metadata: authors: 0: author: Decerf,Benoit Marie A
1: author: Genoni,Maria Eugenia
2: author: Helmy,Imane Abdelfattah Ahmed Refaat
3: author: Sanz,Guillermo Federico
count: Arab Republic of,Egypt, Arab Republic of
docna: 0: docna: Implications of Using Nonstandard Poverty
            Lines : An Illustration Using the Case of the Arab Republic
            of Egypt
docty: Policy Research Working Paper
abstracts: cdata!: Many developing countries&#x2019;
            official poverty methodologies rely on nonstandard poverty
            lines, which complicate poverty comparisons across space or
            time. The paper considers the case of the Arab Republic of
            Egypt, whose official poverty lines have two important
            nonstandard features. First, the line is neither absolute
            nor relative, but rather hybrid or &#x201c;weakly
            relative.&#x201d; Second, the poverty line&#x2019;s
            implicit equivalence scales are not fixed, but are rather
            endogenous. This paper provides a conceptual and
            quantitative understanding of these two nonstandard
            features. The results reveal that the equivalence scale
            implicit in the official methodology is quantitatively very
            similar to the (simpler) per capita equivalence scale.
            Switching to a per capita equivalence scale would help
            address an implicit gender bias that the paper identifies in
            Egypt&#x2019;s official poverty lines. The analysis
            shows that the official distribution of poverty across
            regions is very similar to that associated with a purely
            absolute line. In addition, the change in official poverty
            rates over the period analyzed (2015 to 2017/18) lies
            halfway between the larger increase captured by a purely
            absolute line (10 percentage points) and that captured by a
            purely relative measure (1 percentage point). However, the
            results show that these more standard poverty lines do not
            systematically perform better than the official methodology
            with respect to the identification of disadvantaged households.
display_title: Implications of Using Nonstandard
            Poverty Lines : An Illustration Using the Case of the Arab
            Republic of Egypt
listing_relative_url: /research/2023/05/34058712/implications-using-nonstandard-poverty-lines-illustration-using-case-arab-republic-egypt
topicv3: Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707
publishtoextweb_dt: 2023-05-08T00:00:00Z
dois: 10.1596/1813-9450-10437
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1947055
1: author_key: 793234
2: author_key: 1591461
3: author_key: 2104958
model_extractions: Policy Research Working Paper 10437 Implications of Using Nonstandard Poverty Lines An Illustration Using the Case of the Arab Republic of Egypt _Benoit Decerf Maria Eugenia Genoni Imane Helmy Federico Sanz_ Development Research Group & Poverty and Equity Global Practice May 2023
Policy Research Working Paper 10437

# **Abstract**

Many developing countries’ official poverty methodologies rely on nonstandard poverty lines, which complicate poverty comparisons across space or time. The paper considers the case of the Arab Republic of Egypt, whose official poverty lines have two important nonstandard features. First, the line is neither absolute nor relative, but rather hybrid or “weakly relative.” Second, the poverty line’s implicit equivalence scales are not fixed, but are rather endogenous. This paper provides a conceptual and quantitative understanding of these two nonstandard features. The results reveal that the equivalence scale implicit in the official methodology is quantitatively very similar to the (simpler) per capita equivalence scale. Switching to a per capita equivalence scale would help address an implicit gender bias that the paper identifies in Egypt’s official poverty lines. The analysis shows that the official distribution of poverty across regions is very similar to that associated with a purely absolute line. In addition, the change in official poverty rates over the period analyzed (2015 to 2017/18) lies halfway between the larger increase captured by a purely absolute line (10 percentage points) and that captured by a purely relative measure (1 percentage point). However, the results show that these more standard poverty lines do not systematically perform better than the official methodology with respect to the identification of disadvantaged households.

This paper is a product of the Development Research Group, Development Economics and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at bdecerf@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Implications of Using Nonstandard Poverty Lines: An Illustration Using the Case of the Arab Republic of Egypt**

Benoit Decerf, Maria Eugenia Genoni, Imane Helmy, Federico Sanz<sup>1</sup>

**JEL codes: I32, D63, E31 Keywords: Poverty lines, Absolute poverty, Poverty trend, Egypt, Equivalence scale**

> 1 The authors are Benoit Decerf (Economist, DECRG), Maria Eugenia Genoni (Senior Economist, EMNPV), Imane Helmy (Senior Economist, EMNPV), and Federico Sanz (Consultant, EMNPV). We thank Tara Vishwanath and Gabriel Lara Ibarra for helpful comments. We thank Dr Heba el Laithy for useful discussions. Financial support for this analysis was provided by the SIEF Trust Fund. This paper was cleared by Johannes Hoogeveen (Practice Manager, EMNPV). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **Section 1: Introduction**

Many developing countries’ official poverty methodologies rely on non-standard poverty lines, which are neither absolute nor relative. Examples include the Arab Republic of Egypt, Indonesia and Kenya, among others. The use of non-standard lines may pose challenges to conduct poverty comparisons across space or time. However, not many analyses address these challenges. This paper aims at partially filling this gap using the case of Egypt.

Egypt’s official poverty lines find their foundation in the cost-of-basic needs (CBN) approach developed by Ravallion (1998). In a nutshell, the CBN method computes the poverty line to be the sum of two components: the food component and the non-food component. The food component is the cost of purchasing a given number of calories given local food consumption behavior. The non-food component is the typical non-food expenditures of individuals whose food expenditures correspond to the food poverty line. The CBN method has been shown to be superior in terms of welfare consistency (Ravallion and Bidani, 1994) to the alternative food-energy intake method, which was proposed by Dandekar and Rath (1971) and refined by Greer and Thorbecke (1986). As a result, the official poverty lines of many developing countries are derived from the CBN (Ravallion, 2015a).

Egypt’s official poverty lines are derived from a particular version of the CBN method. The Egyptian version has two “non-standard” characteristics, which distinguish it from the classical definition of absolute poverty lines. The first non-standard feature relates to the way in which these lines are updated over time. In line with the CBN method, their food component is computed from a reference number of calories multiplied by the prevailing price of purchasing one calorie. The non-standard characteristic is that the price of purchasing a calorie depends on the consumption patterns _observed in the survey period_ . Hence, instead of updating over time the price of a calorie for inflation, the Egyptian poverty lines update the price of a calorie as a function of the updated food consumption behavior. This implies that the price of a calorie, and thus the food component of the poverty line, is not necessarily kept fixed in real terms over time. When growth changes consumption possibilities, households may switch their food habits and consume calories of a different quality (say, substitute wheat for animal sourced proteins), thereby changing the real value of the price of a calorie. This feature implies that the Egyptian poverty lines are neither purely absolute nor purely relative.<sup>2</sup> Rather these lines are hybrid, or “weakly relative” (Ravallion and Chen, 2011). Egypt is not an exception in that regard, as the official poverty lines of other developing countries, such as Indonesia or Kenya, have the same feature. This feature considerably complicates poverty comparisons.

The second non-standard feature relates to the equivalence scale implicit in Egypt’s official poverty lines. In line with the CBN method, the non-food component of the poverty line is computed as a fraction of the food component. However, this fraction is not constant but rather depends on the household composition and the consumption patterns _observed in the survey period_ . This implies that the equivalence scale implicit in the official methodology is not fixed, but rather endogenous. In other words, the equivalence scale changes over time and across space. This non-standard feature again makes it challenging to interpret official poverty figures and trends. It is unclear to what extent the departure from more standard poverty lines impacts poverty trends and the distribution of poverty across regions. In this paper, we provide a conceptual and quantitative understanding of the two non-standard features inherent in the official poverty lines of Egypt. We study which of these two non-standard features has the largest impact on poverty figures. We also investigate how Egypt’s poverty methodology compares with more standard methodologies, both in terms of identifying disadvantaged households and in terms of poverty comparisons over space and time.

