Revenue Mobilisation Strategies in Digitised Tax Administrations: The Case of Uganda

Adequate tax revenue is crucial for low- and middle-income countries to close their financing gap, fund investments in public goods, and strengthen State-building. Such needs are even more urgent after the COVID pandemic.
However, many LMICs, especially in Africa, are affected by weak tax collection, mostly due to widespread informality and poor tax compliance. Uganda, the country under study, is no exception. In this context, the Uganda Revenue Authority (URA) has heavily invested in mass registration and formalisation programmes. More specifically, the URA is relying on information from third-party data from other government institutions to forcedly register taxpayers showing active economic activity. While such shift from traditional in-person registration and enforcement strategies to a cheaper, data-driven approach is appealing to budget-constrained revenue authorities, little
is known about their effectiveness. If anything, forced registrations, which became the majority in Uganda, are much less likely to pay taxes than voluntary registrations, indicating deep issue with compliance. Against this background, it is crucial to understand how different registration strategies translate into higher tax revenues, how they shape taxpayers' perceptions and
attitudes, and eventually better compliance behaviour. In addition, more evidence is needed on how the revenue authority can encourage these intrinsically less compliant taxpayers to abide to the tax law. My doctoral work plans to use the case of Uganda to shed light on the potential of the use of third-party data in for formalisation and enforcement in a weak State capacity context. I will do so by collecting and analysing survey data from a representative sample of 2,000 taxpayers, split by forced and voluntary registrations. Survey data will focus on the registration experience of taxpayers, as well as on their tax attitudes and perceptions, so to capture correlations between the registration journey and taxpayers' opinions. Originally combining survey and administrative data, I also intend to connect perceptions with
real life compliance behaviour. Then, I aim to measure, with adequate econometric tools, the causal impact of forced registration on behaviours by analysing rich taxpayer-level URA administrative data. Lastly, I will rigorously test the drivers of compliance behind forced registrations, by setting up a cheap, easily scalable, mass SMS campaign on about 50,000 forced registrations in the form of a randomised control trial. Through the trial, I will identify the causal impact of different nudging messages - including the provision of third-party information - on taxpayers' compliance behaviour, measured from filing and payment data. With my research, I plan to directly inform the URA's and other revenue authorities' policymaking, by providing evidence-based recommendations on the actual potential of third-party data
for strengthening the core function of tax administrations and mobilising revenue.