Based on these results, it seems that both RD and DID estimates may be picking up, at least partially, something other than just the effect of the program. In the best case scenario, the RD estimates on formalization rates should be seen as upper bound effects of the SIMPLES program.

# **5. Conclusion**

The paper revisited the impact of the tax simplification program for SMEs (SIMPLES) in Brazil to reconcile the conflict of findings and conclusions in Fajnzylber et al. (2011) and Monteiro and Assunção (2012). It then checked the validity of the identification strategy used in both studies, but the focus was on the RD as a credible design to assess the impact of this program. The paper used the ECINF data from 1997 as in the original papers, and found that the main reason for disagreement between the studies was to do with the month used as cutoff point to identify the ‘pre and-post-dummy’ variable. While Fajnzylber et al. used the date the provisional measure was sent to the Congress to vote, Monteiro and Assunção (2012) used the date the law was enacted. The difference is just one month, but it seems to be key to explain the difference in findings between the two papers. Firms that formalized in November 1996 apparently anticipated the approval of the law by the Congress. This cluster of firms that came into business in November 1996 seems to drive most of the large results found by Fajnzylber et al. (2011).

To check whether the jump in formalization rates was observed among young firms, the study undertook a couple of validity tests that have become standard among RD design practitioners. A McCrary density test to check for perfect manipulation of the assignment variable and a simple test of difference in means were performed to check balance around the cutoff using different bandwidth sizes. The McCrary test pointed to a perfect manipulation of the assignment variable around the cutoff point. This result usually suggests that ‘treated’ and ‘control’ firms can be systematically different. In fact, a joint test for difference in means suggested that eligible firms created before and after the program do not seem to look much alike on average. But these results seem to be highly influenced by spikes (heaps) in the number of firms created in October each year.

Because data heap could be confounding both the RD and DID estimates, a couple of robustness checks were performed to attest the impact of the program on formalization rates. First, RD regressions were rerun, controlling for data heaping. Second, placebos tests with different cutoff points were undertaken to check if there is some data seasonality by the end of each year that could be confounding the estimates.

It turned out that the available evidence of the impact of the program does not seem to be very robust, given the similar point estimates between what is supposed to be the effect of the program and those found in the placebo tests. The results seem to indicate the program was not effective in increasing the formalization rates of small firms; however, one could have a more cautious interpretation of the data and conclude that the microdata used in both Fajnzylber et al. (2011) and Monteiro and Assunção (2012) do not seem to be ideal to inform the impact of SIMPLES on firms’ formalization decision and performance.

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