# Impacts of income poverty and high housing costs on fuel poverty in Egypt: An empirical modeling approach

Fateh Belaïd, Véronique Flambard

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Fateh Belaïd, Véronique Flambard. Impacts of income poverty and high housing costs on fuel poverty in Egypt: An empirical modeling approach. Energy Policy, 2023, 175, pp.113450. 10.1016/j.enpol.2023.113450. hal-04125482

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# Impacts of income poverty and high housing costs on fuel poverty in Egypt: An empirical modeling approach

Fateh Belaïd<sup>a, *</sup>, Véronique Flambard<sup>b</sup>

## ARTICLE INFO

JEL classification:

C2

D1

Q41

Q48

Keywords:

Fuel poverty

Inequality

Energy demand

Well-being

Egypt

## ABSTRACT

Energy poverty is a new growing dimension of inequality in both developed and developing economies. Here, we develop an empirical model to examine the linkage between fuel poverty, income, and high housing costs in developing countries. Our analysis focuses on Egypt, a country that has undergone rapid development and significant energy reforms in recent years. Employing a probit and ordered multinomial framework to data from the Egyptian HIECS Survey, a nationally representative sample of both households and dwellings, we estimate the fuel, income, and housing cost-induced poverty extent and their key determinants. Our results show that households with low income, high energy, and high housing expenses represent approximately 16.4% of the total population, and 7.44% have low income, high energy, and low housing expenses. Our findings on critical factors driving LIHC poverty types (e.g., house type and family size) have some important policy implications for understanding the fuel poverty phenomenon and inequality reduction in Egypt, not only for the LIHC definition but also for any indicator of (fuel) poverty involving the posthousing (energy cost) concept.

## 1. Introduction

Reliable access to energy services is crucial for reducing various forms of poverty and sustainable economic growth (Tiba and Bélaïd, 2020, 2021). Nonetheless, different developing countries, particularly the low-income economies of Sub-Saharan Africa and the Middle East and North African regions (MENA), have faced a sharp increase in energy demand in recent years, leading to frequent power outages. In Egypt, the related issues are particularly critical.<fnref n="1" /> Egypt's residential energy consumption has grown considerably with the ever-increasing demand. Natural gas shortages and limited transmission and generation capacity have led to frequent power outages (IENE, 2022). Over the past decade, the Egyptian economy has been profoundly destabilized by the global financial crisis and political turmoil in the wake of the Arab Spring, resulting in a sharp increase in the poverty rate, along with a slowdown in growth, loss of tourists, and ballooning budget and exchange rate deficits. Although the government has implemented various economic reforms, they have had limited success in reducing inequality

and lifting vulnerable populations out of poverty. Official figures indicate that the poverty rate is over 28%, with the vulnerable population at approximately 50% (Lara Ibarra et al., 2019). In addition, following the IMF agreement in 2016, the Egyptian government with other countries in the MENA region started to phase out energy subsidies, which may further compromise the poorest's ability to meet basic needs with higher-good and energy prices (Ibrahim, 2021).

Untangling fuel poverty from income poverty and high housing costs is most needed in the context of changing energy subsidies to define how public policies should be reframed in this transition period.

This paper proposes a multidimensional poverty indicator to assess both the incidence and forms of income-fuel poverty and high housing costs in Egypt. It can also indirectly inform about the potential environmental impact of consumption if restrictions were alleviated. It is worth noting that the building sector in Egypt represents approximately 42% of total energy consumption (Bélaïd and Rault, 2021). In addition, activities related to residential energy consumption (space cooling, heating, cooking, lighting, water heating, and refrigeration) together

<footnote><sup>a</sup> King Abdullah Petroleum Studies and Research Center, P.O. Box 88550, Riyadh, 11672, Saudi Arabia</footnote>

<footnote><sup>b</sup> Faculty of Management, Economics & Sciences, Lille Catholic University, UMR 9221-LEM-Lille Économie Management, F-59000, Lille, France</footnote>

<footnote>* Corresponding author. King Abdullah Petroleum Studies and Research Center, Riyad.</footnote>

<footnote>E-mail addresses: fateh.belaid@kapsarc.org (F. Belaïd), veronique.flambard@univ-catholille.fr (V. Flambard).</footnote>

<footnote n="1">¹ "Only a few countries in the world have implemented a comprehensive economic reform package that is comparable in terms of time and scope to the reforms that have been launched in Egypt" (Breisinger et al., 2019). By phasing out energy subsidies, Egyptian reforms alone worsen fuel poverty due to higher energy prices but also through indirect income effect. Cockburn et al. (2018) based on microsimulation models show that the impacts are greater in Egypt than in Jordan for instance which shares some similarities not only due to the reforms but also because a larger share of energy is consumed directly by households.</footnote>
represent one of Egypt's most significant sources of carbon emissions.

Fuel poverty is an umbrella term used to denote a household's inability to obtain sufficient energy for essential services such as cooking, heating, cooling, and lighting (Belaïd, 2018; Belaid, 2022a). Determining fuel poverty and assessing its impact on human health and well-being is a rising social issue worldwide (Lara Ibarra et al., 2019). Understanding the extent of fuel poverty is a critical social issue for several reasons, including enhancing people's mental and physical health in low energy-efficient housing (Hernández, 2016; Kearns et al., 2019), gaining an in-depth appreciation and understanding of the energy efficiency of the housing stock (Belaïd et al., 2020), and improving the quality of the environment (Boardman, 2009; Bélaid, 2019). In recent years, rising housing costs for poor households have fueled public debate. The difficulties in paying growing housing and energy costs cause financial stress and social issues. Workers providing essential services such as nurses, cleaners, and police officers are priced out of unaffordable urban areas (Wetzstein, 2017). Unaffordability hinders children's education and employment prospects, delays marriage, reduces fertility, and hinders mobility (Galster and Lee, 2021).

This study's primary purpose is to investigate the nexus between fuel poverty, income, and high housing costs in Egypt. By extending the fuel poverty literature and Hills' (2012) framework, this study identifies four "dimensions" of poverty: (i) income poverty – high energy and housing costs; (ii) income poverty – high energy costs; (iii) income poverty – high housing costs; and (iv) income poverty – low energy and housing costs. We use a widely used definition for income poverty – people with household incomes below 60% of the national median, adjusted for the family size. To capture the housing poverty dimension, we augmented the calculation of the income poverty line with housing costs.

A key innovation in the LIHC definition, proposed by Hills (2012), is the mechanism for subtracting housing costs from income to better reflect household disposable income. Our analysis's conceptual framework identifies additional dimensions of poverty by explicitly extending Hills's (2012) approach with housing cost dimensions. A distinguishing feature of our framework is the income threshold's definition adjusted to consider both housing cost and energy cost and augmented by the required fuel cost and housing cost. Accordingly, we designed three thresholds that define eight quadrants: the income after energy and housing cost threshold, the energy cost threshold, and the housing cost threshold. Empirical analysis based on our methodological contribution allows for measuring the proportion of the population affected by the combined component of fuel poverty, income, and housing poverty. The causes of poverty can be identified, informing governments about the extent, depth, and nature of possible remedial measures for poverty.

This paper elaborates an original framework that helps clarify how the combination of low incomes and high housing and energy costs impacts the nature of fuel poverty. Using the after-fuel cost and housing cost captures within-country differences in housing and energy markets, which can help to solve the so-called Egyptian inequalities puzzle.<fnref n="2" />

Such an approach goes beyond the main policy instrument stating that improving the energy efficiency of housing is the primary cure for fuel poverty, rather than through other means such as income support.

This paper's empirical analysis is based upon the Egyptian Household Income, Expenditure, and Consumption Survey (HIECS). In this paper, the structure of the low-income and high-cost fuel poverty definition framework is decomposed. Second, an alternative conceptual definition of fuel poverty is proposed. Third, an empirical strategy is proposed to explore the primary sources and reasons behind fuel poverty, to unveil the key factors behind economic inequality and to identify which households most need financial and energy support in Egypt.

This research makes a methodological and empirical contribution to the literature on fuel poverty determinants for a better inclusive development policy in developing countries. More specifically, we first provide new evidence on the extent, determinants, and various facets of fuel poverty in Egypt. Second, we provide an extension of Hills' (2012) fuel poverty assessment approach. A distinguishing feature of our framework is the income threshold's definition, which takes into consideration housing and energy costs as well as restrictions regarding energy and housing expenses. Third, we develop an innovative multivariate probit regression to shed light on the factors explaining different aspects of poverty compounded by high energy and/or housing costs. This approach is quite flexible and allows us to explore the probability of a household falling into one of the above poverty dimensions. Fourth, from a policy-making perspective, although the study does not assess a particular inequality reduction scheme or policies, it highlights the need to consider their differential impact on household vulnerability. Finally, this study provides guidance on accounting for the various dimensions of the fuel poverty phenomenon as a vector of Egypt's inequality trends. Our methodology informs policymakers to better target households most in need and to find the appropriate combination of policy instruments based on the forms of poverty and local differences accounted for in our approach. For instance, fuel poverty is overrepresented in rural upper Egypt and suggests particular scrutiny.

