Primary Outcomes (end points)
Our key outcome data comes from a smart phone application we developed for this project and distributed to households and businesses for daily entry, and weekly upload. Participants in treatment and control groups reported weekly on what they had paid in formal and informal taxes, whether they had negotiated to lower their tax payments, whether that negotiation was successful, and their attitudes towards paying taxes. Since we made sure that the smart phone data collection activity and the ODEP tax intervention activities were independent of one another, we can be confident that any reporting bias is orthogonal to treatment assignment. We also draw on household and business surveys for key variables for checking balance, analysis of heterogeneous effects, and controls. To analyze payments, we use the estimated payments of informal and formal taxes. We allow for informal (and formal) payments to non-state actors. In addition, we also collected the following variables, which we will exploit in the analysis: Whether a negotiation occurred; starting amount, final amount, and difference ; satisfaction with tax payment; and reasons for paying or not paying associated with bargaining. We next provide a rationale for the categorization of taxes by their degree of formality. Additionally, we use project implementation data that informs how the treatments were actually implemented (This data includes information on how often participants were called by the ODEP advisors (client dataset), tracking sheets that provide detailed tracking data on the nature of each phone call (including what taxes were discussed, abuses reported, etc), and qualitative exit interviews conducted with recruited citizens at the end of the smart phone reporting period that checked on the quality of ODEP consulting) In the remainder of the paper, we use the data collapsed at the week level for each respondent.
The smart phone system allows us to overcome under-reporting that may arise in retrospective surveys. The average payments are higher in the smart phone system, likely because respondents do not need to recall their payments over long periods of time, and the proportions of formal and informal are similar.
A key challenge is how to measure formal and informal payments. Definitions of what constitutes formal and informal taxation have been highly contested within existing research. Following recent work, we define taxation as ``all payments---whether cash or in kind, including labor time---that are made as a result of the exercise of political power, social sanction or armed force.'' Within this definition, identifying and defining formal taxes is straightforward: Formal taxes refer to any compulsory tax or tax like payment stipulated in the statutory legal framework. At the local government level this includes levies formally referred to as ``taxes'', but includes licensing fees, rate and user fees for particular services. In practice, user fees are often particularly prominent as a means to finance services provision. User fees are ``imposed on specific persons, activities, or properties that receive a service or benefit'' in return. Common fees in developing countries like the DRC include those to access education and health services, obtain businesses licenses, or operate in markets. Fees are often viewed as distinct from taxes because, unlike with taxation, there is a direct and immediate relationship between fee payments and the goods and services received in return. Yet, given the prevalence of user fees and the fact that they constitute compulsory payments in exchange for government provided goods and services, we also measure them.
In this RCT, we use multiple approaches to examine formal and informal taxation. We use three approaches to measure informal taxes.
We first obtain formality from the households and businesses self-reports if the payments they make are formal, state law backed payments, or instead informal payments to facilitate the process for instance. However, relying on households' self assessment of formality is problematic on multiple grounds. To begin with, a motivation of this paper is precisely that households do not know what their legal liabilities are, hence relying on self-reported formality may contain biases. Furthermore, the treatments themselves may induce households to relabel taxes between formal and informal in their reporting, without changing the payments. This can induce non-classical measurement error correlated with the treatment. Also, we know that a large fraction of payments made by household are "formal'' in the sense that they are payments they should make according to the law, but are nonetheless bribes. There is a sense of formality in the social convention of paying the statutory taxes to tax officials, even if it is common knowledge that these will be used for private consumption of the official and his superior.
Second, we use the pre-treatment survey data to construct scores of formality of each tax category. There is variation in the proportion in the survey of self-reported proportion of formal taxes in each category. To construct a measure of formality of payments where self-declared formality is not endogenous to the treatments, we use these scores in the main subsequent smart-phone analysis to estimate, probabilistically, the share of payments that are formal. This allows us to capture changes in payments that are immune to relabeling/non-classical measurement bias, since relabeling would only occur within categories.
Third, since self-reporting the formality of a payment, and its meaning, raises concerns of non-classical measurement error, we can focus on total payments, where predictions are immune to endogenous relabeling by households. Any payment to a tax official in the DRC has no guarantee to end up in the state coffers, hence one approach is to consider payments to tax officials who conduct visits to be bribes / formal taxes would instead be paid at the office.