New developments in the field of spatial econometrics have made it possible to examine the spatial effects in the stochastic frontier analysis (SFA) ( Areal et al., 2012a Glass et al., 2013, 2014 Tsionas and Michaelides, 2015). In this manner, farm-level spatial dependency may arise though socio-economic, agro-climatic, or institutional similarities and could influence farmers' technical efficiency. Notably, they may have formed similar social preferences through collective irrigation management ( Ostrom, 2000 Tsusaka et al., 2015). Lastly, farmers who belong to the same water users' group in irrigated areas may face similar institutional shocks and regulations. For farmers in irrigated areas who share water within a water users' group, this kind of dependency may emerge more strongly among them as compared to the rainfed farmers who farm more independently. In this regard, farmers who belong to the same agro-ecological system and share a common resource pool depend on the regulations set within the resource pool for their farming management. ( Case, 1992 Foster and Rosenzweig, 1996 Bandiera and Rasul, 2006 Langyintuo and Mekuria, 2008 Conley and Udry, 2010 Maertens and Barrett, 2012 Banerjee et al., 2013 Ward and Pede, 2014 Nakano et al., 2018). However, there is overwhelming evidence that farmers still rely on their social networks for information on input allocation, management practices, etc. Farmers may emulate each other because they may receive the support of agricultural extension agents in farming communities. There are several reasons why farm and household level networks may influence technical efficiency. However, incorporating information on social interaction is more challenging. For instance, Gadanakis and Areal (2020) show how incorporating rainfall and the length of the growing season in the technical efficiency analysis of cereal production matters. In some cases, spatial information can be incorporated into the analysis combining the use of different data sources including climatic and topographic maps and location of farms. In agriculture, unobserved spatial heterogeneity can arise from farmers emulating each other, level of infrastructure, or climatic and topographic conditions ( Areal et al., 2012a).

Neglecting unobserved spatial heterogeneity in technical efficiency analysis may lead to coefficients that are inefficient or biased ( Anselin, 2001). However, most of the technical efficiency literature has ignored unobserved spatial heterogeneity. There is growing literature devoted to the analysis of farmers' technical efficiency in developing countries using a stochastic frontier analysis approach ( Idiong, 2007 Balde et al., 2014 Quilty et al., 2014 Michler and Shively, 2015, etc.). The Asian rice production sector is a particular example where decision for input allocation is critical in the midst of growing resource scarcity and the need for improving productivity to ensure food security. We recommend the use of unobserved effects in both production and efficiency within the stochastic frontier analysis framework to avoid making any misleading recommendations to farmers and policymakers.Īgriculture in developing countries is challenged by a growing scarcity of resources, which imposes the need for efficient resource allocation to increase productivity. More importantly from a policy perspective, the rankings of the farms in terms of efficiency are altered once unobserved spatial heterogeneity is incorporated in efficiency models. When not accounting for unobserved spatial heterogeneity efficiency, models show farms to be relatively more inefficient than they actually are (i.e., once unobserved spatial heterogeneity is incorporated in the models). We show how not accounting for unobserved spatial heterogeneity affects efficiency estimates and farm efficiency rankings. We compare farm level efficiency rankings derived from non-spatial and a variety of spatial model specifications that account for unobserved heterogeneity in both the production and the efficiency sides of the stochastic frontier model in an empirical application on rice farming in the Philippines. 2International Rice Research Institute, Los Baños, Philippines.1Center for Rural Economy, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.

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