additional dimensions like sector and size using difference-in-differences (e.g., exploiting differences in the cross-industry variation across countries).

As will become clear in our discussion of the data in the next section, limitations in the data in different dimensions across countries and compromises that were made to generate ‘comparable’ data, may hamper analysis of certain questions and generally suggest caution in interpreting simple cross country differences. We now turn to a discussion of the data.

# **3. A new dataset of firm-level data from industrial and developing countries**

The dataset used in the study was collected in various stages. Most recently, the firm-level project organized by the World Bank collected indicators for 14 countries (Estonia, Hungary, Latvia, Romania, Slovenia; Argentina, Brazil, Chile, Colombia, Mexico, Venezuela, Indonesia, South Korea and Taiwan.(China)) An earlier OECD study collected indicators based on information on firms from: Canada, Denmark, Germany, Finland, France, Italy, the Netherlands, Portugal, United Kingdom and United States.

These projects made use of a common analytical framework and the data analysis and collection was conducted by active experts in each of the countries.<sup>5</sup> The framework involves the harmonization, to the extent possible, of key concepts (e.g. entry, exit, or the definition of the unit of measurement) as well as the definition of common methodologies for studying firm-level data. The methodology for collecting the country/industry/time panel dataset built up from underlying micro-level datasets has been referred to as ‘distributed micro-data analysis’ (Bartelsman 2004). A detailed technical description of the dataset may be found in Bartelsman, Haltiwanger and Scarpetta (2004).

The distributed micro-data analysis was conducted for two separate analytical themes. The first set of analyses gathered data relating to firm demographics, such as entry and exit, jobs flows, size distribution and firm survival. The second theme gathered indicators of movements of firms and resources related to productivity, such as productivity contributions of entry/exit and other measures of resource reallocation. The synthetic indicators used in the analysis for these two themes are discussed in details in Box 1.

The analysis of firm demographics is based on business registers, census, social security databases, or employment-based register containing information on both establishments and firms (see Table 1). Data for the analysis of productivity growth come more frequently from business surveys. Using these data, time-series indicators on firm demographics were generated for

> 5. In addition to the authors of this paper, the researchers involved in the distributed micro-data analysis network for the various projects are: John Baldwin (Canada); Tor Erickson (Denmark); Seppo Laaksonen, Mika Maliranta, and Satu Nurmi (Finland); Bruno Crépon and Richard Duhautois (France); Thorsten Schank (Germany);  Fabiano Schivardi (Italy); Karin Bouwmeester, Ellen Hoogenboom and Robert Sparrow (the Netherlands);  Pedro Portugal Dias (Portugal); Ylva Heden (Sweden); Jonathan Haskel, Matthew Barnes, and Ralf Martin (United Kingdom); Ron Jarmin and Javier Miranda (United States); Gabriel Sánchez (Argentina), Marc Muendler and Adriana Schor (Brazil), Andrea Repetto (Chile), Maurice Kugler (Colombia and Venezuela), David Kaplan (Mexico), John Earle (Hungary and Romania), Mihails Hazans (Latvia), Raul Eamets and Jaan Maaso (Estonia), Mark Roberts (Korea, Indonesia and Taiwan (China)), Milan Vodopivec (Slovenia).

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