welfare indicators are not adjusted to spatial price differences in many countries. The World Bank however usually recommends spatial price adjustment when measuring poverty and inequality (Deaton and Zaidi, 2002; Ravallion, 1992, 1996). This is due to the fact that different price levels across geographical areas represent different levels of consumption and welfare for the same amount of expenditure.

In the past, PovcalNet, decided not to use spatially deflated household expenditures to facilitate inter‐ temporal and international comparability of extreme poverty indicators. Indeed, the ways spatial price adjustments are conducted differ largely across countries and are for some countries not properly documented. Recently however, the documentation of price adjustments has improved and the adopted methodologies have become more standardized. PovcalNet started to accept spatially adjusted welfare aggregates but for most countries, the extreme poverty and the shared prosperity indices are still estimated with spatially unadjusted welfare aggregates. It is important to examine implications of the use of spatial price adjustments for estimating the shared prosperity index.

Another potential issue is that when calculating shared prosperity for selected countries (and extreme poverty), mean expenditure or income of the poorest 40 percent of the population are estimated using grouped data rather than estimating directly from the distribution of the household expenditure or income. The grouped data approach first calculates means of household expenditure or income for deciles or ventiles and then estimates a Lorenz curve from the grouped data. Finally, the mean of the bottom 40 percent of the population of a country is estimated from the fitted Lorenz curve and the mean of household expenditure or income in the whole population (Datt, 1998; World Bank, 2008; Mitra et al., 2010; or Box 2 in the annex).

The mean of the poorest 40 percent of the population can be directly calculated from the microdata or grouped data. Creating grouped data and estimating a Lorenz curve using grouped data seems unnecessary and some might even argue that the additional steps add non‐negligible estimation errors (Azevedo and Mitra, forthcoming). The grouped data approach was adopted for the monitoring of extreme poverty because the World Bank could not access household unit record data for most countries when the monitoring started. Even though PovcalNet has started using microdata for estimating extreme poverty for most countries since 2015, there are still several large countries such as China, Iran and Algeria that do not share household unit record data with the Bank. As a result, PovcalNet needs to use the grouped data approach for these countries to calculate poverty and shared prosperity indicators. This is currently the case for 15 out of 94 countries.

The last technical issue is associated with the use of income for measuring shared prosperity. There is a clear preference of using consumption data for monitoring extreme poverty, because it has several advantages over income data in terms of welfare measurement (Deaton, 1997). Nevertheless, many middle income countries in LAC and ECA use income data for their official poverty measurement. For the 2014 and 2015 update of the GDSP, the GPWG decided to increase the number of countries for which income, rather than consumption, is used to estimate shared prosperity.

However, the distribution of income is quite different from that of consumption expenditure and thus additional problems, which consumption data did not have, may occur by moving from consumption to income. For example, income data are usually more volatile than consumption. Of particular concern is income with negative or zero values. Such outliers can affect inequality statistics and means of the poorest 40 percent, resulting in a “lumpy” movement of the shared prosperity indicator.

Empirically testing the impact of different methodological choices in this section is based on a different set of countries depending on the type of test conducted. Comparing grouped versus microdata is based on 20 countries representing all regions of the World. Sixteen countries were used to estimate the impact of having spatially adjusted versus nominal welfare aggregates. The latter analysis includes fewer 8