Logit models for household food insecurity classification
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
American Journal of Theoretical and Applied Statistics
Abstract
Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the
global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a
challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and
qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify
which of them significantly influence household food insecurity. Logit models were then developed using the variables
identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models.
Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if
households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that
represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the
combined households for two districts. However, when fitted for separate districts, it was established that 99% of households
in Busia and 96% in Tororo district respectively, were found to be food insecure.
Description
Keywords
Hybrid Dependent Variable, Latent Variable, Factor Analysis, Logit Model
Citation
Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake. Logit Models for Household Food Insecurity Classification. American Journal of Theoretical and Applied Statistics. Vol. 3, No. 2, 2014, pp. 49-54. doi: 10.11648/j.ajtas.20140302.14