1

Political activism and firm innovation

Alexei V. Ovtchinnikov

HEC Paris

Syed Walid Reza

Queensland University of Technology

and

Yanhui Wu

Queensland University of Technology

Abstract

Political activism positively affects firm innovation. Firms that support more politicians, politicians on

Congressional committees with jurisdictional authority over the firms’ industries and politicians who join

those committees innovate more. We employ instrumental variables estimation and a natural experiment

to show a causal effect of political activism on innovation. The results are consistent with the hypothesis

that political activism is valuable because it helps reduce policy uncertainty, which, in turn, fosters firm

innovation. Also consistent with this hypothesis, we show that politically active firms successfully time

future legislation and set their innovation strategies in expectation of future legislative changes.

Keywords: political contributions, innovation, investment policy, policy uncertainty

JEL Classification: D72, D80, G31, G38, O31, O38

August 11, 2014

Ovtchinnikov is the corresponding author. Please send correspondence to: ovtchinnikov@hec.fr or HEC Paris, 1

rue de la Libération, 78351 Jouy-en-Josas cedex, France. We thank Art Durnev, Bill Christie, Mara Faccio, Johan

Hombert, Elena Loutskina, David Parsley, and Philip Valta for helpful comments. The usual disclaimer applies.

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Political activism and firm innovation

Abstract

Political activism positively affects firm innovation. Firms that support more politicians, politicians on

Congressional committees with jurisdictional authority over the firms’ industries and politicians who join

those committees innovate more. We employ instrumental variables estimation and a natural experiment

to show a causal effect of political activism on innovation. The results are consistent with the hypothesis

that political activism is valuable because it helps reduce policy uncertainty, which, in turn, fosters firm

innovation. Also consistent with this hypothesis, we show that politically active firms successfully time

future legislation and set their innovation strategies in expectation of future legislative changes.

1

Political activism and firm innovation

1. Introduction

A considerable body of research finds that firms are politically active and engage in building

long-term connections with politicians. These connections are typically broken into explicit connections

that arise when a politician joins the firm or its board of directors (or vice versa) and implicit connections

that arise when a firm makes political contributions to the politician’s (re)election campaign.1 There is

also growing consensus in the literature that political activism is valuable to shareholders of the

contributing firms.2 Although the relation between political activism and firm value appears well

established, our understanding of the exact mechanisms through which political activism creates value

and affects real economic outcomes is far from complete.3 In this paper, we contribute to this literature

by analyzing the effect of political activism on firm investment decisions, specifically investment in

innovation.

Our analysis is motivated by a long string of theoretical work on irreversible investment under

uncertainty (see, e.g., Bernanke (1983), Bertola and Caballero (1994), Abel and Eberly (1996), Hassler

(1996), Caballero and Engel (1999), Bloom, Bond and Van Reenen (2007)). One of the central tenets in

these models is that when investment is at least partially irreversible, uncertainty depresses investment

because uncertainty increases the value of the firms’ option to wait. Bhattacharya, et al. (2014) note that

the option to wait is especially important for investment in innovation, considering that innovation

represents the exploration of unknown approaches and untested methods (Holmstrom (1989), Aghion and

Tirole (1994)). Thus, firm decisions that reduce future uncertainty should lower the value of the option to

wait and stimulate current investment, especially investment in innovation.

Bhattacharya, et al. (2014) show that policy uncertainty, defined as uncertainty about future

government economic policy, significantly reduces firm innovation. This is consistent with the intuition

that politics is important for firm innovation because politicians’ decision making significantly affects the

operating environment of innovating firms. Furthermore, the negative correlation between policy

uncertainty and innovation implies that firms’ actions that reduce policy uncertainty should stimulate

investment in innovation.

1 See, e.g., Masters and Keim (1985), Zardkoohi (1985), Grier, Munger, and Roberts (1994), Kroszner and

Stratmann (1998, 2005), Faccio (2006), Goldman, Rocholl and So (2009), Cooper, Gulen and Ovtchinnikov (2010),

Akey (2014). 2 See, e.g., Fisman (2001), Faccio (2006), Ferguson and Voth (2008), Faccio and Parsley (2009), Goldman, Rocholl

and So (2009), Cooper, Gulen and Ovtchinnikov (2010), Ovtchinnikov and Pantaleoni (2012), Amore and

Bennedsen (2013), Akey (2014). Aggarwal, Meschke and Wang (2012) and Coates (2012) present evidence that

political connections may be value destroying. 3 Previous research finds that politically connected firms enjoy preferential access to external financing (Claessens,

Feijen and Laeven (2008)) and are more likely to receive government bailouts in financial distress (Faccio, Masulis

and McConnell (2006), Duchin and Sosyura (2012)). Politically connected firms are also more likely to receive

government procurement contracts (Tahoun (2012), Goldman, Rocholl and So (2013)) as well as government

subsidies and other support (Johnson and Mitton (2003)).

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In this paper, we propose that firm political activism, defined as a strategy of making recurrent

political contributions to build long-term connections with politicians, facilitates investment in innovation

because it helps reduce policy uncertainty. First, political contributions purchase access to politicians

who may be willing to trade policy information for contributions (Stratmann (1998, 2002), Coate (2004)).

Second, political contributions allow firms to actively lobby for government policy (Stratmann (2005)),

thereby reducing future policy uncertainty. Restated in the framework of Pastor and Veronesi (2012),

political activism reduces political uncertainty, i.e. the uncertainty about whether government policy will

change in the future. In Pastor and Veronesi (2012), political uncertainty arises because the political cost

of changing government policy is unobservable to outsiders. We argue that politically active firms

engaged in repeat interactions with policymakers are better informed about the policymakers’ political

cost and, therefore, face lower political uncertainty. Lower political uncertainty decreases the volatility of

future cash flows from innovation, which, in turn, lowers the value of the option to wait and stimulates

investment in innovation. The main testable implication of this argument is that political activism

positively affects firm innovation.

In our empirical tests, we use firm patent activity to measure innovation and find strong support

for our hypothesis. Our sample comprises the intersection of the NBER patent citations data file and the

FEC detailed political contributions file for the period 1979 – 2004. In all, our study covers 813,692 hard

money political contributions made by 1,805 U.S. firms and 1,158,693 patents granted to 6,528 unique

U.S. firms as well as 9,673,067 patent citations over our sample period. Using hard money political

contributions data, we build three separate sets of measures of political activism designed to capture

different aspects of firm political activity.

Our empirical strategy follows a four-tiered approach to isolate a causal effect of political

activism on innovation. In each step, we set the identification bar progressively higher in order to rule out

alternative interpretations of the baseline results. In the first step, we employ panel OLS regressions to

establish a baseline correlation between our measures of firm political activism and future innovation.

The results show that political activism is strongly positively correlated with firm innovation one and

three years ahead. We measure firm innovation with patent applications and patent citations and find that

our political activism measures have a large economic and statistical impact on firm innovation. The

results are robust to alternative sample construction methodologies. We first estimate regressions on the

entire cross-section of CRSP/Compustat firms treating firms that are not politically active as firms with

zero political contributions. However, also recognizing that zero contributions for politically inactive

firms may not accurately capture the choice not to contribute, we re-estimate our baseline regressions on

the subsample of firms with observed political contributions. In the second set of regressions, we control

for the resulting self-selection bias with a two-stage Heckman (1979) approach. Regardless of which

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sample we use, we find a strong positive correlation between firm political activism and future

innovation.

In the second tier of our tests, we conduct a wide range of subsample analyses that, in their

entirety, help address the omitted variable bias and thus move us closer to identifying a causal effect of

political activism on firm innovation. Our tactic is to decompose the sample of political contributions

into those that are predicted by our hypothesis to have a stronger effect on innovation and those

contributions predicted to have a weaker or no effect. If there is indeed a differential impact in the data,

this evidence would be consistent with our hypothesis but harder to explain by an omitted variable bias.

We find evidence consistent with our predictions. The effect of political contributions on innovation is

stronger for contributions to politicians who sit on Congressional committees with jurisdictional authority

over a firm’s industry and politicians who join such committees. Moreover, the effect is completely

concentrated in non-election years when Congressional committee appointments are unexpected.

However, we find no effect of political contributions on innovation for contributions to politicians outside

of Congressional committees with jurisdictional authority over a firm’s industry or for contributions to

politicians who leave such committees.

Next, we set the identification hurdle higher and employ instrumental variables estimation.

