Predicting Contract Participation in the Mekong Delta, Vietnam: A Comparison Between the Artificial Neural Network and the Multinomial Logit Model

Abstract The research aims of this study are bi-fold: to study factors influencing the uptake of contract farming (CF) and to compare the predicting power of the artificial neural network model (ANN) and the Multinomial Logit Model (MNL) on predicting CF participation in the Mekong Delta, Vietnam. ANN and MNL were employed to analyze on the basis of the transaction cost theory. To validate the ANN, a 10-fold cross-validation procedure was applied to avoid model overfitting. The sensitivity analysis of ANN was used to elicit the magnitude of the correlation between predictors. Multicollinearity was examined with all VIFs lower than two. Among predictors, the most influential roles of the cooperatives and the extension agents/services in supporting CF participation are reported. Also, farmers who conduct frequent access to the market incline to participate in CF. Risk perceptions and preferences are dissimilar across domains, which are also mainly interpreted that risk-averse farmers tend to opt for CF as an effective solution to risks perceived. Thus, heterogeneous approaches should be tailored to promote CF. The findings suggest that MNL outperforms ANN in terms of accuracy percentage and mean absolute error (MAE). However, this result should not be generalized base on the constraint of the data threshold as articulated in the study. The sensitivity analysis of ANN and the estimation results of the MNL relatively agreed on the importance of model predictors. This study is the first to investigate the impacts of the domain-specific risk perceptions and attitudes on CF and also contribute to the debate over the performance between the conventional econometric models versus machine learning techniques.