Income inequality, shadow economy and environmental degradation in Africa: quantile regression via moment’s approach

Abstract

Purpose The purpose of this examine the impact of income inequality and shadow economy on environmental degradation given the growing income inequality, shadow economy and ecological degradation in developing countries. Thus, this study is motivated to offer empirical insight into how income inequality and shadow economy influence the environment in African countries. Design/methodology/approach Data from 29 countries in Africa between 2000 and 2017 were used, while the novel method of moments quantile regression of Machado and Silva (2019) and Dumitrescu and Hurlin (D-H) (2012) granger causality is used as the estimation techniques. Findings The results established the presence of cross-sectional dependence and slope heterogeneity in the panel, while Westerlund panel cointegration confirmed the long-run cointegration among the variables. The results from the quantile regression suggest that income inequality increases environmental degradation from the 5th to the 30th quantiles, while from the 70th quantiles, income inequality reduces ecological degradation. The shadow economy negatively influences environmental degradation across the quantiles, strengthening environmental quality. Per capita income (economic growth) and financial development positively impact environmental degradation throughout the quantiles. However, urbanization reduces environmental degradation from 60th to 95th quantiles. The D-H causality established a two-way relationship between income inequality and environmental degradation, while one-way from shadow economy, per capita income and urbanization to environmental degradation were established. Originality/value This study provides fresh insights into the nexus between shadow economy and environmental quality in the presence of higher levels of income inequality for the case of African region. The study applies quantile analysis via moment proposed by Machado and Silva (2019). This technique shows that the impact of income inequality and shadow economy on environmental degradation is heterogeneous across the quantiles of ecological footprints in Africa.

The moderating role of financial development in energy poverty–sustainable environment linkages: evidence from Africa

“…Data for this study are extracted from four primary sources – the Global Footprint Network, the World Development Indicators (WDI), the International Monetary Fund and the International Country Risk Guide (ICRG). In line with recent studies in environmental literature, environmental degradation is a proxy by ecological footprint (Dada et al ., 2022a, b, c; Ahmed et al ., 2022), and it is measured as ecological footprint per capita. Ecological footprint provides a comprehensive way of measuring environmental degradation.…”