Assessing the impact of ASYCUDA on customs revenue performance: evidence from the Liberia Revenue Authority

Abstract

This study sought to explore empirically the impact of an Automated System for Customs Data (ASYCUDA) on customs revenue performance at the Liberia Revenue Authority (LRA). We used monthly time series data sourced from the LRA, the Central Bank of Liberia, and various series of the Harmonized Tariff of Liberia. The data spans from January 2015 to December 2018. We employed the bounds testing approach to the Cointegration and Error Correction Model that is established within the Autoregressive Distributed Lag framework. The results revealed that total trade (Import*Export), goods and services tax (GST) and ASYCUDA positively impact customs revenue performance in both the short and long run while export and inflation were found to negatively affect customs revenue performance in both the short and long run. In addition, an error correction term of -0.837 was found, indicating that 83.7 per cent of the deviation created by shocks in the short run will be corrected in the long run; thus, confirming the existence of a long-run relationship among the variables used. For policy purposes, these findings suggest that ASYCUDA be rolled out to other ports of entry and exit to boost the efficiency of customs revenue generation. Moreover, capacity building should be carried out to complement the effective use of ASYCUDA. We also recommend that policies to reduce inflation be prioritised.