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Two-part fractional regression model with conditional FDH responses: an application to Brazilian agriculture

  • S.I. : Agriculture Analytics, BigData and Sustainable Development
  • Published:
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Abstract

We extend the notion of a two-part fractional regression model with conditional free disposal hull efficiency responses to accommodate two-stage regression analysis. The two-part regression model includes the binomial model with a nonlinear specification for the expected response in (0,1] and is a more general formulation in the context of fractional regressions. We use nonlinear least squares to assess the effect of covariates in the conditional efficiency response. The approach is applied to Brazilian agricultural county data, as reported in the Brazilian agricultural census of 2006. The efficiency measure is output oriented and assumes variable returns to scale. Output is rural gross income and inputs are land expenses, labor expenses and expenses on other technological inputs. The covariates affecting production are credit, technical assistance, a rural development index, income concentration, measured by the Gini index, and regional dummies. Overall Brazilian rural production performance responds positively to all covariates.

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Authors and Affiliations

  1. Departamento de Estatística, Universidade de Brasília, Campus Darcy Ribeiro, Prédio CIC/EST, Brasília, DF, 70910-900, Brazil

    Geraldo da Silva e Souza

  2. Secretaria de Inteligência e Relações Estratégicas, Embrapa, Parque Estação Biológica, Av. W3 Norte final, Brasília, DF, 70770-901, Brazil

    Eliane Gonçalves Gomes

  3. Assessoria da Presidência, Embrapa, Parque Estação Biológica, Av. W3 Norte final, Brasília, DF, 70770-901, Brazil

    Eliseu Roberto de Andrade Alves

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  1. Geraldo da Silva e Souza
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  2. Eliane Gonçalves Gomes
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  3. Eliseu Roberto de Andrade Alves
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Correspondence to Eliane Gonçalves Gomes.

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da Silva e Souza, G., Gomes, E.G. & de Andrade Alves, E.R. Two-part fractional regression model with conditional FDH responses: an application to Brazilian agriculture. Ann Oper Res 314, 393–409 (2022). https://doi.org/10.1007/s10479-020-03752-z

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