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Publication Bias in the Returns to R&D Literature

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Abstract

The returns to R&D literature is large and has been surveyed on several occasions. We complement previous surveys by discussing the scope for publication bias and illustrate how formal meta analytic techniques can be used to analyse the problem. We find evidence consistent with positive publication bias and discuss possible interpretations. The bias appears to be particularly strong in the part of the literature that controls for unobserved firm fixed effects. The reason may be that fixed effects specifications are particularly susceptible to measurement errors and therefore have a high probability of producing implausibly low return estimates. Implausible estimates are likely to be filtered out before being reported, and our analysis suggests that 23 % of a hypothetical complete literature is missing. Future reviews should take into account that the full effect of negative specifications biases may be masked by reporting and publication bias.

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Notes

  1. Other surveys include Mairesse and Sassenou (1991), Nadiri (1993), the Australian Industry Commission (1995), Hall (1996), Griliches (1995, 2000), Wieser (2005) and most recently, OECD (2015).

  2. This is supported by a recent and more formal analysis by Castellacci and Lie (2015).

  3. Wieser (2005) constructed his meta sample from earlier narrative reviews.

  4. This being said we fully acknowledge the advantages of systematic reviews, and that this is the gold standard in meta analysis, cf. Stanley et al. (2013). Systematic literature reviews recently made available by Ugur et al. (2015) and OECD (2015) find that there exists a large number of results that have been ignored in the earlier surveys on the returns to R&D literature.

  5. This was pointed out to us by an early referee, “aware of the research and publication history of many of the papers”. Brodeur et al. (2013) find that young researchers distort their reported results more in order to achieve statistical significance than tenured and older researchers.

  6. The model can also be specified with labor productivity or total factor productivity as dependent variable.

  7. In equation (2), R is the net investment in R&D, while only gross R&D is available in empirical work. Using an R&D measure that is too large, obviously causes ρ to be underestimated. Hall et al. (2010) show that this bias may be substantial. Eberhardt et al. (2013), on the other hand, show that private returns to R&D estimates may suffer from a positive bias as they are based on specifications that ignore the effect of R&D spillovers. R&D spillovers are positively correlated with private R&D investments. Much of this bias, however, is likely to be absorbed by firm fixed effects.

  8. See Karlsson et al. (2013) for a formal meta-analysis of the spatial knowledge spillover litterature.

  9. Note that ongoing research by Ugur et al. (2015) follows up on the specification analysis of Wieser (2005) using a more suitable data set than the one we have available. A similar analysis is also presented by OECD (2015).

  10. Strictly speaking, there may also be symmetric publication selection in which case Light and Pillemer (1984) suggest using the ‘hollowness’ of the funnel graph to indicate selection bias. Symmetric selection is less of a concern, since the mean effect found in meta-analyses will remain largely unbiased.

  11. The only studies we know of are by Abreu et al. (2005) who briefly mention the method and present some results based on it, and two recent papers, Haelermans and Borghans (2012) and Nelson (2014).

  12. A common caveat in this literature, however, is that publication bias is only one possible cause of funnel plot asymmetry, see Sterne and Egger (2005). A cautious interpretation of results based on funnel graph asymmetry, therefore, is to consider them sensitivity analyses showing the potential effect of missing studies. We return to this issue in the “Robustness and Caveats” section.

  13. The algorithm works as follows: The most extreme right-hand side effect sizes are removed one by one. Between each iteration, the overall effect size is estimated, and the procedure goes on until the funnel plot is symmetric around the latest computed overall effect size. This trimming gives an unbiased effect size estimate, but also reduces the variance of the effects. Therefore, the algorithm adds back the original results and augments the sample with their mirror images before computing the confidence interval.

  14. In the empirical part of this paper, we use the distribution free moment-based estimator of DerSimonian and Laird (1986). Kontopantelis and Reeves (2012), in a comprehensive simulation study, conclude that researchers may have “confidence that, whichever method they adopt, results are highly robust against even very severe violations of the assumption of normally distributed effect sizes”.

  15. Leaving out the obvious outliers to the left in Figs. 1 and 3 does not change the results noteworthy.

  16. The algorithm uses the non-iterative moment-based estimator of DerSimonian and Laird (1986) to estimate the between studies variance. Hence, our results in Table 3 do not depend strictly on any distributional assumption regarding the random effect, cf. the discussion in the section “Correcting for Publication Bias”.

  17. Testing the random effects model against the fixed effects model using the so-called Q-test presented in Shadish and Haddock (1994, p. 266), clearly rejects the fixed effects model in all three samples. The fixed effects point estimates before adjusting for potential publication bias are 0.151, 0.099, and 0.021 for columns (1), (2), and (3), respectively. The corresponding estimates with correction for publication bias are 0.126, 0.097, and 0.007. Hence, our results are not highly sensitive to the choice between the fixed effects and the random effects model.

  18. Leaving out the obvious outliers to the left in Figs. 1 and 3 does not change the results in Table 3 noteworthy.

  19. As a partial robustness test, we have divided our meta sample in two according to the median sample size of the primary studies. It turns out that our main conclusions based on Table 2 do not depend on combining samples with large and small n in one regression. We get similar results using only the larger half of the samples as using the smaller half of the samples.

  20. This possibility is—as far as we know—not previously discussed in the meta-analysis literature.

  21. We are grateful to Jonas Andersson for providing these simulations.

  22. More examples can be found in Stanley and Doucouliagos (2010). Note that Card and Krueger’s (1995) conclusion was challenged by Neumark and Wascher (1998), but supported by an extended meta-analysis by Doucouliagos and Stanley (2009).

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Authors and Affiliations

  1. Department of Business and Management Science, Norwegian School of Economics, Helleveien 30, N-5045, Bergen, Norway

    Jarle Møen

  2. Department of Economics, Norwegian School of Economics, Bergen, Norway

    Helge Sandvig Thorsen

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  1. Jarle Møen
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Correspondence to Jarle Møen.

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The project is financed by the Research Council of Norway. We are grateful to Jonas Andersson, referees and participants at the MAER-Net 2013 colloquium at the University of Greenwich for useful comments.

Appendix

Appendix

Table 5 Estimates of the rate of return to R&D
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Table 6 Estimates of elasticity of R&D, level dimension
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Table 7 Estimates of the elasticity of R&D, temporal dimension
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Møen, J., Thorsen, H.S. Publication Bias in the Returns to R&D Literature. J Knowl Econ 8, 987–1013 (2017). https://doi.org/10.1007/s13132-015-0309-9

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