percentage goes to zero). One percent decrease in the share of products that are licensed increase product creation rate by six percent. The last column of the table shows how licensing reform affected the total number of varieties produced by firms. It uses log number of products as the dependent variable and applies the same GMM-SYS method. The result shows that the reform increased product scope by around 3 percent.

In the estimations, coefficients of tariff rates and FDI indicator are not significant. However Goldberg et al. (2010b) find significant increase in product growth rate caused by the reduction in input tariffs. My analysis differs from their study in several ways. In their model input tariff rates are used as instruments for a conventional price index and a variety index. They show that both indices affect product growth. According to their model lower tariffs lead to lower prices of inputs which result in increased imported varieties. Higher use of varieties is associated with an expansion of firm scope. In the estimations of product growth they do not control for the licensing reform. Moreover the model presented in their study does not show the dynamics of product growth.

FDI reform is another factor that can affect innovation performances of firms. It can reduce the cost of innovation by increasing R&D capacity or lowering capital and transactions costs. Using the Prowess database, Vishwasrao and Bosshardt (2001) find that foreign ownership is among the variables that impact a firm’s probability of adoption new technology. However they do not use the detailed product information as their measure of technology adoption. The estimation results presented in Table 5 include all these factors and thus provide a more complete set of controls of policy reforms during the time period included in the analysis. Among the three major reform areas, licensing reform is the only one that significantly increases product creation rates.

At the bottom of the table I present Sargan tests of over-identifying restrictions to test the validity of instruments. For consistent estimation, the error term should be serially uncorrelated. In the GMM estimator this depends on the absence of second-order serial correlation in the residuals of the first differenced specification. The table also presents these test results. The Sargan test has an asymptotic χ<sup>2</sup> - distribution under the null hypothesis that these moment conditions are valid.<sup>12</sup> In all specifications the null hypothesis is not rejected at conventional 5 percent level. If the error terms are serially uncorrelated, we expect to reject the test at order one but not at higher orders. This hypothesis holds in all specifications presented in the table. The null hypothesis that Cov(Δεit, Δεit-1)=0 is rejected (i.e. there is no serial correlation between differenced residuals at order one) but cannot reject at order two Cov(Δεit, Δεit-2)=0.

# **Robustness Tests**

The economic reforms that took place around 1991 did not affect all industries in the same magnitude. Share of products that were opened to FDI and tariff rate reductions were not homogenous across industries. I include tariff rates and FDI reform variable in the estimations to control for the potential effects of these reforms on innovation. GMM-System estimation method controls for industries that observed too high or too low changes in these variables by including their over time differences as instruments. Among the firms that had tariff reductions or FDI liberalization, the ones with the highest

> 12 The distribution of Sargan test is known only when the errors are independently and identically distributed. Thus this test is valid only when it is applied to the specifications with standard errors computed without the robust option.

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