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Trade, imitative ability and intellectual property rights


Economic theory suggests some ambiguity concerning the effects of strengthening intellectual property rights (IPRs) on international trade. Here we extend the empirical literature that attempts to resolve this ambiguity. We use panel data to estimate a gravity equation for manufacturing exports, in aggregate and by industry, from five advanced countries to 69 developed and developing countries over the period 1970–1999. In particular, we use threshold regression techniques to determine whether the impact of IPR protection on trade depends upon the level of development, imitative ability and market size of the importing country. We confirm the importance of the importers’ imitative ability, and also find some evidence of a role for market size in this relationship. The individual industries present different patterns of thresholds and coefficients, with Total Manufacturing closely reflecting that of Fabricated Metal Products.

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  1. 1.

    Even where the evidence indicates a relationship between IPR protection and a specific channel of diffusion, it is often the case that there is little evidence of effective technology transfer. This is particularly the case for FDI. See Falvey et al. (2006b) for a review of the impact of IPRs on the channels of diffusion.

  2. 2.

    Nicholson (2007) uses data on the numbers of US firms undertaking FDI and licensing to investigate how these activities are linked to IPRs and industry characteristics. He finds that firms in industries with high capital costs are more likely to choose FDI where IPRs are weak, and firms in R&D intensive industries are more likely to choose licensing when IPRs are strong.

  3. 3.

    Liu and Lin (2005) consider exports by Taiwan in three knowledge-intensive industries (semi-conductor, information and communications equipment). For importing countries with a lower imitative (R&D) ability than Taiwan, the results are analogous to those in the literature (i.e. market power effects in countries with relatively low imitative ability and market expansion effects in the others). For importing countries whose imitative ability exceeds Taiwan’s, there are market expansion effects but no market power effects. In an interesting recent contribution, Ivus (2008) uses developing countries’ previous colonial status and industry IPR-sensitivity to argue that the IPR strengthening under the TRIPS agreement has led to increased high-tech imports by developing countries.

  4. 4.

    A further issue is how to deal with zero trade flows. Using five-year averages did alleviate this problem somewhat, but there were still a few cases where zero trade flows were reported. Several options are available (Frankel 1997, chapter 6), but given that the threshold techniques that we employ below have been developed for OLS we adopt the most straightforward “solution” of adding a small number to the zero observations (equal to $100), which allows us to estimate the log-linear model.

  5. 5.

    Hansen (1999) in particular describes the threshold regression technique for panel data with fixed effects.

  6. 6.

    The alternative hypothesis, tested by Co (2004), is that the marginal effect of IPR protection on trade is a continuous (in fact linear) function of, say imitative ability, which is tested by including the appropriate interactive term. The threshold and interactive approaches are not nested hypotheses, however, and it is possible that each is valid for some industries. Where thresholds are estimated, but an interactive term is more appropriate, we would expect to find a large number of significant thresholds with the coefficient on IPR rising for higher regimes. If however we were to find only one or two significant thresholds, or coefficients that change sign across regimes then the results would not support the interaction hypothesis.

  7. 7.

    The bootstrap distribution of the test statistic was computed using 1,000 replications of the procedure proposed in Hansen (1996).

  8. 8.

    The five exporting countries are France, Germany, Japan, the United Kingdom and the United States. The importing countries are Algeria, Argentina, Australia, Austria, Bangladesh, Belgium-Luxemburg, Bolivia, Brazil, Cameroon, Canada, Central African Republic, Chile, Colombia, Costa Rica, Denmark, the Dominican Republic, Ecuador, Egypt, El Salvador, Finland, Ghana, Greece, Guatemala, Guyana, Haiti, Honduras, Iceland, India, Indonesia, Ireland, Israel, Italy, Jamaica, Kenya, Korea (Republic of), Malawi, Malaysia, Mexico, the Netherlands, New Zealand, Nicaragua, Niger, Norway, Pakistan, Paraguay, Peru, the Philippines, Portugal, Senegal, Sierra Leone, South Africa, Spain, Sri Lanka, the Sudan, Sweden, Switzerland, Syria, Tanzania, Thailand, Togo, Trinidad and Tobago, Turkey, Uganda, Uruguay, Venezuela, Zaire, Zambia, Zimbabwe.

