Abstract
This study empirically tests the relationship between regulation and innovation in the telecommunications sector by deploying an efficient panel threshold model. The balanced panel dataset comprises of 32 OECD countries over the period 1995–2012. The empirical results unveil that beyond a certain threshold, a further increase in the (de)regulatory intensity leads to a decrease of the sectoral innovation activity. The empirical findings do explain the descriptive evidence of an inverted U-shaped relationship between regulation and innovation in the telecommunications sector since structural non-monotonic relationships are uncovered. Lastly, this study provides significant policy implications, arguing that regulators should develop mechanisms fostering innovation activity without much affecting the intensity of market competition.
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Source: OECD International Regulation Database
Source: OECD TRI Data Regulation and OECD Patent Grants
Source: OECD TRI Data Regulation and OECD Patent Grants
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Notes
See Amable et al. (2016) for this discussion.
For an excellent review of the liberalization process in the OECD countries, see Duso and Seldeslachts (2010), as well as Laffont and Tirole (2001). Cave et al. (2019) provide an exceptional review of the transformations made in the European fixed and mobile telecommunications markets during the last 25 years.
A more reasonable alternative could be that of scaling patent counts on the employment of the ICT sector. Moreover, an alternative innovation variable such as the trademarks in telecommunications sector could be also used in the analysis to increase the robustness of the empirical findings. However, due to severe data restrictions the inclusion of both variables was not feasible.
The IPC codes that are included in the telecommunications category of the EPO are the following: G01S, G08C, G09C, H01P, H01Q, H01S3/025,043,063,067,085,0933,0941,103,133,18,19,25), H01S5, H03B, H03C, H03D, H03H, H03M, H04B, H04J, H04K, H04L, H04M, H04Q.
https://stats.oecd.org/index.aspx?DataSetCode=PMR. The related methodology for constructing these variables is described in Égert and Wanner (2016).
Another panel methodology that tackles potential endogeneity problems is the Generalized Methods of Moments (GMM) employed in a static or a dynamic framework (e.g., difference/System GMM estimators). We mention though that the reported results do not differ significantly if we use GMM estimators.
The latter computes a test for endogeneity in a regression estimated via IV, the null hypothesis for which states that an OLS estimator of the same equation would yield consistent estimates. The null hypothesis denotes that any endogeneity among the regressors would not have deleterious effects on OLS estimates. In other words, a rejection of the null indicates that endogenous regressors' effects on the estimates are meaningful, and IV techniques are required (Davidson & MacKinnon, 1993). According to this test, a rejection of the null indicates that endogenous regressors' effects on the estimates are meaningful, and IV techniques are required.
We argue that the null hypothesis of no single threshold is rejected in all the three models since the bootstrap p values are equal to zero. On the contrary, the test for the second threshold is not statistically significant in any of the three models. Therefore, we infer that there is only one threshold in all the regression relationships. The results from these tests are available on request.
The results are available from the authors upon request.
Similarly to Hansen (1999), each regime has to contain at least 5% of all observations. The trimming percentage is set to 0.02 and the Bootstrap replications are set to 1000. By construction, the confidence intervals for the threshold estimates can be highly asymmetric.
The results remain robust after the inclusion of country * year FE. To conserve space, this set of results is also available upon request.
Since the parametric results coincide with the TR model exhibiting an inverted U-shaped curve between upstream regulation and patent intensity, we rely solely on the latter estimations. Due to space constraints the parametric results are available upon request.
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Acknowledgements
We would like to thank Menahem Spiegel (Editor) and an anonymous referee of this journal for constructive comments and suggestions that enhanced the merit of the paper. Special thanks also go to Thanasis Stengos for providing useful comments on an earlier version of this paper. The authors have the sole responsibility for possible errors and omissions. The usual disclaimer applies.
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Polemis, M.L., Tselekounis, M. Threshold effects in the regulation-innovation nexus: evidence from the telecommunications industry. J Regul Econ 60, 74–93 (2021). https://doi.org/10.1007/s11149-021-09433-4
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DOI: https://doi.org/10.1007/s11149-021-09433-4