This paper contributes to the debate on the nutrition-related outcomes of cash crop adoption by using a model with essential heterogeneity and a semi-parametric estimation technique. The model explicitly frames non-separability between production and consumption decisions of farming households providing an original test of separability. The empirical application is run using Malawian data. The results imply rational anticipations and decision process of agrarian households relative to the crop portfolio choice, disparate strength of market barriers faced by the farmers, nonseparability between production and consumption decisions and a weak transmission from agricultural incomes to higher food expenditures and better diet.

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Many households in developing countries allocate their productive assets among various income generating activities in order to develop a portfolio of income from occupations with different degrees of risk, expected returns and seasonal and liquidity constraints. The push and pull factors influencing diversification decisions of households are widely discussed in the literature; however, no study to date has taken into account spatial interdependence of household decisions in spite of various channels of neighborhood effects such as information flow, learning from others, social networks and agglomeration economies. This paper fills in the gap by incorporating spatial dependence in the choice model of diversification by using a spatial auto-regressive probit model and an advanced Bayesian strategy to its estimation. Empirical analysis is run taking advantage of the Nigerian General Household Survey Panel, 2010-2011 providing GIS coordinates for the surveyed households. The results imply endogeneity of the neighbors' decisions to allocate their productive assets among various occupations and signal the importance of social learning and agglomeration effects favoring the spillover of diversification activities through neighbors' networks and local markets. The households' decisions respond to local environment factors such as weather shocks or infrastructural constraints. As shown by the regional differences, taking into account spatial interdependence is particularly important in the event of a wide divide of the data available into different zones, as for example in case of the Nigerian northern region where the states are larger.