Small Area Estimation of Household Consumption-expenditure Pattern in Nigeria During COVID-19 Pandemic

The problem of Small Area Estimation is the non-availability of sample data in areas of interest. The idea behind this study is to adopt and modify some calibration estimators that could produce reliable estimates with minimum mean square error in small areas to determine the pattern of household consumption-expenditure in Nigeria before, during and after COVID-19 pandemic. A combined direct and synthetic ratio/regression estimators are used in the formulation of the longitudinal estimators. The bias and mean square errors of the estimators are derived using Taylor's series approximation techniques different from the existing estimators. It is observed that the calibrated estimators have provided more reliable estimates against the instability of the existing synthetic estimators and the higher variance of the existing direct estimators. Consequently, the gains made on the performance of the modified estimators cannot be overemphasized. From the empirical results, the performance of the suggested estimators are outstanding using the average mean square error, average relative bias and average coefficient of variation across the survey periods (WAVEs) of 2019, 2020 and 2021. This indicates that the use of auxiliary variable (income) into the existing estimators by calibration technique has yielded the desirable result which agrees with the literature. Again, this result is validated since the modified calibrated estimators provide estimates within the acceptable region of 25% benchmark of the average coefficient of variation in the area of interest. In addition, the performance of the estimators in predicting the estimates of the population mean expenditure are also carried out. The pattern of household consumption-expenditure signifies that households in Nigeria consumed more during COVID-19 period while at home and the consumption burden lessens after the pandemic. This study has established the use of auxiliary variable that is strongly correlated with the study variable in domain estimation where there is small/no sample data in areas of interest.