We start by studying the poverty trend over the period 2015 – 2017/18. Over the period, the fraction of poor individuals has increased due to a negative shock to consumption. However, we show that the use of a comparable poverty line leads to an increase in poverty rate over the period about twice as large as that documented by the official poverty lines. Indeed, the poverty increase over the period can be

> 2 See Decerf (2022) for a recent survey of absolute and relative poverty measurement.

2
decomposed between the effect attributed to changes in real consumption and changes in the real value of the poverty lines.<sup>3</sup> The results reveal that the increase in poverty rate would have been about twice as large if the reduction in real consumption had not led to a reduction in the real value of poverty lines. In other words, the increase in poverty over the period would have been even higher if measured using a purely absolute measure of poverty, which keeps the real value of the poverty lines fixed over time. Also, the results show that the evolution of poverty was heterogenous across regions. The Metropolitan areas, where poverty was initially lower, experienced the largest increase. In contrast, Upper Rural Egypt, where poverty was initially the highest, experienced a small _reduction_ in poverty (according to the official methodology). The decomposition reveals that the decrease in poverty in Upper Rural Egypt can be entirely attributed to the reduction in the real value of the poverty lines over this period.

Then, we investigate the equivalence scale implicit in the official poverty lines. We show the existence of an implicit gender bias, whereby the poverty line of women is smaller than that of men in proportions that cannot be entirely attributed to caloric needs differences between men and women. As a result, the incidence of poverty among women is “artificially” reduced. Our results also show that the official equivalence scale is much closer to the per capita equivalence scale than to the square root equivalence scale. Indeed, both the implicit equivalence scale and the per capita equivalence scale evolve with household size in a similar fashion. We also show that the official equivalence scale is primarily affected by calorie requirements and is thus much less affected by the imputed food shares. Hence, imputing the same food share to all households would be a slight additional simplification that does not drastically impact poverty comparisons. Adopting a per capita equivalence scale could also help resolve the implicit gender bias. Interestingly, the results show that the poverty rate under the official methodology is lower among women than men, while the opposite holds when adopting a constant food share and the per capita equivalence scale.

In the latter portions of the paper, we evaluate the impact of the hybrid definition of the official poverty lines, which are not purely absolute or relative. We show that the official methodology does not seem to perform worse than alternatives with respect to identifying disadvantaged households. To investigate this issue, we compare the distribution and evolution of poverty under the official poverty lines with those obtained under two alternative types of poverty lines. These two types are both based on the per capita equivalence scale, but one corresponds to purely absolute poverty lines, while the other to purely relative poverty lines. To be able to compare the performance of each of these three types of poverty lines in terms of identifying disadvantaged individuals, we define the purely absolute and purely relative lines in such a way that they yield the same national poverty rate in 2015. The results show that the official poverty distribution across regions is broadly the same as that associated with the absolute methodology. In contrast, the official distribution of poverty across different household types is more distant from those obtained with the two alternative types of poverty lines. This result likely reflects the fact that the latter two types share the same equivalence scale. Our results do not allow the conclusion that the official poverty lines perform worse than the two alternatives with respect to identifying disadvantaged households. In particular, the official lines better identify households deprived of some welfare-relevant assets or households whose heads have low education. In contrast, the official lines underperform in identifying households with more dependents. We also show that the official poverty trend is halfway between the two trends respectively recorded by the purely absolute lines and the purely relative lines. The trend captured by the purely absolute lines is broadly the same as the effect of the changes in real consumption (as computed in the decomposition of the official poverty trend).

The first contribution of our results is to document, in the Egyptian context, the impact on poverty comparisons of using weakly relative poverty lines. A second contribution consists in uncovering the potential gender bias present in some versions of the CBN methodology.

The remainder of this paper is organized as follows. Section 2 presents the official poverty lines and underlines their non-standard features. Section 3 presents the poverty figures in 2015 and 2017/17, and

> 3 This decomposition follows the standard method of Shorrocks (1999).

3
studies the poverty trend from 2015 to 2017/18 and its decomposition. Section 4 quantifies the impacts of the non-standard features of the official methodology. Section 5 provides concluding comments.

# **Section 2: Description of Egypt’s official poverty lines**

In this section, we briefly present the data and methodology used to produce poverty figures for Egypt. More complete information on Egypt’s official poverty measurement methodology can be found in World Bank (2011) or CAPMAS (2019).

# **Data**

Living standards in Egypt are monitored through the Household Income, Expenditure, and Consumption Survey (HIECS), which captures income, expenditures, and consumption at the household level. These surveys are conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS).

Our analysis is based on two rounds of these surveys. First the 2015 round, which covered a representative sample of 23,976 households for the period January to December 2015.<sup>4</sup> Second, the 2017/18 round, which covered a representative sample of 24,971 households from October 2017 until September 2018.

# **Total household expenditures**

The consumption indicator that we use is annual household total expenditures. Total expenditures include all spending on consumption of goods and services, including food and beverages; clothes and footwear; housing; housing services (water, electricity, gas, and fuel); furniture and household equipment and maintenance; health care; transportation; wired and wireless communication; culture and recreation; education; restaurants and hotels; and miscellaneous goods and services. The implicit value of housing is also added.<sup>5</sup> In the remainder, we sometimes use “consumption” or “expenditure” interchangeably.

# **Official poverty lines**

The definition of the official poverty lines is relatively complex, which makes the interpretation of the poverty trend challenging. The main complexity comes from the way in which the poverty lines are computed and updated.

The constitutive elements of Egypt’s official poverty lines are related to the cost of basic needs approach (Ravallion 1998, 2015a). These lines are computed as the sum of two components: a food poverty line and a non-food poverty line. For a household h living in region r, the poverty line zhr is thus computed as F The food poverty line zhr for household h living in region r is computed as:

> 4 The publicly available microdata of HIECS 2015 only contains 50 percent of the total sample.

> 5 As we explain in Appendix 2, the households’ total expenditures slightly differ from their official welfare aggregates, because the latter also include (small) food subsidies.

4
where pr<sup>c</sup> is the price of a calorie in region r and Ch is the total number of calories necessary for the household to satisfy its minimal energy requirements, which depends on its composition. The non-food poverty line for household h is computed as: where shr denotes the share of food imputed to the households. The regression used for the imputation of shr is presented in Appendix 1. In a nutshell, the observed food shares are regressed on household consumption and household characteristics. Then, the share imputed to a household corresponds to that predicted for a household with the same characteristics but whose consumption is equivalent to the food F poverty line zhr. Given that households whose consumption is equal to the food poverty line must displace food consumption to allow for non-food consumption, this approach yields a rather conservative poverty line.

# **Non-standard aspects of the official methodology**

The computation of these poverty lines is non-standard in the following respects:

# 1. Hybrid poverty lines

The food poverty line is computed from a methodology that is neither purely absolute nor purely relative. The caloric requirement is kept fixed over time, as is typical for an absolute approach to monetary poverty. The price of a calorie is not kept fixed in real terms over time, which introduces an element of relativity. As explained in more detail in Appendix 1, the price of a calorie depends on a reference food bundle that is representative of food consumption _in the year considered_ . As a result, if society gets richer over time and switches to more expensive calories, the real price of a calorie entering the definition of the food line increases, thereby raising the food poverty line (in real terms).