Reverse causality is a concern in our analysis, since it may well be the case that firm innovation actually

drives the demand for political activism. To rule out this explanation, we instrument for political

contributions with a politician’s margin of victory in the prior election. Intuitively (and we confirm this

in the data), a politician’s margin of victory in the prior election should not affect current firm innovation.

However, it should significantly affect the firm’s propensity to make political contributions to that

politician (we also confirm this in the data) because the marginal dollar of contributions matters more to a

vulnerable politician in a close election. In return, a vulnerable politician should promise more favors in

exchange for contributions to attract more campaign financing (Stratmann (1998, 2002), Coate (2004)).

Thus, we hypothesize that firms target politicians in close elections and expect more benefits, including

access to valuable information, in return for their political contributions. We isolate the exogenous

variation in firm political contributions with a politician’s margin of victory in the prior election and show

in the second stage that instrumented political contributions strongly predict future firm innovation.

In our final test, we move the identification bar still higher and perform a natural experiment that

exploits an exogenous shock to the value of political contributions that occurred during our sample

period. We use the surprise Republican win in the 1994 mid-term election and the consequent surprise

Newt Gingrich’s decision to appoint four junior Congressmen to committee chairman positions as an

exogenous shock to the value of political contributions for those firms that supported the four new

chairmen prior to the 1994 election. Because the chairman appointments were unexpected, firms could

not have adjusted their contribution strategies prior to the election. Thus, if political contributions to

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committee chairmen are valuable (Cooper, Gulen and Ovtchinnikov (2010)) and affect firm innovation,

firm patent activity should increase following the election. Consistent with this, we show that patent

activity of the treatment group of firms increased significantly following the election compared to that of

the control group. Specifically, the number of patent applications for the treatment firms grew steadily

from an average of 4.8 applications in 1994 to 8.4 applications in 1999, a 75% increase, while the patent

activity of the control group stayed essentially flat over the same period. We replicate these results in

univariate and multivariate tests and show that the results are statistically and economically significant.

Taken together, the preponderance of evidence shows that political activism positively affects

firm innovation. A caveat is immediately in order. Even though each individual step of our identification

strategy is subject to understandable empirical limitations (for example, our use of a relatively small

sample in the natural experiment and the resulting difficulty of generalizing the results, or the challenge

of addressing the exclusion restriction in instrumental variables estimation), we draw our conclusions

based on the overall weight of combined evidence from our analysis.

Collectively, the results are consistent with our hypothesis that political activism reduces policy

uncertainty, which, in turn, fosters firm innovation. In the last section, we go further and provide

evidence consistent with the negative relation between political activism and policy uncertainty. Whether

firms use political contributions to purchase valuable policy information or actively lobby for government

policy, both arguments imply that politically active firms are able to successfully time future legislation

and set their innovation strategies in expectation of upcoming legislative changes. Empirically, this

means that changes in innovation by politically active firms predict future legislative changes. Consistent

with this hypothesis, we find that firm innovation, especially by politically active firms, strongly predicts

future legislative changes. Specifically, we show that politically active firms are able to successfully time

industry deregulation and start innovating in soon-to-be-deregulated industries prior to deregulation

legislation. In contrast, politically inactive firms do not exhibit this timing ability.

The analysis in this paper is important for three reasons. First, the results in this paper contribute

to our understanding of the sources of value from firm political activism. We show that political

activism, in addition to its impact on firm financing decisions (Claessens, Feijen and Laeven (2008)) and

operating performance (Cooper, Gulen and Ovtchinnikov (2010), Ovtchinnikov and Pantaleoni (2012),

Goldman, Rocholl and So (2013)), also significantly affects firm real investment decisions, such as

investment in innovation. These results are important for building a complete picture of firm political

activism and its impact on firm decisions and value.

Second, our results add to the growing literature exploring the drivers of innovation across firms.

Previous research finds that innovation is related to the firm’s organizational form (see, e.g., Spiegel and

Tookes (2008), Ferreira, Manso, and Silva (2014), Seru (2014)), corporate governance characteristics

(see, e.g., Atanassov (2013)), CEO characteristics (see, e.g., Hirshleifer, Low, and Teoh (2012)), VC

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characteristics (Kortum and Lerner (2000), Tian and Wang (2013)), legal systems (see, e.g., Acharya and

Subramanian (2009), Fan and White (2003), Armour and Cumming (2008), Acharya, Baghai, and

Subramanian (2009)) and institutional and market settings (see, e.g., Aghion, Van Reenen and Zingales

(2013), Acharya and Subramanian (2009), He and Tian (2013), Fang, Tian, and Tice (2014)). We show

that firm political activism is an important determinant of innovation that adds considerable explanatory

power to traditional cross-sectional models of firm innovation. These results are especially significant

considering a critical role of innovation in driving competitive advantage and long-term economic growth

(Sollow (1957), Romer (1987, 1990)).

Third, our results add to the emerging literature on the effects of policy uncertainty on real

investment. A growing consent among researchers asserts that policy uncertainty depresses investment

(see, e.g., Barro (1991), Pindyck and Solimano (1993), Alesina and Perotti (1996), Bloom, Bond and Van

Reenen (2007), Julio and Yock (2012, 2014)).4 In this paper, we argue that political activism can mitigate

the negative effects of policy uncertainty and stimulate real investment of politically active firms. In turn,

if investment of politically active firms does not completely crowd out investment of other firms, political

activism can promote competitive advantage and long-term economic growth. These results have clear

and important policy implications. Many academics, political observers and policymakers have called for

major curbs on different forms of corporate political activism citing potential corruption and an unfair

capture of the legislative process by the corporate sector (see, e.g., Briffault (2011) for a discussion of

legislation actions surrounding the Citizens United Supreme Court case). While these concerns may be

valid in some instances, we caution against such one-sided blanket view of firm political activism. Our

results show that firm political activism can serve an important role in reducing policy uncertainty and

stimulating firm investment, which, in turn, promotes real economic growth.

Section 2 presents the sample and describes the construction of our measures of firm political

activism. Section 3 documents the effect of political activism on firm innovation. Section 4 documents

the effect of political activism on the firm’s ability to predict future legislative changes. Section 5

concludes.

2. Sample

2.1. Data sources

Our sample comprises the intersection of the NBER patent citations data file described in Hall,

Jaffe, and Trajtenberg (2001) and the political contributions data file in Cooper, Gulen, and Ovtchinnikov

4 As a case in point, consider Italy, a country that has had 65 governments in the 69 years since the World War II.

Jones and Mackenzie (2014) argue that political instability created by the new governments regularly ripping up the

predecessors’ policies and starting new has had a significant negative impact on corporate investment and is one of

the central reasons for the country’s current economic decline.

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(2010). The patent citations file consists of 3,279,509 patents granted to firms for the period January 1,

1963 – December 30, 2006 and 24,082,781 patent citations made by patents granted during the period

January 1, 1975 – December 30, 2006. From this file, we extract data on the identity of the patent

recipient, the patent application date, the patent technological category and sub-category, and the number

of patent citations received. After deleting data for private firms and firms with no identification codes,

we are left with 1,158,693 patents granted to 6,528 unique firms and 9,673,067 patent citations.

The political contributions data file is described in detail in Cooper, et al. (2010) and consists of

1,064,830 hard money political contributions made by firms through their political action committees

(PACs) to politicians running for the President, the Senate, and the House of Representatives during the

period January 1, 1979 – December 31, 2004. From this file, we extract data on the identity of the

contributing firm, the date and the amount of each contribution, and the identity of the receiving

candidate. For each receiving candidate, we collect data from the Federal Election Commission (FEC) on

the sought after public office, the state and the district for which the candidate is running, the candidate’s

party affiliation, the election outcome, and the percentage of votes received. In addition, for all

candidates already in office, we obtain data on their Congressional committee assignments and their party

rankings on each serving committee. This data is from Charles Stewart’s Congressional Data Page.5

After deleting contributions from private firms, subsidiaries of foreign firms and firms with no data in the

CRSP/Compustat merged file, we are left with 813,692 contributions made by 1,805 unique firms.

After intersecting the patent citations and the political contributions files, our final sample

includes all 1,805 politically active firms with 404,536 granted patents and 3,814,120 patent citations

during the period January 1, 1979 – December 31, 2004. During this period, the firms in our sample

contribute a total of $933,002,309 in 2005 dollars to 5,584 unique political candidates. Our sample of

political contributions covers 67.1% of the total dollar volume of all corporate contributions reported to

the FEC and 27.2% of all political candidates running for office.