  9. 9.

    UNIDO (2002) notes that the share of R&D financed by enterprises in advanced countries was 98% in the 1980s and 94% in the 1990s.

  10. 10.

    The ANBERD database reports total manufacturing R&D expenditure for 15 OECD countries for 1973–1998, and the average share of R&D expenditure by these five economies over that period was 91.4%. There has been a slight decline in this share over the sample period from 92.8% in 1973 to 89.4% in 1998.

  11. 11.

    This measure is derived from Cohen et al. (2000). The underlying data is from a survey questionnaire administered to 1,478 R&D labs in US manufacturing in 1994. The question asked respondents to report the percentage of their product innovations for which patents had been effective in protecting the firm’s competitive advantage from those innovations.

  12. 12.

    Our observations fall from 2,021 to 1,360.

  13. 13.

    Maskus (2000, p. 118) observes that “in the developing economies R&D data are highly suspect and not comparable to those in developed countries”. He also notes that Smith’s designations of countries into the four groups based on R&D data led to a number of anomalies.

  14. 14.

    The view that secondary education is the broad key to development (shifting countries towards the frontier) is supported by Meier (1995, p. 315) “the most critical manpower requirement tends to be for people with a secondary education who can be managers, administrators, professional technicians or sub-professional technical personnel”. Similarly, Ramcharan (2004, p. 320) conjectures that “it may well be that developing economies need only invest in secondary schooling, importing high-skilled education embodied in the foreign goods”.

  15. 15.

    We also estimated these equations as a system using seemingly unrelated regression (SURE) methods. The SURE results are very similar to the OLS results and are available upon request, but for consistency with the threshold results that we report later which rely on OLS estimates, we report the OLS estimates in the text.

  16. 16.

    If we re-estimate excluding importer, exporter and time dummies, the size of the negative coefficients on exporter GDP tends to fall significantly, and in many cases becomes significant and positive.

  17. 17.

    When fixed effects are excluded, the common border is more likely to be positive and significant for the industries, but still negative for the most R&D intensive products. It should be remembered that while we expect a common border to lead to greater trade flows in aggregate, this will not necessarily be the case for each individual product. The negative coefficients may be an idiosyncrasy of the sample since the number of common borders is limited and, with the exception of the US-Mexico border, involve trade between advanced countries.

  18. 18.

    TRIPs includes agreements on the following forms of intellectual property; copyrights and related rights, trademarks, geographical indications, industrial designs, patents, layout-designs of integrated circuits, and protection of undisclosed secrets.

  19. 19.

    For brevity and ease of presentation we choose not to report the coefficients on the other gravity variables. These results are available upon request. The coefficients on the other gravity variables are found to be remarkably consistent across the remaining tables, and are quite robust in terms of size, sign and significance to the choice of threshold variable and the number of thresholds.

  20. 20.

    Note that in this table there are occasions in which the last estimated threshold was significant. In these cases it was not possible to search for a further threshold whilst maintaining the restriction that 20% of observations must lie in each regime. In these cases we report the results in Tables 4 and 5 based on the last significant estimated threshold.

  21. 21.

    The thresholds are marked in italics and are located horizontally in this and the following two tables so as to give a rough indication of their relative location across industries.

  22. 22.

    We can only deal with one threshold for imitative ability, and the highest thresholds take just two values, 1.56 for total manufacturing and 6 of the industries, and 1.95/6 for the other three industries.

  23. 23.

    To estimate this equation by least squares Hansen (1999) recommends a fixed effects transformation to remove the individual-specific mean.

  24. 24.

    Given the panel nature of the problem Hansen (1999) recommends grouping the residuals by individual and drawing (with replacement) a sample of size n in order to construct the bootstrap series.


