particular, I show that these industries account for more than 50% of total lobbying expenditure, and that this participation increases during the credit crunch. Finally, I provide evidence of heterogeneity in lobbying behavior within sectors of external nance as large rms increased their lobbying expenditure relative to small rms during the crisis. Furthermore, this relative dierence was once again stronger in sectors that depend more on external nance. In fact, small rms in externally nanced sectors reduce their lobbying expenditure for taxation issues which is consistent with the idea that small rms should be more aected with the credit shock.

# 3.1 Data and Summary Statistics

In order to follow rms over time for the empirical part of the paper, I name-match lobbying expenditure data with rm level characteristics. In this section, I describe the main features of each dataset and the matching procedure.

Firm level lobbying data is based on more that 1,100,000 lobbying reports that became available under the lobbying Disclosure Act of 1995.<sup>12</sup> This act, together with the Honest Leadership and Open Government Act (2007) established a set of provisions to be followed by anyone lobbying the federal government at congress.<sup>13</sup> Firms, organizations, or individuals that want to lobby have to le a semi-annual report to the Secretary of the Senate's Oce of Public records (SOPR) including the following information: (i) the name of the client, address and general business description; (ii) the total amount of income or expenditure in the lobbying activity, depending whether it is an inhouse or an external lobbyist; (iii) all of the general issues for which they are lobbying. Firms that are trying to inuence the government to modify the tax code and obtain tax benets targeted to themselves have to declare that they are doing lobbying for taxation, allowing me to focus only on those rms.<sup>14</sup> Finally, since any non-prot organization, individual, or rm can engage in lobbying activity, I clean the original dataset to keep only those observations that correspond to rms. To do this, I scrape the data with text-parsing methods to look for keywords that allow me to eliminate entries that do not correspond to rms. After that, I manually check the remaining observations to eliminate non-prots or individuals.<sup>15</sup> The nal dataset contains information from 2000 to 2014.

introduction, a rm's capital level is important because most of the tax benets granted by the government are tied to capital.

> 12The information is provided by the Center of Responsive Politics (CRP), which collected the data from the Senate Oce of Public Records. Data is available upon request at www.opensecrets.org/lobby/.

> 13A lobbyist is any individual who is employed or under a contract to lobby on behalf of a client. An In-House Lobbyist is an employee hired by an organization to lobby for them. An External Lobbyist is typically an organization or person that works under a contract for the lobbying organization. Organizations could be one of 3 types: non-prot associations, rms, or groups of individuals.

> The Lobbying Disclosure Act denes "lobbying activity" as lobbying contacts and eorts in support of such contacts, including preparation and planning activities, research and other background work that is intended, at the time it is performed, for use in contacts, and coordination with the lobbying activities of others.

> 14There are 77 issues such as trade, taxes, agriculture, etc. A list of all the issues can be found here: https://www.opensecrets.org/lobby/alphalist_issue.php. In online appendix C I include an example of a form led by a lobbying rm.

> 15See the online data appendix for a description of the procedure, including the keywords used to eliminate observations.

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