The firms at the intersection of the patent citations and the political contributions files represent

9.1% of all firms with available data on assets and book equity from CRSP/Compustat and 59.3% of the

total market capitalization of those firms. It is clear that politically active firms are significantly larger

than politically inactive firms. Indeed, the average market capitalization of politically active firms places

them in the top 8th percentile of the NYSE market capitalization in 2004.

Table 1 reports summary statistics for various firm characteristics of politically active and

inactive firms. All variables are defined in Appendix A. In panel A, politically active firms are much

larger, older and more profitable than inactive firms. Politically active firms also have more tangible

assets, higher leverage, lower Q, and invest significantly less in R&D relative to their assets. In terms of

5 We thank Charles Stewart III for generously providing this data on his website

http://web.mit.edu/17.251/www/data_page.html.

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sheer raw innovation, politically active firms receive more patents and patent citations, although this

result likely reflects the firm size effect. To address this issue, panel B reports the characteristics of

politically active and inactive firms within size-ranked deciles. We sort all firms in the patent citations

file by NYSE annually ranked size decile breakpoints and within each decile report the characteristics of

politically active and inactive firms.

The results in panel B show that politically active firms receive more patents compared to

inactive firms, but this result holds only for very large firms. For other firms, there is no discernible

pattern in patent activity between the two subsamples. Similarly, there appears no consistent differences

in patent citations between politically active and inactive firms once we control for firm size. As regards

other variables, the relation between firm size and political activism is particularly evident from the

distribution of politically active firms across size deciles. The percentage of politically active firms is

1.1% in the smallest decile but increases significantly to 14.7% for firms in decile five and 71.5% for the

largest decile. Controlling for firm size, politically active firms tend to be older and less profitable, with

more tangible assets and higher leverage but lower Q and R&D expenditures. Politically active firms also

operate in more competitive industries.

2.2. Measures of political activism

We construct three separate sets of measures of firm political activism. Our first set of measures

is similar to Cooper, et al. (2010) and is designed to capture the general level of firm political activism.

Specifically, we rely on prior evidence that firms build long-term relationships with politicians by making

frequent, roughly constant political contributions and define our first measure of political activism as the

total number of political candidates supported by a firm over a five-year period:

∑

(1)

where i and t index firms and years, respectively, and is an indicator variable equal to one if

firm i made at least one hard money political contribution to candidate j during the period t - 4 – t and

zero otherwise. We use a five-year window in Eq. 1 to capture the firms’ desire to develop long-term

relationships with politicians (Snyder (1992)). To match the timing of political contributions to the

timing of elections, which take place on the Tuesday following the first Monday of November of every

even year, we use election cycle years spanning 12 consecutive months from November of year t - 1 to

October of year t. Our first election cycle year starts in November 1979, and the number of supported

candidates in Eq. 1 is updated once a year from October 1984 to October 2004.

Given prior evidence that hard money political contributions are correlated with other ways in

which firms build relationships with politicians (see, e.g., Wright (1990), Milyo, Primo, and Groseclose

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(2000), Ansolabehere, Snyder, and Tripathi (2002), Bombardini and Trebbi (2008)), the number of

supported candidates in Eq. 1 should be an accurate measure of firm political activism. For robustness,

we supplement this measure with the total dollar amount of political contributions made to political

candidates by a firm over a five-year period:

∑

(2)

where is the total amount of money that firm i contributed to candidate j during the period t

- 4 – t. We use the same timing convention to construct this measure as in Eq. 1.

Our next set of measures of political activism is based on the results in Ovtchinnikov and

Pantaleoni (2012) that show that politicians who sit on Congressional committees with jurisdictional

authority over a firm’s industry receive a greater level of political support from individuals who reside in

a vicinity of the industry firms. Moreover, Ovtchinnikov and Pantaleoni (2012) show that the higher

level of political support from those individuals is associated with better operating performance of local

firms. These results imply that relationships with politicians who sit on Congressional committees with

jurisdictional authority over a firm’s industry are more valuable. We hypothesize that such relationships

are also more relevant for firm innovation because they provide access to valuable legislative information

(that originates in relevant Congressional committees) thereby reducing policy uncertainty. For brevity,

we label Congressional committees with jurisdictional authority over a firm’s industry as influential

committees and politicians on those committees as influential politicians. We then build analogs to Eqs.

1 and 2 that include only the subset of influential politicians:

∑

(3)

∑

(4)

where is an indicator variable equal to one if firm i contributed to politician k who sits on

an influential committee during the period t - 4 – t and zero otherwise, and is the total

amount of money that firm i contributed to politician k over the same period. The mappings between

Congressional committees and their industry jurisdictions are from Ovtchinnikov and Pantaleoni (2012),

Appendix B. We also build compliments to Eqs. 3 and 4, and

, as the difference between Eqs. 1 and 3 and Eqs. 2 and 4, respectively. These

variables track a firm’s relationship with politicians outside of Congressional committees relevant to the

firm. We label these committees as outside committees and politicians on those committees as outside

politicians.

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Our last set of measures of political activism is based on the argument in Kroszner and Stratmann

(1998) that membership on specialized Congressional committees facilitates repeated interactions

between special interest groups and politicians. It also fosters long-term relationships with politicians and

supports a reputational equilibrium in which contributions-for-service, including contributions-for-

information, contracts are enforceable. One implication of this argument is that contributions to

politicians who join a committee become more valuable for those interest groups that operate under the

jurisdiction of that committee. Consequently, politicians who join a particular committee should

experience an increase in the level of contributions from the affected interest groups. Using this logic, we

calculate the total number of politicians supported by a firm who join influential Congressional

committees as well as the total amount of political contributions made to those politicians:

∑

(5)

∑

(6)

where is an indicator variable equal to one if firm i contributed to politician m during the

period t - 4 – t who joined an influential Congressional committee in year t and zero otherwise, and

is the total amount of money that firm i contributed to politician m over the same period.

Finally, and

are defined analogously by focusing on contributions made to

politicians who change their Congressional committees from influential to outside committees.

Table 2 reports summary statistics on our political activism measures. On average, politically

active firms support 84 political candidates over any given 5-year period. This support is divided

between 13.5 candidates who serve on influential Congressional committees and 70.6 candidates who

serve on outside committees. The minimum number of supported candidates is one (315 firms in our

sample support a single candidate) and the maximum is 844 (AT&T in 1984). In terms of contribution

amounts, firms in our sample contribute on average a total of $226,050 over a 5-year period. These

contributions are divided between $44,082 contributed to members of influential Congressional

committees and $181,968 contributed to other politicians. These results show that members of influential

committees receive more in political contributions ($44,082/13.483 = $3,270 on average) compared to

other politicians ($181,968/70.619 = $2,577), which is consistent with Romer and Snyder (1994) and

Kroszner and Stratmann (2000). The minimum amount of political contributions is zero and the

maximum is $6,293,663 (AT&T in 1990).6

6 Zero contributions arise when a firm’s original contribution is refunded by a politician’s campaign, usually after an

election loss. We view them as valid contributions in the sense that the firm attempted to establish a relationship

with a politician based on considerations deemed important by the firm. However, the money was refunded for

reasons most likely outside of the firm’s control.

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In panel B, we present a detailed account of political contributions made by each politically active

firm in each election cycle to members of each Congressional committee in our sample. Ranked by the

average contribution amount, members of the House Energy and Commerce committee collectively

receive the most in political contributions from a typical firm ($13,578 on average), while members of the

House Merchant Marine and Fisheries committee receive the least ($7,335). Similarly in the Senate,

members of the Commerce committee receive the most in political contributions ($9,479), while members

of the Environment and Public Works receive the least ($7,409). Other committees receive between

$7,000 and $10,000 on average from firms, with the Armed Services, Financial Services, and the House

Transportation committee receiving amounts at the top end of the distribution and the Agriculture and

Natural Resource committees at the bottom.

In addition to contribution totals, two other results stand out. First, firm contributions to

Congressional committees are significantly related to the rankings of powerful committees developed in

Edwards and Stewart (2006). Three out of seven House committees in our sample (Energy and

Commerce, Transportation and Infrastructure, and Armed Services/National Security), and two out of six

Senate committees (Commerce, Science, and Transportation and Armed Services) are on the Edwards and

Stewart (2006) list of the ten most powerful committees. Moreover, the correlation between contribution

totals in panel B and committee power rankings is 0.782 for the House committees and 0.538 for the

Senate committees. These correlations are significantly higher than those reported in Ovtchinnikov and

Pantaleoni (2012) who study individual political contributions and show that firms target powerful

committees to a much greater extent than individuals. The results are consistent with prior evidence that

committees with significant power over firms receive more money (see, e.g., Grier and Munger (1991),

Romer and Snyder (1994), Ansolabehere and Snyder (1999)).

