of Albania and Bangladesh, under the log-linear approach, and in Iraq with both methods, rent shares are a decreasing function of households’ consumption.

As a reminder to the reader, in the following analysis we report all results using also the official consumption aggregate (denoted in the tables that follow as _original_ ). The main tables are shown only for the most recent year, with the exception of shared prosperity, where both years are used, and poverty results for Bangladesh, where we show 2005 instead of 2010. This is because the 2005 HIES was used to update the official poverty line, and we were able to get hold and to modify the original code for this year.

# **4.1. Inequality**

The impact of including rent on inequality is assessed by looking at changes in the Gini index. Table 5 shows the Gini index calculated on the consumption aggregate without the rental component (first row) and the difference, in absolute value, between the initial Gini index and the value of the same indicator calculated on consumption aggregates that include imputed rents according to one of the two imputation methods, namely the self-assessment approach and the log-linear model.

Albeit always statistically significant, only in few instances the impact on inequality of including rents in the consumption aggregates is over one percentage point, although the exact magnitude of the changes varies across countries and for the same country across rent-imputation approaches. A notable exception is Iraq 2007 (Table 5, panel a), where we observe an increase of almost 3 percentage points in the case of the log-linear model relative to the case of no rent. In Albania and Peru, the increase in inequality is as large as 2 percentage points when the self-assessment approach is used. This is likely explained by the large variation introduced by selfassessed values of home-owners, who in most cases tend to overestimate the rental market price due to their attachment to the dwelling they own and in some cases they built (see Heston and Nakamura 2009 for the home-owner pride effect, and Ceriani, Olivieri, and Ranzani 2019 for a general discussion on the accuracy of self-assessment and ways to test it).

As for the sign of the impact, in most instances we observe an increase in inequality, in contrast with the rest of the literature where findings seem to build consensus around an equalizing effect of rent imputation (see Balcázar et al. 2017 for a recent review of the literature). Nevertheless, no general conclusion can be drawn: the sign of the impact varies across countries and for the same country across rent-imputation approaches. For instance, the Gini index increases in all countries in the first round of data (Table 5, panel a). In the second waves, instead, the Gini index increases in the case of Albania and Bangladesh using the log-linear approach, while it decreases in the case of Iraq using the log-linear method (Table 5, panel b). These findings seem to corroborate our hypothesis that the sign and magnitude of the rent imputation impact are an empirical question and no a priori theoretical conclusion can be drawn.

# **4.2. Poverty**

The analysis of the impact of rent imputation on poverty is ascertained following two different approaches. First, we show how poverty (as measured by the headcount, poverty gap and squared poverty gap indices) varies when the initial consumption aggregate, which excludes rent, is modified to include imputed rent and the poverty line is left unchanged. Then, we show the impact on poverty indices of including rent both in the construction of the poverty line and in the consumption aggregate.

As expected, poverty decreases if we keep constant the poverty line to the case of no-rent, independently of the rent imputation method. Table 6 shows in fact that the three poverty indices, i.e. incidence, depth and severity of poverty, decrease as a consequence of the shift to the right of the consumption distribution.

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