share of GDP by sector. Data on population come from the U.N. population division and data on poverty come from the World Bank Poverty Database. Finally, data on employment come from the ILO-KILM database.

U.N. data on National Accounts has value added disaggregated into seven sectors (ISCSrevision 3 definitions): agriculture, hunting, forestry and fishing; mining and utilities; manufacturing; construction; wholesale, retail, restaurant and hotels; transport, storage and communications; and other activities. Poverty data from the World Bank in PovCalNet lists several measures of poverty, several measures of income distribution and mean survey income. These data are based on household surveys and measure welfare either by income or by consumption. The KILM database has information for 20 indicators of the labor market, with several disaggregations for each. It is the most comprehensive database for labor market outcomes available. However, it has some limitations for comparability across countries and within countries across time. The main difficulty for cross-country comparability is that definitions vary by country, and the coverage of population and of segments of the labor market are not always the same. In particular, many countries report values for formal employment only, and others leave out sectors such as agriculture. However, the KILM database does provide information when this is the case, so that it is possible to control for differences in measurement and coverage. The main problem for within country comparisons is changes in sample or survey design. Again, the database provides information on these breaks in the series. In addition, the coverage for Sub-Saharan Africa is low. We use indicators on employment by sector. The database presents data for ISCS revision 2 and revision 3. Whenever revision 3 is available for a country year, we use this last classification system; otherwise, we use the available information using revision 2. In either case we make sectoral aggregations consistent with the sectoral aggregation of the U.N. National Accounts information on GDP by sector of economic activity. We also include data on employment for three other African countries based on country studies that use household data in an effort to increase the sample of African countries.<sup>20</sup> To profile growth in terms of employment and productivity by sectors, we construct short run “growth spells.” For each country, short run growth spells are constructed as the percentage change in value added per capita (VA) between _two consecutive comparable points in time_ . One country may have several growth spells. For each growth spell in VA, the corresponding changes (for the exact same years) in employment to labor force ratios (E/A) by sectors, value added per worker (Y/E) by sectors, and ratio of labor force to total population (A/N) are constructed. To link the profile of growth to poverty, the corresponding changes in the poverty headcount ratio are constructed. Care is taken to ensure that the spells are comparable in time by taking into consideration that within a spell measured poverty is constructed using the same welfare indicator (income or consumption) and employment numbers have the same coverage. Consistency in employment measures by sectors is obtained by taking into account breaks in employment series emanating from changes in either coverage or sample, so that within a

> 20 These countries are Senegal, with the source World Bank (2007a); Ghana, with the source World Bank (2007b); and Madagascar, with the source World Bank (2007c). Ghana and Madagascar drop from the sectoral regression to outlier observations.

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