# **5.3 Comparing poverty estimates for countries with actual household surveys**

At the time of this writing, household survey micro data for 2020 is available for only 20 countries of which 12 are in LAC with the remaining countries in EAP and ECA. For these countries, it is possible to compare the poverty estimates using the survey microdata with (i) poverty derived using per capita GDP growth-based distribution-neutral projections, and (ii) for a subset of 13 countries, poverty derived using HFPS. Comparing the estimates based on distribution-neutral and phone survey-based projections with the actual survey micro data-based poverty estimates will give us some sense how these estimates might differ. However, given the limited number of countries and the issues discussed above with the actual household surveys themselves, it is not clear how generalizable the results of the findings to other countries will be.

Panel a of figure 13 shows the correlation of the poverty rate calculated using the actual survey data and poverty derived using per capita GDP growth-based distribution-neutral projection. For 13 countries, panel b compares the change in poverty using survey data with poverty using phone surveys. Panel c shows the percentage points change in extreme poverty from 2019 to 2020 using the two sources in panel a, and panel d compares the change in poverty for the sources in panel b.

The figure shows that for most countries in the sample, the change in poverty derived from survey micro data is fairly close to estimates derived using per capita GDP growth and phone surveys. Poverty derived using survey data is on average 0.25 percentage point higher compared to projections based on per capita GDP growth and 0.72 percentage point higher compared to projections based on phone surveys. The correlation coefficient of poverty changes is 0.60 between survey data and per capita GDP growth-based projection, and 0.62 between survey data and phone survey-based projection. Yet there are clear outliers. The change in poverty is 4.5 percentage points higher in Colombia in survey data compared to per capita GDP growth-based projection and 4.8 percentage points higher compared to phone survey-based projection. On the other extreme, survey data estimates a 3.8 percentage point lower poverty in Brazil compared to the per capita GDP growth projection. The lower poverty in Brazil in the survey data can be explained by the extraordinarily large social protection measures implemented during the pandemic (see Castanada et al. 2022).

The poverty rate and size of increases in poverty using the distribution-neutral per capita GDP projection are similar to those using phone surveys. These are reported in Figure B.9. This similarity in estimated poverty is due to the similar magnitude of changes in growth between those calculated using per capita GDP and those calculated using sectoral growth rates, which are used in the phone survey simulations. The fact that the magnitude of the changes is similar and the phone surveys potentially provide additional signal on the distributional changes is an added benefit of using the phone surveys in conjunction with the sectoral growth rates compared to the distribution-neutral per capital GDP growth rates.

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