adjustment has significant impact on the welfare ranking of households, individuals and areas. These rankings are important for designing policies to alleviate extreme poverty and promote shared prosperity. Unadjusted welfare indicators will likely misguide the identification of the extreme poor and the poorest 40 percent of the population.

# **3.3. Shared Prosperity Using Income Data**

As mentioned, various factors in the poverty measurement literature point to consumption being a better proxy to measure welfare than income. Nevertheless, many countries in the world measure welfare using income. In particular, European countries measure welfare using income data from Eurostat on Income and Living Conditions (EUSILC) including negative and zero income. Countries from LAC also use income to measure social‐economic well‐being, but recode negative income to missing values. In this section, we test the impact of negative income data on shared prosperity using EUSILC data and the impact of zero incomes on shared prosperity using selected countries from the Latin American and Caribbean region.<sup>13</sup>

## **3.3.1 Data with negative numbers**

The shares of households with negative income across EUSILC surveys are minimal, with a maximum of 1.2 percent for Greece in 2011. For most countries collecting EUSILC data, the share is less than 0.5 percent. To observe the impact of negative numbers of income, we run three simulations: (1) keeping negative values; (2) replacing negative values with zero; and (3) excluding negative values. The results of the shared prosperity numbers using these three scenarios are reported in table 5. The differences in estimates for the proposed scenarios are reported in percent and percentage points. In relative terms, the difference in estimates can vary substantially reaching more than 100 percent; however, this is mainly for countries with low numbers on shared prosperity. In absolute terms, the differences are less dramatic ranging from 0 to 0.7 percentage points.

Though not strictly comparable (as different sets of countries are used), the magnitude of the change in shared prosperity estimates after excluding negative numbers seems to be higher compared to the results for grouped versus microdata. For example, exclusion of negative income can increase shared prosperity by 59 percent in the case of Belgium and reduce it by 123 percent in the case of Denmark, whereas the largest change in shared prosperity using microdata did not exceed 14 percent. In absolute terms, excluding negative incomes also has also a higher impact than the choice of grouped versus microdata. Thus, the largest gap for testing the effects of negative income on shared prosperity is 0.7 percentage points compared to 0.14 percentage points for grouped versus microdata (table of figure 5).

The largest absolute differences in estimates (in percentage points) are observed in countries with the highest shares of negative numbers (figure A3) and large negative numbers. For example, in Denmark (income year 2011), 25 percent out of 24 households (0.44 percent of the 5,355 household sample) with negative incomes (per capita per day in 2005 PPP terms) are very large, ranging from ‐20 to ‐400, while the weighted national average is 39 and the maximum is 541. This suggests that the frequency of negative numbers and their magnitude can seriously distort the shared prosperity indicator.

> 13 In addition, we use the opportunity that EUSILC data have both, negative and zero incomes, and compare impacts of dropping these observations. Results are shown in the annex. As one may expect, dropping negative observations has stronger impact than dropping observations with zero incomes on average.

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