This is an Open Access paper distributed under the terms of the Creative Commons Attribution Non Commercial 4.0 International license, which permits downloading and sharing provided the original authors and source are credited -but the work is not used for commercial purposes. http://creativecommons.org/licenses/by-nc/4.0/legalcode SummaryAlthough field experiments in tax compliance represent a growing area of research, the literature has so far focussed exclusively on high and middle-income countries. This paper starts to fill this gap by reporting the results of a tax field experiment in Rwanda, while also highlighting some characteristics that may be common to other low-income countries. We evaluate an intervention carried out by the Rwanda Revenue Authority (RRA), which involved sending messages to taxpayers to nudge their declaration behaviour during the filing period of January-March 2016. Focussing particularly on business profits tax, our study is designed to address two interrelated questions. First, what are the key drivers of compliance in Rwanda? Second, what is the best delivery method to reach taxpayers with messages designed to improve compliance? Although other studies have explored delivery methods in the context of taxpayer communication, our study is the first one to interact these methods with different message contents. As a result, we evaluate a set of nine treatments that combine three message contents (deterrence, fiscal exchange, reminders) and three delivery methods (letters, SMS, emails) -as compared to a control group that received no message. We find that friendly approaches to taxpayers are generally more effective than deterrence. However, small taxpayers are still quite responsive to the possibility of being fined and prosecuted (deterrence). We also show that low-cost delivery methods like SMS and emails can be highly effective as compared to letters.
* This article is part of a broader project on tax compliance in Rwanda carried out in collaboration with the African Tax Administration Forum (ATAF) and the Rwanda Revenue Authority (RRA), with funding from the International Centre for Tax and Development (ICTD). We are extremely grateful to these institutions for the support they have provided throughout the project and especially to ATAF, which has played a vital role through the involvement of Dr Nara Monkam in the broader project. We are particularly thankful to the staff of the RRA for their invaluable inputs and assistance, that made this project possible. We would like to express our gratitude to RRA's officials, at the time of this project: Commissioner General Mr Richard Tusabe, Deputy Commissioner General Mr Pascal Ruganintwali, Deputy Commissioner for Research and Planning Mme Agnes Kanyangeyo, as well as other Deputy Commissioners who provided assistance and feedback throughout the project. Special thanks are due to RRA's Head of Research Mr Denis Mukama, who has coordinated the implementation of the project and has provided excellent inputs throughout the research. We are also very grateful to John Karangwa, Gaudence Uwimana, Lucie Niyigena, Naphtal Hakizimana, Florence Mukakabanda and the entire research and planning unit, as well as Mme Hajara Batamuliza and all officials involved in the letter delivery. This paper benefited from comments provided by Lucio Castro, Laura Paler, Jorge Garcia Hombrados, Carlos Scartascini, anonymous referees, and participants to various conferences and seminars. All remaining errors are our own. Declarations of interest: none.
This paper is the first in a series of three studies looking at tax compliance using administrative data from Rwanda. It discusses the use of administrative data for tax research specifically anonymised taxpayers records, which have become increasingly available on the African continent. The paper starts by critically summarising the key advantages and disadvantages of using this data for tax research in Africa. It proceeds to illustrate these opportunities and challenges in practice, using the case of Rwanda for application of the data to analyse tax compliance and progressivity. By doing this it shows some stylised factsfor example that tax systems designed to be progressive can still be regressive in practice, that a great share of tax revenue is generated by a few very large taxpayers, and that some taxpayers face a negligible probability of being audited. Although these results are specific to Rwanda, they are in line with the situation in other low-income countries in Africa.
Large-scale field experiments on tax compliance have been a thriving field of research in many regions of the world. However, Africa is still lagging behind, as administrative data from anonymised returns is available only in a handful of countries. To the best of our knowledge, there is as yet no published evidence of a tax field experiment from Africa. This paper reports the results of a pilot experiment in Rwanda that served as a stepping stone for a larger experimental study on tax compliance. In this pilot, we test the process of messaging taxpayers to encourage them to comply voluntarily, by providing information on sanctions. The results indicate that communication strategies that aim to inform taxpayers may be effective in increasing tax compliance. However, these results are only indicative. They will be complemented by further evidence from the larger field experiment, where we test different types of messages and delivery methods. Nonetheless, this paper provides some initial insight into the use of tax experiments in Africa, both in terms of initial evidence and lessons learned for future efforts in this field.
Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group affinity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group affinity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. To capture the temporally extended nature of group engagement we implement a time-varying factorization. We test the assertion that latent preferences for groups and users are sparse in investigating elastic-net regularization. Experiments using data from DeviantArt indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the benefit of the added model complexity.
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