Análise de Impacto da Política de Gratificação por Local de Trabalho na Rede Municipal de Ensino da Cidade de São Paulo
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Autores
Catalani, Gabriel Lima
Orientador
Co-orientadores
Citações na Scopus
Tipo de documento
Dissertação
Data
2025
Resumo
Os desafios para atrair e reter docentes para escolas públicas em áreas
periféricas e/ou com menor índice de desenvolvimento social são realidade em
diversos países, tanto desenvolvidos quanto em desenvolvimento, tanto em áreas
urbanas quanto em zonas rurais e são, assim, objeto de muitos estudos pelo mundo.
Esta pesquisa visa analisar o impacto da Gratificação por Local de Trabalho (“GLT”),
implementada pela Prefeitura do Município de São Paulo em agosto de 2022, sobre a
atração e retenção de professores nas escolas com alta rotatividade da rede municipal
de ensino. A GLT se dispõe a conceder bonificação salarial de 300 a 1.500 reais
mensais aos profissionais do magistério, e entre 200 e 500 reais aos profissionais de
apoio, alocados em escolas de difícil lotação. A política tem como objetivo reduzir a
rotatividade de docentes e outros servidores nas escolas municipais de difícil lotação
criando, assim, condições para a melhoria do desempenho dos estudantes. Através
de dados dos concursos de remoção da Prefeitura para os anos de 2019 a 2024
obtidos através de análise primária do Diário Oficial do município, analisamos os
impactos do programa para o aumento da retenção, ou seja, a redução de pedidos de
remoção de professores alocados nas escolas de difícil lotação, e para o aumento da
atratividade dessas escolas, ou seja, se houve mais professores da rede solicitando
remoção para essas escolas. Fazemos uma análise com base na metodologia
econométrica de diferenças e diferenças múltipla para avaliar a efetividade do
programa em suas diferentes faixas referenciais e, adicionalmente, realizamos análise
de estudo de evento para investigar a validade da suposição de tendências paralelas.
Os resultados mostram que a bonificação a partir de 690 reais mensais aumenta a
retenção de profissionais nas escolas do programa, assim como também houve
impacto na atratividade de docentes para as escolas contempladas com gratificação
igual ou superior a 1.100 reais mensais.
The challenges of attracting and retaining teachers for public schools in peripheral areas and/or areas with lower social development indexes are a reality in many countries, both developed and developing, in urban and rural areas alike. As such, they are the subject of many studies worldwide. This research aims to analyze the impact of the Gratification for Workplace Location (GLT) policy, implemented by the City of São Paulo in August 2022, on the attraction and retention of teachers in high-turnover schools within the municipal education system. The GLT provides a monthly salary bonus of 300 to 1,500 reais for teaching professionals and 200 to 500 reais for support staff allocated to hard-to-staff schools. The policy’s objective is to reduce the turnover of teachers and other staff in these schools, thereby creating conditions for improving student performance. Using data from the City Hall’s reassignment competitions from 2019 to 2024, obtained through a primary analysis of the municipality’s Official Gazette, we analyze the program's impact on increased retention—that is, the reduction in reassignment requests from teachers allocated to hard-to-staff schools—and on the increased attractiveness of these schools—that is, whether more teachers from the network requested reassignment to these schools. We conduct an analysis based on the econometric methodology of multiple differencein- differences to evaluate the program's effectiveness across its different bonus tiers and, additionally, we conducted an event study analysis to investigate the validity of the parallel trends assumption. The results show that a monthly bonus of 690 reais or more increases the retention of professionals in the program's schools, and there was also an impact on the attractiveness of these schools for teachers receiving a bonus of 1,100 reais or more per month.
The challenges of attracting and retaining teachers for public schools in peripheral areas and/or areas with lower social development indexes are a reality in many countries, both developed and developing, in urban and rural areas alike. As such, they are the subject of many studies worldwide. This research aims to analyze the impact of the Gratification for Workplace Location (GLT) policy, implemented by the City of São Paulo in August 2022, on the attraction and retention of teachers in high-turnover schools within the municipal education system. The GLT provides a monthly salary bonus of 300 to 1,500 reais for teaching professionals and 200 to 500 reais for support staff allocated to hard-to-staff schools. The policy’s objective is to reduce the turnover of teachers and other staff in these schools, thereby creating conditions for improving student performance. Using data from the City Hall’s reassignment competitions from 2019 to 2024, obtained through a primary analysis of the municipality’s Official Gazette, we analyze the program's impact on increased retention—that is, the reduction in reassignment requests from teachers allocated to hard-to-staff schools—and on the increased attractiveness of these schools—that is, whether more teachers from the network requested reassignment to these schools. We conduct an analysis based on the econometric methodology of multiple differencein- differences to evaluate the program's effectiveness across its different bonus tiers and, additionally, we conducted an event study analysis to investigate the validity of the parallel trends assumption. The results show that a monthly bonus of 690 reais or more increases the retention of professionals in the program's schools, and there was also an impact on the attractiveness of these schools for teachers receiving a bonus of 1,100 reais or more per month.
Palavras-chave
educação São Paulo; GLT; Gratificação por Local de Trabalho; Lei nº 14.660/2007; Lei nº 17.722/2021; decreto nº 61.678; education; São Paulo; location-based incentive
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Idioma
Português