Second, the differences in committee contribution totals reflect differences in the number of

committee members that firms choose to support rather than differences in the total amount of political

contributions made to each committee member. In fact, the latter is roughly constant, with approximately

$1,700 contributed to members of the House committees and $2,800 contributed to members of the

Senate committees. These results are in line with Cooper, et al. (2010) and show that the level of firms’

political activism is determined by the number of political candidates firms choose to support rather than

by the amount of money contributed to each candidate. Therefore, in our analysis below, we focus on

political activism measures based on the number of supported political candidates. We use political

activism measures based on the amount of contributions to political candidates as a robustness check.

3. Political contributions and firm innovation

In this section, we document the effect of political activism on firm innovation. Our

identification strategy follows a four-step approach. First, we establish a baseline positive correlation

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between firm political contributions and future innovation. Second, we conduct a wide range of

subsample analyses that, in their entirety, provide stronger causal evidence of the effect of political

contributions on firm innovation. Third, we set the identification hurdle higher and employ instrumental

variables estimation. We instrument for political contributions with a politician’s margin of victory in the

prior election and show that instrumented contributions strongly predict future firm innovation. Fourth,

we move the identification bar even higher and exploit an exogenous shock to the value of political

contributions that occurred during our sample period. We show that subsequent innovation activity of the

treatment firms was significantly affected by the shock relative to that of the control firms. To preview

our results, the collective evidence below offers strong support for our hypothesis that political activism

facilitates firm investment in innovation.

3.1. Baseline results

Our baseline test of the hypothesis that political activism facilitates firm innovation is to establish

a positive correlation between our proxies for political activism and innovation. We estimate the

following panel OLS regression that relates political activism proxies to firm innovation:

(7)

where is a measure of innovation of firm i during the period ( { }) and

are industry and year fixed effects, respectively, is a measure of firm political activism,

is a vector of control variables, and is a random error term assumed to be possibly

heteroskedastic and correlated within firms (Petersen (2009)). All control variables are winsorized at the

upper and lower one-percentiles.

In our baseline tests, we use two measures of firm political activism, and

defined in Eqs. (1) and (2), respectively, and estimate two separate models of firm innovation. Following

extant literature (see, e.g., Hall, Jaffe, and Trajtenberg (2005), Atanassov (2013), He and Tian (2013),

Seru (2014), Fang, Tian, and Tice (2014)), our first measure of firm innovation, , is the

natural logarithm of the total number of successful patent applications for firm i during the period .

We use the patent application year because it is closer to the timing of actual innovation than the patent

grant year (Grilliches, Pakes, and Hall (1988)). Our second measure of innovation, , is the

natural logarithm of the total number of future patent citations received by each patent. Compared to

simple patent counts, this variable more accurately captures the significance and value of individual

patents and, therefore, may serve as a better proxy for innovation “success” (Hall, Jaffe, and Trajtenberg

(2005); Grilliches, Pakes, and Hall (1988) report that patent value is highly skewed, with most of the

value concentrated in a small number of patents). Because of the well-known truncation problem in the

patent data file, we follow Hall, Jaffe, and Trajtenberg (2005), Trajtenberg (2005), Atanassov (2013) and

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Seru (2014) and estimate effective patents and patent citation counts by dividing the annual number of

patent applications and patent citations for a given firm in a given technology class by the average

number of patent applications and patent citations received by all firms in the same technology class in

the same year. Our results are robust to an alternative truncation adjustment used in He and Tian (2013)

and Fang, Tian, and Tice (2014).

Table 3 reports the results of estimating Eq. 7. Panel A presents the results for the total number

of patent applications; panel B presents the results for the total number of patent citations. Models 1 and

2 test whether political activism affects firm innovation during the year immediately following the year

when political contributions are measured. Models 5 and 6 allow for a longer three-year lag between

political contributions and their effect on subsequent innovation.

The results in panel A show that firm patent activity is strongly related to political activism. The

coefficient is positive and significant at the 1% level in all models, which implies that firms that

support more political candidates and firms that contribute more capital to political candidates innovate

more. The results are significant at both the one- and three-year horizons. It is important to note that all

models include firm size as a standard control variable, so our results do not simply reflect the firm size

effect (table 1). The inclusion of political activism variables improves the R2 statistic in model 1 from

0.295 to 0.326 and from 0.333 to 0.357 in model 5. These figures represent a 10.1% and a 7.2%

improvement in R2, respectively, and show that firm political activism adds significant explanatory power

to the baseline model of innovation.7 The results for Camount in models 2 and 6 are similar.

To get a better sense of economic magnitudes of our political activism variables, note from table

2 that firms in the 25th percentile of the number of supported political candidates support 14 candidates,

while firms in the 75th percentile of that distribution support 117 candidates. Thus, an interquartile

increase in the number of supported candidates is associated with an additional 0.489 patent applications

over the next year ( ([ ] ) ) and an additional 0.517 patent applications over the

next three years. Compared to the unconditional mean number of patent applications by politically active

firms (6.1 patents), the results appear economically large. As another benchmark, the impact on

innovation of an interquartile change in each control variable ranges from 0.357 additional patent

applications for leverage to 0.461 for total assets. Thus, the number of supported political candidates has

the largest economic effect on firm innovation, even larger than firm size. This is especially compelling

given prior evidence that firm size is one of the most dominant determinants of firm innovation (see, e.g.,

Atanassov (2013), Cornaggia, et al. (2014), He and Tian (2013)). The results for Camount are

7 The estimated coefficients on traditional control variables line up well with expectations and previous studies. In

particular, firm innovation is positively related to firm size, age, profitability, investment, and Q and negatively

related to firm leverage and the ratio of fixed assets to total assets. Firm innovation is higher in more concentrated

industries as measured by the Herfindahl index, although the relation is non-linear. Similar results are reported in

Atanassov (2013), Cornaggia, et al. (2014), He and Tian (2013), and Fang, Tian and Tice (2014).

13

economically equally significant and indicate that an interquartile increase in the amount of political

contributions is associated with an additional 0.419 (0.430) patent applications over the next year (three

years).

The results in panel B similarly show a strong positive association between firm patent activity,

as defined by patent citations, and political activism. The is positive and significant at the 1% level for

both measures of political contributions and at both time horizons. The results also appear economically

large. The magnitude of the coefficient implies that an interquartile increase in Pcand and Camount is

associated with an additional 0.381 and 0.374 patent citations over the next year and an additional 0.395

and 0.380 patent citations over the next three years. Compared to economic magnitudes of the control

variables, which range from 0.360 additional patent citations for an interquartile change in leverage to

0.370 for total assets, the results again indicate that political activism has the largest economic effect of

all variables on firm innovation.

In the current estimation, we use all firms with available data on Compustat to estimate Eq. 7,

irrespective of whether those firms have an established PAC. Firms without a PAC are assigned a value

of zero for political activism variables, which may be problematic for two reasons. First, a zero political

contribution implies a choice in the sense that a firm could have contributed money to a politician but

decided not to do so. This is not the case for firms without a PAC that decided not to participate in the

political process altogether. Second, firms do not randomly decide to get involved in politics, and this

choice introduces a potential self-selection bias into our observed sample. To control for this bias, we use

a two-stage Heckman (1979) approach in the remainder of table 3. In the first stage, we estimate a probit

model of whether a firm has an established PAC on determinants of PAC participation. These

determinants come from prior literature that analyzes the determinants of firm political involvement (see,

e.g., Masters and Keim (1985), Zardkoohi (1985), Grier, Munger, and Roberts (1994), Hart (2001)). The

first-stage probit results are presented and discussed in Appendix B. From the probit, we calculate the

inverse Mills ratio (IMR) and include it in second-stage regressions in the rest of table 3. The inclusion of

IMR in innovation regressions helps control for firm self-selection into the politically active group.

Models 3 and 4 and models 7 and 8 present the results for one-year ahead and three-year ahead innovation

measures, respectively, for the sample of politically active firms.

The results of the two-stage Heckman model show that firm innovation is strongly positively

related to political activism after controlling for self-selection. The coefficient remains significant at

the 1% level in both panels and at both time horizons despite the fact that our estimation utilizes only

politically active firms. Put differently, our baseline results do not simply reflect differences in

innovation between politically active and inactive firms. Rather, within politically active firms, those

firms that support more political candidates and contribute more capital, also innovate more. In terms of

economic significance, the Heckman results are quite similar to full sample results. An interquartile

14

increase in Pcand and Camount is associated with an additional 0.435 and 0.393 patent applications and

0.379 and 0.372 patent citations over the next year and an additional 0.453 and 0.399 patent applications

and 0.391 and 0.377 patent citations over the next three years. These results are quite similar in

magnitude to those reported for the whole sample. Firms that are more politically active innovate more.

