Given differences in production technologies across industries, the analysis estimates heterogeneous, industry-specific (2-digit NACE) production functions using the Levinsohn and Petrin technique to obtain consistent and unbiased estimates of _λj_ , _μj_ , and _κj_ for the derivation of log TFP estimates according to equation (3), which takes two step.<sup>7</sup> In the first step, the coefficient on labor is obtained using semi-parametric techniques. Assuming that the firm’s demand for material inputs increases monotonically with its productivity conditional on its capital, the inverse demand function for material inputs then depends only on observable materials usage and capital and its nonparametric estimate can be used to control for unobservable productivity, thus removing the simultaneity bias.<sup>8</sup> In the second step, the coefficients for material inputs and capital are obtained using generalized method of moments techniques. The identification assumption is that capital adjusts with a lag to productivity, specifically productivity is assumed to follow a Markov process,  _ijt_  _E_ [  _ijt_ |  _ijt_  1<sup>]</sup>  _ijt_ , where  _ijt_ is the unexpected part of current productivity to which capital does not adjust. The estimates of firm log TFP are given by the residuals from equation (3), _TFPijt_   _ijt_   _ijt_ , and capture the efficiency in transforming inputs into outputs and may include changes in factor utilization.<sup>9</sup>

Table 1 presents descriptive statistics of log TFP estimates, calculated according to the technique of Levinsohn and Petrin (2003), for firms in the manufacturing sector (NACE 1500 to 3663) of Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. For years 2001 through 2004, means are provided for the whole sample, by country, and also by groups defined by country, sub-national location, and firm size upon which the BEEPS manufacturing dataset is merged. On average, firms experienced an overall increase in log TFP

> 7 Alternatively, a fixed effects model can be used to address the simultaneity problem if the part of the error that influences input factor decision, _ωi_ , is assumed to be a firm-specific attribute and time invariant (e.g., managerial skills, organizational efficiency, etc.).  In this case, unobserved firm heterogeneity that remains constant over time can be removed (for example, by subtracting the means from each variable for each observation) before estimating the production function so that _l_ , _m_ , and _k_ are no longer correlated with the error term.

> However, evidence from BEEPS suggests that managerial skills and organizational structure have changed significantly over time, and therefore, preclude the adoption of fixed effect methods for the analysis in the paper. Among the 1,416 firms from Bulgaria, Croatia, Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine that comprise the BEEPS 2005 sample, 22 percent had “some reallocation of responsibility and resources between departments”, 11 percent had “major reallocations of responsibility and resources between departments”, and 5 percent had a “completely new organisational structure” over the last three years.

8 Making mild assumptions about the firm’s production technology, Levinsohn and Petrin (2003) show that the demand function is monotonically increasing in  _ijt._

> 9 ˆ ˆ The estimated <sup>ˆ</sup> _j_ ,  _j_ , and  _j_ show the importance of the simultaneity bias when compared to OLS.  The production function parameters are available from the authors upon request.

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