firms still report according to the old system. For those firms, we have used the conversion accounting tables for each country as produced by PriceWaterhouse. The analysis is then based on IAS across all firms. Sales and inventory changes are reported in all cases. This allows us to adjust the revenue numbers to account for sold (rather than produced) output during the period when countries still used old accounting conventions. Firm-specific output prices are not available. Instead, we use output price indices at the industry level, as reported by the respective statistical offices. All nominal values are deflated using these price indices. This limits the comparisons between firms within the same sector and country, but would not appear to introduce a significant bias in comparisons across sectors or countries. We recognize, however, that the use of separate inflation corrections across firms may be beneficial, given the likely variation across firms. The last year of the sample (1995) is taken as a base year on the assumption that relative prices are closer to relative market prices than in earlier years.

The data on factor inputs include detailed information on firm expenditures, employment, and average hours worked. Expenditures on energy are available separately from other material inputs' expenditures. We use industry-level input and energy price indices reported by the statistical offices to deflate nominal values. Following Gordon and Li (1995), we use purchased inputs rather than used inputs in constructing our material input variable. Under this definition, output produced using materials drawn from inventory results in increased productivity.<sup>2</sup> Data on firm-specific fixed assets are also available but not used in the analysis. Table 2 lists for each country the number of firms in the dataset, aggregate employment in 1992, and its share of total manufacturing employment, and the sectoral distribution of employment, as well as descriptive statistics on employment (mean and median number of employees).

The data cover 6,354 firms with over 6.5 million employees. Polish and Romanian firms are the largest among the seven countries - they have a mean number of employees of 911 and 1,521 respectively. This is hardly surprising since the two countries have also the largest total population among the sample countries. The sectoral distribution of employment in the dataset varies across countries, with Bulgaria, the Czech Republic, and Poland displaying a high concentration in the Non-Electrical machinery sector, Hungary in Textiles, Romania and the Slovak Republic in Fabricated metals, and Slovenia in Electrical machinery.

Each firm reports its ownership status starting in 1991. This is reported as a discrete variable (privatized\state-owned). In addition, the data show what ownership share of the firm went private in a particular year. To avoid possible differences in the definition of private firms, we choose to call a firm privatized when more than a third of its shares is privately-owned. This choice was made based on the existing corporate laws in the Central and Eastern European countries. In all seven countries, major strategic and investment decisions at the firms' Board of Directors can be taken with only two-thirds majority. Thus if more than one-third of shares are privately owned, collectively private owners can block decisions at the Board. Based on this

> 2 We also took the used material expenditures as a basis for the material input variable (not reported). The results are robust to either specification.

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