Abstract
In this chapter, we investigate the geographical and sectoral profiles of poverty in Lebanon using data from the country’s 2004 National Survey of Households Living Conditions (NSHLC). With this objective in mind, we have adopted both unidimensional monetary approaches and multidimensional approaches to poverty. In the case of multidimensional approaches we focus on four dimensions of poverty: expenditure, education, housing conditions, and access to basic services. The poverty measures are estimated according to standard monetary FGT indices and their extension, based on Alkire and Foster’s method, in the multidimensional case. The robustness of rankings (by mohafaza and by occupational sector) resulting from these measures is then tested using stochastic dominance procedures. Our findings suggest that caution should be exercised when the conclusions drawn from typical poverty profiles depend on the researchers’ arbitrary decisions. Also, they shed light on the limitations of conducting an analysis that is solely based on the monetary dimension of poverty, as it may not necessarily corroborate the results provided by multidimensional analysis.
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- 1.
The authors gratefully thank the Ministry of Social Affairs of Lebanon for providing access to the dataset from the Lebanon Consumption Household Budget Survey: 2004–2005. The authors also wish to thank Rita El Araj for her helpful and efficient assistance in contacting the Ministry of Social Affairs of Lebanon. Lastly, thanks are also due to Adib Yazbeck and Alain Safa for useful information on the Lebanese institutional context.
- 2.
It is important to mention that this methodology was a source of inspiration for the recently developed Multidimensional Poverty Index (MPI) (UNDP 2010).
- 3.
A mohafaza (or mohafazat in the plural form) is an administrative division. We can consider a mohafaza as an administrative region or province.
- 4.
We will be using these FGT indices in the empirical section.
- 5.
The Watts index can be interpreted as a transformation of the Clark, Hemming, and Ulph index.
- 6.
Note that if the (s–1)th derivative of a function is piecewise differentiable, the (s–1)-th derivative is necessarily continuous and the function itself and its first (s–2) derivatives are continuous and differentiable everywhere. Note that the continuity condition we impose is more restrictive than that in Zheng (1999), which only postulates continuity on the interval (0, z) without any restriction on p (t) (z, z) = 0 for t = 0, 1,…, s – 2. This difference between his and our assumptions has implications for the analysis developed in this chapter. Specifically, we are able to consider dominance criteria for orders greater than two, even when there is significant uncertainty regarding the value of the lower limits for the ranges of possible poverty lines (for detailed information, see Duclos and Makdissi 2004).
- 7.
- 8.
To describe the reasoning behind multidimensional poverty identification, we will first consider a situation where we measure poverty in relation to a single poverty line. Then, by switching to stochastic dominance we will generalize through the use of a set of poverty lines.
- 9.
Note that the Alkire and Foster (2011) framework includes two limiting cases, namely the “intersection” and the “union” approaches of poverty identification. In the “intersection” approach (c = m), a person is considered poor if he or she is deprived in all dimensions. This same person would be regarded as poor if he or she is deprived with respect to any attribute when the “union” approach (c = 1) is used.
- 10.
In fact, the Alkire and Foster (2011) approach is even more general, as the authors also consider the case where different weights are given to each dimension for the comparison with the threshold c. The approach can then be summarized formally by the following identification function \( \Upphi \):
$$ \Phi \left( {\mathbf{y},\,\mathbf{z},\,\omega ,\,c} \right)\;: = \;\left\{ {\begin{array}{*{20}c} 1 \; {if\;\sum\nolimits_{j = 1}^{m} {\omega_{j} \varphi \left( {y_{j} ,\;z_{j} } \right)\; \geq \;c} } \\ 0 \; {if\;\sum\nolimits_{j = 1}^{m} {\omega_{j} \varphi \left( {y_{j} ,\;z_{j} } \right)\; \geq \; c} } \\ \end{array} } \right. $$With ω being some m-vector of nonnegative weights ωj such that \( \sum\nolimits_{j = 1}^{m} {\omega_{j} } = m \) and \( \varphi \left( {a,\;b} \right) \) is a function that returns 1 if a is <b and 0 otherwise.
- 11.
The generalization of higher order principles to the multidimensional framework is not available in the literature and is beyond the scope of this empirical chapter.
- 12.
It is a technical requirement.
- 13.
It is worth noting that \( F_{m}^{1} \) corresponds to a multidimensional headcount index consistent with the “intersection” view of poverty identification, though its use for stochastic dominance checks is consistent with any approach of poverty identification that fits the Duclos et al. (2006) framework such as the “intermediate” views of Alkire and Foster (2011) used for the present study.
- 14.
- 15.
More specifically, children under 5 years old are supposed to be less demanding with respect to housing, and therefore represent the equivalent of half an older household member.
- 16.
The service sector is vital to the Lebanese economy.
- 17.
It is worth emphasizing that with respect to this result, though not as rich as Beirut, Nabatieh exhibits a greater value for the minimum income with our dataset (see Table 5.1).
- 18.
Since first-order dominance implies second-order dominance, we will not discuss the cases where first-order dominance can be observed.
- 19.
On the grounds of privacy, unit prices were not made available in the data.
- 20.
Of course, the two measures are equal with the intersection view since poor individuals are then by definition systematically deprived with respect to all attributes.
- 21.
These correspond to the first-and second-order dominance tests, respectively.
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Bérenger, V., Bresson, F., Makdissi, P., Yazbeck, M. (2013). Regional and Sectoral Distributions of Poverty in Lebanon, 2004. In: Berenger, V., Bresson, F. (eds) Poverty and Social Exclusion around the Mediterranean Sea. Economic Studies in Inequality, Social Exclusion and Well-Being, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-5263-8_5
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