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Can licensing induce productivity? Exploring the IPR effect

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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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  1. See, for example, Bettig (2018), Brüggemann et al. (2016), Lai (1998) and Yang and Maskus (2001).

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

  3. For more examples, see

  4. For further information, see Instituto Nacional de Propiedad Industrial (

  5. 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%.

  6. 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.

  7. 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.

  8. The entire methodology for constructing the two-digit deflators can be obtained from

  9. An excellent survey of TFP estimation can be found in Van Beveren (2012).

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

  11. 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 \(EFWi = [(GCRi - 1)/6] * 10\).

  12. 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).

  13. 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.

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

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

  16. 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.

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

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

  19. 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).

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

  21. 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.

  22. 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”).

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

  24. 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.

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

  26. 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.

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


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We would like to thank Keith Maskus, Wolfgang Keller, Scott Savage, and Thibault Fally for their invaluable advice. We would also like to thank the editor, two anonymous referees, and Joze Damijan and Ricardo Lopez for their helpful comments. Felipe Bedoya-Maya provided superb research assistance. The findings, interpretations, and conclusions expressed in this document do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent.

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Correspondence to Luis Castro Peñarrieta.

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A. Appendix I: robustness

See Tables 5, 6, 7, 8, 9 and 10.

Table 5 Placebo regressions
Table 6 Spillover effects under different IPR measures panel IV in levels
Table 7 Spillover effects under different IPR measures (panel IV and first differences, with lagged control variables)
Table 8 Robustness checks for specification issues
Table 9 Different IV following (Lu et al. 2017)
Table 10 Different IV following (Javorcik et al. 2018)

B. Appendix II: TFP estimation methods

We follow closely the excellent survey of Van Beveren (2012) regarding TFP estimation which departs from an standard Cobb–Douglas production function with capital, unskilled labour and skilled labour: \(Y_{_{ijrt}}=A_{_{ijrt}}K^{^{\beta _{k}}}_{_{ijrt}}L^{s^{\beta _{ls}}}_{\ _{ijrt}}L^{u^{\beta _{lu}}}_{\ _{ijrt}}\); where \(Y_{ijrt}\) is output of the firm i in sector j and region r at time t; \(K_{ijrt}\) is the capital stock; while \(L^s\) and \(L^u\) are the number of skilled and unskilled workers, respectively. If we apply logarithms our production function becomes: \(TFP_{ijrt}=y_{ijrt}-\beta _kk_{ijrt} -\beta _{ls}l^s_{ijrt}-\beta _{lu}l^u_{ijrt}\).

If we were to estimate this equation using OLS, the main assumption would be that all inputs in the production function are exogenous. However, inputs in the production function are not chosen independently, creating an endogeneity problem. Moreover, selection bias is introduced when, in an unbalanced panel of firms, the decision on allocation of inputs (especially variable inputs) is not independent of the decision to continue operating in the market. Therefore, the estimation technique has to take into account that the estimates are conditional on the survival of the firm. To overcome endogeneity and selection bias, Olley and Pakes (1996) assumes that capital is a fixed input that depends on an investment process. Therefore, capital in period t depends on capital in \(t-1\) and investment \(i_{t-1}\). This timing corrects the endogeneity issue concerning the capital since the decision of capital is taken before the knowledge of the productivity shock. To estimate this, Olley and Pakes (1996) propose a three-step estimation process where the investment of the firm is a monotonically increasing function of productivity and existing capital. If the relationship is monotonically increasing, as explained in Arnold (2005), the investment decision is a function of the productivity and the capital stock.Footnote 26 Thus for the first step, the estimating equation becomes: \(y_{it}=\beta _0+\beta _{l}l_{it} +\phi (i_{it}, k_{it})+\eta _{it}\).

Since the functional form of \(\phi (.)\) is unknown, it can be proxied non-parametrically by a third or fourth-degree polynomial. Thus, in the first stage, both \(\widehat{\beta _l}\) and \(\widehat{\phi }\) can be estimated using regular Ordinary Least Squares (OLS). In the second step, they introduce a correction for the selection bias (exit decision), and in the third step, they recover consistent estimates of \(\beta _l\) and \(\beta _k\). However, the main limitation of this methodology is that there could be a large number of “zero” investment observations (not all firms invest every single period). Thus a considerable amount of information is potentially lost. Since there is a monotonic relation between investment and productivity, all the observations with zero investment must be dropped from the sample, which could imply a significant loss of observations in the dataset (depending on how many firms do not invest). Moreover, since investment is not always positive, Levinsohn and Petrin (2003) proposes using intermediate inputs as a proxy for productivity shocks. As Arnold (2005) points out, typically, there are significantly less zero- observations in intermediate inputs than in investment.

More recently, Ackerberg et al. (2015) have re-examined the estimation methods for production functions. They shed some light on some issues that might hinder the methodology proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). They argue that there may be significant problems with the estimation of production functions if the methods mentioned above are used. The most critical issue is collinearity problems that arise in both methods.Footnote 27 The estimation has to be performed using either non-linear least squares or an optimization routine. The OLS and the Olley and Pakes distributions are very asymmetric, while the Levinsohn and Petrin, and the ACF estimators behave more smoothly. Levinsohn and Petrin estimation overestimates TFP compared to the ACF estimator.

Using the Chilean Encuesta Nacional Industrial Anual (ENIA), which is a national representative survey of the manufacturing sector for 2001 – 2007, we follow (Ackerberg et al. 2015) employing the number of skilled and unskilled workers within each establishment to identify \(\beta _{ls}\) and \(\beta _{lu}\) from the production function and electricity consumption as a proxy for investment to identify the function \(\phi (\cdot )\) and thus be able to recover \(\beta _k\).

Table 11 Summary statistics of different TFP estimators
Fig. 4
figure 4

Comparison of TFP densities from different methods

Table 11 shows summary statistics of different TFP estimators, while Fig. 4 shows the distribution for those estimators. Moreover, Table 13 illustrates the coefficients for the different methods.

In order to check the robustness of the main results of the paper, estimates of Table 2 are depicted in Table 12. As it is shown, the results are qualitatively similar to the ones presented in the main section of the paper (Table 13).

Table 12 Main specification under different TFP methodologies
Table 13 TFP estimation by different methodologies

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Canavire-Bacarreza, G., Castro Peñarrieta, L. Can licensing induce productivity? Exploring the IPR effect. Empir Econ 61, 549–586 (2021).

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