# **1. Introduction**

Housing, measured as the welfare value of the flow of services households derive from their dwelling, is one of the most relevant components of households’ welfare aggregate, which is used as a basis for distributional analysis (Deaton and Zaidi 2002). Nevertheless, due to data limitations and the difficulty of estimating rental values for non-market tenants and owners, the housing component is often neglected in the construction of welfare aggregates. In fact, although we can reasonably assume that the flow of services enjoyed by living in a dwelling is correctly approximated by the monthly rental payment to landlords among tenants, no such measure is readily available for home-owners. Furthermore, not all tenants pay a market price for the dwellings they live in, as many enjoy subsidized rental arrangements (see, for instance, Lara Ibarra et al 2017), or live for free in a dwelling provided by their employer or by a family member. Therefore, in order to include in a measure of the welfare aggregate the value of the flow of services households derive from the dwellings they live in, one would have first to estimate rental values for home-owners and adjust those for non-market tenants.

As argued by Frick et al. (2010), excluding estimated rental prices from the welfare aggregate used in distributional studies may undermine the validity of comparisons of any distributional outcome, both in timeseries within a particular country and in cross-sectional analysis across countries. The need for including the housing component for the purpose of measuring welfare has been stressed by different communities of practitioners. For instance, the US Interagency Technical Working Group on Developing a Supplemental Poverty Measure Census (2010) acknowledges the need to adjust poverty thresholds by housing status. The European Commission financed the project “Accurate Income Measurement for the Assessment of Public Policies” (Sutherland et al., 2009) with the objective, among others, of understanding the distributional impact of the inclusion of rents in income aggregates. Törmälehto and Sauli (2010 and 2013) analyze the implications of including rents in the income aggregate for official poverty and inequality estimates published by Eurostat and conclude that “ _the present data quality does not support adding this component to disposable income concept without major improvements in the comparability of the data between countries and within countries across time”_ (Törmälehto and Sauli 2013, 39).

By contrast, in order to guarantee meaningful international and inter-temporal comparability of countries’ welfare, observed and imputed rental prices are included in National Accounts according to internationally agreed recommendations (SNA 2008). In a sample of OECD countries, for instance, the share of observed and imputed rents in households’ total consumption expenditures range from 6.5 percent in Poland to 25 percent in Finland, according to the latest available National Accounts data (OECD 2018). For the same countries, this share may vary sensitively over time, even within a short period. For instance, in Ireland the share almost doubled from 11 percent in 1996 to 20 percent in 2016, in Spain it increased from 10 percent in 1996 to 16 percent in 2016. In some other countries the share decreased: over the period 1996-2016, in Sweden it declined from 25 to 20 percent and in Mexico from 19 to 14 percent. It is clear then that the exclusion of this large component of household consumption from National Accounts would distort country-rankings in terms of Gross Domestic Product (GDP).

The World Bank has recently started collecting information for understanding the key elements of national methodologies used to produce official poverty estimates. Preliminary results shed light on a sample of 69 country-year observations of recent poverty estimates. Only for 28 observations is it possible to understand whether the welfare aggregate included observed and imputed rents (World Bank, 2015). Nonetheless, when housing is included, researchers and practitioners do not usually test how different imputation methods may affect distributional indicators nor justify why a particular method has been chosen. For example, Ceriani, Olivieri and Ranzani (2019) tell a cautionary tale about the accuracy of rents imputed by using the self2