The remainder of this article is organized as follows. The next section covers the literature review and conceptual framework (Section 2). Section 3 presents the conceptual framework and data and develops the empirical strategy. The following section (Section 4) summarizes the empirical findings and discusses the main results. Finally, Section 5 concludes the paper and discusses policy implications and future works.

## 2. Literature review and conceptual framework

Fuel poverty, which is conceptualized as a household's inability to meet its primary energy needs, is a pervasive concern in developed countries and elsewhere (Boardman, 2009). Different concepts have been used in the literature to apprehend similar circumstances that entail both affordable and reliable access to energy sources, including energy vulnerability, energy poverty, and fuel poverty (Bouzarovski, 2014). Despite the necessity of energy services for individual well-being (e.g., cooking, cooling, heating, and lighting), policymakers struggle to identify the extent and scope of fuel poverty even in developing economies.

Although relatively new literature examines the relationship between fuel poverty, poverty, and housing vulnerability, this literature primarily focuses on European countries (Galvin and Sunikka-Blank, 2018; Burlinson et al., 2018). Galvin and Sunikka-Blank (2018) recent work puts into perspective and discusses the approaches mobilized to analyze energy demand and poverty, calling for more research to better capture the growing inequalities experienced in recent decades and the impacts of their energy behavior, including the detrimental effect of declining homeownership on thermal housing quality, fuel poverty, the role of wealth distribution on CO2 emissions, and gender inequality of wealth. Furthermore, they claim that the macroeconomic dimension of income inequality is a crucial driver of household energy consumption behaviors.

A recent study by Burlinson et al. (2018), relying on the U.K. Housing Survey incorporates the definition of poverty induced by the cost of housing in the Low Income-High Cost indicator. This analysis provides additional insight into households' composition in fuel poverty by distinguishing between three specific poverty dimensions, including income poverty, housing poverty, and fuel poverty. Under this conceptualization framework, these dimensions indicate that paying the necessary amount of income for energy drives households in the poor income group. This segmentation of the low-income, high-cost (LIHC) group illustrates the potential shortcomings of aggregation implemented in U.K. government policy and subsequent research on poverty.

<footnote n="2">² A prevalent hypothesis is that inequalities have played an important role in fueling discontent in Egypt despite the difficulties for scholar to accurately measure geographical and intergroup differences.</footnote>
Contributing to the nascent stream of literature, the theoretical background foundations of this article draw from two significant constructs, namely, the intersection of capability and climate justice theories (Schlosberg, 2012). The argument here is that a capabilities-based vision of justice offers the potential to accommodate a range of energy transition and climate change concerns – ranging from the diffusion of vulnerability to considering individuals, locations, and interactions to a spectrum of fundamental human rights at stake. Such a framework, according to Schlosberg (2012), may afford the appropriate flexibility to accommodate local interplay in the effects, experiences, and resolutions of climate change.

Capability stands as a normative lens for assessing individual well-being, social arrangements, and the impact of poverty; as Adam Smith claimed, people's social competencies could have a straightforward impact on their relative income (Belaïd, 2022b).

Meanwhile, Nussbaum and Sen (1993) contend that fairness has to focus not only on distributional ideals but also on the full range of capabilities required for individuals to pursue a free and productive life of their own design. The emphasis is not only on the availability of resources but also on how those resources assist individuals to operate.

Broadening climate justice and capability theories to fuel poverty is a reasonably forthcoming avenue of research toward a capability-based conception of climate equity. It is critical to comprehend and articulate practical ways in which poorly designed climate policy can generate new forms of poverty and reassert existing patterns by sapping the underpinning of individuals' capabilities. Using these two doctrines, this article offers an original analysis to reflect on the different facets of fuel poverty in a developing country. It also paves the way for discussion on the possible impacts of poorly designed climate policies on energy justice.

## 3. Data and methodology

The conceptualization of the critical factors driving fuel poverty and a certain number of cycles in which vulnerable individuals could be trapped is illustrated in Fig. 1.

Economic inequality, fuel poverty, and housing consumption have long been analyzed by many researchers and policymakers but are often

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Conceptualization of factors causing fuel poverty and its socioeconomic impacts. Source: Belaïd (2022b).</figcaption></figure>

examined separately in developing nations (Datt and Jolliffe, 2005). Only recently has the wave of investigation of the dimensions of household energy consumption and fuel poverty been studied predominantly in developing countries due to the previous lack of microdata that provides the necessary information to analyze the dynamics of household consumption habits and link them to social issues such as fuel poverty and housing inequality. The Egyptian Household Income, Expenditure, and Consumption Survey (HIECS), which provides information on household expenditures and income level, is well suited to poverty and inequality studies.<fnref n="3" />

Egypt's housing markets face many challenges due to population growth, especially in urban areas, driving up housing prices (United Nations- Habitat (2020)). The Oxford Business Group reports that the housing affordability crisis is exacerbated by a supply shortage (the most significant supply deficit in the MENA region) and the nascent mortgage industry.<fnref n="4" /> Hoek-Smit (2015) estimates that 60 percent of households migrating to urban areas are not able to afford shelter in the formal sector.

### 3.1. Data

This analysis is based on a nationally representative survey of Egyptian households conducted in all Egyptian governorates. The Egyptian Household Income, Expenditure, and Consumption Survey (HIECS) of 2015 is made available by the Economic Research Forum and Central Agency for Public Mobilization & Statistics (ERF-LIS). This survey includes households' economic, social, and geographic characteristics, descriptions of their dwellings and assets, yearly household income, and expenditures. Most pertinently for this research, the survey provides detailed housing costs (actual and imputed rents as well as maintenance expenses), energy costs (electricity, gas, and other fuels), and additional related dwelling charges. On this basis, we can link the household expenditure of energy to inequality.

The variables used in the descriptive statistical and econometric analysis are presented and summarized in Tables 1 and 2. We divide income by the number of consumption units of the households, using the OECD scale, to account for the economy of scale within a household. The weighted distribution of the HIECS survey is representative of the Egyptian population. All statistical and econometrical analyses are therefore conducted using the sample weight.

Some households may be unable to purchase sufficient energy for everyday use of refrigeration, equipment, cooling, or heating. We impute energy expenditure based on a modeled energy demand equation estimated on households with a total disposable income per consumption unit greater than income from the first quintile (10,953 EGP). This constitutes an additional contribution to our analysis. In fact, by reasoning in terms of the energy required to obtain an energy service above a minimum comfort level, energy restrictions that specific households might adopt are not left undetected.

The factors influencing energy demand are income, dwelling tenure and number of rooms, household composition, geographical location,

<footnote n="3">³ Media pointed to economic and social perception of injustice as a reason for 2011 uprising in Egypt. Several studies have explored this research question based on the HIECS survey. Assaad et al. (2018) demonstrated that middle class families' labor income and consumption fell toward lower class families while the gap between the less well-off and upper class narrowed somehow hiding changes in inequalities of opportunities. Van der Weide et al. (2018) have been able to better estimate higher incomes and to show that when accounted for inequalities are higher than suggested by studies. Ramadan et al. (2018) have unveiled that relatively to Jordan or Palestine inter group expenditure gaps are higher in Egypt. In addition, Dang and Ianchovichina (2018) have shown that downward mobility was higher in Yemen and Egypt than in other Arab countries.</footnote>

<footnote n="4">⁴ https://oxfordbusinessgroup.com/analysis/financing-demand-rising-de-mand-affordable-housing-causes-egypt-address-home-financing.</footnote>
<table><caption>**Table 1**

Descriptive statistics.</caption><thead><tr><th></th><th>Mean</th><th>Min</th><th>Max</th><th>Std Dev</th><th>Median</th><th>60% of Median</th></tr></thead><tbody><tr><td>Annual disposable income per consumption unit (cu) (in EGP)</td><td>20,033</td><td>2745</td><td>3,531,818</td><td>42,711</td><td>15,406</td><td>9244</td></tr><tr><td>Variable totdincrott</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total disposable income after housing cost plus energy cost per consumption unit (RINC) (in EGP)</td><td>17,496</td><td>6729</td><td>3,496,438</td><td>41,005</td><td>13,430</td><td>8058</td></tr><tr><td>RINC=(totdinc-houseexp + edwe)/cu</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total disposable income after energy cost plus housing cost per consumption unit (RINC_AE) (in EGP)</td><td>19,550</td><td>2070</td><td>3,527,388</td><td>42,578.71</td><td>14,955</td><td>8973</td></tr><tr><td>RINC_AE=(totdinc-edwe)/cu</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Modeled energy cost in EGP</td><td>1086</td><td>73</td><td>19,942</td><td>361</td><td>1069</td><td></td></tr><tr><td>Maximum of modeled energy cost and actual cost in EGP</td><td>1250</td><td>126</td><td>21,540</td><td>673</td><td>1156</td><td></td></tr><tr><td>Modeled housing cost in Egyptian £</td><td>5647</td><td>251</td><td>312,292</td><td>7522</td><td>4271</td><td></td></tr><tr><td>Maximum of modeled housing cost and actual cost in EGP</td><td>5628</td><td>251</td><td>312,292</td><td>7519</td><td>4271</td><td></td></tr></tbody></table>

**Note:** Full housing expenditures (houseexp) include rental and maintenance, repair, water, and energy. Modeled housing costs include all the above costs related to housing except energy expenses. The original variable houseexp includes energy cost (EDWE).

