Countering COVID-19: Real-time data from Pakistan

The on-going pandemic put a strain on the developed economy with a sharp collapse of GDP and mounting unemployment. Simultaneously, the fragile socio-economic structure of low and middle-income countries are extremely vulnerable to the pandemic’s effects. Amongst the many constraints that local authorities face in responding to the pandemic, these countries have few instruments to monitor the economic indicators in real-time, making policy design particularly challenging. The research aims to design innovative tools for nowcasting the economic output of these regions, while constituting the grounding for future researchers in Pakistan.

During recent years, economic literature has explored the use of real-time data from satellite technologies. In this field, a predominant role is played by night-lights also in presence of two major drawbacks: they consider as homogeneous the workers’ productivity, and they attribute to each unit of lights the same value-added of manufacturing and services. In our study, to counter these issues, we plan to integrate other sources of real-time data. The proposed research has the ambitious goal to combine for the first time all this information through cutting-edge AI technologies.

This project has been initiated by the State Bank of Pakistan, to monitor the economic impact of COVID-19. Several institutional stakeholders will collaborate on the study, providing data and expertise from different fields.  Pakistan represents an interesting case-study for its heterogeneity in geographical and social composition. This fast-growing economy is already facing harsh challenges from terrorist organizations, unfriendly neighbours, and climate change. The current pandemic opens a new front for the local policymakers.

Outputs

  • Publication - Project Report

    Subnational income, convergence, and the COVID-19 pandemic

    The COVID-19 pandemic severely affected the growth and subnational distribution of income in low-income countries. To understand these effects, we produce monthly aggregates on gross national income (GNI) for 147 Pakistani districts through real-time data and machine learning between 2012 and 2021. Three findings emerge from our analysis. First, urban districts...

    25 Oct 2021 | Ali Choudhary, Ijlal Haqqani, Federico Lenzi, Nicola Limodio

  • Publication - Policy Brief

    Subnational income, convergence, and the COVID-19 pandemic

    This brief investigates the effects of the COVID-19 pandemic on gross national income in Pakistan and its districts. To address the lack of available data, we developed a machine-learning algorithm that can predict economy activity in real time by leveraging administrative, night-light, and other real-time data. Urban districts in Pakistan drove the slow-down in...

    8 Nov 2021 | Ali Choudhary, Ijlal Haqqani, Federico Lenzi, Nicola Limodio