3.2. Subsample analysis

Our baseline result establishes a strong positive correlation between political contributions and

firm innovation. Whereas this result is consistent with the hypothesis that political activism facilitates

firm innovation, this clearly is not the only possible interpretation. Firm political activism is plausibly

endogenous to innovation, so to comment on causality, we must tackle the omitted variable bias as well as

the simultaneity of political contributions and firm innovation (Roberts and Whited (2010)).

Despite including all standard determinants of firm innovation in table 3 (Atanassov (2013),

Cornaggia, et al. (2014), He and Tian (2013), Fang, Tian, and Tice (2014)), we cannot dismiss a

possibility that some omitted variable, correlated with political contributions, actually drives innovation.

For example, political contributions are correlated with firms’ access to external financing (Claessens,

Feijen, and Laeven (2008)) which may, in turn, predict innovation. Likewise, political contributions are

related to certain governance characteristics (Aggarwal, Meschke, and Wang (2009)), and it is possible

that those characteristics drive firm innovation (Atanassov (2013)). This problem is especially

challenging because the complete set of omitted variables is potentially infinite and, by construction,

unobservable to an econometrician. So, instead of relying on additional controls in Eq. 7, we go a

different route. Our second step toward identification is to decompose the sample of political

contributions into those that are predicted by our hypothesis to have a stronger impact on firm innovation

and those contributions predicted to have a weaker or no effect on innovation. If there is indeed a

differential impact in the data, this evidence would be consistent with our hypothesis that political

contributions facilitate innovation. However, it would be more difficult to interpret the results as driven

by an omitted variable since that interpretation would require that the omitted variable is correlated with

one particular subset of political contributions but not with others. This analysis is also important in

ruling out the possibility that the relation between political contributions and firm innovation is spurious.

Table 4 reports the results for various subsamples of political contributions. If political

contributions facilitate innovation because they grant access to politicians who, in turn, either supply

valuable information or trade legislative favors for contributions, the relation between political

contributions and firm innovation should be stronger for contributions to influential politicians. Because

these politicians sit on Congressional committees with jurisdictional authority over the firm’s industry,

they have direct legislative control over the firm and possess valuable private information about future

legislative changes. The evidence in table 2 shows that influential politicians receive more in political

15

contributions compared to other politicians.8 Hence, under our hypothesis, contributions to influential

politicians are more significant in predicting future innovation compared to contributions to outside

politicians.

Panels A and B present the results of estimating Eq. 7 for subsamples of contributions to

influential and outside politicians, respectively. All models include the full set of control variables in

table 3 and control for firm self-selection into the politically active group with IMR. Consistent with our

hypothesis, the results show a much stronger relation between political contributions to influential

politicians and firm innovation compared to the relation between political contributions to outside

politicians and firm innovation. In panel A, the coefficient on PcandCommittee and CamountCommittee is

positive and statistically significant, which implies that political contributions to influential politicians are

significant predictors of future firm innovation. This result holds for firm innovation measured by patent

applications and patent citations and at both the one- and three-year horizons. In contrast, the

coefficient on PcandNon-Committee and CamountNon-Committee in panel B is insignificant. It is actually

marginally negative in model 1, although this result is not robust to alternative specifications. The

differences in results across panels A and B are stark and provide further evidence that political

contributions, especially to influential politicians, are strongly related to firm innovation. This evidence

is consistent with our hypothesis but is more difficult to explain by an omitted variable bias. The latter

explanation would require that an omitted variable is positively correlated with political contributions but

only to influential politicians while uncorrelated with contributions to outside politicians.

In our next set of tests, we switch from levels to changes in firm political activism and focus on

politicians who join or leave influential Congressional committees. Kroszner and Stratmann (1998) argue

that membership on specialized Congressional committees facilitates a reputational equilibrium in which

politicians trade services to special interest groups for their political contributions. Under our hypothesis,

this argument implies that contributions to politicians who join (leave) influential committees become

more (less) significant in predicting future firm innovation after the committee change. On average, 144

(123) politically active firms in table 4 make at least one political contribution over any five-year period

to a politician who joins (leaves) an influential committee. These firms represent, respectively, 26.7%

and 22.8% of an average annual number of politically active firms in our sample. In panel C, we estimate

the following regression:

(8)

8 We analyze the relation between a politician’s status and firm contributions in greater detail. In unreported tests,

we find that firms (i) are more likely to contribute to influential politicians, (ii) contribute more frequently to

influential politicians, and (iii) contribute greater amounts to influential politicians. These results are available upon

request.

16

where and

are the number of politicians joining and leaving influential

Congressional committees defined in section 2.2 and the rest of the variables are as defined above.

Consistent with our hypothesis, the results show a significant positive relation between

contributions to politicians who join influential committees and firm innovation while no relation between

contributions to politicians who leave those committees and firm innovation. The coefficient on

is positive and significant in all specifications while the coefficient on

is either

insignificant or marginally negative. We also estimate a variant of Eq. 7 that uses and

in place of

and . The results are very similar, so we do not report them

in the interest of space.

The variables and

are clearly not mutually exclusive. In fact, 508 unique

firms in our sample simultaneously support politicians who join influential committees and politicians

who leave those committees. So, in the rest of panel C, we replace and

with

indicator variables that isolate firms that net gain or lose connections with influential politicians.

Specifically, we define ( ) as an indicator variable set to one for firms that support

a greater number of politicians joining influential committees than politicians leaving those committees.

Analogously, we define ( ) as an indicator variable set to one for firms that

support a lower number of politicians joining influential committees than politicians leaving those

committees. As an illustration, AT&T supported seven politicians during the 1998 – 2002 period who

joined influential committees following the 2002 election and one politician during that period who left

an influential committee. So, ( ) takes a value of one for AT&T in 2002. During

the same time period, Boeing supported eight politicians who joined influential committees and 14

politicians who left influential committees in 2002. So, ( ) takes a value of one

for Boeing in 2002.

Consistent with our hypothesis, the results show a strong positive effect of political contributions

on firm innovation for firms that support a greater number of politicians joining relevant committees than

politicians leaving those committees. The coefficient is positive and significant for both measures of

innovation and at both the one- and three-year horizons. In contrast, there is no effect of political

contributions on innovation for firms that support fewer politicians joining relevant committees than

politicians leaving those committees. The coefficient is negative when innovation is measured by the

number of patents and insignificant when innovation is measured by the number of patent citations. The

results are very similar if the indicator variables are computed based on the total amount of political

contributions to politicians joining and leaving influential committees. The results are available upon

request. Collectively, the results in panel C are consistent with our hypothesis but seem, at least to us,

more difficult to explain by an omitted variable bias.

17

In our last set of subsample tests, we interact ,

and indicators (

) and ( ) with election year and non-election year dummies. Election

outcomes and, consequently, many upcoming Congressional committee changes are often predictable

during election years, especially in weeks just prior to general elections. Mullins and Mundy (2010)

suggest that firms exploit this predictability and strategically shift their contributions to politicians likely

to gain influence in the upcoming elections. If political contributions are set optimally and firms

redistribute contributions during election years to politicians expected to join influential Congressional

committees, we expect a lower, if any, impact of those contributions on subsequent innovation.

Conversely, if firms make political contributions to politicians who unexpectedly join influential

committees during non-election years, we expect a stronger impact of those contributions on future

innovation.9

Panel D presents the results. On average, 65 (39) out of 539 politically active firms in table 4

make at least one contribution to a politician who joins (leaves) an influential committee during a non-

election year. In comparison, 217 (200) firms support at least one politician who joins (leaves) an

influential committee during an election year. Naturally, committee changes in non-election years are

rarer although far from infrequent. Consistent with our hypothesis, the entire effect of political

contributions on firm innovation is concentrated among politicians who join influential committees

during non-election years. The coefficient on is positive and significant but only in non-

election years. In contrast, the coefficient is insignificant in election years. The results hold for firm

innovation measured by patent applications and patent citations and at both the one- and three-year

horizons. The results are identical if is replaced with ( ) in Eq. 8.

Firms that support a greater number of politicians joining influential committees than politicians leaving

those committees subsequently innovate more but only for committee changes that take place in non-

election years. There is no effect of political support on firm innovation in election years. Also

consistent with our hypothesis, the coefficient is insignificant in all years. Our results are robust if all

variables are computed based on the total amount of political contributions to politicians joining and

leaving influential committees. The results are available upon request.

