the date the law was passed. As will be shown below, this explains in great part the disagreement between the studies.

This paper revisits the identification strategy used in both studies and performs a thorough check of the RD estimates of the impact of the SIMPLES program on firms’ formalization rates. I focus on the RD estimates because the identification strategy of both RD and DID relies on a jump in formalization rates after the program started. Notice that an invalidation of the first-stage results would call into question any treatment effect estimate of the program on outcomes related to firms’ performance obtained through an IV method.

In my analysis I use the same data set as in both studies, the Brazilian Survey of the Urban Informal Sector (ECINF) collected in October 1997 by the Brazilian Bureau of Statistics (IBGE), but in a format that is closest to Monteiro and Assunção (2012), that is, I do not restrict the sample as in Fajnzylber et al. (2011), and I look at the sample of all micro firms without distinguishing between micro-entrepreneurs and small firms.<sup>2</sup> It is important to emphasize that this paper should not be seen as a replication exercise. The objectives of this paper are (1) to identify reasons for the studies coming to different conclusions, even though they used the same data set, and (2) to test the robustness of the observed jump in formalization rates detected by Fajnzylber et al. (2012).

Even though ECINF has more than one variable that could be used to capture formalization rates (see Fajnzylber et al. 2011), I will focus on the results obtained with the variable, ‘license’. This variable tells us whether a firm had a municipal or state license to operate. This variable was used in both studies to measure the impact of SIMPLES on formalization rates and to estimate the impact of formalization on firms’ performance through two-stage least squares by Fajnzylber et al. (2011).<sup>3</sup> Fajnzylber et al.’s (2011) main contribution in terms of identification strategy is that they explore more deeply the information on firms around the threshold. It is unfortunate, however, that they do not perform RD validity tests. When revisiting their RD estimates, I will perform a couple of validity tests that are now standard among RD design practitioners. These include the McCrary density test (2008) for the manipulation of the assignment variable and a balance test around the cutoff point to check whether the covariates are similar for firms created just after and just before the law passed (see Lee and Lemieux, 2010).

I will also investigate whether the potential measurement error in the assignment variable, as discussed in Monteiro and Assunção (2012), may bias RD estimates to some extent and, if so, how (see Barreca et al. 2015 for this point). Finally, I will run a placebo test using different cutoffs to check whether the discontinuity in formalization rates around November/December 1996 is exclusively likely due to the law or influenced by some other factors, such as seasonality issues that could be correlated to firms’ creation.

Beyond this brief introduction, this paper is organized as follows. The next section briefly describes the program, while section 3 revisits the first-stage estimates using an RD design. Section 4 discusses some robustness checks of the RD design. The main findings are summarized in the conclusion.

> 2 Fajnzylber et al. (2011) estimates the effect of the program for all micro-firms and then separately for microenterprises and small firms with at least one employee.

> 3 Fajnzylber et al. (2011) show first stage estimates for five alternative measures of formalization, but license showed larger point estimates for the sample of all micro-firms and micro-entrepreneurs. For firms with at least one employee, the variable ‘legal entity’ showed the largest point estimate.

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