The non-food poverty line also has a non-absolute feature, which is that the share of non-food imputed is not fixed over time but rather depends on the share of total expenditures spent on food _in the year considered_ .

These non-absolute characteristics complicate the interpretation of the official poverty trend because this trend is the combined result of the evolution of welfare aggregates in real terms and the evolution of the poverty lines in real terms.

2. Endogenous equivalence scale The equivalence scales are neither fixed across regions nor time. Households of different sizes and compositions have different needs and cannot take advantage of the same household public goods.<sup>6</sup> Poverty lines typically account for these differences using a factor that scales up or down the poverty line as a function of household composition. The equivalence scale is the function that computes this factor.

The particularity of the Egyptian methodology is that the factor used to scale the poverty line is not fixed across time and space for a given household size and composition, but rather varies over time and across space. This endogeneity does not come from the energy requirements, which are fixed for a given household composition. Rather, this endogeneity results from the fact that the share of food imputed to the household depends on the region, and also that this share depends on the year considered (through observed food shares). Again,

> 6 An example of the implications of different needs is that children have smaller calorie requirements than adults. An example of the implications of household public goods is that larger households typically have lower per capita expenditures on housing because they share the same house.

5
the endogeneity of the equivalence scales complicates the comparison of poverty trends across household types. Indeed, the real values of poverty lines do not grow at the same rate for all households, because the equivalence scale evolves over time.

# **Section 3: Poverty figures over the period 2015 - 2017/18**

# **3.1 Poverty figures in 2015**

We start with some descriptive poverty statistics in 2015.

As shown in Table 1, the national poverty rate estimated in 2015 is 24.8 percent. Table 1 shows that, in 2015, the poor lived more than proportionally in rural areas and in Upper Egypt, where per capita total expenditures are lower.

_Table 1: Spatial distribution of poverty in Egypt in 2015 (total expenditures)._

|Statistic|National|Metropol.|Lower<br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% of population*|100.0%|6.4%|7.7%|29.8%|14.3%|38.6%|3.2%|
|Per capita total<br>expenditures*|8599|12088|9884|8075|8479|5745|9177|
|Poverty rate (total<br>exp.)*|24.8%|11.0%|9.6%|17.1%|23.9%|54.8%|15.5%|

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

Table 2 complements these insights by providing a more complete picture of Egypt’s poor in 2015. Poverty affects 29 percent of children, 25 percent of adults, and 17 percent of the elderly. Hence, children are more affected by poverty than adults. On average, poor households have a larger size (5.6 members) than non-poor households (3.9 members). The former also have more dependents (2.7 dependents) than the latter (1.6 dependents). Poor households are more often headed by a person with low education than non-poor households. Poor households are more likely to have a head who is working. Finally, poor households are less likely to own welfare-relevant assets, such as private cars, washing machines, or smartphones. This unequal distribution of welfare-relevant assets is not surprising but confirms that the poverty measurement methodology adequately identifies households with fewer assets.

_Table 2: Profile of the poor in 2015 (total expenditure)_

|Profiles among poor households<br>(total expenditures)|Poor<br>household|Non-poor<br>household|
|---|---|---|
|Fraction<br>of<br>poor<br>among<br>Children<br>Elderly<br>Men<br>Women|29%<br>17%<br>26%<br>24%||
|Average HH size|5.60|3.93|
|Average number of dependents|2.67|1.64|
|Fraction of poor HHs whose head has low education|95%|81%|
|Fraction of poor HHs whose head is not currently working|25%|31%|

6
|Fraction of poor HHs that have some welfare-relevant assets|33%|68%|
|---|---|---|
|Fraction of poor HHs that have a private car/motorcycle or bike|15%|24%|
|Fraction of poor HHs that have any washing machine (electric, semi-<br>automatic, or automatic)|16%|53%|
|Fraction ofpoor HHs that have a smartphone|8%|26%|

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

# **3.2 Poverty figures in 2017/18**

Table 3 presents our estimated poverty rates for all regions in 2017/18. Our estimate of the national rate in 2017/18 is 31 percent. As in 2015, the two regions with the highest poverty rates are Upper Urban and Upper Rural Egypt, while the two regions with the lowest poverty rates are Lower Urban Egypt and the Metropolitan Areas.

_Table 3: Contrasting 2017/18 poverty estimates based on total expenditures with official poverty rates_

|Statistic|National|Metropolitan|<sup>Lower</sup><br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% of the population*|100%|6%|8%|30%|14%|39%|3%|
|Per capita total<br>expenditures*|7366|9626|8560|6886|7507|5817|5924|
|Poverty rate (total<br>expenditures) *|31.0%|25.3%|12.6%|25.3%|28.2%|51.4%|40.7%|

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

To get a better sense of the types of households that are poor, we partition the population of households into eight categories that differ in composition. These categories differ in terms of household size and number of dependents (either children or elderly individuals). Table 4 provides summary statistics on these household types as well as their average total expenditures per capita and their respective poverty rates in 2017/18. Total expenditures per capita decrease sharply with household size and number of dependents. As a result, households with larger size and more numerous dependents have higher poverty rates. For instance, households without dependents have an average size of 2.7 members, average total expenditures per capita close to 11,000 Egyptian pounds (deflated in 2015 prices), and a poverty rate of 16 percent. In contrast, households with two adults and at least four dependents have an average size of 6.4 members, average total expenditures per capita close to 5,000 Egyptian pounds (deflated in 2015 prices), and a poverty rate of 48 percent.

_Table 4: Decomposition of 2017/18 poverty estimates by household types_

|HH category|% of<br>population|Age|HH<br>size|Total<br>expenditure<br>pc|Poverty<br>rate (total<br>exp.)|
|---|---|---|---|---|---|
|HHs without dependents|14%|43|2.7|10926|16%|
|HHs with only 1 adult and at least 1 dependent|6%|38|2.8|9144|11%|
|HHs with 2 adults and 1 or 2 dependents|14%|28|3.6|8301|14%|
|HHs with 3 or more adults and 1 or 2 dependents|20%|31|5.0|7711|35%|
|HHs with 2 adults and 3 dependents|16%|20|5.0|6069|27%|

7
|HHs with 2 adults and at least 4 dependents|16%|19|6.4|4898|48%|
|---|---|---|---|---|---|
|HHs with 3 or more adults and at least 3 dependents|13%|25|7.2|5358|57%|
|Other HHs|2%|72|1.4|14305|4%|
|Total|100%|29|5.0|7499|31%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Table 5 complements this analysis of the profile of the poor in 2017/18. Again, children are more affected by poverty than adults. On average, poor households still have a rather large size (5.5 members) and have several dependents (2.6 dependents). Poor households are still very often headed by a person with low education. Finally, the fraction of poor households who owned a private car had barely changed since 2015, but the fraction of poor households who owned a smartphone had increased four-fold, likely reflecting the evolution of communication technologies rather than an improvement in their welfare.