Overall, the analysis in table 4 shows that an omitted variable bias is not likely to explain our

baseline results. Such an explanation would require that an omitted variable is correlated with the overall

level of firm political activism because it is correlated with the firm’s support for politicians on influential

Congressional committees and for politicians who join such influential committees. Moreover, the

correlation between the omitted variable and the firm’s political support has to exist only in non-election

years. We are unaware of a reasonable candidate for such a variable.

9 Committee reassignments in non-election years take place because of a politician’s death, resignation from

Congress, or appointment to a different post.

18

3.3. Instrumental variables estimation

We next turn to analyses that represent a higher hurdle to identify the effect of political

contributions on firm innovation. While the results in the previous section serve an important first step

toward identification, we cannot yet completely rule out the existence of an omitted variable that causes

both firm innovation and political contributions. Moreover, the above analysis does little to address the

simultaneity of political contributions and firm innovation. It may well be the case that innovative firms

choose to make political contributions not only to access information from politicians but also to protect

their current innovative status. If the latter is true, the relation between political contributions and firm

innovation runs in the direction opposite to that predicted by our hypothesis. The cleanest example is

firms lobbying members of Congress to exert influence on various government officials, including those

in the U.S. Department of Commerce and its Patent and Trademark Office.10 If such lobbying is

successful, firms can protect their innovative status with political contributions by increasing the odds of

approval of their own future patents or decreasing the odds of approval of their competitors’ patents.

Under this explanation, innovation drives the supply of political contributions.

To tackle both issues, we turn to instrumental variables (IV) estimation. We use a politician’s

margin of victory in the prior election as an instrument for firms’ political contributions to that politician

in the current election cycle. Intuitively, a politician’s margin of victory in the prior election is unlikely to

affect current firm innovation. However, it should significantly affect the firm’s propensity to make

political contributions to that politician because the marginal dollar of contributions matters more to a

vulnerable politician in a close election. Consistent with this, several studies show theoretically and

empirically that politicians in close elections raise more campaign financing (see, e.g., Jacobson (1980,

1985), Kau, Keenan, and Rubin (1982), Poole and Romer (1985), Stratmann (1991)). Because the value

of a marginal dollar of campaign funds is higher in close elections, a politician running in a close race

ought to promise more favors in return for contributions to attract more campaign financing. The

assumption that a politician trades favors in exchange for contributions is common in theoretical

arguments including Coate (2004) and has received empirical support (see, e.g. Stratmann (1998, 2002)).

Thus, we hypothesize that firms target politicians in close elections and expect more benefits, including

access to information valuable for future innovation, in return for their political contributions.

Firms typically support multiple political candidates per year, so we calculate our instrument as

follows. For every year and for every firm, we estimate a first-stage logit regression that relates a firm’s

choice to contribute to a politician to her margin of victory in the prior election. The coefficient on the

10 Wiley (1988) argues that Congress indeed has significant influence and oversight of government agencies

including control by statute, the power of the purse, the continuing oversight of standing committees, and pressures

from individual politicians and congressional staff members.

19

margin of victory measures the sensitivity of the firm’s decision to contribute to the politician’s prior

election result. A low (in fact, negative) sensitivity identifies firms that choose to support politicians with

a low prior margin of victory who, we argue, should be more vulnerable in the current election cycle and,

therefore, more likely to trade political favors for contributions going forward. Conversely, a high

sensitivity identifies firms that choose to support politicians firmly entrenched in their Congressional

positions.

Because of the well-known “errors-in-the-variables” problem with the first-stage sensitivity

estimates, we sort firms into 20 equal- and value-weighted portfolios based on their estimated sensitivities

to the margin of victory. Firms with the lowest sensitivity are placed in portfolio one and firms with the

highest sensitivity are placed in portfolio 20. Fama and MacBeth (1973) show that as long as the errors in

the estimated first-stage coefficients are less than perfectly correlated, the portfolio coefficients are much

more precise estimates of the true coefficients. In the second-stage regression, we then estimate

predictive regressions that relate portfolio average growth in innovation over different time horizons to

the lagged average portfolio sensitivity to the margin of victory. Our hypothesis predicts that firms that

target politicians with a low margin of victory subsequently benefit from a faster growth in innovation.

So, we expect a negative relation between firm sensitivity to the margin of victory and future innovation

growth.

Table 5 reports the results of IV estimation. Panel A presents the first-stage logit results, with

emphasis on the instrument relevance condition. The results show that firm political contributions are

strongly related to a politician’s margin of victory. The average coefficient on the margin of victory is

negative and significant, which indicates that firms are much more likely to support politicians with a low

margin of victory in prior elections. The analysis of the distribution of the first-stage coefficients shows

that 11,439 out of 17,873 coefficients on the margin of victory are negative (64% of firm-year

coefficients) and 1,024 of the negative coefficients are significant at the 5% level or higher (9% of all

negative coefficients). At the same time, only 297 positive coefficients are statistically significant (4.6%

of all positive coefficients). These results imply that the entire distribution of the first-stage coefficients

is centered below zero and exhibits a significantly fatter left tail than what would be expected by chance.

This evidence is consistent with our hypothesis that firms target vulnerable politicians with a low margin

of victory in prior elections.

To address the exclusion restriction, in unreported tests, we sort firms into deciles based on the

average margin of victory of their supported politicians and analyze future innovation growth across the

different deciles. Irrespective of the time horizon considered, we find no consistent differences in

innovation growth across the deciles. We also regress our instrument on lagged innovation growth and

find a positive but insignificant coefficient (p-value > 0.3). Although not a formal proof of the exclusion

restriction, these results are an important building block in our IV estimation. They corroborate our

20

intuition that a politician’s margin of victory has no impact on firm innovation except through its impact

on political contributions. Together with panel A results, this evidence shows that the margin of victory

is a valid instrument.

The remainder of table 5 reports the results of the second-stage regressions that relate our

instrument to firm innovation. Panel B presents the results for innovation measured by the number of

patent applications; panel C presents the results for innovation measured by the number of patent

citations. The results in panel B show that firm innovation is strongly related to its propensity to support

politicians with a low margin of victory. The coefficient on the sensitivity to the margin of victory is

negative and significant at the 1% level in regressions that employ both equal- and value-weighted

portfolios and for innovation growth measured at one-, two- and three-year horizons. These results imply

that firms that choose to support vulnerable politicians who narrowly beat their opponents in prior

elections subsequently experience a faster growth in patent applications. This evidence is consistent with

our argument that vulnerable politicians are more likely to trade favors for political contributions; hence,

firms that target vulnerable politicians obtain higher tangible benefits, including access to information

valuable for future innovation.

To gauge economic magnitudes of our results, the interquartile range in the sensitivity to the

margin of victory is 2.9% in our sample, so an interquartile decrease in the sensitivity to the margin of

victory is associated with a 3%, 5.1% and 7.4% (2.5%, 4.3% and 6.1%) faster growth rate in patent

applications over the next year, two years, and three years respectively using EW (VW) portfolios. The

results are economically important.

The results in panel C appear statistically weaker. Nevertheless, the sign of the coefficients in all

but one specification is consistent with our hypothesis that changes in firm patent citations are positively

related to the propensity to support politicians with a low margin of victory. In addition, the results for

three year changes in patent citations are statistically significant at the 5% level. In terms of economic

significance, an interquartile decrease in the sensitivity to the margin of victory is associated with a 0.17%

(0.14%) faster growth in patent citations over the next three years using EW (VW) portfolios.

Overall, the results in table 5 provide stronger evidence of a causal effect of firm political

contributions on innovation. By instrumenting political contributions with a politician’s margin of

victory, our analysis isolates the exogenous variation in firm political contributions, which, in turn, allows

us to more precisely pin down the causal effect of contributions on innovation. The IV results are

consistent with our hypothesis that political activism facilitates firm innovation.

3.4. Difference-in-difference estimation

Our next set of tests moves the identification bar still higher and exploits a significant exogenous

change in the value of political contributions that occurred during our sample period. The 1994 midterm

21

Congressional election was a dramatic and unexpected victory for Republicans that not only put them in a

leadership position in the House of Representatives but also resulted in significant changes to the rules

and practices of the House. Aldrich and Rohde (1997) detail the changes, the most important of which for

us was Newt Gingrich’s unexpected departure from the traditional Republican practice of assigning

Congressional committee chairman positions based on committee seniority to that based on party loyalty.

