Conference Agenda

This study assesses the impact of the reverse-charge mechanism (RCM) on VAT compliance proxied by the VAT gap, defined as the difference between expected and realized VAT revenues. Using annual information in 2000-2020 across 27 EU member states, we first exploit the staggered adoption of RCM by member states to compare VAT gap changes before and after RCM implementation and, additionally, consider variation in the size of economic activity targeted by RCM-adopting member states in the pre-implementation year. Our estimates indicate that RCM adoption does not lead to EU-wide VAT gap reductions and illustrate the mixed effectiveness of the RCM across goods and industries, suggesting its application on durable goods can be effective but not universally. These findings do not provide support for policy changes that cast the net of the RCM wider, although bilateral coordination in RCM adoption with top trading partners may assist in curbing VAT fraud relocation.

The Value-Added Tax (VAT) is widely recognized as being inherently self-enforcing because it features third-party reporting and spread withholding along the production chain. However, a growing literature documents significant VAT tax gaps, sparking interest in possible improvements to VAT design. Reverse Charge (RC) is among the main examples: recently adopted by multiple countries to a variety of industries, RC shifts the entire tax payment on retailers, which, however, remain subject to third-party reporting. Leveraging administrative firm-level data from Italy and a quasi-experimental variation, we show that RC leads to an increase of reported value added (+20 percent). The effect of RC is present across the distribution of firm size although it is stronger among small firms and companies that used to bunch around the zero-liability tax kink.

The Value-Added Tax (VAT) collection plays a role in achieving positive domestic revenue objectives through improved and reformed taxation, but VAT gap estimation is infrequently conducted in developing countries. This study uses novel tax declaration and audit data to estimate the VAT gap in Tanzania and its heterogeneity. We perform a machine learning approach to predict evasion assessment on unaudited firms and periods. We document evidence about the firm's strategic behavior to avoid forceful auditing and increased evasion. Those firms show the largest VAT gap in the data. By the machine learning approach, we estimate a VAT gap equal to 62%, and the Agricultural sector shows the largest VAT gap. Finally, we provide a cost-benefit ratio to indicate that it is cost-effective to redistribute resources to audit more intensive sectors with larger evasion and smaller audit probability.

Assessing tax gaps—the difference between the potential and actual taxes raised—plays a vital role in achieving positive domestic revenue objectives through improved and reformed taxation. This is particularly pertinent for growth outcomes in developing countries. This study uses a bottom-up approach based on micro-level audit information to estimate the extent of tax misreporting in Zambia. Our methods predict the extent of tax evasion using regression and a machine learning algorithm with a sample of audited firms, after which we estimate tax gaps using a standard approach. We estimate total tax gaps as 55% and 47% for the two approaches. This represents potential revenue with the least assumed tax evasion. These gaps are mainly driven by Corporate Income Taxes (CIT). Applying our gap to key industries shows that the extractives sector in Zambia records the highest gaps in terms of CIT and one of the lowest in terms of VAT.