_GDP_ of the high externally dependent sector is 70.4 % . I use this moment to calibrate _γ_ .

Finally, we have two parameters related to the stochastic process of productivity: _{ψ, σ}_ . The parameter _ψ_ measures the persistence of the process and therefore it has a direct impact on the wealth share of the top 10 % of households. I target the 2007 wealth share, which was equal to 73 _._ 1% .<sup>42</sup> In the case of _σ_ , it is calibrated to match the fraction of labor employed by the top 10 % of establishments, which is equal to 63 % according to the U.S. Census in 2012. The calibrated technological parameters can be found in table (7).

Lobbying and Tax Benet Parameters To calibrate _{h, φ, ν, μ, fl}_ , I resort to the microdata on lobbying and eective tax rates analyzed in section (3).

Although lobbying is in principle available to all rms, based on the micro data, there is a striking dierence between public and private rms: most of the lobbying rms are public rms. In fact, as a share of private rms, lobbying rms are negligible in number. For this reason, I assume that there is a fraction _m ∈_ [0 _,_ 1] of all rms in the model that map to the "private" rms in the data. The dierence between _m_ and all the rms in the model is going to be dened as "public rms". These rms will be used as a reference sample to match the moments related to lobbying activity.<sup>43</sup> To choose _m,_ I compute the domestic (U.S.) gross value of production by public rms in Compustat and I compare this aggregate of public rm output against the value of production of the non-nancial corporate sector from the BEA. Using those numbers, I nd that 57 % of the production in the non-nancial corporate sector is carried out by public rms for the pre-crisis period. In other words, 43% of the production of the non-nancial corporate sector is due to private rms. Given that private rms usually have low level of employment and that in the model small rms do not choose to lobby, the set _m_ of rms is going to be composed of the smallest non-lobbying rms that accumulate 43 % of the production in the model.<sup>44</sup> The xed cost of lobbying _fl_ has a rst order eect on the share of lobbying rms in the economy. By increasing this parameter, the number of rms that can aord this costly activity is reduced. Given that in Compustat the number of lobbying rms for taxation issues is 7 _._ 1% on average between 2004 and 2007, I calibrate _fl_ to obtain that fraction over the sub-sample of public rms from the model. The scale parameter _h_ controls how lobbying eort translates into lobbying expenditure. For this reason, I choose to match the average lobbying expenditure to sales from the microdata, which is equal to 0.08 % .

The common component associated to the tax benet _φ_ is targeted to match the average eective tax rate of non-lobbying rms in Compustat, which is equal to 21.4 % . The parameter _ν_ controls

> 42This moment is taken from Wol (2012). Using data from the Survey of Consumer Finance (SCF) he computed wealth distributions for a series of periods of time. For an intuition of the relationship between wealth concentration and persistence of the process refer to Moll (2014). To calibrate these parameters, I exploit the fact that the autocorrelation of the process depends on _ψ_ and that given _ψ,_ the stationary variance is given by 2<sup>_<u>σ</u>_</sup> _ψ_<sup>2.</sup>

> 43We can think of _m_ as the fraction of potential agents that could start a private rm. In general, these agents will not have the connections or the information necessary to lobby at congress, at least for the short run.

> 44For a discussion of the main rm level characteristics of private rms versus public rms in the U.S. see Davis et al. (2007) and Asker et al. (2011).

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