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Falvey and Greenaway acknowledge financial support from the Leverhulme Trust under Programme Grant F/00 114/AM. We thank a referee for helpful comments.

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Correspondence to David Greenaway.


Appendix 1: threshold regressions

Threshold regression addresses the issue of whether regression functions are identical across observations or whether there exists evidence of non-linearity with observations split into discrete classes. Hansen (1996, 2000) developed a method of estimating such thresholds and testing for their significance as well as constructing confidence intervals. Hansen (1999) extended this approach to fixed effects panel data. In this appendix we briefly describe the methods used in this paper.

The method can be described using the following two variable panel regression model

$$ y_{it} = \mu_{i} + \delta_{1} x_{it} I(q_{it} \le \lambda ) + \delta_{2} x_{it} I(q_{it} > \lambda ) + \varepsilon_{it} $$

where I(·) is the indicator function and q it is the threshold variable.Footnote 23 Here the observations are divided into two regimes depending upon whether the threshold variable, q it , is smaller or larger than the threshold, λ. The coefficient on x it is given by δ 1 for observations with q it less than or equal to λ and by δ 2 for q it greater than λ. Chan (1993) and Hansen (1999, 2000) recommend estimation of λ by least squares, which involves finding the value of λ that minimises the concentrated sum of squared errors, S 1 , that is \( \hat{\lambda } = \arg \min_{\lambda } S_{1} (\lambda ) \). In practice this involves searching over distinct values of q it for the value of λ at which the concentrated sum of squares is smallest. Hansen (1999) notes that it is undesirable to have two few observations in a particular regime, a possibility that can be excluded by constraining the possible values of λ to those for which a minimum percentage of observations are in each regime. In our analysis we impose the restriction that at least 20% of observations must lie in each regime.

Hansen (1999) suggests the following steps to estimate the threshold value: first, sort the distinct values of the observations on the threshold variable, q it . Second, eliminate the smallest and largest η percent. For each of the remaining values of q it estimate Eq. A1 and save the sum of squared residuals. Third, choose the estimate of \( \hat{\lambda } \) as the value of q it with the minimum sum of squared errors.

Having found a threshold it is important to determine whether it is statistically significant or not, that is, it is necessary to test the null hypothesis H0: δ 1 = δ 2. Given that the threshold is not identified under the null hypothesis this test has a non-standard distribution and critical values cannot be read off standard distribution tables. Hansen (1999) proposes a bootstrap procedure to simulate the asymptotic distribution of the likelihood ratio test. This involves the following steps: first, estimate the linear model where no threshold is assumed and save the sum of squared residuals, S 0 . Second, calculate the likelihood ratio test of the null hypothesis, H 0, given by \( F_{1} - [S_{0} - S_{1} (\lambda )]/\hat{\sigma }^{2} , \) where \( \hat{\sigma }^{2} = \left\{ {{1 \mathord{\left/ {\vphantom {1 {n[t - 1}}} \right. \kern-\nulldelimiterspace} {n[t - 1}}]} \right\}S_{1} (\hat{\lambda }), \) with n the number of cross-sectional units and t the number of time periods. Third, construct a bootstrapped sample under the null hypothesis by drawing from the normal distribution of the residuals from the linear model.Footnote 24 Fourth, using the bootstrap sample estimate the model under the null (linearity) and the alternative (threshold at \( \hat{\lambda } \)) and calculate the bootstrap likelihood ratio statistic, F 1. Finally, repeat this procedure a large number of times (in our case 1,000) and calculate the percentage of draws for which the simulated statistic exceeds the actual one. This is the bootstrap estimate of the p-value for F 1 under H 0.

