There has never been a serious effort to allocate intellectual capital to determine poverty rates and project incomes at a district level in Pakistan. In order to fill this gap, this project aims to develop an innovative IT infrastructure that will equip policymakers with the necessary tool to monitor and evaluate progress on the Sustainable Development Goals (SDGs) at a district level.
By using a mixed-method approach to project incomes and poverty rates, the researchers aim to build dynamic models based on combining Night Light Data from outer space with data published by IMF through its World Economic Outlook programme.
The researchers investigate whether nighttime light (NTL) can be used as an instrumental variable for the GDP growth at the district level. Through the use of the U.S. Department of Commerce's NOAA National Geophysical Data Centre's (NGDC) data from its DMSP satellites, the researchers will test the validity of night light data as a statistically reliable proxy for income projections. They will also use the income and consumption data provided by the Pakistan Bureau of Statistics (PBS) through its PSLM Project.
The granular income projections and poverty spatial analysis at the district level will help policymakers identify and respond to poverty pockets that are seldom part of the national discourse. Boosting Pakistan’s SDG monitoring capabilities is perfectly aligned with the UN strategy for an effective and coherent implementation agenda called MAPS (Mainstreaming, Acceleration, and Policy Support). Additionally, the researchers aim to provide a tool that is user-friendly and updated in real-time.