Within a week of the November 8 election, the new leadership in the House announced the appointment

of four junior members of Congress to committee chairman positions, thereby bypassing the expected

senior candidates for the posts.11 Aldrich and Rohde (1997) note that this was the most significant

departure from seniority in the selection of committee chairs since 1974, when four senior conservative

Southern Democrats were stripped of their chairs following the election of the Democratic class of 1974

known as the “Watergate babies”.

The 1994 event represents a particularly convenient setting for us to analyze the importance of

political contributions, especially to powerful politicians with chairman roles, on firm innovation. First,

the election results and the new procedures for assigning committee chairman positions were completely

unexpected. Note Aldrich and Rohde (1997):

“Well before the Republican Conference could meet, and before any formal change in the method of

choosing committee chairs could be considered, Gingrich and his allies simply asserted the power to

choose the chairs of the committees that were most important to them.” (p. 550).

Second, the changes in committee chairmanships were clearly exogenous to firm innovation. Third,

various firms were impacted differently by the event. So, our treatment group comprises 126 firms that

supported the four junior politicians who unexpectedly became committee chairs in November 1994. To

be included in the treatment group, a firm must have contributed to at least one of the four would-be

committee chairs during the 1990 – 1994 period. We use two control groups to identify the treatment

effect. The first group, firms with chair contributions, includes 475 firms that supported one or more

existing committee chairs during the 1990-1994 period. The second control group, firms without chair

contributions, includes 197 firms that supported no existing committee chairs during the 1990-1994

period. If political contributions to powerful politicians are important for firm innovation, we expect an

increase in innovation for the treatment firms relative to the control firms without chair contributions

following the 1994 election. We also expect the level of innovation of the treatment firms to converge to

that of the control firms with chair contributions following the 1994 election.

11 During the week following the election, Robert Livinston (LA), ranked fifth in committee seniority, was promoted

to the chairman position on the Appropriations committee, bypassing the second ranking John Myers (IN). Henry

Hyde (IL) and Thomas Bliley (VA) were promoted to the chairman positions on the Judiciary and Energy

committees, respectively, bypassing a higher ranking Carlos Moorhead (CA). David McIntosh (IN), a House

freshman was appointed the chair of the National Economic Growth, Natural Resources, and Regulatory Affairs

subcommittee of the Government Reform and Oversight committee.

22

Figure 1 presents the results. The solid line tracks the treatment firms. The dashed line tracks the

control firms without chair contributions. The dashed line with a triangular marker tracks the control

firms with chair contributions. The shaded area designates the November 1994 election. The first

important result in figure 1 is the dynamics of patent activity of the treatment and control firms prior to

the election. It is clear that the level of patent applications varies considerably across the three

subsamples. However, no clear differences in trends are evident across the groups. This result, while not

a formal test of the “parallel trends” assumption, nevertheless suggests that in the absence of treatment,

the average trend in patent activity would have been the same across the treatment and control firms. The

differences in levels of patent activity is not a cause for concern as those will be differenced out in our

formal difference-in-difference tests below.

The post-election results in figure 1 show that the treatment firms significantly increase their

patent activity following the 1994 election. The number of patent applications hovers at roughly five

prior to 1994 but increases steadily to 5.3 applications in 1995, 5.8 in 1996, 7.1 in 1997, 7.5 in 1998 and

8.4 applications in 1999. The almost two-fold increase in patent activity of the treatment firms is

particularly striking given a relatively constant patent activity of the control firms without committee

chair contributions. Those firms apply for slightly fewer than two patents per year on average throughout

the event window. Thus, firms that supported politicians who were unexpectedly appointed committee

chairmen in 1994 subsequently significantly increased their patent activity relative to firms that did not

support committee chairs. The results in figure 1 also show that patent activity of the treatment firms

converges to that of the control firms with chair contributions after the 1994 election. Indeed, while a

large difference in patent applications exists between the two groups at the time of the election, it is

completely wiped out by 1999. Firms that supported would-be committee chairs increased their

innovation exactly to the level of innovation of firms that supported committee chairs.

Table 6 reports formal tests of the difference-in-difference estimates. Columns 1 – 3 analyze

differences in levels of patent activity between the treatment and control firms; columns 4 – 6 analyze

differences in changes in patent activity. The results show that both the level and the growth in patent

applications is significantly higher for the treatment firms following the 1994 election compared to the

pre-1994 period. The differences between the pre- and post-1994 estimates are positive and economically

large for both measures of patent activity. This stands in sharp contrast to the dynamics of patent activity

for the control firms, which shows no discernible trend in patent applications or patent application growth

from before to after 1994. The bottom two rows show that the resulting difference-in-difference estimates

are significantly positive in tests using both sets of the control firms and for patent activity measured in

levels and changes.

Table 7 decomposes the post-1994 period by year. We estimate the following regression:

23

( ) ( )

(9)

where { } is a measure of patent activity of firm i

during the year t+1, is the firm fixed effect, ( ) is an indicator time variable that identifies post-

1994 years, is an indicator variable set to one for firms in the treatment group and zero

otherwise and is an error term. Note that the indicator by itself is not in Eq. 9

because it is perfectly collinear with the firm fixed effect .

Panel A (panel B) presents the results comparing patent activity of the treatment firms with that

of the control firms with committee chair contributions (without committee chair contributions). Models

1 and 3 corroborate our findings in figure 1 and table 6 and show a significant treatment effect in the post-

1994 period. Models 2 and 4 show that when the post-event window is decomposed by year, the

treatment effect is concentrated in the post-1996 period. The lag between the committee chair

appointments and subsequent patent activity makes intuitive sense given that it takes considerable time

and resources for firms to adjust their innovation activity to new information (Atanassov (2013)). Finally,

we repeat the analysis in this section by measuring patent activity with patent citations rather than patent

applications and obtain qualitatively similar results.

Overall, the results in this section provide possibly the strongest evidence of a causal effect of

firm political contributions on innovation. By identifying an exogenous shock to the value of political

contributions, our analysis isolates firms that were significantly impacted by the shock and shows that

those firms responded with greater patent activity in subsequent years. We interpret these results as

robust evidence that political activism facilitates firm innovation.

4. Firm innovation and future legislative changes

The evidence in the previous section offers strong support for our hypothesis that political

activism positively affects firm innovation. This is consistent with the view that political activism

reduces policy uncertainty, which, in turn, fosters firm innovation. In this section, we explore in greater

detail how political activism may reduce policy uncertainty and stimulate firm innovation. We discuss

two channels, first introduced in section 1, and offer evidence consistent with both of them. We note

right away that the channels we describe are not mutually exclusive. Albeit the evidence below is

consistent with both explanations, we believe it is important to devise empirical tests that could isolate the

relative importance of each channel. This exercise, however, falls outside the scope of the current study

so we leave it to future work.

24

4.1. How does political activism reduce policy uncertainty?

We put forward two channels through which political contributions may reduce policy

uncertainty and stimulate firm innovation. First, firms may use political contributions to purchase access

to politicians who, in return for contributions, supply legislative information relevant for firm innovation.

For example, a politically active auto manufacturer will benefit significantly from its connections to

members of Congress who reveal private information to the firm that Congress plans to take a hard stance

on environmental or product safety issues. This information is clearly valuable to the firm and allows it

to adjust its innovation strategy in anticipation of upcoming legislative changes.

There is surprisingly little evidence in the existing literature whether firm political contributions

purchase access to politicians purely for information acquisition reasons. Hojnacki and Kimball (2001)

note that many studies define access to politicians as an opportunity to (i) persuade or make a case or

lobby, (ii) secure a legislator’s attention, (iii) meet with a legislator to communicate policy views, or (iv)

influence a legislator to consider and act on arguments. Yet, theirs is the only study of which we are

aware that considers a broader view of access that includes not only active lobbying but also purely

passive acquisition of information. Hojnacki and Kimball (2001) find that special interest groups with

established PACs contact more members of Congressional committees compared to groups without

established PACs. This is consistent with the view that political contributions purchase access, including

access to legislative information. If access to such information is valuable for firm innovation because it

reduces policy uncertainty and the value of the firms’ option to wait, we expect politically active firms to

successfully time future legislation and set their innovation strategies in expectation of upcoming

legislative changes.

