Utilizing Night-Time Light and Hierarchical Bayesian Small Area Model for Consumption Expenditure Estimation - Siregar, Sri Indriyani (DAS Institute, 2024)
As an indicator that plays an important role in determining the level of poverty and inequality, per capita household consumption expenditure needs to be estimated at a smaller area level. The Small Area Estimation (SAE) method is considered as an answer to the challenge of providing this data. The increasing popularity of this method causes the need to utilize good auxiliary variables to increase. Auxiliary data that is real-time and easily accessible such as big data is interesting to involve, especially data that comes from satellite imagery such as night-time light intensity. This data offers benefits such as time and cost efficiencies and being global and easily accessible. This research aims to apply night-time light intensity as an auxiliary variable for the Hierarchical Bayesian (HB) – SAE model to estimate household per capita consumption expenditure in Bandung City. There are three scenarios for using auxiliary variables to employ the HB model: official data on village characteristics, night light intensity, and a combination of these data. The results show that the HB model with official data on village characteristics and night-time light as auxiliary variables can provide the best accuracy with relative root mean square error (RRMSE) as an evaluation.