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Empirical Economics

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23-06-2020

Can licensing induce productivity? Exploring the IPR effect

Authors: Gustavo Canavire-Bacarreza, Luis Castro Peñarrieta

Published in: Empirical Economics | Issue 2/2021

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Abstract

Licensing is one of the main channels for technology transfer from foreign-owned multinational enterprises (MNEs) to domestic plants. This transfer, occurring within and across industries, results in technology spillovers that may affect both intra- and interindustry productivity. Intellectual property rights (IPR) legislation may increase (or reduce) this effect. Using Chilean plant-level data for the 2001–2007 period and an exogenous variation from an IPR reform in 2005, we explore whether or not IPR affects the spillover effects of licensing on productivity. We find that stronger IPR reduces backward spillovers but increases forward spillovers. Moreover, the IPR legislation effect is stronger on firms that are on average smaller, and have low productivity. Our results are robust not only to a series of definitions of IPR, licensing, and productivity, but also to a set of different specifications.

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For a survey on spillover effects, see Görg and Greenaway (2004).

For more examples, see www.franquicia.cl/nacionales.html.

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This could present a problem if the majority of firms have more than one plant; however, as noted by Pavcnik (2002), using a previous version of this dataset, approximately 90% have a single plant. For the 2001–2007 period, this figure is approximately 89%.

The covered industries are, in terms of ISIC (Rev.3) codes, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, and 36. The ISIC (Rev.3) codes of the manufacturing sector range from 15 to 36. Industries 16 (tobacco) and 23 (coke, refined petroleum products, and nuclear fuel) have no observations in the dataset.

Although there may be a loss of information, in this case the number of observations lost is not large (approximately 6 percent of the original dataset). When estimating a model using fixed effects, these fixed effects will capture all the inherited characteristics of a firm that do not change over time. Thus, a change in industry or region would invalidate the interpretation of the results.

The entire methodology for constructing the two-digit deflators can be obtained from https://www.ine.cl/estadisticas/economia/industria-manufacturera/estructura-de-la-industria.

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Table 12 in “Appendix B” presents results for the main specification using different TFP methods

The formula used by the Fraser Institute is based on the index presented by the World Economic Forum in its Global Competitiveness Report. The formula used is \(EFWi = [(GCRi - 1)/6] * 10\).

For this variable, Lopez (2008) uses two methods, the stock method and the flow method. The method described here refers to the latter. For a detailed explanation of both, see Lopez (2008).

An alternative definition of horizontal spillovers uses license payments instead of purchases of licenses of the focal firm. We have tested our results with this definition and the results remain valid.

The average exchange rate in 2003 was 691.54 pesos per US dollar.

This can be seen in Table 13 in the columns listing observations used per method.

Of concern are two potential sources of spillovers, one driven by the acquisition of foreign licenses and another by a foreign presence that may be endogenous. We estimated our models following Lu et al. (2017) and Javorcik et al. (2018) and our results are qualitatively similar.

For a more detailed discussion, see (Cameron and Trivedi 2005).

The other estimation methods have been calculated and are available from the corresponding author upon request.

Here the assumption is that this imitative capacity is not high enough to create bias. Still, this could be checked by introducing an interaction term of the spillover effects with skilled labour, as in Damijan et al. (2013).

Since there could be an issue with overidentification, we present in “Appendix A” the correspondent tests that support a correct identification of the models.

To make this claim, the dummy IPR and the Fraser Institute measures should be considered interaction terms that reflect the difference between before and after the reform. However, this is not a difference-in-difference estimation, since the changes IPR law affect each firm in the same way.

Following (Javorcik et al. 2018) we evaluate the model lagging explanatory variables to mitigate simultaneity, and the results remain the same (see Table 7 in “Appendix A”).

We conducted a Kolmogorov–Smirnov test between backward and forward spillovers.

The decision to split the sample into quartiles makes it possible to estimate the model. If the division were into smaller bins, there would not be enough observations in each group for the estimation.

Recall from equations (2) and (3) that these two variables are \(H_{jt}\) relative to the IO table.

For ease of exposition, we will only include one variable that represents labour, although in this study, we use skilled and unskilled labour as two different inputs. Since it is estimated as a function of investment and capital, it is possible to estimate it even for firms that have zero capital.

Recall that in the LP methodology; investment is replaced by intermediate inputs.

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Title: Can licensing induce productivity? Exploring the IPR effect
Authors: Gustavo Canavire-Bacarreza
Luis Castro Peñarrieta
Publication date: 23-06-2020
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 2/2021
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-020-01880-w

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