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Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines

Pede, V. O., Areal, F. J., Singbo, A., McKinley, J. and Kajisa, K. (2018) Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines. Agricultural Economics, 49 (3). pp. 301-312. ISSN 0169-5150

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To link to this item DOI: 10.1111/agec.12417

Abstract/Summary

We investigated the role of spatial dependency in the technical efficiency estimates of rice farmers using panel data from the Central Visayan island of Bohol in the Philippines. Household-level data were collected from irrigated and rainfed agroecosystems. In each ecosystem, the geographical information on residential and farm-plot neighborhood structures was recorded to compare household-level spatial dependency among four types of neighborhoods. A Bayesian stochastic frontier approach that integrates spatial dependency was used to address the effects of neighborhood structures on farmers’ performance. Incorporating the spatial dimension into the neighborhood structures allowed for identification of the relationships between spatial dependency and technical efficiency through comparison with non-spatial models. The neighborhood structure at the residence and plot levels were defined with a spatial weight matrix where cut-off distances ranged from 100m to 1,000m. We found that spatial dependency exists at the residential and plot levels and is stronger for irrigated farms than rainfed farms. We also found that technical inefficiency levels decrease as spatial effects are more taken into account. Because the spatial effects increase with a shorter network distance, the decreasing technical inefficiency implies that the unobserved inefficiencies can be explained better by considering small networks of relatively close farmers over large networks of distant farmers.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Agriculture, Policy and Development > Economic and Social Sciences Division > Food Economics and Marketing (FEM)
ID Code:73582
Publisher:Wiley

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