Judicialización del Beneficio de Prestación Continuada (BPC/LOAS) en los tribunales federales: análisis a gran escala de patrones y determinantes

Organizational Context

The Economic Commission for Latin America and the Caribbean (ECLAC) is part of the UN Secretariat. This role is based in Brasília and focuses on analyzing the judicialization of the "Benefício de Prestação Continuada" (BPC/LOAS) in Brazil's federal courts. The aim is to improve administrative procedures and reduce litigation through evidence-based interventions.

Job Purpose

This consultancy aims to develop an automated workflow for collecting and analyzing judicial proceedings related to the BPC/LOAS in Brazil's federal justice system. The work will investigate national patterns of judicialization, determinants of case outcomes, and the socioeconomic profile of claimants. By combining jurimetric methods, large language models, and administrative data, the project seeks to generate insights that will inform public policy recommendations. These recommendations will focus on enhancing the INSS administrative procedures and developing evidence-based strategies to reduce litigation, ultimately supporting the development of public platforms for monitoring BPC judicialization nationwide. The ultimate goal is to ensure the fiscal balance of the program and improve access to social benefits.

Responsibilities

Develop and implement a web scraping module for systematic collection of BPC/LOAS cases from federal court public portals. Design and implement a structured and vector database for efficient retrieval of collected documents. Calculate aggregated procedural metrics, such as average duration, success rates, appeal rates, and reversal rates, disaggregated by Federal Regional Court (TRF), year, and legal representation type. Produce a national analytical report and a repository containing the collection and analysis pipeline, associated source code, and public policy recommendations. Identify factors determining case outcomes, claimant socioeconomic profiles (including race, education, and family composition), and key motivations for claims, highlighting disparities across TRFs and legal representation types.

Work Experience

A minimum of 3 years of experience in quantitative empirical studies or research, and data analysis using Python, applied to public policy, law, social sciences, or related fields. Experience in text mining, natural language processing (NLP), computational methods for legal data, relational or vector databases, and automated data collection (web scraping) is also required.

Skills

Quantitative empirical research, Data analysis (Python), Text mining, Natural Language Processing (NLP), Computational methods for legal data, Relational and vector databases, Web scraping, Public policy analysis, Jurimetrics, Large Language Models (LLM).

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