01 A successful recovery from the COVID-19 pandemic and the implementation of the 2030 Agenda for Sustainable Development require strong policy responses.
02 The optimization model developed by the Economic and Social Commission for Western Asia (ESCWA) facilitates the optimized allocation of resources between population groups and Multidimensional Poverty Index (MPI) indicators.
03 Using survey information on household deprivation trends and data from ESCWA’s Social Expenditure Monitor (SEM), the optimization model aligns the recording of deprivations with the projected resources needed for their mitigation. to provide effective interventions.
Identifying beneficiaries and allocating aid is a well-known challenge in the implementation of social protection programs, especially when these interventions take many different forms. Interventions can be implemented by a wide range of stakeholders, including governments, domestic private sector donors, international donors, religious institutions and households. The MPI expands traditional measures of financial poverty and captures deprivation across multiple dimensions related to individuals’ capabilities and well-being. The MPI is a useful tool for determining the distribution of multiple deprivations across population groups, including taking into account geographic and demographic differences. To date, however, MPI research and applications have not provided national planners with the relevant instruments to ensure the efficient use of limited resources and the achievement of poverty reduction goals.
The study described in this report is a first attempt to address these challenges. Two optimization models have been developed for this purpose, each with its own strengths, weaknesses, assumptions and results. Each model is characterized either by the type of information available to the policy maker or by political and technological restrictions for targeting interventions, and more specifically whether measurement and targeting can be done at the household level or through geographic or demographic assessments. A complete mathematical formulation for each of the two optimization models has been developed.
Both models are tested against sample data from Lebanon. We discuss the process involved and the performance of the two models and highlight how the results can help decision-makers identify the most effective interventions to achieve IPM reduction goals and which demographic cells to prioritize.
The household-level targeting scenario is conceptual by design, as no state is expected to have the necessary information or political and technological capacity to put this scenario into practice. Cell-level demographic targeting, however, is a realistic scenario that can be used to improve efficiency provided the state can target a large number of geographic cells. It prompts policy makers to explore differences between geographic cells, enabling them to spot mismatches between resource allocation and poverty measures.
A custom illustration using survey data from Lebanon confirms that informed interventions at the household level can achieve poverty reduction goals and require less effort than limited targeting at the government level. Indeed, targeting at the national level can sometimes yield inferior results. However, examples of limited targeting at the governorate level show comparable results to informed targeting at the household level.