**Number of observations:** 11,966.

and the amount of equipment requiring energy (such as air conditioning, vacuum cleaner, computer). We proceed similarly to impute the theoretical housing expenditure by estimating housing demand. The estimation is performed only on households that do not live in overcrowded dwellings, whose income per consumption unit exceeds the first quintile and for house costs below 4300 EGP (roughly the median house cost). The estimation results are provided in Table 3.

## 3.2. Methodology

### 3.2.1. Assessing poverty dimensions

To assess the extent of the various dimensions of poverty, the analysis stems from three economic variables, including energy costs, housing costs, and income. Thus, we develop a conceptual and statistical framework emphasizing the three dimensions of poverty that underlie the LIHC concept.

According to the LIHC indicator, a household is defined as fuel poor if they 1) “have required fuel costs that are above the national median level” and 2) “were to spend that amount they would be left with a residual income below the official poverty line” (Hills, 2012: 9). These two thresholds create the quadrants of the LIHC framework, as illustrated in Fig. 2, and fuel poor are in the southwest quadrant.

Burlinson et al. (2018) define additional categories within the fuel poor. For that, they define the income poor (IP) as households earning less than the poverty threshold before and after deducting housing costs (the green area in Fig. 2). Following Kutty (2005), they define housing-induced poverty HIP for households falling into poverty after deducting housing costs but not before (the yellow area in Fig. 2). They extend this notion to fuel-induced poverty, FIP, for households pushed to poverty after deducting energy costs (the pink area in Fig. 2).

Instead, we propose an extension of Hills' (2012) approach. Hills (2012) relies on the after-housing cost rather than the before-housing cost, as it better reflects the household's purchasing power by controlling for regional disparities in housing affordability and housing quality. We argue that the same holds for the energy market, with significant geographical disparities concerning climate (Besagni and Borgarello, 2019), infrastructure, and housing insulation. Low versus high income are separated by the lower orange threshold in Fig. 2 (relative to Hills or Burlinson' works, the energy cost is deducted). We define low-income households (income poor) as those who earn less than the poverty threshold of 60% of median after-housing-and-energy-cost plus calculated household energy and housing requirements. In the income-energy space, the sloping threshold in our framework is, therefore, to the left and parallel to Burlinson' income threshold since the threshold increases with energy cost on the x-axis for a constant housing cost (orange sloping threshold in Fig. 2 at T'). The energy threshold equals the current median energy requirements. We define, similarly, the housing threshold with the contemporary median housing modeled cost to define low versus high housing cost not used in the aforementioned

frameworks. Instead of working with housing-induced poverty high energy costs (HIP-HC in yellow in Fig. 2), we define low income, high energy, and high housing cost. In the HIP-HC condition, households are pushed to poverty if their housing cost is more significant than their net income of the income poverty threshold.<fnref n="5" /> In contrast, high housing cost households are not defined relative to their income but by reference to the population's median. A distinguishing feature of our framework is that the income threshold's definition is adjusted to take into account both after housing cost and after energy cost and augmented by the required fuel cost and housing cost (allowing us to take into account different geographical needs). We will now give practical details of the implementation.

To untangle the different situations, we calculate the residual income after all housing-related expenditures per consumption unit (that is, housing expense minus rent, maintenance, water and energy costs). The median value is 12,962.78 EGP. We define as a reference for the income threshold 60% of this value, which is 7777.67 EGP.

The sloping threshold, which varies with modeled energy and housing cost (respectively denoted max_e and max_h), is given by:

$$ \text{Sloping Threshold (max_e, max_h)} = 7777.67 + \text{max_e} + \text{max_h} \quad (1a) $$

Low-income households are defined as households with actual residual income per consumption unit below that sloping threshold. Households are considered to experience high energy costs if their modeled energy cost exceeds the median modeled level of 1155.55 EGP. Similarly, households spend a high amount on housing if their modeled housing costs (defined as housing-related cost before energy cost) exceed the median modeled level of 4271 EGP. We can deduce the distribution of households according to these eight quadrants, as shown in Table 4. A detailed description of each profile is provided in Annex B.

We then adapt the methodology of Hills (2012) to deduce the fuel and housing poverty gap (see Fig. 3).

A household experiences a situation of fuel poverty, which can be

<footnote n="5">⁵ In other words, households are tipped to housing poverty if their housing costs is greater or equal to their total income minus 60% of the AHC equivalized income plus required energy cost. For example, assume 60% of the AHC equivalized income is 8000 EGP. Additionally, suppose that for a given family, the required energy cost is 1000 EGP. For this family, the income threshold that defines income poverty is 9000 EGP. If that family earns more than 9000 EGP, it is not income poor before housing costs. If that family earns for instance 13,000 EGP, then housing costs above 4000 EGP would push them into housing poverty after housing cost with the HIP-HC framework. However, if this same family earns 11,000 EGP then housing costs above 2000 EGP would be enough to drive them into poverty. With our definition, we do not distinguish between households pushed into poverty or not, but rather between those spending more and less than the median contemporary housing costs. If the median contemporary housing costs is 4000 EGP then all households who spend more than this amount would be considered as high cost households.</footnote>
<table><caption>**Table 2**
List of variables.</caption><thead><tr><th>Variable</th><th>Categories</th><th>Frequency (in %)</th></tr></thead><tbody><tr><td>Household Income per consumption unit per quartiles (totdinc CU4)</td><td>Less than 11,703<br>From 11,704 to 15,402<br>From to 15,403 to 21,440<br>More than 21,441</td><td>25.00<br>25.00<br>25.00<br>25.00</td></tr><tr><td>Household Income per consumption unit per quintiles (totdinc CU5)</td><td>Less than 10,953<br>From 10,954 to 13,882<br>From to 13,883 to 17,261<br>From to 17,262 to 23,493<br>More than 23,494</td><td>20.00<br>20.00<br>20.00<br>20.00<br>20.00</td></tr><tr><td>Gender of the head (SEXHD)</td><td>1 Male</td><td>82.14</td></tr><tr><td>Educ_primary<br>Education level of the head (using EDUHD)</td><td>1 Education up to lower secondary</td><td>55.57</td></tr><tr><td>Educ_secondary<br>Education level of the head (using EDUHD)</td><td>1 Secondary</td><td>26.54</td></tr><tr><td>Educ_postsecondary<br>Education level of the head (using EDUHD)</td><td>1 University or postgraduate</td><td>16.91</td></tr><tr><td>Head living in couple (MARRIED)- Used as categorical variables.</td><td>0 couple in the household<br>1 couple in the household<br>2 or more cohabiting couples</td><td>22.25<br>77.31<br>0.44</td></tr><tr><td>Seniors (using NUM65)</td><td>1 Households with at least one member older than 65 years old</td><td>19.73</td></tr><tr><td colspan="3">Continued Table 2</td></tr><tr><td>Number of persons (HNUM)</td><td></td><td></td></tr><tr><td>Number of children (CH14)</td><td></td><td></td></tr><tr><td>Household composition (HCOMP)</td><td>1 1-2 adults, no children<br>2 1-2 adults, 1-2 children<br>3 1-2 adults, 3 or more children<br>4 3 or more adults, 0-1 children<br>5 3 or more adults, 2-3 children<br>6 3 or more adults, 4 or more children</td><td>18.18<br>18.63<br>22.60<br>24.41<br>12.61<br>3.56</td></tr><tr><td>Source of Income (using INCS)</td><td>1 From salary or business</td><td>77.09</td></tr><tr><td>Employed (using MASHD)</td><td>1 Employed</td><td>72.03</td></tr><tr><td>Pensioner (using MASHD)</td><td>1 Pensioner</td><td>18.02</td></tr><tr><td>Dwelling Type (using DWLТУР and the detailed version for estimation of housing costs)</td><td>1 House<br>2 Vila<br>3 Apartment<br>4 Others</td><td>14.27<br>0.18<br>80.57<br>4.98</td></tr><tr><td>Type of tenure (using DWLTEN)</td><td>1 Rented<br>2 Owned<br>3 Provide free<br>4 Others</td><td>17.24<br>69.37<br>13.21<br>0.18</td></tr><tr><td>Number of rooms (ROOM) Area</td><td>1 Urban Governorates<br>2 Urban Lower Egypt<br>3 Rural Lower Egypt<br>4 Urban Upper Egypt<br>5 Rural Upper Egypt<br>6 Urban Frontier<br>7 Rural Frontier<br>27 Governorates</td><td>20.80<br>12.70<br>31.80<br>11.33<br>21.96<br>0.97<br>0.45</td></tr><tr><td>Region</td><td></td><td></td></tr><tr><td>Rural (RURURB)</td><td></td><td>54.21</td></tr><tr><td>Overcrowded generated with HNUM, CH14, ROOM, HCOMP, MARRIED</td><td>The dwelling is overcrowded if the household has less than 1 living room, 1 room per couple, 1 room for 2 children</td><td>46.19</td></tr></tbody></table>

measured by the income gap to the high-income quadrant (point B) or by the energy consumption gap to the median energy cost (point A). For households in the first situation (point B), the gap is the difference between the individualized income threshold after the modeled fuel and housing bill (the sloping threshold income defined in Eq. (1)) and the actual residual income of the household after housing and energy expenditure for the households of quadrant 2.