_Table 5: Profile of the poor in 2017/18 (total expenditure)_

|Profiles among poor households (total expenditures)|Value for<br>poor HH|
|---|---|
|Children|36.3%|
|Fraction of<br>Elderly|16.3%|
|poor among<br>Men<br>Women|31.8%<br>30.1%|
|Average HH size|5.49|
|Average number of dependents|2.60|
|Fraction of poor HHs whose head has low education|95.0%|
|Fraction ofpoor HHs whose head is not currentlyworking|23.8%|
|Fraction of poor HHs that have some welfare-relevant assets|-|
|Fraction of poor HHs that have a private car/motorcycle or bike<br>Fraction of poor HHs that have any washing machine (electric, semi-automatic,<br>or automatic)|14.9%<br>-|
|Fraction ofpoor HHs that have a smartphone|34.6%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Overall, the previous tables show that poverty increased over the period 2015 to 2017/18 but that the profile of the poor did not radically change. However, the interpretation of this increase in poverty is complicated by the hybrid nature of the official poverty measurement methodology. We now turn to the interpretation one can give to the increase in poverty.

# **3.3 Decomposition of the poverty trend over 2015 to 2017/18**

We address the difficulty associated with the interpretation of a hybrid definition of poverty lines by decomposing the evolution of poverty into the effect of changes in real total expenditures and the effect of changes in real poverty lines.

To correctly interpret the poverty trend, we apply a Shapley-Owen-Shorrocks (SOS) decomposition (Shorrocks 1999). To simplify its exposition, assume that all households have the same composition and 8
that there is a single region. Consider two periods of time t ∈ {0,1}, where 0 is the initial period and 1 is the end period. Let yt denote the distribution of _real_ total expenditures in time t. Taking period 0 as reference, the distribution of real total expenditures y1 is obtained by deflating the total expenditures observed in period 1 for the inflation between period 0 and period 1. Let zt denote the distribution of _real_ poverty lines in time t, where again the effect of inflation has been removed. We denote the poverty rate in period t (computed using the official methodology based on total expenditures) by P(zt, yt).

As the poverty lines have a hybrid definition, we typically have z1 ≠z0. The poverty trend can be decomposed either as P(z1, y1) - P(z0, y0) = P(z1, y1) - P(z0, y1) + P(z0, y1) - P(z0, y0) or alternatively as P(z1, y1) - P(z0, y0) = P(z1, y1) - P(z1, y0) + P(z1, y0) - P(z0, y0) where the difference between the two decompositions lies in the order in which the arguments of P are modified. Indeed, the former decomposition considers the change from period 0 to period 1 by changing yt first and zt second, while the opposite order is considered in the latter decomposition. The SOS decomposition of the change in poverty rates, which we denote by ∆P(z∆, y∆), considers the average effect of each change over all possible orders, which is where the first term captures the effect of the change in real total expenditures, which is and the second term captures the effect of change in real poverty lines, which is To dissipate any potential confusion, we now provide some additional details on how to compute P(z1, y0). To check whether household h is poor under P(z1, y0) we compare its 2015 total expenditure to the (deflated) poverty line that household h would face in 2017/18, where this poverty line is obtained by considering the 2017/18 price of a calorie and the food share that would have been imputed to household h by the regression (run on 2017/18 data).

# **Decomposition results**

The results of the decomposition exercise for all regions are presented in Table 6. At the national level, the poverty rate increases from 24.8 to 31 percent, which is an increase by 6.2 percentage points. This increase can be decomposed into a 9.9 percentage point increase due to the reduction in real total expenditures and a 3.7 percentage point reduction due to the decrease in real poverty lines. This decomposition reveals that the increase in poverty over the period would have been even higher if measured using an absolute measure of poverty, which keeps the real value of the poverty lines fixed over time. The hybrid nature of the official poverty lines yields a smaller increase in poverty rates, because the real values of poverty lines are smaller in 2017/18 than in 2015. As shown in Table 7, the real price of a calorie is, in all regions, smaller in 2017/18 than in 2015. One likely reason for these lower calorie prices has to do with the reduction in real total expenditures. As shown in Table 6, total expenditures per capita (expressed in 2015 prices) are about 8,600 Egyptian pounds in 2015 but decrease to below 7,400 Egyptian pounds in 2017/18. Hence, the reduction in households’ consumption possibilities provided an incentive to switch their food consumption towards cheaper calories.

9
_Table 6: Shapley-Owen-Shorrocks decomposition of the poverty trend from 2015 to 2017/18 between the effect of change in total expenditures and the effect of change in poverty lines_

|Region|Poverty<br>rate 2015|Δ poverty rate<br>(2017/18-<br>2015)|Δ<br>total<br>expenditure<br>effect|Δ poverty<br>line<br>effect|Total<br>expenditures<br>2015|Total<br>expenditures<br>2017/18<br>(deflated)|
|---|---|---|---|---|---|---|
|Metropolitan|11.0%|14.3%|14.4%|-0.2%|12,088|9,626|
|Lower Urban|9.6%|3.0%|6.8%|-3.8%|9,884|8,560|
|Lower Rural|17.1%|8.2%|14.6%|-6.4%|8,075|6,886|
|Upper Urban|23.9%|4.3%|7.7%|-3.4%|8,479|7,507|
|Upper Rural|54.8%|-3.4%|-0.1%|-3.3%|5,745|5,817|
|Borders|15.5%|25.2%|36.9%|-11.7%|9,177|5,924|
|National|24.8%|6.2%|9.9%|-3.7%|8,599|7,366|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

_Table 7: Evolution of the real price of a calorie from 2015 to 2017/18_

|Region|Price of calorie in<br>2015|Price of calorie in<br>2017/18<br>(2015 prices)|General CPI<br>(2015<br>to<br>2017/18)|
|---|---|---|---|
|Metropolitan|0.00393|0.00388|1.59|
|Lower Urban|0.00369|0.00349|1.65|
|Lower Rural|0.00362|0.00342|1.66|
|Upper Urban|0.00376|0.00366|1.61|
|Upper Rural|0.00375|0.00366|1.63|
|Border|0.00407|0.00364|1.65|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

The evolution of poverty was heterogeneous across different regions. Putting aside the Border regions, where the smaller sample size provides a less reliable picture, Table 6 shows that the largest increase in poverty took place in the Metropolitan area, where the poverty rate doubled. This is indeed the region in which total expenditures per capita underwent the largest decrease, from about 12,000 Egyptian pounds in 2015 to about 9,600 Egyptian pounds in 2017/18. As a result, poverty increased from 11 percent in 2015 to 25 percent in 2017/18. Poverty also increased in all other regions (although to a smaller extent), except in Upper Rural Egypt. The latter region is the only one where real total expenditure per capita did not decrease over the period. In fact, the poverty rate decreased by 3.3 percentage points in Upper Rural Egypt. However, the decomposition reveals that this reduction is almost entirely due to the reduction in the real poverty lines, and thus does not signal an increase in real consumption in that region. Upper Rural Egypt remained by far the region with the highest poverty rate in 2017/18.

10
# **Section 4: Quantitative analysis of the official poverty lines**

In this section, we analyze the two non-standard features of Egypt’s official poverty lines and contrast their poverty comparisons with those that would be obtained using two alternative poverty lines, one purely absolute and one purely relative.

# **4.1 Implicit equivalence scales**

# **Male versus female**

The food poverty line is anchored to individual energy requirements, which vary by gender. This design is perfectly sensible. However, the fact that the non-food line is computed as a share of the food line may potentially bias the poverty evaluation against women. To see this, assume for a moment that the imputed share of food was common to both genders. In that case, the smaller energy requirement of women implies a smaller food line and _therefore also a smaller non-food line_ . Conceptually, this smaller non-food line for women is not justified by their smaller energy requirements. The implication of this smaller non-food line is that the poverty lines faced by women are “artificially” lower than the poverty lines faced by men, which may lead to an underestimation of poverty rates among women.

