**Table 8.  Atkinson (1) measure before and after adjusting for OLD‐law tenants, by region**

|**Region**|**A(1)**|**A(1) with**<br>**adjusted rent**|
|---|---|---|
|Metropolitan|.179|.162|
|Lower Urban|.108|.105|
|Lower Rural|.072|.072|
|Upper Urban|.145|.134|
|Upper Rural|.08|.08|
|Total|.136|.133|

Source: HIECS, 2012/13. Note: Based on annual consumption expenditure per capita In contrast to what had been suggested before, these results point to an _overestimation_ of inequality in Egypt. Overall, however, the inequality estimates at the national‐level seem to be only slightly overestimated. These results show, to some extent, that using reported rents instead of imputed rents, in the presence of a strong regulatory rental market leads to an underestimation of welfare and an overestimation of inequality in urban Egypt especially the metropolitan areas. This is to be expected as a third of the households in this region live in regulated dwellings.

# _Testing other hedonic models_

We estimated several specifications of the hedonic model as a robustness check on the results presented so far. Besides the model used to estimate the results shown above, we estimate three different specifications. The results are reproduced in **Table 9** . The table shows different indicators obtained directly from the survey data, as well as those obtained by using different hedonic models to correct for the rental regulation. The results include the estimate of housing services (i.e. the component that is incorporated into the welfare aggregate), the poverty rate among Old Law renters and across entire population, and inequality estimates based on the Atkinson Index A(1). Model 1 in the table reproduces the results previously described. Model 2 estimates are obtained from a hedonic model that uses information exclusively from the urban regions of the country (Metropolitan, Lower Urban and Upper Urban) to conduct the adjustments for Old Law tenants. This specification is run to address the potential concern that New Law renters are just a small fraction of dwelling ownership in Rural regions and may affect the quality of the results ( **Malpezzi, 2003** ). Under model 3, we run three separate regressions for each of the urban regions to allow the returns to dwelling characteristics to vary. Model 4 replicates the same specification as model 1, but performs an additional correction for ‘owner’s pride’. There is a possibility that owners might overestimate the market value of rent for their dwelling due to “pride”, which refers to the affinity that owners might have to their own home ( **Frick et al. 2010** ). Similarly, in‐kind dwellers may not have a good grasp of the market value of rental units and may over‐ or underestimate the value of the dwellings they inhabit. Under model 4, we use the regression estimates to correct (i.e. we adjust downward) for the average premium found for owners’ and in‐kind dwellers. The corrections are based on the interaction variables of region and ownership status.<sup>15</sup> As shown in **Table 9** , the results are qualitatively the same across all specifications: i) housing services are underestimated among Old Law tenants if data from the survey is used as face value to calculate the household welfare aggregate; ii)

> 15 Results of all regressions are available upon request.

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