Introduction

Measurements of light captured at night have become an increasingly popular data source for economists and social scientists. Nighttime lights (NTL) statistics have been used extensively to measure the extent of urban areas and to proxy economic activity. Given its ubiquity and ease of access, NTL data has become a popular choice as an economic variable, particularly in countries where economic statistics are lacking. Two public satellites – Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) – combined provide data with global coverage and monthly cadence since 1992. A new partnership between the World Bank and NOAA has unlocked access to daily scenes from both archives.

Researchers have used NTL to study economic dynamics at various scales, whether to estimate growth in GDP, or map the distribution of economic activity at local levels (Zhao et al. 2019). The main assumption behind the use of lights as an economic proxy is that consumption and production activities during the evening require some form of lighting. However, there are several reasons why this relationship might not be as strong in the Pacific Islands.

For one, the overpass time of the more accurate VIIRS satellite is after-midnight when centers of production may not be emitting light. Satellite data for the Pacific territories is highly susceptible to clouds, causing frequent interruptions in data coverage. Populations in the Pacific Island states are predominately rural and produce too little light to be captured by these satellites (Gibson, Susan, and Boe-Gibson 2020). Perhaps most importantly, there is a risk that important economic activities from the islands (fishing, agriculture, and tourism) are not captured well by luminosity signals.

The objective of this feasibility note is to test these assumptions and assess the usability of NTL to generate statistics for the Pacific Island Countries (PICs). The note is divided into four sections. As a preamble, we will review the main approaches to generating economic statistics from NTL data and note previous efforts to apply them in the Pacific context. In the second section, we present a quality assessment of nighttime lights data for PICs, exploring the distribution of values and issues with cloud coverage. The third section investigates whether lights data can be used to predict output from the extractives industry in Papua New Guinea, and if they correlate with sub-national poverty. The last section explores two other use cases for NTL in PICs: tracking recovery from natural disasters and estimating electrification rates.