To consider the rm-level implications of the model for lobbying after a credit crunch, I simulate a sample of 500,000 rms from the model and follow them for four periods after the shock, keeping track of the size in terms of employment. In section (3.4) I use the small-medium rm (SME) label for a rm with fewer than 500 employees following the classication used by the Trade Commission. The data from the Business Dynamics Statistics (BDS) for 2007 shows that the employment share of rms with fewer than 500 employees was equal to 50.4 % _,_ which is almost equal to the median of employment. In order to map my model to the size variable I look for the employment size such that below that level the employment share is equal to 50.4 % . Then, I classify those rms below that level as SME and those above that as a large rms. As before, sector one in the model is represents sectors that rely less on external nance and sector two represents the remaining.

Table (11) reports the results of running the regression on equation (1) using the simulated data and it reproduces the same results from section (3.4). To run this regression, I control for the same variables as in the empirical regression (capital, asets and sales). The table decomposes the results into two groups, low external dependence and high external dependence sectors. The model delivers almost all the signs of the regression, but misses the change in lobbying expenditure for small-medium rms in the sectors that relies less on external nance. While the data delivers a positive correlation, the model displays a negative one. Nevertheless, that coecient is not statistically signicant in the data. The model captures the sign of the changes for lobbying expenditure before and after the crisis for 3 groups: large rms in sectors that depend less on external nance; small-medium rms in sectors that rely more on external nance; and large rms in sectors that rely more on external nance. The model-based regression nds that the change in lobbying expenditure for small-medium rms in the sectors that rely less on external nance should be negative. In the data, the coecient was found to be negative. However, given that the coecient is not statistically signicant and close to zero, the negative coecient coming from the model it is certainly possible.

In the model, a reduction in input prices implies an increase in capital demand and lobbying activity for unconstrained public rms in both sectors. This mechanism drives the signs in the model based regression for the change in the intensity of lobbying in column (4) for rms in the low external dependence sector and in the high external dependence sector. On the other hand, with the credit crunch some public rms become nancially constrained in the model and therefore the amount of capital used is lower than before. According to the model, the lobbying expenditure of those rms is also smaller and this eect drives the signs in columns (3) for both groups of rms.

To summarize, the evidence provided in this section shows that the modeling strategy is successful in capturing the micro level implications of lobbying during a nancial crisis. In addition, it reinforces the validity of the calibration strategy. Together, these two features imply that the model could be used to study policy relevant questions with the certainty that the model represents closely the most salient features of this activity.

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