<table><caption>**Table 3**
Estimated demand for energy and housing.</caption><thead><tr><th></th><th>Coefficient of energy consumption</th><th>Coefficient of housing consumption</th></tr></thead><tbody><tr><td>Household Income (respectively after housing cost; after energy cost)</td><td>0.005***</td><td>0.004**</td></tr><tr><td>Dwelling Type (Villa vs. House)</td><td>-100.714</td><td>-688.802</td></tr><tr><td>Dwelling Type (Apartment vs. House)</td><td>21.667</td><td>381.451***</td></tr><tr><td>Dwelling Type (Others vs. House)</td><td>6.529</td><td>NA</td></tr><tr><td>Detailed-Dwelling Type (More than 1 Apt vs. House)</td><td>NA</td><td>233.134</td></tr><tr><td>Detailed-Dwelling Type (One or more separate rooms vs. House)</td><td>NA</td><td>-631.427**</td></tr><tr><td>Detailed-Dwelling Type (One or more Separate Room in housing unit vs. House)</td><td>NA</td><td>-246.251*</td></tr><tr><td>Detailed-Dwelling Type (Tent, Slum, etc.)</td><td>NA</td><td>1567.173*</td></tr><tr><td>Dwelling Tenure (Owned vs. Rented)</td><td>147.602***</td><td>1536.209***</td></tr><tr><td>Dwelling Tenure (Provided Free vs. Rented)</td><td>73.298***</td><td>1469.188***</td></tr><tr><td>Dwelling Tenure (Others vs. Rented)</td><td>-1.805</td><td>265.747</td></tr><tr><td>Household Composition (HCOMP2 vs. HCOMP1)</td><td>127.246***</td><td>242.730***</td></tr><tr><td>Household Composition (HCOMP3 vs. HCOMP1)</td><td>182.664***</td><td>259.093***</td></tr><tr><td>Household Composition (HCOMP4 vs. HCOMP1)</td><td>243.991***</td><td>117.426**</td></tr><tr><td>Household Composition (HCOMP5 vs. HCOMP1)</td><td>290.297***</td><td>181.617*</td></tr><tr><td>Household Composition (HCOMP6 vs. HCOMP1)</td><td>408.470***</td><td>68.899</td></tr><tr><td>Number of rooms</td><td>79.942***</td><td>75.119***</td></tr><tr><td>27 regions</td><td>Not reported</td><td>Not reported</td></tr><tr><td>Secondary Area (Urban Lower Egypt vs. Urban Governorates)</td><td>64.747**</td><td>-63.012</td></tr><tr><td>Secondary Area (Urban Upper Egypt vs. Urban Governorates)</td><td>203.604***</td><td>-8.604</td></tr><tr><td>Secondary Area (Urban Frontier vs. Urban Governorates)</td><td>182.979</td><td>769.370</td></tr><tr><td>Seniors (vs. no senior in the household)</td><td>7.484</td><td>-205.061***</td></tr><tr><td>Number of equipment requiring energy</td><td>72.132***</td><td>N.A.</td></tr><tr><td>Intercept</td><td>158.961***</td><td>1123.464***</td></tr><tr><td>Estimated on households</td><td>With RINC&gt;9240.93</td><td>With (Income per cu greater than 1st quintile or totdinc CU5&gt;10,953 EGP &amp; HouseCost&lt;4300 EGP &amp; overcrowded = 0)</td></tr><tr><td>Number of observations</td><td>9553</td><td>2258</td></tr><tr><td>Continued Table 3</td><td>F (44, 9508) = 69.57<br>Prob &gt; F = 0.00<br>Adj R-squared = 0.24</td><td>F (45, 2212) = 48.65<br>Prob &gt; F = 0.0000<br>Adj R-squared = 0.4872</td></tr></tbody></table>

Note: *** refers to p < 0.01; ** refers to p < 0.05 and * refers to p < 0.1.
<figure><img src="image_4.png" /><figcaption>Fig. 2. The various dimensions of fuel poverty under the Hills framework. This figure constitutes an extension of the Hills illustration.</figcaption></figure>

<table><caption>**Table 4**
Distribution of households per income and expense.</caption><thead><tr><th>LIHC_all</th><th>Income</th><th>Energy</th><th>Housing</th><th>Proportion of Households (in %)</th></tr></thead><tbody><tr><td>1</td><td>Low</td><td>High</td><td>High</td><td>16.40</td></tr><tr><td>2</td><td>Low</td><td>High</td><td>Low</td><td>7.44</td></tr><tr><td>3</td><td>Low</td><td>Low</td><td>High</td><td>10.51</td></tr><tr><td>4</td><td>Low</td><td>Low</td><td>Low</td><td>20.94</td></tr><tr><td>5</td><td>High</td><td>High</td><td>High</td><td>16.72</td></tr><tr><td>6</td><td>High</td><td>High</td><td>Low</td><td>9.45</td></tr><tr><td>7</td><td>High</td><td>Low</td><td>High</td><td>6.36</td></tr><tr><td>8</td><td>High</td><td>Low</td><td>Low</td><td>12.10</td></tr></tbody></table>

Note: 11,966 observations. The number of households in 2015 was approximately 23.976 million (https://www.ceicdata.com/en/egypt/number-of-housesholds). According to the Hoek-Smit, 2015, the calculated average household size is 4.27 persons.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Calculation of the fuel poverty gap.</figcaption></figure>

### 3.2.2. Exploring the critical drivers of poverty dimensions

We seek to untangle the critical drivers of the various facets of poverty in Egypt. First, we develop a multivariate probit econometric model.

Every aspect of poverty considered corresponds to a dichotomous variable, which takes the value 1 if the household is considered to be in such a situation of poverty and 0 otherwise. The multivariate probit model accounts for correlation through the error terms of each equation (Greene, 2003; Capellari and Jenkins, 2003) and is generally preferred to a multinomial logit model because it allows for a more flexible IIA hypothesis (independence of irrelevant alternatives). Thus, the random components of the utility of various alternatives are allowed to be nonindependent and nonidentical. Here is the general specification of the econometric model:

$$Y_{in}^{*} = \beta_{n}X_{in} + \mu_{in}; \quad n = 1, \dots, N; \quad Y_{in} = 1 \text{ if } Y_{in}^{*} > 0 \text{ and } 0 \text{ otherwise,} \quad (1b)$$

$Y_{in}^*$ is an unobserved variable that represents the latent utility or propensity of individual *i* in poverty situation *n*. $X_{in}$ is a matrix of variables assumed to drive the poverty situation *n*. $\beta_n$ are vectors of coefficients to be estimated for each poverty situation *n*. Finally, $\mu_{in}$ are error terms that are assumed to follow a multivariate normal, each with a mean of zero, and variance-covariance matrix *V*, where *V* has values of 1 on the leading diagonal and correlations $\rho_{jk} = \rho_{kj}$ as off-diagonal elements.

In the first step, we develop an empirical model based on multivariate probit regression on three forms of poverty (LICH_all = 1 to 3 of Table 4). The main objective is to identify some variables that increase the probability of being in one form relative to another.

Consider the three-equation multivariate probit model:

$$P(y_1 = 1 | x) = \beta_0 + \beta_1 x_1 + \dots + \beta_k x_k, \quad (2)$$

$$P(y_2 = 1 | x) = \alpha_0 + \alpha_1 x_1 + \dots + \alpha_k x_k, \quad (3)$$

$$P(y_3 = 1 | x) = \gamma_0 + \gamma_1 x_1 + \dots + \gamma_k x_k, \quad (4)$$

where $y_1$ to $y_3$ are the dependent variables corresponding to the three poverty forms defined as a binary variable (1 if the households fall under this category of poverty, 0 otherwise). The independent variables are the *x* variables ($x_1$ to $x_k$). The error terms of the equation system are normally distributed, with a matrix of variance-covariance, with 1 on the leading diagonal and correlations as off-diagonal elements $\rho_{jk} = \rho_{kj}$.

