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

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.

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Notes

  1. 1.

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

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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).

  5. 5.

    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.

  6. 6.

    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.

  7. 7.

    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.

  8. 8.

    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.

  9. 9.

    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.

  10. 10.

    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.

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Acknowledgements

The authors are grateful to the comments and suggestions from Patrick Alves, Daniel Da Mata and Danilo Coelho, and also to the methodological contributions of Fernando Freitas. The first author thanks CNPQ for research fellowship (project nº 303217/2005-7). All errors remain ours.

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Correspondence to Sérgio Kannebley Jr..

Appendix

Appendix

Logit model results: probability of the firm assigns treatment—innovation activity and covariates (mean 1996–1997, t−1)

Dependent variable/covariates (t−1) Inov Inovproc Inovprod Inovprod mark Inovprod firm Inovproc mark Inovproc firm Prodmark
procmark
Prodfirm
procfirm
Prodmark
procfirm
Prodfirm
procmark
Employment (log) 0.204***
(0.063)
0.12
(0.079)
0.151
(0.111)
0.314*
(0.161)
0.087
(0.127)
−0.004
(0.157)
0.142*
(0.084)
0.851***
(0.156)
0.182**
(0.090)
0.449***
(0.148)
0.279
(0.244)
Years of education (log) 0.468*
(0.170)
0.113
(0.194)
0.911***
(0.306)
2.02***
(0.487)
0.423
(0.326)
0.149
(0.453)
0.068
(0.202)
1.48***
(0.390)
0.782*
(0.255)
1.13***
(0.412)
0.813*
(0.418)
Labor productivity (log) 0.271***
(0.072)
0.077
(0.075)
0.240**
(0.103)
0.469**
(0.184)
0.177
(0.112)
0.575***
(0.173)
0.032
(0.078)
0.616***
(0.141)
0.356**
(0.141)
0.821***
(0.160)
0.358
(0.243)
Capital productivity (log) 0.126*
(0.075)
0.004
(0.079)
0.020
(0.111)
0.068
(0.210)
−0.006
(0.122)
0.402**
(0.171)
−0.040
(0.082)
0.160
(0.205)
−0.250**
(0.114)
−0.036
(0.161)
−0.021
(0.204)
Market share 0.040*
(0.020)
−0.013
(0.026)
0.002
(0.025)
−0.012
(0.030)
0.029
(0.032)
0.038
(0.038)
−0.043
(0.032)
0.041
(0.030)
0.005
(0.027)
0.034
(0.031)
0.088***
(0.032)
Markup 0.026
(0.049)
−0.028
(0.097)
−0.018
(0.078)
−0.306
(0.281)
0.025
(0.075)
−0.309
(0.250)
0.068
(0.101)
−0.037
(0.231)
0.098
(0.098)
0.054
(0.074)
0.041
(0.193)
Export activity (dummy) 0.206**
(0.089)
0.095
(0.112)
0.402**
(0.158)
0.964***
(0.282)
0.231
(0.183)
0.632***
(0.243)
0.018
(0.120)
0.513*
(0.281)
0.07
(0.131)
0.602***
(0.226)
0.657**
(0.330)
Foreign ownership (dummy) −0.071
(0.146)
0.122
(0.185)
−0.274
(0.244)
−0.178
(0.304)
−0.485
(0.310)
0.226
(0.308)
−0.010
(0.204)
0.124
(0.263)
−0.457**
(0.209)
−0.477*
(0.273)
0.013
(0.318)
(Mills)−1 −0.534*
(0.285)
−0.900**
(0.349)
0.285
(0.454)
1.09
(0.717)
0.138
(0.528)
−1.97***
(0.686)
− 0.793**
(0.369)
−1.50
(0.979)
−0.641
(0.493)
−0.408
(0.609)
−1.26
(1.06)
Constant −4.44***
(0.890)
−2.13**
(0.965)
6.94***
(1.39)
−14.65***
(2.11)
−5.11***
(1.49)
−8.94***
(1.81)
−1.82*
(1.013)
−16.95***
(1.98)
−6.85***
(1.62)
−16.27***
(1.97)
−9.08***
(2.83)
P score (mean) 0.568
(0.156)
0.319
(0.092)
0.214
(0.162)
0.105
(0.125)
0.159
(0.121)
0.082
(0.078)
0.279
(0.086)
0.151
(0.215)
0.270
(0.138)
0.143
(0.162)
0.129
(0.169)
Observations 5,002 3,112 2,485 2,132 2,316 2,247 2,964 2,344 2,824 2,279 2,230
Pseudo R 2 0.0742 0.0285 0.136 0.222 0.105 0.121 0.027 0.417 0.073 0.24 0.169
Log-likelihood −3,209.3 −1,791.62 −968.225 −380.309 −765.011 −365.851 −1,634.38 −348.619 −1,329.15 −447.616 −287.934
Treated 2.908 1.015 382 169 311 148 867 295 732 255 131
Non-treated 2,103 2,103 2,103 2,103 2,103 2,103 2,103 2,103 2,103 2,103 2,103
No. of blocks 7 7 8 8 5 5 5 7 7 5 7
  1. *Significant at 10%; **significant at 5%; ***significant at 1%
  2. Standard error in brackets

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Kannebley, S., Sekkel, J.V. & Araújo, B.C. Economic performance of Brazilian manufacturing firms: a counterfactual analysis of innovation impacts. Small Bus Econ 34, 339–353 (2010). https://doi.org/10.1007/s11187-008-9118-x

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Keywords

  • Technological innovation
  • Average treatment effect
  • Propensity score matching

JEL Classifications

  • O31
  • O33
  • C40
  • L26