Non-banking sector development effect on economic growth. A nighttime light data approach

Abstract

This paper uses nighttime light (NTL) data to measure the nexus of the non-banking sector, particularly insurance, and economic growth in South Africa. We hypothesize that insurance sector growth positively propels economic growth due to its economic growth-supportive traits like investment protection and optimal risk mitigation. We also claim that nighttime light data is a better economic measure than gross domestic product (GDP). We used weighted regressions to measure the relationships between nighttime light data, GDP, and insurance sector development. We used time series South African GDP data collected from the World Bank for the period running from 2000 to 2018, and the nighttime lights data from the National Geophysical Data Centre (NGDC) in partnership with the National Oceanic and Atmospheric Administration (NOAA). From the models fitted and the reported BIC, AIC, and likelihood ratios, the insurance sector proved to have more predictive power on economic development in South Africa, and radiance light explained economic growth better than GDP and GDP/Capita. We concluded that nighttime data is a better proxy for economic growth than GDP/capita in emerging economies like South Africa, where secondary data needs to be more robust and sometimes inflated. The findings will guide researchers and policymakers on what drives economic development and what policies to put in place. It would be interesting to extend the current study to other sectors, such as microfinance and mutual and hedge funds.

Impact statement

This study examines nexus between the non-banking sector, particularly the insurance sector and economic growth in South Africa using nighttime light data (NTL). The study tests the hypothesis that insurance sector growth drives economic growth due to its economic growth supportive traits such as investment protection and optimal risk mitigation. Additionally, we claim that nighttime light data is a good economic measure than gross domestic product (GDP) and GDP/Capita. Using the weighted regressions, we build and fitted two different models: for GDP/Capita and radiance light. From the models fitted, the insurance sector proved to have more predictive power on economic development in South Africa and radiance light proved to explain economic growth better than GDP/Capita. The results from the statistical tests show that, there is indeed a difference between the two and nighttime data is a good proxy for economic growth than GDP/Capita in emerging economies like South Africa. Our results are useful to any country with irregular and poor statistics, especially in developing economies.

Keywords:

• Nighttime lights data
• GDP/capita
• radiance light
• saturated light
• economic growth
• insurance sector
• insurance premium density

REVIEWING EDITOR:

• Goodness Aye, University of Agriculture, Makurdi, Nigeria

SUBJECTS:

• Economics and Development
• International Economics
• Development Economics

Disclosure statement

The author(s) reported no potential conflicts of interest.

Additional information

Notes on contributors

Leonard Mushunje

Leonard Mushunje is a master’s and pre-doctoral student in the Department of Mathematics and Statistics at Columbia University in New York, USA. Before joining Columbia University, he graduated from Midlands State University in 2021 with degrees in mathematics and statistics and a minor in computer science as a Higherlife-ECONET Scholar. He spent his undergraduate years working at the intersection of pure mathematics and statistical modelling with applications to economics and finance. Additionally, in 2021, he was a Global UGRAD fellow at the University of Montevallo in Alabama, USA. Currently, Leonard is a quantitative risk analyst at Dura Capital, where he is working on model building, pricing, and risk management. Before joining Dura Capital, he was a quantitative analyst at Commercial Bank of Zimbabwe in the CEO’s office.

Maxwell Mashasha

Maxwell Mashasha is a senior lecturer in the department of mathematics at Midlands State University. He holds a BSc in mathematics from the same institution and a master’s in operations research from the National University of Science and Technology. He is in the final years of his PhD studies in Statistics. He is interested in exploring the application of mathematical and statistical methods to economics, agriculture, and health.