In this system, the coefficient of each variable *x* indicates the change in probability of success following a one-unit increase in *x*.

In a second step, we use an ordered probit with more categories to identify the variables contributing to poverty depth. We order households from no poverty to low, medium, and high poverty levels according to our quadrants of Table 4. Our primary interest is to identify the factors contributing to a “higher” level of poverty. To simplify, all households in the high-income categories are pulled together (last four quadrants, 5 to 8). We are left with 5 categories. We also want to take into account the endogeneity of income. With the same notation as above, the regression of outcome *y* (with *y* between 1 and 5) on exogenous variables $x_1$ to $x_k$ and endogenous variable *e* may be written as:

$$P(y = v_h | x, e) = \beta_0 + \beta_1 x_1 + \dots + \beta_k x_k + \beta_e e, \quad [5]$$

with $\delta_h \le \beta_0 + \beta_1 x_1 + \dots + \beta_k x_k + \varepsilon \le \delta_{h+1}$.

The different cutoff points $\delta$ are estimated along with the coefficients of the regressions. The unobserved error term $\varepsilon$ is normally distributed.

$$E(e|z) = \theta_0 + \theta_1 z_1 + \dots + \theta_n z_n, \quad [6]$$

The error term of this last equation of the endogenous variable (income) and $\varepsilon$ from the main regression are multivariate normal with mean 0 and a covariance that is estimated in the model.

The estimated coefficient in the main equation represents the change in probability of being in the top end of the *y* scale by opposition to the bottom end following a one-unit increase of *x*.

## 4. Empirical results

We shall now empirically evaluate the depth and drivers of the various forms of poverty. According to our analysis framework, four different categories of poverty are defined (Fig. 4). Two thresholds define high energy cost (the green plane for required fuel costs above 1155 EGP, represented on the green axis) and high housing cost (the blue plane for modeled expenses above 4271 EGP on the blue vertical axis). Income poor earn less than a sloping income threshold defined by equation (1) and which is a plane intersecting the two previous ones in light red. The income axis is represented in red in the figure. The three planes define the eight categories of Table 4.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Graphical representation of the poverty forms.</figcaption></figure>

## 4.1. Depth of poverty

Households fall under the extended LIHC framework into one of the eight quadrants. For the low-income households with high energy costs and low housing costs, the fuel gap is defined by the excess required fuel bill relative to its median (point A, in Fig. 3) and the amount of energy expenditure that pushed the households below 60% after housing and energy cost equivalized income (point B, in Fig. 3). Fig. 5 shows the breakdown of the population across the quadrants. The median gap value for households in fuel poverty is 143.57 EGP.

The results suggest that the Egyptian government would need to allocate a total annual budget of approximately 257 million EGP to assist the population in escaping fuel poverty. The population in quadrant two that would benefit from this policy represents 7.43% of Egyptian households or approximately 1,783,814 households.

We proceed similarly to estimate the housing cost gap. The amount to draw the median household of quadrant three from housing cost is 891 EGP. 10.51% of households are in this situation, which represents approximately 2,519,878 families).

Finally, some households experience both fuel and housing costs in quadrant 1 (16.40% of the households or approximately 3.93 million households). They would need to receive the equivalent of both their fuel plus the house gap. The median household would need an additional 332.74 EGP for fuel and 1553 EGP for housing.

<figure><img src="image_7.png" /><figcaption>Fig. 5. The breakdown of the population across the quadrants. Note. The number of households in 2015 was approximately 23.976 million (https://www.ceicdata.com/en/egypt/number-of-households).</figcaption></figure>

## 4.2. Estimating the core determinants of the assessed poverty dimensions

We develop an empirical model based on multivariate probit regression to shed light on the factors explaining poverty's three defined dimensions. This approach is quite flexible and allows us to explore the probability of a household falling into one of the aforementioned dimensions of poverty.

The dependent variables (LICH_all = 1 to 3; 1 being defined as fuel and housing poor; 2 being fuel poor as defined originally by Hills (2012) and 3 defined as house poor) are explained by the same set of independent variables characterizing the household, the dwelling, and location.

We estimate a first model without disposable income to avoid the issue of endogeneity. Education levels, age, or employment status are proxies of income. We intend to estimate the factors that influence whether a household is likely to be in category 1, 2, or 3 (all low-income households with 1 high costs for both housing and energy, 2 high energy costs and 3 high housing expenses, respectively).

If the independent variable's coefficient is positive, the variable increases the probability that a household falls in the studied category, presented in columns. For example, households with primary education or secondary education, relative to those who are more educated, are more likely to be poor (in the three categories) than not being in those categories. Younger households are more likely to suffer from 1 (fuel-house) poverty and 3 (house poverty). These variables are correlated with lower income levels. Education is notably a stronger predictor of fuel poverty than housing or combined housing and fuel poverty. Fuel poverty also corresponds to the more precarious form of the three explored poverty types. Energy expenses represent a lower amount than nonenergy housing costs, let alone the total housing burden. These households are therefore closer to the poverty line. Descriptive statistics by quadrants confirm this (see Appendix B).

When the respondent who is interviewed is a male, the household is more likely to be poor, either of type 1 or 2. Families in apartments or slums are more likely to be in categories 1 (house and fuel poverty) and 3 (house poor) but less likely to be in category 2 (fuel poor). The tenure and dwelling type help to untangle categories 1 and 2: renting and being in a house reduces the odds of being in 1 while increasing the probability of being in 2. Household composition also plays a key role. The larger households (HCOMP4, 5, and 6) are more likely to be in categories 1 and 2 but less likely to be in category 3, with a higher coefficient in category 2. Being a larger household increases the odds of falling into fuel precariousness. In sum, the estimated equations show that fuel poor are more often households that rent and live in a house than those that own and stay in an apartment. Moreover, this category consists of large families, at least three adults, with a lower level of education. Geographical location matters, not only because of the difference in the cost of living and infrastructure but also because of the prevalence of poverty. According to the existing studies, there is not only an urban-rural gap but also significant inequalities in the most urbanized governorates, most notably in Cairo, a phenomenon observed in cities of similar size in developing countries (Verme et al., 2014). Table 5 shows that all households, except those in urban upper Egypt, are less likely to be in category 1 than the households in the urban governorates. Households of category 2 are more often in Upper Egypt, while those in category 3 are more likely to be from urban lower Egypt.

When looking at the distribution by geographical areas for each category (appendix B), we can see that without correcting for the other factors, there is an overrepresentation of all urban areas for category 1 (house and fuel induced poverty). This goes along with the fact that these households are more likely to live in apartments. There is also an overrepresentation of rural upper Egypt for fuel-poor households and lower Egypt for house-poor people. These results show that fuel poverty is significantly differentiated from the other groups by both household characteristics (education level, size) and dwelling covariates (building type, tenure, geographical location). This is consistent with the
<table><caption>**Table 5**

