topics covered in the standard WBES questionnaire, it allows for investigations on a wide range of research questions. Here, we will build on the approach suggested in Chapter 6 of Taglioni and Winkler (2004). They describe how consistent indicators for three dimensions of GVC integration can be calculated based on responses to the WBES surveys and they illustrate the results for a few countries.

In Section 2.2 we calculate these three indicators for each country in sub-Saharan Africa and illustrate some patterns that emerge. We exploit both the firm, country, and time dimension of the surveys to document the range and depth of GVC integration and the rate of change over time. A table in the Appendix shows the averages and changes over time for all individual countries, averaged over all survey years. We also make a data file with the firmlevel indicators an a set of firm characteristics available to interested researchers. We show how patterns of GVC engagement differ along a few dimensions of interest, e.g. they differ between foreign-owned and domestic firms, between large and small firms, or between manufacturing and service firms. This rich source of information can be exploited to condition on several more firm characteristics of interest. For example, the data also records each firm’s location, detailed sector, capital intensity, owner characteristics, and many more dimensions of firm heterogeneity.

While it is useful to explore the extent that firms integrate in GVCs in its own right, the WBES information can also be used to construct the average extent of integration for each country. However, the representativeness of the surveyed firms for the entire economy will naturally vary by country. Freund and Pierola (2015) have shown that a few firms account for an extremely large fraction of exports in most developing countries. To have a sense of the fraction of a country’s interaction with the world economy that the sample of firms represents, we calculate for each country-year survey the ratio between the aggregate exports over all sampled firms and the aggregate exports for the country as recorded in the UN Comtrade database. On average, this fraction is 19%, but it ranges widely across countries, which is important to keep in mind if one wants to generalize from the WBES results.

The second source of information that we exploit is the multi-region input-output table (MRIO) which is part of the Eora database. In contrast with two widely-used comparable global IO tables, the WIOD (maintained by researchers at the University of Groningen) and the TiVA database (of the OECD), the Eora database includes information on all countries in the world, including all individual sub-Saharan African economies.<sup>3</sup> Naturally, the more disaggregate country dimension comes at the cost of greater reliance on proportionality assumptions and imputations, but the advantage is that the MRIO includes bilateral input flows between all African country-sector pairs. As a result, it provides an unprecedented wealth of information on the regional production network in Africa and its connections with the rest of the world economy.

In order to exploit the staggering amount of information, as discussed below the full MRIO contains more than 24 million input-coefficients, we need to aggregate the table to reduce its dimensionality. We describe the process of collapsing the global IO table in a set of country-

> 3 In the WIOD and TiVA databases, the African countries are grouped together in one or two country-groupings or in the rest-of-the-world aggregate.

3