-employment in time and space: evidence from British Census microdata

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It means that the validity of the innovation impacts on firm’s performance does not critically depend on the formulation of the structural equations.

In comparison with an alternative specification of the model, based on a constant elasticity curve for the inversed demand function, given by p = H(q ₁ q ₂)^−1/η and on a Cobb-Douglas production function q _i = A _i L ^a K ^b, i = 1, 2, the only different result is the one for the mark-up variable, if respected the conditions of decreasing returns to scale. In the second alternative specification, the mark-up becomes, obviously, invariant given the supposition of constant elasticity of demand.

This section is based on papers of Dehejia and Wahba (1998), Abadie et al. (2001) and Cameron and Trivedi (2005).

This is called independence of treatment assignment. The output for the control group (not treated) is \({y}^{0} \bot \hbox{INOV}\left| {x_i} \right..\) The PSM relies on this assumption.

The construction of the capital stock variable is based on the method of perpetual inventory, using data from Annual Survey of Manufacturing firms (PIA). The survivorship probability model is similar to the one described in De Negri et al. (2007).

According to Ashenfelter (1975) and Ashenfelter and Card (1985) apud Dehejia and Wahba (1998), the use of more than one pretreatment period is essential to an improved estimation of treatment effect.

The radius matching assumes that observations have fixed weights that disable the adoption of sample weighting in ATET estimation. In this article the distance (radius) of the neighborhood is 0.01 which is the maximum value allowed for the difference between distances in the propensity scores of treated and untreated units.

The unit’s weight for kernel function is assigned by the bandwidth parameter between treated and untreated units. However, our sample has its own sampling weight (Pintec-2000), which is also used in the matching procedure and not only in those observations belonging to that distance. This distance was limited to 0.06.

There are two strata in the PIA survey. The first stratum comprises a non-random sample of all Brazilian manufacturing firms with more than 30 employees. The second stratum is a randomly selected sample composed by firms with 5–29 employees.

Following the survivorship model estimated in De Negri et al. (2007), the identification variable for the selection equation was the log of the ratio of financial expenses (including factoring) to net revenue—this variable was separated in four quantiles categories.

One must remember that, in general, the growth on the market share of innovators happens at the expenses of the decrease on the market share of non-innovators.

Abadie, A., Drukker, D., Herr, J. L., & Imbens, G. W. (2001). Implementing matching estimators for average treatment effects in Stata. The Stata Journal, 1, 1–18.

Ashenfelter, O. (1975). The effect of manpower training on earnings: Preliminary results. In Industrial Relations Research Association, Proceedings of the twenty-seventh winter meeting (pp. 252–260). Madison, Wis.: IRRA.

Ashenfelter, O., & Card, D. (1985). Using the longitudinal structure of earnings to estimate the effect of training programs. The Review of Economics and Statistics, 67(4), 648–660.CrossRef

Benavente, J. M., & Lauterbach, R. (2006). Technological innovation and employment: Complements or substitutes? Serie Documentos de Trabajo No. 221. Universidad de Chile.

Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. NY, USA: Cambridge University Press.

Crepon, B., Duguet, E., & Mairesse, J. (1998). Research, innovation, and productivity: An econometric analysis at the firm level. NBER Working Paper No. 6696.

De Negri, J., Esteves, L., & Freitas, F. (2007). Knowledge production and firm growth in Brazil. Paper presented at the Micro Evidence on Innovation in Developing Economies Conference, Maastricht.

Dehejia, R. H., & Wahba, S. (1998). Propensity score matching methods for non-experimental studies causal studies. NBER Working Paper Series, No. 6829, Cambridge.

Freel, M. S. (2000). Do small innovating firms outperform non-innovators? Small Business Economics, 14(3), 195–210.CrossRef

Gerosky, P., & Machin, S. (1992). Do innovating firms outperform non-innovators? Business Strategy Review, 3(2), 79–90.CrossRef

Goedhuys, M. (2007). The impact of innovation activities on productivity and firm growth: Evidence from Brazil. UNU – MERIT, Working Paper Series No. 2007-2, Maastrich.

Huergo, E., & Jaumandreu, J. (2004). Firm’s age, processes innovation and productivity growth. International Journal of Industrial Organization, 22(4), 541–559.CrossRef

Jaumandreu, J. (2003). Does innovation spur employment: A firm-level analysis using Spanish CIS data. Mimeo, Universidad Carlos III de Madrid, Madrid.

Kemp, R., Folkeringa, M., Jong, J. & Wubben, E. (2003). Innovation and firm performance: Differences between small and medium-sized firms. Research Report H200207, SCientific AnaLysis of Entrepreneurship and SMEs, Netherlands.

Titel: Economic performance of Brazilian manufacturing firms: a counterfactual analysis of innovation impacts
verfasst von: Sérgio Kannebley Jr.
Júlia Valeri Sekkel
Bruno C. Araújo
Publikationsdatum: 01.04.2010
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 3/2010
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-008-9118-x

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