However, for most of the analysis I restrict my attention to the period 2004-2014.

Financial data by parent rm is primarily taken from Compustat North America. This dataset contains information on publicly traded companies in the United States, including sales, employment, industry classication, assets, and useful information to compute eective tax rates, which I describe below. The balance sheet presentation in Compustat is consolidated at the parent level. This is a problem, because a single organization could have more than one entry. To deal with this issue, I aggregate the information at the ultimate owner level using parent-subsidiary identiers from the NBER patent data project to assign each entry from Compustat to one unique parent. In addition, I also use the dataset ORBIS compiled by the by Bureau van Dijk Electronic Publishing (BvD) to check Parent-subsidiary relationships. After obtaining these relationships, I name-match the lobbying data with Compustat using open rene, which provides a reconciliation service that uses a probabilistic matching algorithm to pair entries between the two datasets.<sup>16</sup> Data on external nancial dependence of 63 2-digit SIC sectors is computed with data from Compustat. To construct this measure (proposed by Rajan and Zingales (1998)), I follow the methodology described in Cetorelli and Strahan (2006). External nancial dependence is dened as the fraction of capital expenditure that is not nanced with internal cash ows from operations . A positive value implies that a rm must use external sources of funds to nance investment, while a negative value indicates that rms have enough cash ows to fund investments. Appendix C.4 contains the method used to construct the index and the measure of external nancial dependence for each sector.

According to Rajan and Zingales (1998), the external nance measure varies across industries due to technological factors aecting initial project scale, gestation period, the cash harvest period, and the requirement for continuing investment. Consequently, these technological factors determine the demand for external nancing and as a result, industries like metal mining or oil and gas extraction heavily dependent on external annce should be more aected by a credit shock than industries like leather. For the remainder of the paper, I exclude the nancial sector and the agricultural sector.

Later in the paper, I will classify rms into two broad sectors: those producing in sectors that rely more on external nance and those producing in sectors that depend less on external nance. The former includes all the 2-digits SIC sectors with a measure of external nancing need below 0. The rest of the sectors will be categorized as sectors that rely more on external nance.

Lastly, to compute eective tax rates I also use data from Compustat. To compute this measure, I use the denition provided by Gupta and Newberry (1997) and used in Richter et al. (2009) and Arayavenchkit et al. (2014). The eective tax rate for each rm is computed as The numerator is a measure of how much a rm paid in taxes, while the denominator computes the taxable income coming from balance sheet data. In general, the eective tax rate will be below 16 Open rene is available at www.openrene.org. See online appendix C.2 for additional details of the procedure.

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