to market output (and the inputs to measured physical capital and labor), but in so doing we, and almost everyone else, are mismeasuring real GDP and TFP. Second, our starting point of a representative-consumer framework implies that we automatically ignore issues of distribution that intuition says should matter for social welfare. We believe that distributional issues are very important. However, our objective of constructing a better welfare measure from aggregate data alone implies that we cannot incorporate measures of distribution into our framework. Thus, we maintain the representative-consumer framework, but without in any way minimizing the importance of issues that cannot be handled within that framework.

Having established that aggregate TFP is the natural measuring stick for aggregate welfare, we then ask the next natural question: Can one show what contribution a subset of the economy (which may be as small as a single ...rm) makes to the aggregate welfare index? The answer is yes, as shown by Domar (1961). Domar established that a correctly-weighted average of sectoral TFP residuals sums to Solow’s familiar aggregate index. We use a variant of his result to present the welfare contributions made by large sectors of the economy using the EU-KLEMS dataset. We compare the sources of welfare di¤erences across countries, asking what fraction of cross-country di¤erences are due to di¤erences in industrial structure as opposed to di¤erences in the welfare contributions of the same sector across countries. We then do a similar exercise using our ...rmlevel data over the period 1998-2004, and investigate the extent to which di¤erences in the relative productivity distribution of ...rms across countries contributes to di¤erences in welfare.

Finally, we decompose aggregate TFP into components. As we noted, while TFP is itself meaningful in welfare terms without any additional assumptions, we need to make assumptions about ...rm technology and behavior in order to decompose TFP in a meaningful way. We use a variant of the decomposition of Basu and Fernald (2002), which is derived by assuming that ...rms minimize costs and are price-takers in factor markets, but may have market power for the goods they sell and might produce with increasing returns to scale. As we also noted, di¤erent assumptions about technology would give di¤erent decompositions, without changing the essential features of the results. For example, Basu and Fernald (2002) assume that factors are freely mobile across ...rms, without adjustment costs, while Basu, Fernald and Shapiro (2001) extend the framework to include costly factor adjustment. Abel (2003) and Basu et al. (2001) show that adjustment costs are a special type of intangible capital, of a sort that needs to be accumulated in ...xed proportions with physical capital. Thus, accounting for adjustment costs in the empirical results would require us to impute an addition to measured output, which is conceptually the same issue as accounting for non-market consumption goods or for more general forms of intangible capital accumulation.

Some of the components in the decomposition we use can be clearly identi...ed as being due to reallocation, since they depend on marginal products of identical inputs not being equalized across ...rms. Other components depend on aggregate distortions, such as the average degree of market power and various tax rates. In order to estimate the reallocation terms, we need to estimate ...rmlevel marginal products. We do so using ...rm-level data for a number of manufacturing industries 5