# **B.3 Are Untreated Blocks Affected by the Intervention?**

A crucial aspect of partial population experiments is the unit within which the experimenter will test the presence of spillovers. In some settings, these are relatively straightforward to establish: electoral precincts for political outcomes, towns for regional policies, and schools or school districts for educational interventions. In our application, we aim to measure information spillovers among taxpayers. Discussions with municipal tax authorities and with taxpayers, as well as the context of our intervention, led us to select city street blocks as the relevant clusters for potential information spillovers about tax reminders and deadlines and their effects on tax compliance. Specifically, the campaign was motivated by the sharp drop in compliance in April 2020 induced by the severe lockdown imposed in the Greater Buenos Aires area in Argentina during the COVID pandemic in a context where most payments were made in person (see Figure B.11). The lockdown was strongly enforced, and as a result, citizens’ mobility was severely limited, which justifies the choice of the city street block—a relatively small cluster—as the relevant unit for information spillover since it reflects the limited physical interactions generated by the lockdown. A further justification is the city’s street layout, which consists mainly of relatively homogeneous straight streets with orthogonal intersections in square/rectangular city blocks (see Figure A.2).

A potential concern with this setup is that the city street block may not be the relevant unit to capture information spillovers. The random assignment process and the city’s physical layout imply that taxpayers in pure control street blocks (i.e., blocks where no one received a tax reminder) were still adjacent and/or surrounded by blocks with treated taxpayers, as shown by inspection of the map in Figure A.2. Interference between adjacent blocks is possible, and this would induce a downward bias in our results, since individuals in pure control (untreated) blocks would be affected by the information campaign via spillovers from adjacent (treated) blocks. Our empirical setup allows for an auxiliary test to rule out this concern and establish that units in pure control blocks indeed provide a valid counterfactual in our analysis.<sup>4</sup> To test the robustness of untreated blocks as pure controls, we leverage our experimental assignment process, which implies that the “intensity” of treatment in the surrounding blocks is random by definition. Pure control units are by chance surrounded by blocks with varying degrees of treatment intensity (0%, 20%, 50%, or 80%), and thus by a random number of treated taxpayers. If there is interference between treated and untreated blocks, we should observe that pure control payment rates increase with the exposure of untreated blocks to treated blocks.

We construct our measure of the potential exposure of a street-block to the intervention in two steps. First, we use GIS software to calculate a buffer of 100, 200, and 300 meters around the centroid of each street-block (see the three figures in the top panel of Figure B.12), given the typical street block length of 100 meters. Second, for each street-block and radius, we calculate the share of properties receiving a letter (treated) relative to all the properties in the buffer zone. The three figures in the middle panel of Figure

> 4This is an auxiliary analysis in the sense that while it exploits features of our experimental design, it does not correspond to our pre-registered analysis and only represents an ex-post robustness check.

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