estimate _ε_ using OLS with robust standard errors. The resulting value for _ε_ is 0.67, which is in line with the estimates of Acemoglu and Guerrieri (2008) for a similar group of industries.<sup>38</sup> Table (5) presents the results.

# 5.2.2 Moment Selection and Calibration

Standard Parameters The impatience rate _ρ_ is calibrated to match the real interest rate for the period 2004-2007. Given an annual nominal interest rate for the period of 5 % and a core annual ination of 3%, I target a real interest rate _r_ of 2 % . This implies a calibrated impatience rate of 0.05. The depreciation rate _δ_ is taken to imply an average investment to capital ratio of approximately 6 % _,_ which corresponds to the average value for the private capital stock in the U.S. xed asset tables, after considering growth. Finally, I use a constant relative risk aversion coecient _σ_ equal to 1.5, which is in the range of values used in quantitative studies with heterogeneous agents. The values of these parameters can be found in table 6.

Technological Parameters Given _ε,_ we have 6 remaining technological parameters to calibrate: _{η, α, f_ 2 _, γ, ψ, σ}_ . Based on empirical evidence on estimates of the degree of returns to scale at the rm level, I set _η_ = 0 _._ 78 , in line with Thomas (2002), Pavcnik (2002) and Restuccia and Rogerson (2008).<sup>39</sup> As usual, _α_ controls the share of payments to capital observed in the data, which is equal to 0.3. However, due to the presence of xed cost and nancial frictions, that share of payments will no longer be equal to _α_ .<sup>40</sup> In particular, the aggregate capital income share could be lower than the value of _α._ To capture the sector specic xed costs I match the relative capital intensity between the low and high nancially dependent sectors. If there were no xed costs, the capital intensities would be equalized across sectors since the nancial frictions and lobbying xed cost aect both sectors in the same way. Given a xed value for _f_ 1 and using the fact that _f_ 1 _< f_ 2 , an increase in _f_ 2 implies that the sector 2 is more capital intensive, that is, ( _k_ 2 + _f_ 2) _/l_ 2 _>_ ( _k_ 1 + _f_ 1) _/l_ 1 .<sup>41</sup> In this economy, the production of the intermediate sectors _y_ 1 and _y_ 2 are the only contributors to value added since the nal producer only "bundles" those goods. In addition, they do not require any other intermediate good to produce. This implies that _pjyj_ can be interpreted as value added in sector _j._ Following this logic, we can think of 1 _− γ_ driving the share of the externally dependent sector in _GDP_ . Using the same data and the same classication for sectors used to estimate the elasticity of substitution of intermediates _ε_ , on average during the period 1970 to 2005 the share in

> 38 They estimate _ε_ using data from NIPA for 22 industries classied with NAICS. Then, they separate those industries by capital intensity and they run the same regression.

> 39Values for this parameter used in the literature range from 0.7 to 0.9. See references in Restuccia and Rogerson (2008) and in Atkeson and Kehoe (2005).

> 40Even with no nancial frictions this statement would be true due to the presence of rents to entrepreneurship. However, if we assume as in Gollin (2002) that those rents are split evenly between workers and capitalists, we return to a world with capital share of 1 _/_ 3 .

> 41See Blaum (2013) for a discussion of partial versus general equilibrium identication of xed costs in these types of models.

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