Trivariate probit to explain fuel and housing poverty.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Model 1</th><th colspan="3">Model 2</th></tr><tr><th>Coeff<br>LICH = 1<br>Fuel+<br>Housing<br>Poor</th><th>Coeff<br>LICH = 2<br>Fuel Poor</th><th>Coeff<br>LICH = 3<br>Housing<br>Poor</th><th>Coeff<br>LICH = 1<br>Fuel + Housing</th><th>Coeff<br>LICH = 2<br>Fuel Poor</th><th>Coeff<br>LICH = 3<br>Housing<br>Poor</th></tr></thead><tbody><tr><td>Total Disposable Household Income per c.u. (in,000)</td><td>NA</td><td>NA</td><td>NA</td><td>-0.003***</td><td>-0.169***</td><td>-0.045***</td></tr><tr><td>Age of the Head</td><td>-0.005**</td><td>0.001</td><td>-0.012***</td><td>-0.002</td><td>0.006***</td><td>-0.010***</td></tr><tr><td>Source of Income (Salary or Business vs. others)</td><td>-0.049</td><td>-0.085</td><td>0.007</td><td>0.002**</td><td>0.072</td><td>0.040</td></tr><tr><td>The Head is a Male</td><td>0.256***</td><td>0.2603***</td><td>0.011</td><td>0.194***</td><td>0.138*</td><td>-0.062</td></tr><tr><td>None or Primary Education (vs. Postsecondary education)</td><td>0.265***</td><td>0.607***</td><td>0.313***</td><td>0.059</td><td>0.159**</td><td>0.106</td></tr><tr><td>Secondary Education (vs. Postsecondary education)</td><td>0.214***</td><td>0.379***</td><td>0.229***</td><td>0.046</td><td>0.107</td><td>0.083</td></tr><tr><td>Employed (vs. other status)</td><td>-0.132</td><td>-0.151</td><td>-0.046</td><td>-0.060</td><td>0.002</td><td>0.035</td></tr><tr><td>Pensioners (vs. other status)</td><td>-0.037</td><td>-0.019</td><td>0.093</td><td>-0.011</td><td>0.019</td><td>0.109</td></tr><tr><td>Dwelling Type (Villa, Apartment, Others vs. House)</td><td>0.414***</td><td>-0.136***</td><td>0.220***</td><td>0.444***</td><td>-0.008</td><td>0.261***</td></tr><tr><td>Dwelling Tenure (Rented, Provided Free; Others vs. Owned)</td><td>-0.501***</td><td>0.134***</td><td>0.024</td><td>0.580***</td><td>-0.054</td><td>-0.027</td></tr><tr><td>Household Composition (HCOMP2 vs. HCOMP1)</td><td>0.612***</td><td>0.602***</td><td>0.227***</td><td>0.439***</td><td>0.219**</td><td>0.029</td></tr><tr><td>Household Composition (HCOMP3 vs. HCOMP1)</td><td>0.847***</td><td>0.849***</td><td>0.075</td><td>0.633***</td><td>0.265**</td><td>-0.193**</td></tr><tr><td>Household Composition (HCOMP4 vs. HCOMP1)</td><td>0.587***</td><td>0.665***</td><td>-0.314***</td><td>0.457***</td><td>0.496***</td><td>-0.444***</td></tr><tr><td colspan="7">Continued Table 5</td></tr><tr><td>Household Composition (HCOMP5 vs. HCOMP1)</td><td>0.994***</td><td>1.015***</td><td>-0.334***</td><td>0.754***</td><td>0.514***</td><td>-0.594***</td></tr><tr><td>Household Composition (HCOMP6 vs. HCOMP1)</td><td>1.270***</td><td>1.359***</td><td>-0.557***</td><td>0.993***</td><td>0.641***</td><td>-0.901***</td></tr><tr><td>Number of rooms</td><td>0.130***</td><td>0.003</td><td>0.009</td><td>0.193***</td><td>0.105***</td><td>0.053***</td></tr><tr><td>Secondary Area (Urban Lower Egypt vs. Urban Governorates)</td><td>-0.176***</td><td>-0.098</td><td>0.208***</td><td>-0.277***</td><td>0.026</td><td>0.202**</td></tr><tr><td>Secondary Area (Rural Lower Egypt vs. Urban Governorates)</td><td>-0.668***</td><td>0.087</td><td>0.005</td><td>-0.796***</td><td>0.202***</td><td>-0.030***</td></tr><tr><td>Secondary Area (Urban Upper Egypt vs. Urban Governorates)</td><td>0.166***</td><td>0.357***</td><td>-0.247***</td><td>0.061</td><td>0.364***</td><td>-0.319***</td></tr><tr><td>Secondary Area (Rural Upper Egypt vs. Urban Governorates)</td><td>-0.537***</td><td>0.348***</td><td>-0.128</td><td>-0.673***</td><td>0.201***</td><td>-0.228***</td></tr><tr><td>Secondary Area (Frontier vs. Urban Governorates)</td><td>-0.340***</td><td>-0.417***</td><td>-0.106</td><td>-0.391***</td><td>-0.216</td><td>-0.031</td></tr><tr><td>Number of equipment requiring energy</td><td>0.109***</td><td>-0.022*</td><td>-0.116***</td><td>0.1675***</td><td>0.136***</td><td>-0.067***</td></tr><tr><td>Intercept</td><td>-2.605***</td><td>-2.463***</td><td>-0.684***</td><td>-2.507***</td><td>-1.100***</td><td>-0.136</td></tr><tr><td>Correlation between errors terms</td><td colspan="3">rho21 = -0.431***; rho31 = -0.279***; rho32 = -0.177***;<br>Likelihood ratio test of rho21 = rho31 = rho32 = 0;<br>Chi2 (3) = 588.149 Prob > chi2 = 0.000</td><td colspan="3">rho21 = -0.568***; rho31 = -0.305***; rho32 = -0.131***<br>Likelihood ratio test of rho21 = rho31 = rho32 = 0; chi2 (3) = 836.756; Prob > chi2 = 0.000</td></tr><tr><td>Number of observations</td><td colspan="3">11,966</td><td colspan="3">11,966</td></tr><tr><td>Wald chi2 (63) = 2117.43 Prob > chi2 = 0.00</td><td colspan="3"></td><td colspan="3">Wald chi2 (66) = 3513.53 Prob > chi2 = 0.000</td></tr><tr><td>Log pseudolikelihood = -11,375.707</td><td colspan="3"></td><td colspan="3">Log pseudolikelihood = -10,295.317</td></tr></tbody></table>

Note: *** refers to $p < 0.01$; ** refers to $p < 0.05$ and * refers to $p < 0.1$.

literature but not directly comparable because of differences in fuel poverty definitions between country development level, characteristics of their infrastructures (both energy and housing markets) and availability of data. Our population of interest here is low-income households with either high energy or housing costs, which are defined by extending Hill's framework (Hills, 2012). Legendre and Ricci (2015) compare three definitions of fuel poverty (the 10% energy-income ratio, the after-fuel-cost-approach and Hills' framework) on French data. Our approach provides a lower bound estimate of fuel poverty (in the aforementioned research: the after-fuel-cost poverty identifies the largest rate of fuel poverty in France with 20.9% of the population, followed by the 10% ratio concept (16.6% of the population) and the hills method (9.2% of the population)). ...

Our multivariate probit analysis shows that the typical profile of the fuel poor household is a large size family (three or more adults with dependent children), living in a detached house, with a head of household that lacks education. These results are consistent with previously cited work (Bélaïd, 2020; Burlinson et al., 2018; Karpinsky and Śmiech, 2020). Most of the time, those households according to our results rent rather than own. Unsurprisingly, lower income households more often rent than own due to credit constraints. This, however, contrasts with results from Legendre and Ricci (2015), who found that renting is a marker of fuel poverty mostly with the after-housing cost approach but

not with Hills' approach in France. Differences might come from different household profiles or differences in the quality isolation of existing housing stock through renting or owning. Finally, we find that households from rural upper Egypt are overrepresented among low-income high-energy cost and low housing cost households. This can be partly explained by the fact that rural upper Egypt has a poverty rate equal to twice the national average and is the area most affected by poverty, even though spatial differences within governorates are also important (World Bank report, 2019; pages 11–16). According to the same World Bank report, income poverty is also closely correlated with all types of deprivation and dwellings of lower quality (18 percent have dirt floors, for instance).

Karpinsky and Śmiech (2020) focused on what they call hidden energy poverty and defined it as households with total disposable income after housing cost below an estimated threshold in Poland. They find two groups of households likely to underconsume energy: one in a detached house with dependent children and another one composed of retired people in apartments. Other authors (Bélaïd, 2018, 2020; Primc et al., 2019; Karpinsky and Śmiech, 2020) suggest the existence of different profiles of fuel-poor households. With our approach, the four categories of poverty are also differentiated and call for policies targeted to address the high cost of energy and/or housing and the lack of income and adapted to the profile of the household.
When introducing income, the education level becomes less significant or not significant. Unsurprisingly, a higher disposable income reduces the probability of being in any of the three dimensions of poverty. The results are presented only as a robustness check since we do not correct for endogeneity in model 2.

We also assume that based on our quadrants, we can order households by increasing order of precariousness (LICH_all = 1 poverty with the combined effect of high energy and housing costs; 3 poverty with high housing costs; 2 poverty with high fuel cost and 4 are income poor despite low expenses). See appendix B for descriptive statistics. The ordering is ordinal with no assumed poverty gap between each category.

We estimate an ordered probit, as a robustness check, with this dependent variable controlling for the household and dwelling characteristics. Income is the defining variable of poverty and is intrinsically linked to our four smallest quadrants. The ordered probit is estimated, taking into account the endogeneity of disposable income. This model estimates a single equation (in the top half of Table 6) over the four levels of poverty's dependent variable (from low to high). The second part shows the estimated equation of disposable income.

Each variable with a negative coefficient decreases the probability of being in a higher poverty level, while a variable with a positive coefficient increases this probability. The coefficient for disposable income is -0.180. The proportional odds ratio is calculated by exponentiating the coefficient and is equal to 0.835 for this variable. When disposable income increases by 1000 EGP, the odds of being in the “high” poverty level (Low Income-Low Energy and Housing Cost) versus the combined three other categories are 0.835 times lower (holding the other factors constant). We proceed similarly with all variables. Factors that reduce the probability of experiencing the highest degree of poverty (here LIHC_all = 4, income poor) by opposition to the three others are households with a male head, living in a dwelling other than a house, owning, having a household composition larger than a couple, being able to afford a dwelling with more room and living in urban upper Egypt. Living in rural areas increases the probability of being among the worse off. The two variables that have the highest impact are the family composition HCOMP5 and HCOMP6, with coefficients of -1.174 and -1.503 or odds ratios of 0.309 and 0.222, respectively. Large households of 2-3 adults with at least 2 children are approximately 3-4.5 times less likely to be in the “high” deprivation category than smaller households. Looking at the descriptive statistics of Appendix B, fuel-poor households, which are financially better off than the income poor, are larger households (with an average size of 5.5 against 4 for the income-poor). This analysis is a rather important perspective to enhance fuel poverty phenomenon understanding, given that ordering poverty dimensions by precariousness depth (in line with income poverty) has not been considered previously in Egypt.

