volatile, reflecting short-run fluctuations in demand conditions and possibly the fact that wages are not negotiated on an annual basis. In order to minimize these short-run fluctuations, the share of labor compensation was regressed on country-industry specific fixed-effects and on the logarithm of capital-labor ratio. Fixed effects account for unobserved factors influencing the technology used (such as endowments, available technologies, institutional factors). Next, we use as a country/sector-specific measure of the labor share the fitted value from this equation, which accounts for country-industry fixed components plus variations due to changes in the capital intensity.

Finally, the calculations of MFP levels requires the use of comparative product price levels across countries in order to convert the value of production to common units, while taking into account differences in the purchasing power of each country’s currency. Ideally, comparative product prices should be measured at the producer level, but survey data on production prices are usually available only for a few countries and for even fewer products. Thus, we use estimates of industry-specific expenditure PPPs.<sup>11</sup> Data on R&D intensity are drawn from the OECD ANBERD database. R&D intensity is defined as the ratio Business Expenditure in Research and Development (BERD) to value-added. Value-added is from the main dataset discussed above. Different measures of human capital were considered. First, macro-economic proxy for general human capital, such as the proportion of individual with secondary school attainment and the average number of years of schooling of employment were considered. The coefficients on either of these two variables turned out to be statistically non significant in the MFP regressions. The alternative indicator is an industry-level proxy of human capital, based on the skill composition of employment and relative wages by skill level. Thus, the measure of human capital is defined by (the subscripts _j_ , _i_ and _t_ are omitted): where ω _HWh_ , ω _LWh_ , ω _HBl_ , ω _LBl_ are respectively the wage rate for the white-collar high-skill, whitecollar low-skill, blue-collar high-skill and blue-collar low-skill workers. _LHWh_ , _LHBl_ and _L_ are respectively white-collar high-skill employment, blue-collar high skill employment and total employment. Thus, this measure is rising with the wage premium of (white-collar and blue-collar) skilled workers relative to unskilled workers, weighted with the proportion of (white-collar and bluecollar) skilled workers in total employment (see the appendix for a description of the data).

> 11 . The alternative is to use aggregate purchasing power parities (PPPs) for GDP as often done in the empirical literature (see e.g., Bernard and Jones, 1996 _a,b_ ). However, aggregate PPPs are problematic if relative prices evolve differently across countries (Harrigan, 1997 _a)_ . For example, Sørensen (2001) shows that while relative productivity levels are independent of the choice of the base year, using PPPs for total GDP leads to different degree of convergence depending on the base year chosen for PPPs. This may be due to the fact that relative prices of manufacturing have evolved differently across countries, but may also reflect the fact that PPPs for total GPD have improved over time.

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