ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

TL;DR

ReGNL is a neural network model that uses nightlights data to rapidly estimate regional GDP, providing accurate predictions even during disruptive events like the COVID-19 pandemic, aiding timely policy decisions.

Contribution

The paper introduces ReGNL, a neural network model that predicts GDP from nightlights data, demonstrating robustness during disruptions and outperforming traditional time series methods.

Findings

ReGNL accurately predicts GDP during normal and disruptive years.

ReGNL outperforms ARIMA models in GDP prediction during COVID-19.

Nightlights data can serve as a reliable proxy for economic activity.

Abstract

Policy makers often make decisions based on parameters such as GDP, unemployment rate, industrial output, etc. The primary methods to obtain or even estimate such information are resource intensive and time consuming. In order to make timely and well-informed decisions, it is imperative to be able to come up with proxies for these parameters which can be sampled quickly and efficiently, especially during disruptive events, like the COVID-19 pandemic. Recently, there has been a lot of focus on using remote sensing data for this purpose. The data has become cheaper to collect compared to surveys, and can be available in real time. In this work, we present Regional GDP NightLight (ReGNL), a neural network based model which is trained on a custom dataset of historical nightlights and GDP data along with the geographical coordinates of a place, and estimates the GDP of the place, given the other parameters. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and is able to predict the GDP for both normal years (2019) and for years with a disruptive event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even during the pandemic. Following from our findings, we make a case for building infrastructures to collect and make available granular data, especially in resource-poor geographies, so that these can be leveraged for policy making during disruptive events.