In the real world, the immensity of data problems is overwhelming. In fact, it proved to be impossible to produce a simultaneous assessment of multiple categories of public expenditures over a long period of time. First, such an assessment would require data on expenditure that would be consistent across sectors. The IMF Government Finance Statistics database (GFS) proved to be the only data source that achieved this consistency across sectors. GFS is the main data source for most empirical cross-country studies on government expenditures<sup>13</sup> ; it is the only database offering comparable data on public expenditure for all countries in the world, including developing countries.

This does not mean that the data are good. For instance, the GFS ignores a wide range of sectoral expenditures by public enterprises and other alternative public providers. It tends to have much better data on the central government than on subnational governments. For most countries, the data reported in this widely quoted publication tend to underestimate actual expenditure levels. In addition to the gaps in coverage, the GFS does not credit specific sectors for subsidies debited from general government expenditures to support sector-specific activities managed from the sectoral budget. In sum, any study relying on that data will be biased. Yet everyone uses the data, simply because they are the only set available.<sup>14</sup> Second, many of the data are collected sporadically. The time coverage of expenditures and outcomes differs for many countries; in other words, inputs are available for some years and outputs for others. There are hardly any relevant data on sector-specific institutional characteristics. We thus had to pick between (i) maximizing the number of variables used, irrespective of time-coverage compatibility across countries and across subsectors, so as to minimize the loss of sample size, and (ii) getting the cleanest possible dataset, irrespective of the impact on the sample size. We adopted a hybrid solution. We gave up on presenting a number of outcomes available for only a few countries or years in favor of maintaining a reasonable sample size while accepting that the data quality and comparability would not be free from criticism.

Third, because we were unable to generate useful country-specific data given the limited data coverage for each country, we focused on generating _enough_ country coverage in four country-income groups: low, lower-middle, upper-middle, and higher. These classifications turned out to be broad enough not to be influenced by any remaining countryspecific problems. The breadth also gives a sense of the gap that can exist among countries at different stages of development. The availability of time series on these broad groups was essential in the choice of countries and indicators because it also allowed us to track the evolution of this gap among country groups.

> 13 The OECD and European Union have their own databases, which are widely used in the empirical literature but are of limited use here because there are no comparable data for developing countries.

> 14 Note that the World Health Organization has started to generate its own expenditure series on health. It is more detailed and covers almost the whole world, but it does so only since 1998 **.**

5