Now, it is a priori unclear whether the share of food imputed by the regression will be the same for men and women. Potentially, both men and women could be attributed the same non-food lines if men are imputed a larger share of food than women. This is in fact what could be expected for single males and females who earn the same income, with the males spending more on food because of their higher energy requirements.

To quantify this potential bias, we contrast the poverty lines across households that only differ by their gender composition. For instance, we compare a household composed of two adult males and one adult female with a household composed of one adult male and two adult females. As shown in Table 8, the energy requirements of the second (“female”) household are 93 percent of those of the first (“male”) household, resulting in a smaller food line. However, the “female” household’s smaller food line is not compensated by a smaller imputed share of food. Indeed, the share of food imputed to the “female” household is (slightly) larger than that imputed to the “male” household. As a result, the (total) poverty line for the “female” household is 93 percent of the (total) poverty line for the “male” household. The comparison of households consisting of a single adult (either male or female) and a single child yields the same conclusion. Hence, this analysis points to a risk of underestimating the poverty rate among females (when compared to the poverty rate of males). The order of magnitude of this bias is revealed by our analysis using alternative poverty measurement methodologies (see below).

_Table 8: Comparison of male versus female poverty lines_

|Household type|Household<br>size|Region|Number<br>calories|Imputed<br>share of<br>food|Ratio #<br>calories<br>(% ref<br>HH)|Ratio<br>NON-food<br>shares (%<br>ref HH)|Ratio<br>poverty<br>line (%<br>ref HH)|
|---|---|---|---|---|---|---|---|
|2 males + 1 female|3|Upper Rural|9,550|0.46|1|1|1|
|1 male + 2 females|3|Upper Rural|8,900|0.47|0.93|0.99|0.93|
|1 male + 1 child|2|Upper Rural|5,400|0.53|1|1|1|
|1 female + 1 child|2|Upper Rural|4,750|0.54|0.88|0.98|0.88|

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : The calorie requirements considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

11
# **Comparison with alternative equivalence scales**

The equivalence scale inherent in a poverty measurement methodology defines how the poverty line varies with household composition. It is typically expressed as the ratio of a household’s poverty line divided by the poverty line of a reference household. The equivalence scale implicit in Egypt’s official poverty lines is not constant across time, because the imputed share of food depends on consumption behavior in the period. It is also not constant across space, because the imputed food share also depends on the region. This makes the official poverty lines rather complex. One could wonder whether this additional complexity is justified by a non-negligible impact on the results. To investigate this, we study whether the implicit equivalence scale strongly differs from alternative, more standard equivalence scales. More precisely, we contrast the implicit official equivalence scale with two standard equivalence scales, the per capita and the square root equivalence scales, for a set of household types, when taking as a benchmark a household of two adults with two children.

Both the per capita and the square root equivalence scales only depend on household size, meaning that they do not depend on the gender or age composition of the household (see Ravallion 2015b). In contrast, the official methodology will attribute different equivalence scales to households of the same size as a function of their composition. In Table 9, we consider a series of 13 different household types, whose size varies between two and seven members. For each household size, several compositions are considered. We then compute the three equivalence scales, which in the case of the official methodology requires computing calorie requirements as well as imputed food share from the regression.

The results in Table 9 show that the imputed food share decreases with household size, from 0.53 for a household of two elderly individuals to 0.42 for the two household types consisting of seven members. This finding is surprising in the sense that the economies of scale that are expected from larger household sizes (e.g., coming from public goods such as housing) could imply that lower per capita expenditures on non-food could lead to the same level of non-food consumption, thereby freeing up more resources for food consumption. However, the results show that the implicit equivalence scale is more affected by the caloric requirements than by the imputed share of food. This can be seen by contrasting the results in the third and fourth columns, which respectively present the ratio of caloric requirements and the ratio of imputed non-food shares (still with respect to the reference households with two parents and two children). These columns show that the ratios of caloric requirements vary much more than the ratios of imputed non-food shares. In fact, the impact of calorie requirements on the equivalence scale is even more dominant than what is suggested in Table 9. The reason is that the official equivalence scale between household h and household h<sup>ref</sup> that lives in the same region can be written as where the first factor is the ratio of calorie requirements, while the second ratio is the ratio of one plus non-food share. The first ratio varies between 0.52 for a household of two elderly individuals and 1.66 for one household type of seven members. In contrast, the second ratio merely varies between 0.96 for a household of two elderly individuals and 1.03 for the two household types that consist of seven members. Observe that the variation in the second ratio is much smaller than the variation observed for the non-food share (fourth column in Table 9), which varies between 0.88 for a household of two elderly individuals and 1.10 for one household type of seven members.

12
_Table 9: Comparison of official equivalence scale with square root equivalence scale and per capita equivalence scale_

|Household type|Food<br>share<br>imputed|Ratio #<br>calories<br>(%<br>ref<br>HH)|<br> <br>Ratio<br>NON-<br>food<br>share (%<br>ref HH)|HH<br>size|Official<br>equiv.<br>scale (%<br>ref HH)|SQRT<br>equiv.<br>scale<br>(% ref<br>HH)|<br>Per<br>capita<br>equivalence<br>scale (% ref<br>HH)|
|---|---|---|---|---|---|---|---|
|1 male + 1 female|0.51|0.61|0.93|2|0.59|0.71|0.5|
|2 elderly|0.53|0.52|0.88|2|0.50|0.71|0.5|
|1 male + 2 children|0.51|0.73|0.93|3|0.71|0.87|0.75|
|1 female + 2 children|0.51|0.67|0.92|3|0.65|0.87|0.75|
|1 male + 1 female + 1 child|0.49|0.80|0.96|3|0.79|0.87|0.75|
|1 male + 1 female + 2 children|0.47|1|1|4|1|1|1|
|1 female + 3 children|0.48|0.86|0.97|4|0.85|1|1|
|1 male + 1 female + 3 children|0.45|1.20|1.03|5|1.21|1.12|1.25|
|1 male + 2 female + 2 children|0.44|1.27|1.05|5|1.29|1.12|1.25|
|1 male + 1 female + 4 children|0.43|1.39|1.06|6|1.42|1.22|1.5|
|1 male + 1 female + 3 children +<br>elderly|1<br>0.43|1.46|1.07|6|1.49|1.22|1.5|
|1 male + 1 female + 4 children +<br>elderly|1<br>0.42|1.66|1.10|7|1.71|1.32|1.75|
|1 male + 1 female + 5 children|0.42|1.59|1.08|7|1.64|1.32|1.75|

**Source** : Authors’ calculations based on HIECS 2015. **Note** : The calorie requirements considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

Figure 1 plots the three equivalence scales as a function of household size. For each household size, the two alternative equivalence scales only have one value, whereas the official methodology has several values, because it depends on household composition as well as household size. The main message coming out of Figure 1 is that the official equivalence scale is closer to the per capita equivalence scale than to the square root equivalence scale. This is very clear for larger household sizes, but it is also valid for smaller household sizes. Another message is that household size is the dominant factor in the determination of the official equivalence scale. Indeed, the differences between official equivalence scales at a given household size seem smaller than the difference between average equivalence scales across different household sizes. Together, this suggests that poverty comparisons would not be radically affected when switching from the official equivalence scale towards a more conventional per capita equivalence scale, a move that would simplify the official poverty measurement methodology.

