From Informality to Green Development: How the Shadow Economy Affects Green Finance, Green Innovation and Green Trade

Achieving green transition has become an essential policy priority for which economies need to mobilize green finance, increase green innovation and promote trade in environmentally sustainable goods and services. However, the effectiveness of policies for enhanced green development not only depends on their design but also on their enforcement and mobilizing revenue, which may potentially be undermined by the shadow economy. This study investigates the impact of the shadow economy on green development dynamics—green finance, green trade and green innovation—in the 28 OECD countries, over the period of 1996 to 2024. To overcome the data limitation, the study estimates the size of the shadow economy till 2024 by employing the Multiple Indicators and Multiple Causes (MIMIC) technique, following Medina and Schneider (2019). Methodologically, the study integrates advanced econometric techniques, such as Methods of Moments Quantile Regression and CS-ARDL, with machine and deep learning models (Lasso, Ridge and Quantum-Inspired Neural Forecasting Model) to estimate and forecast the relationship among the variables, respectively. The study found a negative and significant impact of the shadow economy on green development, as increasing economic informality is negatively associated with green finance, innovation and trade across quantiles. The forecasting models corroborate these outcomes and explore that the unfavourable impact of informality on green development intensifies in regimes with higher green transition. The results suggest that economic informality not only hinders the initiation but also the scaling of green development. The study suggests several policy recommendations to reduce the shadow economy and its adverse impact on green development.

Keywords: Shadow economy, Green finance, Green innovation, Green trade, Deep learning, CS-ARDL