In the dual-threshold model we have

$$ y_{it} = \mu_{i} + \delta_{1} x_{it} I(q_{it} \le \lambda_{1} ) + \delta_{2} x_{it} I(\lambda_{1} < q_{it} \le \lambda_{2} ) + \delta_{3} x_{it} I(q_{it} > \lambda_{2} ) + \varepsilon_{it} $$

where the two thresholds are ordered such that λ1 < λ2. It is a straightforward extension to search for the values of λ1 and λ2 that minimise the sum of squared errors. At the same time it can be expensive in terms of computation time to search for both thresholds simultaneously. Chong (1994), Bai (1997) and Bai and Perron (1998) have shown however that sequential estimation is consistent. This involves fixing the first threshold at \( \hat{\lambda }_{1} \) and searching for a second threshold assuming that the first is fixed. The search takes place for values of q it both above and below the first threshold, though the method can be adapted to ensure that a minimum of η per cent of observations are in each of the three regimes. It can be shown that the estimate of the second threshold, \( \hat{\lambda }_{2} \), is asymptotically efficient using this method. This is not the case for \( \hat{\lambda }_{1} \) however, because it was estimated from a sum of squared errors function that was contaminated by the presence of a neglected regime. To overcome this problem Bai (1997) recommends a refinement estimator for \( \hat{\lambda }_{1} \) that involves fixing the second threshold at \( \hat{\lambda }_{2} \) and searching for the first threshold again, now including the second threshold. This approach can be extended to consider the possibility of more than two thresholds.

In Sect. 4.3 we investigate the possibility of thresholds on more than one variable. Specifically, we examine interactions between the level of imitative ability and both the level of IPRs and market size. To allow such a possibility we adopt an approach similar to that described in the previous paragraph. We begin by fixing the threshold on our measure of imitative ability. In particular, we fix the threshold at the highest significant threshold on imitative ability and then search for a threshold on the other variable in the high imitative ability regime (i.e. for observations for which the measure of imitative ability is above the estimated threshold). Finally we consider the possibility of a threshold on these variables in the low imitative ability regime. Where the second threshold, that is the threshold in the high imitative ability regime, is significant we include it when searching for a third threshold. But then the estimated thresholds in the high imitative ability regime are not asymptotically efficient, since they were estimated from a sum of squared errors function contaminated by the presence of a neglected regime. To deal with this we follow Bai (1997) and re-estimate the high imitative ability threshold now including the estimated threshold in the low imitative ability regime.

Appendix 2: data

Our data is averaged over six 5-year periods, 1970–1974, 1975–1979, 1980–1984, 1985–1989, 1990–1994 and 1995–1999. Due to missing data for various variables the maximum number of observations is 2021. The data for population, GDP and GDP per capita came from the World Bank’s World Development Indicators (2001) database. Data on distance, common language and borders and landlockedness came from a website maintained by Jon Haveman. Trade data came from the OECD’s International Trade by Commodity Statistic (Historical Series, 1961–1990) and International Trade by Commodity Statistic (1990–1999). The trade data from 1961 to 1990 was in SITC rev. 2 and was converted to ISIC rev. 2 using a concordance supplied by the OECD. The data for the period 1990–1999 was in SITC rev. 3 and was converted to SITC rev. 2 and then ISIC rev. 2 again using a concordance supplied by the OECD. The education data was taken from the Barro and Lee (2001) database. The index of IPR protection is provided in Ginarte and Park (1997) and is the most commonly used indicator of IPR protection. This index was constructed for 110 countries quinquennially for the period 1960–1990. Five characteristics of patent laws are included: extent of coverage; membership in international patent agreements; provisions for loss of protection; enforcement mechanisms and duration of protection. Each was assigned a value ranging from zero to one and their unweighted sums formed the index, with a higher number signalling stronger IPR protection. This data has been updated to 1995 by Park who kindly supplied us with the full set of data. Table 10 provides summary statistics for all the variables.

Table 10 Summary statistics

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Falvey, R., Foster, N. & Greenaway, D. Trade, imitative ability and intellectual property rights. Rev World Econ 145, 373–404 (2009).

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  • Intellectual property rights
  • International trade
  • Gravity equation
  • Imitative ability

JEL Classification

  • F10
  • F13
  • O34