independent impacts on the business, through alleviating credit constraints. If such impacts are positive, using the treatment assignment as an instrument will overstate the gains to formalizing, providing us with an upper bound of the consequences of formalizing.

An alternative approach is to control for capital stock in regression (2), and thereby attempt to identify the impact of formalizing through channels other than changing capital stock. We use log capital stock as the control, given the skewness of this variable. This will net out any impact of the grants on capital stock. But the inclusion of capital stock as a control also removes the effect of changes in capital stock that come from formalizing. This might arise, for example, if formalizing increases access to credit, enabling firms to invest more. This specification should therefore serve as a lower bound for the impact of formalizing. In order for this approach to be valid, we have to make what Imai et al. (2011) refer to as a sequential ignorability assumption. In our context, this amounts to assuming that, conditional on treatment assignment, the lagged dependent variable, and the strata randomization controls, capital stock is independent of the outcome of interest. Given the rich set of controls used here, this may be reasonable.

# **5.3 Impact on Major Firm Outcomes**

Table 5a presents the results of estimating equation (2) for the key outcomes of firm profits, sales, employment, and capital stock. Consider first the impact on firm profits. Column 1 shows an upper bound estimate of 13,706 LKR increase in monthly profits from formalizing, which is large relative to the mean profits in the control group of 30,537 LKR and significant at the 10 percent level. The lower bound estimate obtained when we control for capital stock is one-third smaller (8,996), and not significant at standard levels (p=0.265). These point estimates are sizeable relative to both the upfront costs of registering (1000 LKR), and the annual income taxes for a typical firm of this level (3000 LKR). They are also sizable relative to the incentives provided to register. Columns 2 and 3 show the results of truncating profits at the 99<sup>th</sup> and 95<sup>th</sup> percentiles respectively. The latter more than halves the point estimate of the impact on profits. In contrast, log profits are also significant (column 4). Columns 5-7 show large positive coefficients for sales, which are not statistically significant in levels, and which halve when we control for capital stock. The remaining columns show positive, but insignificant, impacts on employment and capital stock.

To see where this statistically significant increase in profits is coming from, Figure 3 plots the cumulative distribution function of profits in our final round survey for the combined monetary treatment groups and for the control group. We see that the distributions are extremely similar to one another right up until the very top tail, where they diverge. The significant mean effect is thus being driven by this handful of firms which formalized and experienced large increases in profits, whereas for most of the distribution the treatment does not influence profits.

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