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The purpose of this study is to assess the impact of the shadow economy on three sustainable development indicators while considering the moderating effect of the governance quality, and to highlight the non-linearity of the considered relationship.

A sample of 82 countries covering the period from 1996 to 2017. The dynamic first-differenced generalized method of moments (FD-GMM) panel threshold model is implemented to control for non-linearity.

The shadow economy hinders sustainable development in countries with low-governance quality, while the opposite result holds in countries with high-governance quality. The critical thresholds triggering the switch from one regime to another vary across the sustainable development indicators. Boosting growth requires enhancing the legal system and the economic dimension of governance, while promoting environmental quality requires the implementation and enforcement of specific environment-friendly regulations.

The study addresses non-linearity and the moderating effect of governance quality. The use of six governance indicators allows to gauge the ability of each governance dimension to curb the negative effects of the shadow economy. Considering the three objectives of sustainable development allows to identify specific policy recommendations for each of them.

The purpose of this study provide answers to the following research questions: Whether and to what extent money laundering affects the achievability and the trend of Sustainable Development Goals (SDGs)?; Does the influence of money laundering on the SDGs’ achievement differ from developing to developed countries?; How does the influence of money laundering vary among the 17 SDGs?

The paper’s analysis involves two key parts. In the first part, the authors perform a multivariate analysis to examine the influence of money laundering on the achievement of SDGs, and then in the second part, the authors make use of an ordered probit regression model to investigate the impact of money laundering on the trend of attaining each SDG.

Using a sample of 98 developed and developing countries, the regression results from multivariate analysis estimates show that money laundering has a strong inhibiting effect on the achievement of almost all the SDGs in the whole sample of countries and the sub-sample of developing countries, whereas no significant effect is observed for developed countries. However, for the SDG trends, the ordered probit estimates reveal that the harmful effect of money laundering occurs for all countries regardless their development level. In addition, perhaps surprisingly, the results from both the approaches yield also evidence advocating that money laundering activities might be associated with positive externalities on production and consumption. In fact, money laundering is found to have a significant positive influence on the achievement and the trend of SDG12 (Sustainable Consumption and Production). Overall, this study’s findings do have interesting policy implications, especially for developing countries. In these countries, prioritising the formulation and implementation of sound anti-money laundering policies is a necessary requirement for their progress towards achieving the SDGs.

The long-standing tradition of previous empirical studies examining the nexus between money laundering and sustainable development concentrates mainly on the economic dimension of sustainability (i.e. economic growth). However, little is known about the consequences of money laundering activities on the environment and the societies. Consequently, this study seeks to fill this gap by assessing the influence of money laundering on the achievement of the economic, environmental and social goals of sustainable development. To the best of the authors’ knowledge, this is the first integrated study to analyse the potential repercussions of money laundering on the SDGs’ achievement.