## 5. Conclusions and policy implications

This article provides a new perspective on fuel poverty analysis in the context of developing countries. Its insights are driven by considering various dimensions of the phenomenon. Using an empirical approach based on the recent Egyptian HIECS survey, we have been able to assess the extent of fuel poverty and link various dimensions of inequality, including income and housing vulnerability. After understanding the dynamics of the three dimensions of fuel poverty (income poverty, energy costs, and housing costs) and their causes, the results shed light on ways to redirect household energy consumption that would bring better equality opportunities. The study also illustrated the specific combinations of the three defined dimensions of poverty and socioeconomic characteristics that determine the likelihood of being in one of the three dimensions.

Implementing a probit and ordered multinomial framework to recent and rich data from the Egyptian HIECS Survey, a nationally representative sample of both households and dwellings, this research argues that the extended LIHC's three dimensions of poverty are differentiated

<caption>**Table 6**</caption>

Ordered probit with income as an endogenous variable to explain ordered poverty dimensions.

<table><thead><tr><th></th><th>Coeff</th></tr></thead><tbody><tr><td colspan="2">Estimation of the probability to fall into a given poverty dimension</td></tr><tr><td>Dependent variable: four categorical variables (based on LIHC_all)</td><td></td></tr><tr><td></td><td>1 Low income with high energy and housing costs</td></tr><tr><td></td><td>2 Low income with low energy cost and high housing costs</td></tr><tr><td></td><td>3 Low income with high energy and low housing costs</td></tr><tr><td></td><td>4 Low income with low energy and low housing costs</td></tr><tr><td>Total Disposable Household Income per c.u. (in,000)</td><td>-0.180***</td></tr><tr><td>Age of the Head</td><td>-0.003</td></tr><tr><td>The Head is a Male</td><td>-0.270***</td></tr><tr><td>Dwelling Type (Villa, Apartment, Others vs. House)</td><td>-0.410***</td></tr><tr><td>Dwelling Tenure (Rented, Provided Free in Others vs. Owned)</td><td>0.396***</td></tr><tr><td>Household Composition (HCOMP2 vs. HCOMP1)</td><td>-0.590***</td></tr><tr><td>Household Composition (HCOMP3 vs. HCOMP1)</td><td>-0.926***</td></tr><tr><td>Household Composition (HCOMP4 vs. HCOMP1)</td><td>-0.901***</td></tr><tr><td>Household Composition (HCOMP5 vs. HCOMP1)</td><td>-1.174***</td></tr><tr><td>Household Composition (HCOMP6 vs. HCOMP1)</td><td>-1.503***</td></tr><tr><td>Number of rooms</td><td>-0.269***</td></tr><tr><td>Secondary Area (Urban Lower Egypt vs. Urban Governorates)</td><td>0.133</td></tr><tr><td>Secondary Area (Rural Lower Egypt vs. Urban Governorates)</td><td>0.665***</td></tr><tr><td>Secondary Area (Urban Upper Egypt vs. Urban Governorates)</td><td>-0.271***</td></tr><tr><td>Secondary Area (Rural Upper Egypt vs. Urban Governorates)</td><td>0.544***</td></tr><tr><td>Secondary Area (Frontier vs. Urban Governorates)</td><td>0.232*</td></tr><tr><td>Equipment</td><td>-0.219***</td></tr><tr><td colspan="2">Estimation of the total disposable income per consumption unit</td></tr><tr><td>Dependent variable:</td><td>Total Disposable Household Income per c.u. (in,000)</td></tr><tr><td>Age of the Head</td><td>0.102***</td></tr><tr><td>Source of Income (Salary or Business vs. others)</td><td>0.682</td></tr><tr><td>The Head is a Male</td><td>-0.862***</td></tr><tr><td>None or Primary Education (vs. Postsecondary education)</td><td>-8.982***</td></tr><tr><td>Secondary Education (vs. Postsecondary education)</td><td>-7.000***</td></tr><tr><td>Employed (vs. other status)</td><td>0.327</td></tr><tr><td>Pensioners (vs. other status)</td><td>-0.423</td></tr><tr><td>Secondary Area (Urban Lower Egypt vs. Urban Governorates)</td><td>-3.339***</td></tr><tr><td>Secondary Area (Rural Lower Egypt vs. Urban Governorates)</td><td>-4.108***</td></tr><tr><td>Secondary Area (Urban Upper Egypt vs. Urban Governorates)</td><td>-4.214***</td></tr><tr><td>Secondary Area (Rural Upper Egypt vs. Urban Governorates)</td><td>-5.879***</td></tr><tr><td>Secondary Area (Frontier vs. Urban Governorates)</td><td>-2.601***</td></tr><tr><td>Intercept</td><td>19.562***</td></tr><tr><td>Cutoff Point 1 and standard error into parenthesis</td><td>-5.960 (0.221)</td></tr><tr><td>Cutoff Point 2 and standard error into parenthesis</td><td>-5.423 (0.219)</td></tr><tr><td>Cutoff Point 3 and standard error into parenthesis</td><td>-4.600 (0.216)</td></tr><tr><td>var (e.totincK) and standard error into parenthesis</td><td>60.682 (29.905)</td></tr><tr><td>Continued Table 6</td><td>0.030 (0.062)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 6 (continued)**</caption><thead><tr><th>Coeff</th></tr></thead><tbody><tr><td>Estimation of the probability to fall into a given poverty dimension</td></tr><tr><td>corr (e.totincK,e.Poverty) and (standard error)</td></tr><tr><td>Number of obs = 6810</td></tr><tr><td>Wald chi2 (17) = 2023.35; Prob > chi2 = 0.000</td></tr><tr><td>Log pseudolikelihood = -29358.822</td></tr></tbody></table>

statistically. The finding has valuable policy implications, not only for the LIHC definition of fuel poverty but also for any indicator of (fuel) poverty involving the posthousing (energy cost) concept. Taking into account housing and energy costs in the definition of income poverty allows greater flexibility in the model by allowing us to take into account regional disparities in energy quality services, house insulation qualities and other aspects relevant to the energy market, such as possible differences in prices or energy income elasticity. This was overlooked in the previous contributions.

This paper provides an analytical tool for policymakers to identify different household profiles according to their income and expenses by breaking down energy and housing costs. Housing and energy are merit goods that exhibit both positive and negative externalities of consumption. A better knowledge of the phenomenon allows for identifying the needed actions (retrofitting for high energy costs or reforms of local housing markets to act on high land prices or low construction rates). In sum, with this new approach, regional differences are accounting for better targeting households in Egyptian public policies and using the most blunt instruments (whether it is retrofitting, acting on the affordability of the local housing market, or enhancing income support).

The results show that fuel poverty, housing poverty, and fuel-housing poverty represent approximately 7.44%, 10.51%, and 16.40% of the total Egyptian population, respectively. The econometric analysis reveals that fuel poverty affects mostly less educated households with a lower income of 10,765.59 EGP on average by comparison to 10,254.43 EGP for income-induced poverty; 13,699.05 EGP for nonenergy-housing poverty and 17,069.15 EGP for high housing cost. Coaching or counseling made more readily available for those households could be experimented with. The critical determinants of fuel poverty, relative to other poverty forms, are renting, living in a house, and being a larger household (with at least three adults). Policymakers could design energy policies targeted by tenure, for instance. Upper Egypt, predominantly rural upper Egypt, accounts for more fuel-poor households than other regions (36.82% of fuel-poor households live in rural Upper Egypt, even though this region is home to only 26.45% of the Egyptian population). This suggests that if, due to budget constraints, government policies should be implemented in successive phases, this region could be among the national priorities.

From a policy perspective, our analysis has important practical implications for the energy strategy in Egypt. The empirical results could constitute a strategic information vector in the implementation of future energy policies and programs aiming to reduce fuel poverty and inequalities in general. The largest proportion of low-income households (income poor low costs) can be helped with welfare programs to support basic consumption needs. The second largest category of low-income households faces both high energy and housing costs, suggesting a

combination of policy instruments. However, in terms of precariousness, fuel-poor households are in greater need, for instance, than this last group and should not be left undetected. The results suggest that bridging the inequality gap and alleviating fuel poverty in developing countries, particularly low-income economies, mandates supplementary policies that complement conventional energy tariff instruments. Such policies could include both supply-side interventions, such as lowering the cost of energy services or energy retrofitting, and demand-side policies targeting wealthy consumers with a higher willingness to pay for higher energy services. Furthermore, with Egypt's steady increase in domestic energy demand, the focus should be on energy sector reforms, adding capacity, improving energy efficiency, and existing infrastructures. Using energy more efficiently and thus consuming less, Egypt can reduce its overall energy bill, contribute to environmental protection, mitigate climate change, improve citizens' quality of life, and enhance its energy security. To harness these benefits and reduce final energy consumption, aggressive energy efficiency instruments are needed.

