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Optimization model for poverty reduction strategies

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ESCWA
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Key messages

01 Successful recovery from the COVID-19 pandemic and 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 across population groups and multidimensional poverty index (MPI) indicators.

03 Using survey information on trends in household deprivation and data from the ESCWA Social Expenditure Monitor (SEM), the optimization model aligns the record of deprivations with the projected resources required for their mitigation with a view to proposing efficient interventions.

Executive summary

Beneficiary identification and support allocation is a well-known challenge in the implementation of social protection programmes, particularly when those interventions take several different forms. Interventions can be implemented by a wide range of stakeholders, including governments, national private sector donors, international donors, religious institutions and households. The MPI expands traditional measures of financial poverty and captures deprivations in 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 by incorporating geographic and demographic differences. To date, however, MPI research and applications have not provided national planners with relevant instruments to ensure the efficient use of limited resources and the achievement of poverty reduction targets.

The study outlined in the present report is an initial attempt to address those challenges. Two optimization models have been developed to that end, each with its own strengths, weaknesses, assumptions and results. Each model is characterized either by the type of information available to the policymaker, or by political and technological restrictions for targeting interventions, and specifically whether measuring and targeting can be conducted at the household level or through geographic or demographic assessments. A complete mathematical formulation for each of the two optimization models has been developed.

The two 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 support decision makers in identifying the interventions that are most effective at meeting MPI reduction targets and the demographic cells that should be prioritized.

The household-level targeting scenario is conceptual by design, as no State is assumed to have the necessary information or the political and technological capacity to put that scenario into practice. Demographic cell-level targeting, however, is a realistic scenario that can be used to improve efficiency provided that the State can target a large number of geographic cells. It directs policymakers to explore differences among geographic cells, thus enabling them to spot mismatches between resource allocation and poverty measures.

A custom illustration using survey data from Lebanon confirms that informed household-level interventions can achieve poverty reduction targets and require less effort than limited targeting at the government level. Indeed, targeting at the national level may sometimes yield inferior outcomes. However, examples of limited information governorate-level targeting show results comparable to those of informed household-level targeting.