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This paper aims to examine the effect of corruption on foreign direct investment (FDI) inflow in Ghana. This provides answers to the call for further empirical examination of the contextual impact of corruption on FDI inflow.

The study uses a non-linear ADRL time series econometric model to estimate data from the World Bank and the international country risk guide (1984–2019).

The study confirms the sand in the wheel and the grabbing hand hypothesis of the impact of control of corruption (CoC) on FDI both in the short and long run. However, degradation on the CoC index has a significant and more than a proportionate constraint on FDI inflows, while an improvement in CoC has no significant impact on improving FDI inflows. An explanation for this outcome was proposed after comparing this finding to a similar prior study with a Nigerian data set (Zangina and Hassan, 2020). The proposed explanation relied mainly on the rational expectation hypothesis and drawing elements of the efficient market hypothesis. FDI inflows do not react to outcomes or trajectories reasonably expected because such rationally expected future outcomes will have been modelled into existing FDI movement decisions. Instead, FDI flows react to “surprises” and often respond in a more than proportional manner.

Political leadership in Ghana should be conscious of the severe adverse effects of inaction or ineffective action in curbing corruption, leading to slippering in CoC rankings. In the case of Ghana, the dependence of FDI on CoC is even more pronounced as the other variables within the specified model show an insignificant impact on FDI. Additionally, admittedly aggregated cross-country data in econometric modelling is appealing and has some empirical basis, but these must not erode the relevance of country-specific studies as both are needed to support theorization.

The paper is among the first to test for the asymmetric relationship between corruption or its control thereof and FDI with a time series approach, and hence, the findings offer new insight.