over _i_ and _t_ , panel method leads to efficiency gains.<sup>9</sup> The table shows that this correction does not significantly improve the results from the fifth column. In the bottom of the table, I present the likelihood ratio test statistic which provides evidence on the existence of random effect in the model.

The last two columns apply random effects method to the product growth rates. As discussed in the previous paragraph random effects estimators allow over time correlation for each firm and leads to efficiency gains. This method is applied only when innovation is measured over three years.<sup>10</sup> The result from the random effects model yields a similar result to the pooled OLS method presented in column one. Contribution of licensing reform to product growth is 0.4 percentage points higher in random effects model than the result from pooled OLS method. Last column repeats the estimation in column seven for firms that were in the panel from 1989 to 1995. In the bottom of the table, I present Breusch and Pagan (1980)’s likelihood ratio test statistic for product growth which provide evidence on the existence of random effects for the results presented in column seven and eight.

# **Dynamic Model Estimation**

In the descriptive analysis performed in the previous section, although the inclusion of additional controls, clustering standard errors, and the use of panel estimation methods alleviate the endogeneity problem, they do not completely solve it. First of all the main determinant of innovation, sales is endogenous to innovation decision. For a firm with _n_ products, total sales is computed as summation of _nit_ revenues from each product _Salesit_   _j_  1 _saleijt_ . Hence number of products created this period will affect total sales of the future periods. This effect makes the specification dynamic. The common methods of dealing with firm-specific effects like within-group or difference estimators might not be appropriate given the dynamic nature of the regression. The advantage of the methods applied in the previous section was that I could identify the coefficient of an indicator variable that does not vary over time. Industries were delicensed at various years and some of them were never delicensed. I introduce a new indicator variable denoted as _Delicen_ which is zero until the industry is delicensed and changes to one after delicensing. Using this continuous variable, I apply a variation of Arellano-Bond (1991) generalized method of moments (GMM) method for dynamic panels. With this method, I can control for the biases that are due to unobserved firm-specific effect such as efficiency level-  and the endogeneity of firm sales. These estimators are based on differencing regressions to control for unobserved effects and using appropriate lags of explanatory variables and dependent variable as instruments. It is important to note that within estimator is inconsistent once lagged regressors are introduced to the model because the within model has the first regressor that is correlated with the error term.

I present two-step GMM (Two-step GMM) estimator where optimal weighting matrix is computed in the first step which is then used in the second step. This estimator is more efficient than the two-stages least square-2SLS (one-step) estimator in overidentified models. The GMM estimator which is based on the differenced model can have statistical shortcomings. Blundell and Bond (1998)

> 9 Random effect method is used in panel probit model and standard errors are not clustered for this specification. 10 When growth rates are computed over two years, share of individual effect in overall variation (the interclass correlation) was ignorable which means random effects method is not appropriate. On the other hand when growth rates are computed over three years, the interclass correlation is around 0.22.

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