Where Are the Unbanked in Belize?: Using Machine Learning Small Area Estimation to Improve Financial Inclusion Geographic Targeting

This study aims to contribute to the efficient and effective implementation of Belize's National Financial Inclusion Strategy (NFIS) that was launched by the Central Bank of Belize in 2019. It employs Machine Learning Based Small Area Estimation to develop granular estimates of financial inclusion at the smallest geographical level know as Enumeration Districts (ED) that were previously unavailable for Belize. To gain deeper understanding of the population's financial characteristics at the ED level, the study builds five measures of access to banking and financial services. Significant clustering of financial inclusion metrics that are not apparent in the district level averages are identified.

This study also analyzes the factors that influence the use of financial services and instruments in order to propose appropriate adjustments in the strategies implemented by authorities in each geographical area. Both the spatial distribution of financial inclusion indicators and the factors influencing the adoption of financial services shed light on specific recommendations relevant to each of the four Thematic Financial Inclusion Task Forces included in the NFIS.

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By Jonathan Hersh, Lucia Martin Rivero , Janelle Leslie