13
_Figure 1: Comparison of the official equivalence scale with the square root equivalence scale and the per capita equivalence scale_ <!-- Start of picture text --> Comparison of three equivalence scales<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00<br>Household size<br>Official ES SQRT ES Per-capita ES<br>Equivalence scale<br><!-- End of picture text -->

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : The calorie requirement considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

# **4.2 Comparison with alternative poverty lines**

In the final subsection, we contrast the official poverty lines with two benchmark alternative poverty lines. The objective is to further investigate the joint implications of the two non-standard aspects of the official poverty lines, namely their hybrid definition and their endogenous implicit equivalence scale. We assess: (a) how different the poverty picture would be if these aspects were taken out; and (b) whether the official poverty lines perform well in terms of identifying disadvantaged households. We also explore whether the official poverty lines behave more similarly to purely absolute or purely relative poverty lines.

# **Definition of the purely absolute and purely relative lines**

As shown in the previous subsection, the most natural alternative to the current endogenous equivalence scales would be to use the per capita equivalence scale. That is, the poverty line of any household is obtained by multiplying the poverty line for a single individual by household size. The most natural alternatives to the current hybrid poverty line would be to define either a purely absolute line, whose real value is fixed across time and space, or a purely relative line, established as a fixed fraction of median per capita total expenditure.

To allow a direct comparison of the characteristics of individuals who are identified as poor under the three different poverty lines, we define the purely absolute line (as well as the purely relative line) in such a way that the national poverty rate in 2015 is the same as that obtained by the official poverty lines. That is, we select through iterations the value of the absolute line (as well as the value of the constant fraction of median per capita total expenditure) in such a way as to match the national poverty rate in 2015.

14
To reflect the regional emphasis embedded in the official poverty lines,<sup>7</sup> we define the two alternative lines at the regional level. For the purely absolute line, we consider Lower Urban Egypt to be the reference region (given the fact that the Metropolitan region faced a different inflation path from the one seen in the rest of the country). Let x denote the value in 2015 of the per capita poverty line in this reference region. The per capita poverty line in other regions is obtained by spatial deflation of x, using the relative prices of a calorie (defining the food poverty line) as spatial deflator. The value of x is selected such that the national poverty rate in 2015 is the same as that obtained with the official methodology (based on total expenditures). When computing poverty rates in 2017/18 for the purely absolute measure, we simply inflate the regional per capita poverty lines obtained in 2015 for the inflation that was observed in the region over 2015 to 2017/18.

For the purely relative line, we proceed similarly. Let f denote the fraction of median per capita total expenditure defining the per capita poverty line. The same value for f is used in all regions. Regions have different relative lines because they differ in their median per capita total expenditure. Again, the value of f is selected such that the national poverty rate in 2015 is the same as that obtained with the official poverty lines. When computing poverty rates in 2017/18 for the purely relative measure, we keep the value of f used in 2015 but consider the 2017/18 values for median per capita total expenditure.

# **Results**

First, we contrast the 2015 profile of the poor obtained under the three poverty measures. We investigate whether these profiles are very different, and which of the two alternative measures yields results that are closest to the official measure.

Table 10 shows that the three poverty lines yield almost identical national poverty rates in 2015, respectively of 24.8, 24.8 and 24.7 percent. This table shows that the regional distribution of poverty associated with the official poverty measure is very close to that of the purely absolute measure. In contrast, the regional distribution of poverty associated with the purely relative measure is close to attributing the same poverty rate to all regions, which suggests that intra-region inequalities are not too heterogenous.

When moving to the distribution of poverty across household types, we find that the purely absolute and purely relative measures seem closer to one another than to the official measure. Indeed, there is no household type for which the official measure yields a poverty rate that is intermediate between those obtained using the other two measures. In contrast, both of the other poverty measures yield an intermediate poverty rate for some household type. The fact that the alternative measures distribute poverty across household types in a more similar manner is likely the result of the fact that they use the same per capita equivalence scale. However, the difference between the three measures is not very large. This similarity is also suggested when comparing the fraction of poor among children, the elderly, men, and women. Again, the purely absolute and purely relative measures are closer to one another, but they do not differ drastically from the official measure. One difference worth noting is that the official measure finds a higher poverty rate among men than among women, whereas the opposite result appears for the alternative measures, based on the per capita equivalence scale. This suggests that relative to the per capita equivalence scale, the official equivalence scale is more focused on detecting poverty among men than among women. The differences do not appear very large, as moving from the official to the alternative measures increases the poverty rate among women by one percentage point and decreases that among men by two percentage points. However, these differences should not be dismissed as negligible, because the unit of analysis is the household, not individuals. It is much more difficult to capture differences in poverty rates by gender when the unit of analysis is the household, because households typically have similar numbers of men and women, thereby tuning down the potential gender features inherent in a given measure.

> 7 The official methodology computes the food line with a price of calories that is regional and uses regional dummies in the regression imputing food shares.

15
Table 10 also shows poverty rates among households that exhibit different characteristics traditionally associated with economic disadvantage. The results are mixed, and it is not possible to conclude that the alternative poverty lines better identify households with characteristics associated with economic disadvantage. The alternative poverty measures do find a larger average number of dependents among poor households. However, the official measure finds a higher poverty rate among households whose head has low education. The official measure also finds a higher poverty rate among households whose head is currently not working, but Table 2 suggests that these households may in fact be, on average, better off than households whose heads are currently working. Finally, the official poverty line seems to better identify as poor households that do not have welfare-relevant assets. In this regard, the purely relative line performs worse, particularly in terms of washing machine ownership. Overall, we cannot conclude that the official poverty lines perform worse than the alternative poverty lines, which implies that a change in the methodology could only be defended on grounds of simplifying monitoring and interpretation.

_Table 10: Comparison of three poverty measurement methodologies in 2015_

|HH t||Official|Absolute|Relative|
|---|---|---|---|---|
|caegor||2015|2015|2015|
||National|24.8%|24.8%|24.7%|
||Metropolitan|11%|13%|28%|
|Prt|Lower Urban|10%|10%|24%|
|ovey<br>rate|Lower Rural|17%|17%|24%|
||Upper Urban|24%|23%|25%|
||Upper Rural|55%|52%|24%|
||Borders|15%|18%|22%|
||HHs without dependents|13%|6%|6%|
||HHs with only 1 adult and more than 1 dependent|15%|22%|17%|
||HHs with 2 adults and 1 or 2 dependents|10%|11%|14%|
||HHs with 3 or more adults and 1 or 2 dependents|26%|16%|19%|
|Fraction of|HHs with 2 adults and 3 dependents|18%|25%|28%|
|poor|HHs with 2 adults and 4 or more dependents|42%|55%|49%|
|individuals|HHs with 3 or more adults and 3 or more dependents|48%|47%|42%|
|among|Other HHs|6%|6%|5%|
||Children|29%|33%|32%|
||Elderly|17%|15%|15%|
||Men|26%|24%|24%|
||Women|24%|25%|25%|
|Average HH|size among poor HHs|5.6|5.7|5.6|
|Average nu|mber of dependents among poor HHs|2.7|3.1|3.0|
||Whose head has low education|95%|93%|91%|
||Whose head is not currently working|25%|22%|23%|
|Fraction of|That have some welfare-relevant assets|33%|35%|43%|
|poor HHs|That have a private car/motorcycle or bike|15%|16%|14%|
||That have a washing machine|16%|19%|31%|
||That have a smartphone|8%|7%|8%|

**Source** : Authors’ calculations based on HIECS 2015.

16
**Note** : The official methodology is run based on total expenditures.