Furthermore, to counter this phenomenon effectively, it is essential to design a dedicated fuel poverty entity with a more comprehensive and pragmatic vision that mirrors the definition, metrics, and responses required to combat fuel poverty. Its purpose will consist of designing a policy to assess the extent and pervasiveness of fuel poverty in Egypt, investigating its causal factors, and setting plausible targets for reducing energy poverty. In addition, energy efficiency targeted actions for buildings and devices, while not systematically tailored to tackle fuel poverty directly, are critical to reduce energy consumption and enhance indoor thermal comfort while addressing the underlying structural drivers of fuel poverty. Other dimensions of poverty could be explored using our proposed framework to give a more detailed picture of fuel poverty. For instance, socially desirable minimum health, education, or labor market access could constitute other dimensions and thresholds to consider in policy design at the cost of a complexified implementation. Furthermore, in the Egyptian context of phased reforms, identifying induced trends in fuel poverty would be helpful in identifying corrective policies, but this would require data availability (with frequent waves of HIECS surveys).

### CRediT authorship contribution statement

Fateh Belaïd: Conceptualization, Formal analysis, Methodology, Visualization, Writing – review & editing. Véronique Flambard: Conceptualization, Formal analysis, Methodology, Visualization, Writing – review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

The authors do not have permission to share data.

## Appendix A

### A1. Bivariate Analyses

We can represent the distribution of households as in Hills (2012) theory by considering only two dimensions: income poverty and energy-induced poverty. Four quadrants separate the households according to their situation. In Fig. A1, the fuel-poor households (who are not poor after housing costs) are represented in the bottom left quadrant. In this representation, the origin of the axis is on the top left corner, and greater expenditures in fuel are represented by a downward movement along the vertical axis. To adopt this representation easily, we have represented the negative values of the
energy expenditure instead of the positive values. The sloping income threshold is a function of the modeled energy cost: Sloping Threshold (max_e) = 8058.144 + max_e, where 8058.144 is the calculated income for a household that earns 60% of the median residual income per consumption unit after housing cost.

<figure><img src="image_8.png" /><figcaption>Fig. A1. Distribution of Households by Low Income and High Energy Costs.</figcaption></figure>

The methodology can be adopted in a straightforward manner to address income poverty and housing (excluding energy) costs. The distribution of households per type of poverty using the same conventions is presented in Fig. A2.

<figure><img src="image_9.png" /><figcaption>Fig. A2. Distribution of Households by Low Income and High Housing Costs.</figcaption></figure>

## A2. Trivariate Analyses

By defining whether households spend more or less than median fuel expense (plan y = 1155 EGP) and whether they spend more or less than median house expense (plan z = 4271 EGP), we generate two perpendicular planes (as in Fig. A3).
<figure><img src="image_10.png" /><figcaption>Fig. A3. Median plans for fuel and housing expenses.</figcaption></figure>

Finally, identify households with low versus high income with the sloping income threshold condition. Low-income households have actual residual income x below 7778 + y + z. This adds another plane, as shown in Fig. A4.

<figure><img src="image_11.png" /><figcaption>Fig. A4. Median plans for expenses and income threshold condition.</figcaption></figure>

Focusing only on positive values, the quadrants can be identified as follows in Fig. A5.

1: Low Income High Energy Cost High Housing Cost
2: Low Income High Energy Cost Low Housing Cost
3: Low Income Low Energy Cost High Housing Cost
4: Low Income Low Energy Cost Low Housing Cost

5: High Income High Energy Cost High Housing Cost
6: High Income High Energy Cost Low Housing Cost
7: High Income Low Energy Cost High Housing Cost
8: High Income Low Energy Cost Low Housing Cost

<figure><img src="image_12.png" /><figcaption>Fig. A5. Identifying poverty quadrants.</figcaption></figure>
## Appendix B. Descriptive statistics of the different profiles

We decomposed poverty based on income level and expenditures. As expected and confirmed in the table below, the households that are financially worse off are those that are income poor and have low expenses. Then, comes the fuel poor households who are worse off than those who are house poor or fuel and house poor. There is also a natural ordering for those who are better off financially. The better off not only enjoy a higher income but also have a higher standard of living and afford better quality and lifestyle, reflected in higher fuel and housing expenses (see Fig. B1).

<figure><img src="image_13.png" /><figcaption>Fig. B1. Poverty Dimensions from the worse-off Households (LIHC_all = 4) to the better-off ones (LIHC_all = 5) based on the eight quadrants.</figcaption></figure>

Descriptive statistics are provided for each type of household in the eight quadrants. The fuel poor (LIHC_all = 2; Low income; High Energy Cost and Low Housing Cost) represents the households with the lower income with those who are income poor even with low energy and housing costs. They spend a high share of the total housing expenses on energy. They are large households but are not housed in the larger houses (budget constraints are binding). A quarter of them live in countryside houses. One-fifth rent their dwelling, which is the highest share among the low-income households (LIHC-all<5).

In contrast, the house poor (LIHC-all = 3) contains individuals who are slightly better off financially with relatively low energy expenditure but have high house costs relative to the household size and are the youngest households. They mostly live in apartments; 20% of them do not actually pay for housing; they benefit from in-kind transfer but would not be able to afford it otherwise.

<table><caption>**Table 7**

Characterization of the households by quadrant with selected variables</caption><thead><tr><th>LIHC_all</th><th>Proportion of Households (in %)</th><th>Housing Cost +Fuel Cost In 000 s</th><th>Fuel Cost In 000 s</th><th>Income per c. u. In 000 s</th><th>Household size</th><th>Number of rooms</th><th>Age</th><th>Male Head In %</th></tr></thead><tbody><tr><td>1 House and Energy Induced Poverty</td><td>16.40</td><td>10</td><td>1.3</td><td>17</td><td>5.0</td><td>3.9</td><td>49</td><td>88.5</td></tr><tr><td>2 Fuel Induced Poverty</td><td>7.44</td><td>4.2</td><td>1.1</td><td>11</td><td>5.5</td><td>3.6</td><td>49.3</td><td>87.44</td></tr><tr><td>3 House Induced Poverty</td><td>10.51</td><td>6.5</td><td>0.8</td><td>14</td><td>3.9</td><td>3.4</td><td>45</td><td>81.69</td></tr><tr><td>4 Income Poor</td><td>20.94</td><td>3.5</td><td>0.7</td><td>10</td><td>4.0</td><td>4.0</td><td>47.8</td><td>75.73</td></tr><tr><td>5</td><td>16.72</td><td>10.3</td><td>1.4</td><td>38</td><td>1.6</td><td>4.1</td><td>52.2</td><td>85.11</td></tr><tr><td>6</td><td>9.45</td><td>3.9</td><td>1.1</td><td>23</td><td>4.4</td><td>3.8</td><td>52.7</td><td>83.53</td></tr><tr><td>7</td><td>6.36</td><td>6.2</td><td>0.8</td><td>24</td><td>2.9</td><td>3.4</td><td>50.1</td><td>74.19</td></tr><tr><td>8</td><td>12.10</td><td>3.3</td><td>0.7</td><td>19</td><td>2.9</td><td>3.1</td><td>52.8</td><td>70.76</td></tr></tbody><thead><tr><th>LIHC_all</th><th>Proportion of Households (in %)</th><th>Proportion of households of 3 adults or more with 4 or more children In %</th><th>Proportion of countryside House In %</th><th>Proportion of Apartment In %</th><th>Proportion of Rented In %</th><th>Proportion of Freely Housed (In-kind-Benefit or Gift)In %</th></tr></thead><tbody><tr><td>1</td><td>16.40</td><td>6.45</td><td>7.62</td><td>89.22</td><td>10.13</td><td>11.86</td></tr><tr><td>2</td><td>7.44</td><td>10.62</td><td>25.30</td><td>69.48</td><td>20.02</td><td>9.42</td></tr><tr><td>3</td><td>10.51</td><td>1.09</td><td>10.12</td><td>88.19</td><td>15.63</td><td>21.64</td></tr><tr><td>4</td><td>20.94</td><td>2.26</td><td>27.49</td><td>55.39</td><td>13.47</td><td>19.66</td></tr><tr><td>5</td><td>16.72</td><td>1.53</td><td>3.77</td><td>92.61</td><td>8.18</td><td>10.16</td></tr><tr><td>6</td><td>9.45</td><td>2.92</td><td>14.23</td><td>83.10</td><td>40.65</td><td>7.21</td></tr><tr><td>7</td><td>6.36</td><td>3.05</td><td>9.01</td><td>88.12</td><td>10.90</td><td>18.14</td></tr><tr><td>8</td><td>12.10</td><td>4.92</td><td>17.70</td><td>71.84</td><td>27.68</td><td>13.22</td></tr></tbody></table>

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