Abstract
National framework conditions directly affect the productivity of firms, but also moderate returns on their technological efforts. Although this has long been recognised, there is a dearth of quantitative analyses that openly consider this hypothesis. Using a data set of 15 425 manufacturing firms in 32 developing countries, we investigate the impact of national institutions on firms’ total factor productivity with the help of multilevel modelling. The results indicate that technological infrastructure and educational system make a large difference, and also most significantly interact with firms’ technological capabilities. However, governance measures that are conventionally considered in the literature explain surprisingly little.
Abstract
Les institutions nationales déterminent directement la productivité des entreprises, mais aussi la rentabilité de leurs efforts en matière de technologie. Bien que cela ait été reconnu depuis longtemps, il y a bien peu d’analyses quantitatives qui ouvertement analysent cette hypothèse. On a étudié l’impact des institutions nationales sur la productivité multifactorielle des entreprises utilisant un modèle hiérarchique et une base de données incluant 15 425 entreprises manufacturières en 32 pays en voie de développement. Les résultats indiquent que l’infrastructure technologique et le système éducatif ont une forte influence, et qu’ils interagissent avec les capabilités technologiques des entreprises. Cependant, les mesures de gouvernance conventionnellement utilisées dans la littérature ont bien peu de pouvoir explicatif.
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Notes
Reliable data on the amount of R&D expenditure of the firms is not available in most of the national data sets.
Two hundred and forty-nine observations, 1.6 per cent of the eligible sample, are omitted, because they have been identified as multivariate outliers at the 0.1 per cent level of statistical significance with the help of Mahalanobis distance computed on the basis of ln(Y), ln(K), ln(L), ln(W) and ln(AGE).
Even this could be seen as a relatively low number. Nevertheless, micro data from developing countries are extremely scarce, especially on technological variables.
Since there is no information on R&D in Bangladesh, we imputed the missing value by the average of 0.17 per cent of other least developed countries (nine observations) presented in the sample.
Since outliers can distort correlations significantly, the country-level data were checked even before computing the factor analysis; however, the Mahalanobis distance criterion did not identify multivariate outliers at any reasonable significance level.
Because the γ coefficients apply to all countries, they are referred to as ‘fixed coefficients’, whereas the u j error terms, which represent the unexplained between-country variation, are referred to as the ‘random coefficients’; the former should not be confused with the so-called fixed effects panel data models.
Centring of dummy variables is strongly recommended (Raudenbush and Bryk, 2002, p. 34).
It should be pointed out that we included each of the six individual governance indicators by Kaufmann et al (2009) in the model instead of the GOV composite to see whether the results are affected by the factoring procedure. However, the estimated coefficients turned out qualitatively very similar.
As recommended in Stata (2009), the covariance matrices are based on the estimated disturbance variance from the consistent estimator.
Admittedly, the Hausman test has its own problems but this result indicates that at least there is not likely to be a fatal bias in this regard. Nevertheless, it should be acknowledged that there is also the potential simultaneity bias due to orthogonality assumptions on the firm-level residual, as some of the covariates can be correlated to eij. Unfortunately, a lack of relevant instruments prevents us from addressing this issue, and hence the results should be interpreted in terms of causality with caution.
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Acknowledgements
Financial support from the Czech Science Foundation (GAČR) project P402/10/2310 on ‘Innovation, productivity and policy: What can we learn from micro data?’, STRIKE (Science and Technology Research in a Knowledge-based Economy) funded as Action No. IS0604 by COST for short term scientific missions (COST-STSM-IS0604-04148), and institutional support RVO 67985998 from the Academy of Sciences of the Czech Republic and VINNOVA Core Funding of Centers for Innovation Systems Research project 2010-01370 on ‘Transformation and Growth in Innovation Systems: Innovation Policy for Global Competitiveness of SMEs and R&I Milieus’ is gratefully acknowledged. Earlier versions of the article were presented at the Globelics 7th International Conference in Dakar, Senegal, October 2009, the 6th Biennial Conference of the Czech Economic Society in Prague, November 2010 and the DRUID 2011 Conference in Copenhagen, June 2011. The article has benefited from comments and suggestions from participants at these and other events, in particular Théophile Azomahou, Bart Verspagen, Cristina Chaminade, Gustavo Crespi, Jan Fagerberg, Mark Knell, Lubomír Lízal, Bengt-Åke Lundvall, Pierre Mohnen, Roberta Rabellotti, Adam Szirmai and Reinhilde Veugelers, as well as from the referees of this journal. All the usual caveats apply.
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Goedhuys, M., Srholec, M. Technological Capabilities, Institutions and Firm Productivity: A Multilevel Study. Eur J Dev Res 27, 122–139 (2015). https://doi.org/10.1057/ejdr.2014.32
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DOI: https://doi.org/10.1057/ejdr.2014.32