**Table 4: Impact of message assignment on payment amounts**

||**IT**|**T estimat**|**es**|**2S**|**LS estima**|**tes**|
|---|---|---|---|---|---|---|
||Start of<br>experiment|First tax<br>deadline|One month<br>after frst<br>deadline|Start of<br>experiment|First tax<br>deadline|One month<br>after frst<br>deadline|
|**Pooled treatment arms**|||||||
|Treated|-18.44|395.7<sup>_∗∗∗_</sup>|468.6<sup>_∗∗∗_</sup>|-18.77|460.1<sup>_∗∗∗_</sup>|542.3<sup>_∗∗∗_</sup>|
||(-0.72)|(5.16)|(5.25)|(-0.66)|(5.38)|(5.45)|
|**Separate treatment arms**|||||||
|T1: Reminder|-47.06<sup>+</sup>|354.5<sup>_∗∗∗_</sup>|401.3<sup>_∗∗∗_</sup>|-52.97<sup>+</sup>|399.0<sup>_∗∗∗_</sup>|451.7<sup>_∗∗∗_</sup>|
||(27.01)|(84.13)|(97.71)|(30.41)|(94.70)|(110.0)|
|T2: Reciprocity|12.31|559.1<sup>_∗∗∗_</sup>|660.0<sup>_∗∗∗_</sup>|12.96|590.6<sup>_∗∗∗_</sup>|695.7<sup>_∗∗∗_</sup>|
||(33.94)|(100.3)|(115.8)|(35.98)|(106.4)|(122.8)|
|T3: Social pressure|8.130|313.8<sup>_∗∗_</sup>|411.3<sup>_∗∗∗_</sup>|9.469|362.9<sup>_∗∗_</sup>|478.1<sup>_∗∗∗_</sup>|
||(33.54)|(98.08)|(114.2)|(40.32)|(117.9)|(137.2)|
|Constant|262.1<sup>_∗∗∗_</sup>|2434.8<sup>_∗∗∗_</sup>|3540.1<sup>_∗∗∗_</sup>||||
||(23.10)|(67.89)|(79.25)||||
|Control mean|259.641|2435.597|3544.343|259.641|2435.597|3544.343|
|First stage f-stat||||135,432|135,432|135,432|
|R<sup>2</sup>|0.019|0.047|0.055|0.000|0.000|0.000|
|Obs|143,425|143,425|143,425|143,425|143,425|143,425|
|Test: T1 = T2|0.037|0.021|0.011|0.034|0.049|0.029|
|Test: T1 = T3|0.050|0.638|0.920|0.064|0.725|0.824|
|Test: T2 = T3|0.904|0.016|0.034|0.932|0.060|0.119|

**Notes:** Outcome is the amount in Tanzanian shillings a taxpayer paid to the TRA by the date indicated. ITT estimates indicate impact of being assigned to treatment. 2SLS estimates instrument the receipt of each message type with assignment to treatment. Payment amounts winnorsized at the 99th percentile. Robust standard-errors in parentheses.<sup>+</sup> _p <_ 0 _._ 10 _,_<sup>_∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗∗_</sup> _p <_ 0 _._ 001

reciprocity group (Appendix Table A2). Likewise, the point estimates on total amount paid are highest for the reciprocity group, although in this subsample, the amount paid is quite high for the social pressure group as well (Appendix Table A3).

The remainder of the results section presents some extensions beyond the main results. This discussion is exploratory in nature and may be extended in future research.

# **4.1 The geographic distribution of effects**

In this subsection, we ask a simple question: had the TRA targeted a specific region of Dar es Salaam with messages, would they have had the same impact observed across the entire city? Recent meta-analyses of nudges have revealed a significant amount of heterogeneity across contexts (Antinyan and Asatryan 2020; DellaVigna and Linos 2020). This heterogeneity is important to the policymaker, who will need to know the likelihood a given treatment will be effective in a new setting. If messages sent to taxpayers in different parts of the city have drastically different effects, then a revenue authority may want to consider targeting areas where the intervention is more cost effective, or tailor its messaging to be more effective in those areas.

As mentioned above, the randomization was conducted within 1,211 different strata, which were defined both by the location of the property (sub-ward) and whether a bill had been issued. This allows us to compare effect sizes across different locations to see if any systematic relationships appear.

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