The estimation results with the pooled OLS method are presented in the first four columns of Table 4. The first column shows annual change in product mix. Firms in industries that were delicensed before 1989 are more innovative than the firms in the remaining industries. They increase their product mix at a rate 1.3 percentage points faster than the rest of the firms. Size and R&D investment significantly increase product creation rate. Higher tariff rates decrease the rate of product growth; however the coefficient of this variable is not significant. The industries that had large decrease in nontariff barriers also have higher product growth rates. The second column in the table repeats the estimation in column one restricting the sample to firms that were in the sample from 1989 to 1995. Since the Prowess data is not balanced and sample size increases from 1989 to 1995, the estimation results could be biased as successful firms are more likely to survive and innovate compared to inefficient firms who are more likely to exit. The results are similar and the impact of licensing reform on innovation is only slightly higher.

Introduction of new varieties to the economy shows a sporadic behavior. As Table 2 showed there are many firms that have zero product growth rates annually. In the third column, I compute product growth rate over a three year period instead of two years. With this definition of product growth rate, firms in delicensed industries grow at 1.8 percentage points faster than firms that are in licensed industries. Although standard errors are clustered at firm level and the estimation equation includes year dummies, serial correlation can still be a concern. To alleviate this concern in the fourth column I include only the data from 1991. The first year that product growth can be computed is 1990 but the sample size is larger in 1991. Although a large group of industries were delicensed towards the end of 1991, the effects of the reform on these firms would not be seen right away. Hence _Delicen89_ will still be able to capture the difference in innovation performance between firms that were delicensed before and after 1989. The estimation result confirms the significant positive relationship between delicensing and innovation. In this column since only one year of data is used, standard errors cannot be clustered at firm level. Hence robust standard errors are presented at 4-digit industry level.<sup>8</sup> The magnitude of the coefficient of delicensing indicator is higher when the sample is restricted to 19891994 rather than 1989-1995 period. This probably due to shorter period for firms that were delicensed at the end of 1991 to reach to the innovation performance levels of those firms that were delicensed before 1989.

The fifth and sixth columns analyze the relationship between probability of adding a product and the licensing reform. The fifth specification uses pooled probit model for which the estimation equation is given in equation 8 Pr  _zit_  1   Pr  1 _Delicen_ 89 _i_   2 _Log_ ( _Sales_ ) _it_  1   3 _R_ & _Dit_   4 _Tarifft_  1   5 _Industry_   6 _Export_  _t_   _it_  0  (8) where zit is a discrete random variable equal to one if the _i_ th firm at period _t_ introduced any new product. Licensing reform increases probability of innovation by around two percent. Column six applies panel probit model which has the advantage over pooled probit model by correcting the panel complication that observations are correlated over time for a given individual. By allowing correlation

> 8 The results are quite similar if the clustering of standard errors is made at 2-digit industry-year level.

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