Next, we contrast the poverty trends associated with the three different measures. We investigate whether these trends are very different, and which of the two alternative measures yields results that are closest to the official measure.

Table 11 shows the trends associated with the three different methodologies for the period 2015 to 2017/18 for all regions. The trend at the national level provides a goods summary of the messages of this table. At national level, our estimate of the official poverty rate increase over the period is 6 percentage points (while the estimate by CAPMAS suggests an increase slightly below 5 percentage points). This increase is halfway between the larger increase captured by the purely absolute measure (10 percentage points) and that captured by the purely relative measure (1 percentage point). In fact, the increase captured by the purely absolute measure corresponds to the effect of the change in real total expenditures computed when decomposing the trend in the official measure, which is shown in the last column. The comparisons in each region yield a very consistent picture with that observed at national level. We also note that the relative measure changed little over the period, which suggests that intraregion inequalities do not radically change over this interval.

_Table 11: Comparison of trends for the three methodologies from 2015 to 2017/18_

||Offici|al||Absol|ute||Relati|ve||Δ<br>total<br>expenditure<br>effect|
|---|---|---|---|---|---|---|---|---|---|---|
||2017<br>/18|2015|Δ<br>2017/18<br>-2015|2017<br>/18|2015|Δ<br>2017/18<br>-2015|2017<br>/18|2015|Δ<br>2017/18<br>-2015|Δ 2017/18-<br>2015|
|National|31%|25%|6%|34%|25%|10%|25%|25%|1%|10%|
|Metropolitan|25%|11%|14%|25%|13%|12%|29%|28%|2%|14%|
|<br>Lower Urban|13%|10%|4%|19%|10%|9%|25%|24%|2%|7%|
|Poverty<br>rate<br>Lower Rural|26%|17%|8%|31%|17%|14%|23%|24%|-1%|15%|
|<br>Upper Urban|28%|24%|4%|32%|23%|9%|26%|25%|2%|8%|
|Upper Rural|52%|55%|-3%|53%|52%|0%|24%|24%|0%|0%|
|Borders|42%|15%|27%|60%|18%|42%|30%|22%|8%|37%|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : The official methodology is run based on total expenditures.

# **Section 5: Concluding remarks**

Several countries rely on non-standard poverty measurement methodologies, which may complicate the interpretation of poverty changes across time or space. This paper focuses on one element, which is the use of non-standard poverty lines, particularly lines that are neither absolute nor relative. There is limited evidence on how these departures may impact the interpretation of poverty empirically, beyond obvious conceptual differences. This paper aims to fill this gap by looking at the case of Egypt. The analysis presented in this paper has provided several insights from comparing Egypt’s hybrid poverty lines with more standard lines.

The analysis first shows that consumption poverty increased in Egypt over the period 2015 to 2017/18, mostly due to the reduction in real consumption. The hybrid nature of the poverty lines inherent in the official poverty measurement methodology leads to a smaller increase in the poverty rate, compared to an approach where the poverty lines are updated in real terms. Yet, the profile of the poor has not been 17
dramatically modified, with poor individuals disproportionally living in rural areas, in Upper Egypt, in larger households, and in households whose head has low education.

Second, the two non-standard features of the official poverty lines have been analyzed, and their impacts have been contrasted to standard benchmarks. The equivalence scale implicit in the official poverty lines pays relatively more attention to poverty among men (versus poverty among women) than the more standard per capita equivalence scale. However, the differences between the official equivalence scale and the per capita equivalence scale are not very large. The hybrid official poverty lines have been contrasted to purely absolute and purely relative poverty lines. The results show that the poverty distribution across regions using the official measure is very similar to that of a purely relative measure, but the poverty trend captured by the official measure is situated about halfway between that captured by a purely absolute measure and that captured by a purely relative measure. The official poverty lines do not seem to perform worse than alternatives in terms of identifying households with economic disadvantage.

One interesting open question is how the official poverty lines would compare to an alternative type of poverty lines that combines both absolute and relative aspects. For instance, it would be interesting to compare the official poverty trend with that associated with a measure constructed using a weakly relative poverty line (Ravallion and Chen 2011; Chen and Ravallion 2021). Alternatively, it would be interesting to compare the official trend to the trend in the hierarchical headcount ratio, an index that consistently combines absolute and relative aspects of monetary poverty (Decerf et al. 2022).

18
# **Appendices**

# **1) Details on food and non-food poverty lines**

The food poverty line each year is computed as follows. The total caloric requirements of a household are the sum of the caloric requirements of its members. These individual requirements differ by age categories and sex and are also separately specified for urban and rural individuals. The price of a calorie is based on a reference food bundle, which corresponds to the average food consumption for the poorest 40 percent of households when ranked by nominal per capita consumption. This reference bundle is thus defined at the national level. The price of purchasing this bundle is then computed in each region (using for each item its median unit values observed in the region, accounting for potential food subsidies). The regional price of a calorie corresponds to the regional cost of this bundle divided by the total calorie content of this bundle.

The non-food poverty line is computed as a given fraction of the food poverty line using the imputed share of food. Following Engel’s law, this imputed share is computed by regressing the _observed_ food shares against the logarithm of total household expenditure relative to the food poverty line and its square, which is O where α is the intercept, shr is the observed share of total consumption spent on food, xhr is the household total consumption, ε is an error term, and dhr is a vector of household demographic characteristics: region, logarithm household size and its square, share of children, share of adult males, share of adult females (and the complement is the share of elderly). The coefficients of this regression are then used to impute the food share shr when assuming that total consumption is equal to the food line.<sup>8</sup> This assumption implies that the imputed food share corresponds to the share of food of households with the same characteristics but whose total consumption corresponds to the food line. These households have a consumption strictly smaller than the poverty line, hence they typically spend a larger share on food, which yields a rather “low” poverty line.<sup>9</sup>

# **2) Details on the measure of household consumption**

# **Total expenditures versus welfare aggregates**

The welfare indicator used in Egypt’s official monetary poverty figures is annual household consumption. The consumption aggregate includes all spending on consumption of goods and services, including food and beverages; clothes and footwear; housing; housing services (water, electricity, gas, and fuel); furniture and household equipment and maintenance; health care; transportation; wired and wireless communication; culture and recreation; education; restaurants and hotels; and miscellaneous goods and services. The implicit value of housing is also added. Food subsidies, which lower the price of a few basic food items, are accounted in the welfare aggregates, while energy subsidies are not.

When performing this analysis, a series of obstacles prevented us from satisfactorily replicating the official poverty figures in 2015 and 2017/18. First, we only have access to half of the sample for HIECS 2015. Second, we do not have information on the exact food subsidy schemes used in 2015 and in 2017/18.

> 8 Under this assumption, the two coefficients β and γ “disappear” because ln(1)=0.

> 9 The alternative is to impute the share of food for households for which food consumption is equivalent to the food line. This alternative requires proceeding by iterations until one reaches a fixed point.

19
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.

