Our inability to satisfactorily account for the role of subsidies implies that we cannot confidently build welfare aggregates as defined in the official methodology. For this reason, we conduct our analysis on an alternative definition for welfare aggregates. This alternative definition corresponds to total expenditures with imputed rents.<sup>10</sup> In other words, we drop food subsidies from the definition of welfare aggregates, both in 2015 and 2017/18. Hence, we compute poverty figures in 2015 and 2017/18 following the official methodology but using total expenditures instead of welfare aggregates.

Our use of a slightly different definition for household consumption implies that the poverty rates we compute do not exactly match official poverty rates, as we document below. However, if the food subsidies do not evolve over the period, as we assume,<sup>11</sup> the poverty trend obtained when excluding subsidies should be a decent approximation of the poverty trend obtained when including subsidies.

# **Implications of ignoring food subsidies**

In order to get a sense of the implications of replacing welfare aggregates by total expenditures, we contrast in Table A.0 their respective values in 2015.<sup>12</sup> In all regions, we observe that total expenditures per capita differ from welfare aggregates per capita by less than 3 percent.<sup>13</sup> Given the fact that food subsidies represent a larger share of consumption for disadvantaged households (CAPMAS 2019), the differences are larger when contrasting the poverty rates obtained with welfare aggregates to those obtained with total expenditures. As shown in Table A.0, the national poverty rate estimated with the former is 29.2, while with the latter it is 24.8 percent, a 4.4 percentage point difference. The official national poverty rate in 2015 was 27.8 percent, an intermediate value between these two estimates.

_Table A.0: Contrasting 2015 poverty estimates based on welfare aggregates with those based on total expenditures_

|Statistic|National|Metropol.|Lower<br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% ofpopulation*|100.0%|6.4%|7.7%|29.8%|14.3%|38.6%|3.2%|
|Per capita welfare*|8614|12371|10069|7952|8598|5841|9015|
|Per capita total<br>expenditures*|8599|12088|9884|8075|8479|5745|9177|
|Poverty rate<br>(welfare)*|29.2%|14.1%|11.6%|23.2%|26.9%|58.1%|24.1%|
|Poverty rate (total<br>exp.)*|24.8%|11.0%|9.6%|17.1%|23.9%|54.8%|15.5%|
|Poverty rate<br>(official)**|27.8%|15.1%|9.7%|19.7%|27.4%|56.7%|-|

**Source** : *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

> 10 Given the fact that homeowners tend to misreport the value of the rent (Ceriani et al. 2019), we imputed the rent of homeowners using a hedonic regression on household characteristics.

> 11 A discussion with CAPMAS validated the assumption that subsidies did not significantly evolve over the period 2015 to 2017.

> 12 The poverty figures based on welfare aggregates are obtained through a code from past work at the World Bank. This code makes assumptions about the distribution of food subsidies. This code also differs slightly from ours in terms of outlier treatments. The authors imputed and identify “outliers” as all the observations for which the log <u>(rent)− mean[log(rent)]</u> following condition applies: > 2.5. Given the fact that 82 percent of the “outliers” in the sd[log(rent)] 2015 sample are landlords, we decided not to apply this process for renters. As a result, the differences documented in Table 1 are not solely attributable to food subsidies.

> 13 Food subsidies are part of the social safety net in Egypt. In 2017, food subsidies amounted to 10 percent of households’ total consumption of food and drink (CAPMAS 2019); thus they represented less than 5 percent of households’ total consumption.

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