IASS Webinar 31: Producing Small Area Estimates for Labor Market Indicators in Latin America: a Bayesian Perspective

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Labor market statistics are of major importance when addressing social and economic challenges. These statistics are typically derived from complex survey data collected on a monthly or quarterly basis. However, when it comes to small geographical areas or domains, survey samples often fail to provide reliable estimates. To address this issue, the Economic Commission for Latin America and the Caribbean (ECLAC) has put forth a standardized approach that utilizes area-level models, taking into account the multinomial nature of occupation statuses. Within a Bayesian framework, we define a both a linkage and a sampling model that enable the simultaneous estimation of a diverse set of indicators of interest, such as unemployment rates, occupation rates, and participation rates. This innovative approach proposed by ECLAC aims to overcome the limitations associated with traditional survey sampling methods, particularly in smaller areas where the precision of estimates may be compromised. To validate the effectiveness of our methodology, we conducted a design-based simulation, which revealed promising results and demonstrated the favorable performance of the proposed approach. Building upon this success, we proceeded to implement the methodology across several countries in Latin America, leveraging the extensive and reliable data repository provided by ECLAC's BADEHOG survey database. By adopting this standardized approach, ECLAC aims to enhance the accuracy and reliability of labor market statistics at both national and subnational levels. The utilization of area-level models and the incorporation of Bayesian techniques allow for more reliable estimations, thus enabling policymakers, researchers, and practitioners to make informed decisions and develop effective strategies to address labor market challenges in Latin America and the Caribbean region.