where Yj is the either the proportion of informal workers in fi rm j’s total employment or a measure of fi rm performance (log value added per worker, log average wage, log sales per worker, log pro fi t per worker, log capital per worker, log total employment and a dummy variable indicating use of advanced technologies), Ej is enforcement of regulation faced by fi rm j,<sup>24</sup> Xj is a vector of exogenous variables and εj is a i.i.d. error term.<sup>25</sup> As described in the previous section, enforcement of labor regulation in Brazil is decentralized at the level of the subdelegacia (subregion). This is the relevant level of variation for the enforcement data. We have administrative data on the number of labor inspections and on the number of fi nes issued in each subregion. We prefer to use labor fi nes rather than labor inspections as a measure of enforcement because we can discriminate fi nes by type of violation whereas we cannot discriminate inspections by type of offense (because inspections are general). In particular, we know the number of fi nes related speci fi cally to irregularities that concern worker registration, registration fi nes. This is ultimately the enforcement measure that is most relevant for fi rms when deciding to evade (or not) the law and hire informally. Because some subregions are larger and have more fi rms than others, we normalize number of fi nes by number of fi rms in the subregion.<sup>26</sup> A priori we expect β to be negative when Yj is informal employment, but as we suggest in section (2.2) β can be either negative or positive in the case of all other outcomes. We hope that our measure of enforcement is reasonably exogenous, and varies due to arbitrary differences in inspections across regions. However, for several reasons, it may happen that

> 24 We use contemporaneous measures of enforcement instead of lagged measures of enforcement. This is because the current available data on enforcement is only for 2002. We do not believe this affects the results in our paper.

> 25In the case where Yj is the proportion of informal workers hired by the fi rm, equation (4.1) is not an explicit labor demand equation but can be related to it in a partial equilibrium framework, where the prices of different inputs are kept fi xed when enforcement varies across fi rms. However, a labor demand equation would have the number of informal workers employed by the fi rm on the left hand side instead of the proportion of informal workers in total employment. Our empirical results are similar when we use number of informal workers as our dependent variable in this regression (available on request). A similar reasoning applies when Yj is total employment in the fi rm or capital per worker. In the case where Yj corresponds to fi rm outcomes such as sales per worker or value added per worker, we do not estimate explicit production functions. However, the (reduced form) relationship we estimate between fi rm outcomes and enforcement of laws can be derived from a standard pro fi t maximization problem with multiple types of labor which have different fl exibility and different costs. It is also similar to the speci fi cation of Besley and Burgess (2003) and of other papers in the literature on labor regulation and economic performance.

> 26In practice, we will use total number of fi nes per 1000 fi rms in the subregion.

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