Micro and Macro Indexes of Economic Activity: Multiple Indicators and Multiple Methods Using Bangladesh as a Test Case

The first chapter explores, the use of night-time lights as a proxy for estimating annual GDP per capita and subsequently the GDP per capita growth rate. It is observed that even though, Bangladesh’s, GDP per capita is under-estimated, the annual growth rate is over-estimated. The second chapter explores the quality of the household surveys conducted in Bangladesh through the application of the Benford’s Law and triangulation against administrative data. Sampling errors are detected in all rounds of the household surveys. The results showed that the micro dataset over-sampled wealthier households. This indicated that the income and expenditure levels of the three lowest quartiles, estimated from the household surveys, is likely over-estimated. The results of the first two chapters are then combined to comment on the state of inequality in Bangladesh. It is observed that GDP per capita is higher than expected, while the income of the lowest three income quartiles is lower than estimated. Thus, true inequality is likely to be much higher than what is indicated by the published Gini-coefficients. The fourth chapter assesses the accuracy of a proxy-means test, the Poverty Probability Index, in classifying household poverty, in the absence of sound data. Applicability of the PPI, over years and across population sub-groups, was tested. It was seen that the index overestimated poverty probability in both cases. In the last chapter, machine learning algorithms are used to develop alternatives to the Poverty Probability Index, in the absence of extensive domain knowledge. These models out-performed the PPI by 3 percentage points in terms of accuracy and ROC-AUC.