Targeting humanitarian aid using administrative data: model design and validation

Onur Altindag, Stephen D. O’Connell, Aytug Sasmaz, Zeynep Balcioglu, Paola Cadoni, Matilda Jerneck, and Aimee Kunze Foong

Journal of Development Economics (forthcoming)


This paper presents the design and validation of an econometric targeting model that uses routinely collected administrative data to target over US$380 million per annum
of unconditional cash and in-kind assistance to Syrian refugees in Lebanon. The authors compare the prediction accuracy of the proposed model to a traditional short-form survey Proxy Means Test (PMT) approach. A PMT approach relies on representative household expenditure survey data to determine the relative importance of predictors of household consumption, which are then used to generate a metric for program eligibility, as well as a short-form survey (scorecard) of the entire potentially eligible population.
The analysis relies on: (1) nationally representative survey data from the 2018 Vulnerability Assessment of Syrian Refugees in Lebanon (VASyR); (2) UNHCR administrative data; and
(3) the Refugee Assistance Information System (RAIS), which includes information on all refugee families who receive assistance in Lebanon from any of the major international organizations or their partners.

Key findings:

  • The use of basic demographic information from typical administrative records
    held by aid organizations and governments is approximately as accurate in
    targeting the poor compared to a short-form PMT, which requires a household
    survey for the entire population. There is no substantive difference in the capacity of
    administrative data—which does not include any information on assets—to predict
    poverty, relative to traditional survey-based methods While the survey-based approach
    yields decreases in inclusion and exclusion error of about two percentage points, these
    differences are not statistically significant.
  • A small number of fields in the survey data provide additional predictive power. A
    small number of basic household furniture questions provide modest improvements.
    Adding a single type of housing question to the administrative database would improve
    the targeting accuracy by around two percentage points in overall error.

The authors conclude that routinely collected administrative data can potentially offer
an equally reliable and less costly alternative to existing PMT approaches to targeting
social or aid programs.


Big Data | Technology


Lebanon | Syria



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