Analysis of Household Food Insecurity and the Implication of Measurement Error, Mandera County, Kenya

Abstract

The objective of this study was to analyze food insecurity, underlining the significance of accurate measurement to formulate the required policies for addressing food deprivation. The need for accurate measurement of food requirement is essential to generate adequate information to support decision making especially in areas vulnerable to food shortages and famine. Using random sampling techniques and employing Fisher’s formula, a total of 323 households were selected for the study. Informed by demand theory as articulated by Engel’s law of inverse relationship between total household income and the expenditure on food, plus adding a quadratic term in the equation, the study sought to estimate the magnitude of food insecurity in Mandera County. The cost of basic needs (CBN) method was employed to provide preliminary estimate for the households’ food expenditure level. In order to deal with the problem of measurement error econometric models including ordinary least squares and using instrumental variable in generalized method of moment (IV-GMM) techniques were applied to quantitatively analyze data on quadratic Engel curve. The study established that Mandera County experiences food deprivation of significant magnitude. The study has revealed that, observed household expenditure is not a perfect measure of the actual food insecurity situation. This is because microeconomic data are contaminated by measurement error which reduces reliability of parameters and if not addressed will result to erroneous conclusion in economic analysis. The results show negative and significant quadratic coefficients for both OLS and IV-GMM. Accordingly the results shows that for the estimator that corrects for measurement error 81% of the households are food insecure as opposed to 64%. In this study it is observed that measurement error reduces parameter reliability by 32% which leads to underestimation of food insecurity by about 17%. Among the recommendations resulting from the study include; first it is easy to underestimate the proportion of food insecure households if they are incorrectly estimated and therefore superior statistical and sampling techniques should form the basis of quantifying food insecurity to facilitate decision making process. Secondly, the study supports for policy formulation that is guided by economic limitations not only as a gauge to measure food insecurity but also to guide intervention and evaluating policies aimed at alleviating it. Lastly, to increase food availability and reduce food insecurity, sound data-based analysis anchored on statistical theory that provides inferential basis for guiding policy and program interventions in of paramount importance.