20
Note that poverty rates estimated with welfare aggregates are _higher_ than poverty rates estimated with total expenditures. This result is a priori surprising, given that the former include food subsidies whereas the latter do not. The reason for this is that, given the official poverty measurement methodology, ignoring food subsidies not only decreases welfare aggregates, but also decreases poverty lines. Indeed, the price of a calorie, which defines a household’s food poverty line, depends on the unit values of food items. Ignoring subsidies implies smaller unit values for subsidized food items, which in turn reduces the price of a calorie and thus the household’s food poverty line. One might think that, on average, this reduction in the food poverty line could approximately compensate for the reduction in household consumption, implying that the net effect of ignoring subsidies might cancel out. This is not the case, however, as we illustrate with a simple example. Assume that ignoring subsidies decreases the value of household consumption by  . Under simplifying assumptions,<sup>14</sup> the food poverty line is also reduced by  . However, this does not mean that the poverty rate is unchanged, because the non-food poverty line is computed as a fraction of the food poverty line. If the imputed share of food is 0.5, this implies that the non-food line is reduced by 0.5  . Overall, the poverty line is reduced by 1.5  , which is a larger reduction than the decrease in the value of household consumption. This explains why poverty rates are lower when ignoring food subsidies.

This explains why, in all regions, the poverty rates estimated with welfare aggregates are higher than those estimated with total expenditures. Yet, if the difference is non-negligible, we deem that the two estimates are sufficiently close to one another for our purposes, namely assessing the poverty trend over the period 2015 to 2017/18 and contrasting the official poverty measurement methodology with more standard alternatives. Nevertheless, when interpreting the results, one should keep in mind that our estimates are computed while ignoring food subsidies.

# **3) Additional results**

Table A1 contains the value of coefficients for the regression imputing food shares as run based on total expenditures in 2015 and 2017/18.

_Table A1: Values of coefficients for the regression imputing food shares_

|VARIABLES|2015|2017/18|
|---|---|---|
|Ln(expenditure/food poverty line)|-0.114<br>(0.00410)|-0.135***<br>(0.00275)|
|[Ln(expenditure/food<br>poverty|||
|line)]^2|-0.0021|-0.00113|
||(0.00175)|(0.00136)|
|Ln(HH size)|-0.113|-0.102***|
||(0.00844)|(0.00601)|
|[Ln(HH size)]^2|0.0181|0.00883***|
||(0.00287)|(0.00215)|
|share of kids|0.0413|0.0283***|
||(0.00549)|(0.00379)|
|share of males|-0.0327|-0.0736***|
||(0.00584)|(0.00392)|
|share of females|-0.0183|-0.0168***|
||(0.00598)|(0.00407)|

> 14 This would be the case if all households have the same composition and all consume the reference poverty bundle.

21
|Lower Urban|0.00858|0.0435***|
|---|---|---|
||(0.00298)|(0.00211)|
|Lower Rural|0.0205|0.0639***|
||(0.00238)|(0.00168)|
|Upper Urban|-0.0193|0.0217***|
||(0.00307)|(0.00218)|
|Upper Rural|0.00783|0.0375***|
||(0.00271)|(0.00182)|
|Borders Urban|0.0168|0.0825***|
||(0.00802)|(0.00603)|
|Borders Rural|0.0363|0.100***|
||(0.0111)|(0.00782)|
|Constant|0.586|0.592***|
||(0.00652)|(0.00430)|
|Observations|11,091|23,466|
|R-squared|0.324|0.383|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 **Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : Regression run using total expenditures.

Table A2 contains the value of coefficients for the regression for the hedonic regressions in 2015 and 2017/18.

_Table A2: Values of coefficients for the hedonic regression (dependent variable log of rents)_

||(1)|(1)|
|---|---|---|
|VARIABLES|2015|2017/18|
|HH size|0.00121|-0.00637***|
||(0.00288)|(0.00211)|
|Apartment|0.170***|0.150***|
||(0.0327)|(0.0167)|
|Country House|0.0653**|0.341***|
||(0.0328)|(0.0841)|
|#rooms|0.135***|0.108***|
||(0.0253)|(0.0221)|
|(#rooms)^2|-0.00874***|-0.00871***|
||(0.00276)|(0.00256)|
|area in m2|0.00598***|0.00669***|
||(0.000610)|(0.000537)|
|(area in m2)^2|-6.26e-06***|-1.09e-05***|
||(2.23e-06)|(2.12e-06)|
|tap water|0.0298|0.0340**|
||(0.0201)|(0.0136)|
|Dwelling connected to public network|0.187***|0.110***|
||(0.0168)|(0.0107)|
|Dwelling connected to local network|0.0282|0.0561***|

22
||(0.0204)|(0.0138)|
|---|---|---|
|Dwelling connected to trench/private network|0.0390***|-0.00679|
||(0.0151)|(0.00976)|
|Dwelling has brick/cement walls|0.0669***|0.0264*|
||(0.0200)|(0.0153)|
|Dwelling has steel ceiling|0.130***|0.163***|
||(0.0166)|(0.0119)|
|Dwelling has ordinary tile floor|-0.345***|-0.239***|
||(0.0587)|(0.0423)|
|Dwelling has ceramic tile floor|-0.182***|-0.0491|
||(0.0609)|(0.0427)|
|Dwelling has concrete floor|-0.442***|-0.330***|
||(0.0582)|(0.0425)|
|Dwelling has soil floor|-0.546***|-0.342***|
||(0.0606)|(0.0437)|
|Lower Urban|0.106|-0.105*|
||(0.0872)|(0.0627)|
|Lower Rural|-0.172*|-0.322***|
||(0.0899)|(0.0577)|
|Upper Urban|0.134|-0.0746|
||(0.0938)|(0.0601)|
|Upper Rural|0.0632|-0.301***|
||(0.105)|(0.0748)|
|Borders Urban|0.488***|-0.0291|
||(0.0808)|(0.0910)|
|Borders Rural|0.683***|0.433***|
||(0.0692)|(0.0499)|
|Region*Owner|Yes|Yes|
|Region*Not-owner|Yes|Yes|
|Constant|7.122***|7.653***|
||(0.101)|(0.0737)|
|Observations|11,841|23,970|
|R-squared|0.505|0.411|
|Robust standard errors in parentheses.|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|**Source**: Authors’ calculations based on HIECS 20|15 and 2017/18.||
|**Note**: Regression run using total expenditures.|||

23
_Table A3: Decomposition of the evolution of poverty 2017/18 versus 2015_

|Region|P(z0,y0)|P(z1,y0)|P(z0,y1)|P(z1,y1)|Δ<br>total<br>expenditure<br>effect|Δ<br>poverty<br>line<br>effect|Total<br>change|
|---|---|---|---|---|---|---|---|
|Metropolitan|11.0%|11.0%|25.6%|25.3%|14.4%|-0.2%|14.3%|
|Lower Urban|9.6%|6.9%|17.4%|12.6%|6.8%|-3.8%|3.0%|
|Lower Rural|17.1%|11.8%|32.8%|25.3%|14.6%|-6.4%|8.2%|
|Upper Urban|23.9%|20.5%|31.6%|28.2%|7.7%|-3.4%|4.3%|
|Upper Rural|54.8%|51.7%|54.9%|51.4%|-0.1%|-3.3%|-3.4%|
|Borders|15.5%|10.9%|59.5%|40.7%|36.9%|-11.7%|25.2%|
|National|24.8%|21.6%|35.3%|31.0%|9.9%|-3.7%|6.2%|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18.

24