this rarely seems to lead to any observable demographic imbalance in completed observations (12%). This is a mixed blessing because we lack pre-treatment covariates to narrow the Lee bounds (precisely because attrition is uncorrelated with observables). Since the Lee bounds do cross zero, we cannot mechanically dismiss the possibility that attrition partially explains some of our results (see table 7).

Nonetheless, the balance of evidence suggests that attrition is not driving our results. First, there is a very strong relationship between the effect sizes of the two treatments across countries (which is consistent with attrition), but only a very weak relationship between the effect size and attrition rate (which suggests that the country correlation is driven by other factors). The appendix provides further analysis on this point. Second, the lack of demographic imbalance post-treatment (including by taxpayer type) suggests that the attrition is not differentially affecting subgroups we can measure, which is how we would expect attrition to drive a treatment effect. Third, the robustness checks we run on subsets of countries that fail the various checks do not show smaller effect sizes (and in fact usually show larger ones). This is inconsistent with the results being driven by attrition. We therefore think the most likely cause of the differential attrition is simply that the control group was slightly less onerous than the treatment groups and that there was a general tendency among all subgroups exposed to the control condition to drop out of the survey at a slightly higher rate.

Table 7: Lee bounds analysis

||**Comb**<br>**Top down**|**ined**<br>**Botto**|**m up**|**Top**|**Tax**<br>**down**|**fine**<br>**Bottom up**|**Tax m**<br>**Top down**|**orale**<br>**Bottom up**|
|---|---|---|---|---|---|---|---|---|
||95% CI|95%|CI|95%|CI|95% CI|95% CI|95% CI|
|**Lower**|-0.30<br>-0.19|-0.66|-0.58|-0.15|-0.09|-0.37 -0.30|-0.17 -0.11|-0.43 -0.39|
|**Upper**|0.64<br>0.73|0.86|0.96|0.33|0.39|0.54<br>0.60|0.31<br>0.36|0.46<br>0.50|
|**Obs**|174612|174|439|174|612|174439|174612|174439|
|**Selected obs**|57899|549|42|626|38|59439|63100|59739|
|**Trimming prop**|0.13|0.2|3|0.|14|0.23|0.14|0.23|

Notes: Each column shows the results of Lee Bounds analysis showing the worst case scenario effect that differential attrition could have on the causal estimates from the experiment. The confidence intervals include zero meaning that we cannot definitively rule out the role of differential attrition.

# **Mechanisms**

Taken together, the above analyses strongly suggest that the top-down accountability treatment, and to a somewhat lesser extent the bottom-up participatory treatment, are effective at increasing tax morale among respondents.

Although inherently difficult to answer, one interesting question is whether one can identify beliefs that mediate this effect. With a view towards some initial answers, we asked respondents questions about whether abuse of money was common in their country, and whether politicians cared about what ordinary people needed. Our hypothesis was that both treatments might improve respondents’ view of politicians, and that the top-down accountability treatment might reduce respondents’ estimates of public sector corruption by increasing the salience of enforcement institutions and stressing the punishments they can inflict on wrongdoers.

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