The second way in which political contributions may reduce policy uncertainty and stimulate

innovation is through active lobbying. Firm political contributions and lobbying expenditures are

positively correlated (see, e.g., Wright (1990), Ansolabehere, Snyder, and Tripathi (2002), Akey (2014)),

so firms that contribute more also lobby more. There is also evidence that political contributions affect

legislative outcomes (see Stratmann (2005) for a review of the literature), which implies that lobbying is

successful.12 When applied to firm innovation, this argument suggests that firms may use political

contributions to lobby for legislation that increases their returns to innovation. For example, Aghion, et

al. (2005) present evidence that product market competition and innovation are linked. So, firm lobbying

for legislative changes that impact industry competition, such as foreign trade policy legislation, may

impact returns to innovation and subsequent supply of innovation. Under this explanation, we expect

politically active firms to have an advantage in timing their innovation to their own lobbying efforts and

the resultant future legislative changes.

12 Ansolabehere, et al. (2003) argue that endogeneity issues make inferences regarding the effects of political

contributions on legislative outcomes difficult.

25

Note that even though the two explanations are not mutually exclusive, they are distinct in one

important way. The “acquisition of information” explanation implies that firms are passive with respect

to future legislation and simply use political contributions to learn about upcoming legislative changes.

In contrast, the “active lobbying” explanation implies that firms are actively engaged in the legislative

process and use their political capital to shape future legislation. We believe it is important in future work

to report evidence on the relative importance of the two explanations. However, in this study, we simply

note that under both explanations, political contributions positively affect firm innovation. This relation

exists because under both explanations, politically active firms successfully time future legislation and set

their innovation strategies in expectation of upcoming legislative changes. Empirically, this implies that

changes in innovation by politically active firms predict future legislative changes. We now turn to

testing this prediction.

4.2. Political activism, firm innovation and future legislative changes

In predicting legislative changes, we focus on major deregulatory initiatives affecting the

petroleum and natural gas, utilities, telecommunications, and transportations industries as defined by the

Fama-French 49-industry definitions. Those deregulatory initiatives are from Ovtchinnikov (2010, 2013)

and are listed in table 8, panel A. We focus on economic deregulation, defined as deregulation of entry,

exit, price, and quantity, because it represents a major change in the operating environment of U.S.

industries in the 1980s and 1990s (Winston (1993)) and, therefore, serves a useful laboratory to study the

effects of firm political activism on subsequent innovation. Ovtchinnikov (2010) reports that deregulation

significantly affected firms’ decision making including their financing and investment choices and

resulted in significant changes in operating performance.

Given the impact of deregulation on the industry’s operating environment, we expect that firms

invest heavily in information about the timing of different deregulatory steps. Importantly, the incentive

to invest in information should arise not only among existing industry firms but also among potential new

entrants. If successful, access to legislation timing information gives politically active firms competitive

advantage because it allows them to set their innovation strategies in expectation of upcoming

deregulation.

In addition to acquiring information, we expect that firms, especially new entrants, actively lobby

for deregulation. The incentive to lobby should be particularly high in industries with positive producer

rents that are shielded by entry regulation. In those industries, new entrants have the most to gain if entry

barriers are removed and, therefore, ought to lobby aggressively for deregulation. If politically active

firms expect their lobbying efforts to be successful, their innovation strategies are set in expectation of

upcoming deregulation.

26

To analyze whether firm innovation predicts legislative changes, we focus on firms that are new

innovators in soon-to-be-deregulated industries and test whether innovation of those firms predicts future

deregulation. We expect this relation to hold but only for politically active firms. New innovator firms

are defined as follows. Every year and for every firm in our sample, we record all technology classes for

which the firm submits new patent applications. For every new technology class in a particular year, we

track the firm’s patent activity in that class over the remaining sample period. If the duration to the

subsequent patent application is no longer than that of the average firm in the same technology class, we

classify the firm in question as a new innovator in that technology class in the first year of new patent

activity. The explanatory variable in our tests below, New innovatort, is the sum of new innovator firms

in each technology class in year t scaled by the total number of firms with patent activity in that

technology class in year t-1. The technology classes are then mapped into 4-digit SIC codes using the

Silverman (1996, 1999) concordance procedure.

An example may be useful to fix ideas and outline the intuition for our tests. Northrop Grumman

Corp., a politically active firm, which itself operates in the Search, Detection, Navigation, Guidance,

Aeronautical, and Nautical Systems and Instruments industry (SIC 3812), unexpectedly began innovating

in the Railroads, Line-Haul Operating industry (SIC 4011) in 1994. Two years later, the firm continued

its innovation activity in the new industry. Because the Northrop Grumman’s two-year duration between

successive patent applications was shorter than the industry average of 3.43 years, we define Northrop

Grumman as a new innovator in the railroad industry in 1994. The Northrop Grumman’s timing of

innovation in the railroad industry is particularly noteworthy given that the following year Congress

passed the ICC Termination Act that abolished the Interstate Commerce Commission and fully

deregulated the railroad industry. In our view, the Northrop Grumman’s strategy to start innovating in the

railroad industry just prior to its full deregulation is quite suggestive and consistent on the anecdotal level

with the hypothesis that political contributions are valuable because they allow firms to set their

innovation strategies in anticipation of future legislative changes.

In table 8, panel B, we build on the above example and analyze whether firm innovation,

especially that of politically active firms, systematically predicts future legislative changes. We estimate

industry-year probit models that relate the year of industry deregulation to the inflow of new innovator

firms into each industry during the prior year. The baseline results in model 1 show no relation between

the inflow of new innovators into an industry and the likelihood of its subsequent deregulation. However,

when we split the sample of new innovator firms into those that are politically active and inactive, the

results in model 2 show that the inflow of new innovator politically active firms strongly predicts future

deregulation. In terms of economic magnitudes, an interquartile increase in the inflow of politically

active new innovators increases the probability of deregulation by 6.84%. In contrast, the inflow of new

innovators that are not politically active does not predict future deregulation. These results are consistent

27

with our hypothesis and show that changes in innovation of politically active firms predicts future

legislative changes.

In models 3 – 6, we split the sample of deregulatory initiatives into those passed by Congress and

those adopted by the Executive Order. We hypothesize that politically active firms have significant

advantage predicting legislative changes adopted by Congress (because of a direct connection to members

of Congress) compared to those adopted by the Executive Branch. Consistent with this, we find that

changes in innovation of politically active firms strongly predict deregulatory Congressional Acts (models

3 and 4) but have little ability to predict deregulatory Executive Orders (models 5 and 6). In economic

terms, an interquartile increase in the inflow of politically active new innovators increases the probability

of a deregulatory Congressional Act by 10.21%. Compared to the unconditional probability of

deregulation of 21%, these results are economically significant. In a series of robustness tests, we repeat

the analysis with different lags between firm innovation and subsequent deregulation. We find that

changes in innovation of politically active firms predict industry deregulation, especially deregulation

adopted by Congress, two and three years in the future.

In sum, the results in table 8 show that firm innovation, especially by politically active firms,

strongly predicts future legislative changes. This result is consistent with the view that political

contributions allow firms to acquire information that is relevant for innovation decisions and with the

view that political contributions allow firms to lobby for legislation that impacts returns to innovation.

Both effects are consistent with our hypothesis that political activism facilitates investment in innovation

because it helps reduce policy uncertainty.

5. Conclusion

In this paper, we show that firm political activism positively affects investment in innovation.

Using data on political contributions over the period 1979 – 2004, we show a positive and economically

significant correlation between different measures of firm political activism and innovation. In a series of

further empirical tests, we establish a casual positive effect of political activism on innovation. These

results are consistent with the hypothesis that political activism allows firms to reduce policy uncertainty,

which, in turn, stimulates firm investment, specifically investment in innovation. We also find that

politically active firms are able to successfully time future legislation and set their innovation strategies in

expectation of future legislative changes. These results are consistent with the negative relation between

political activism and policy uncertainty. Overall, the results add significantly to our understanding of the

sources of value from political activism and indicate that political activism is an important determinant of

firm innovation.

An important question that arises at this point concerns the impact of political activism on

aggregate investment. Our results indicate that political activism encourages innovation of politically

28

active firms. Whether this translates into higher aggregate investment depends on how investment of

politically active firms interacts with that of inactive firms. If the former does not completely crowd out

the latter, political activism positively affects aggregate investment. This is clearly an important question

but it falls outside the scope of this study. We hope that future work will address this question in detail.

The results will be important for our understanding of the effects of political activism on aggregate

investment, competitive advantage and long-term economic growth.

29

References

Abel, A. B. and J. C. Eberly, 1996. Optimal investment with costly reversibility. Review of Economic

Studies 63, 581-593.

Acharya, V. V. and K. Subramanian, 2009. Bankruptcy codes and innovation. Review of Financial

Studies 22, 4949-4988.

Acharya, V. V., R. Baghai, and K. Subramanian, 2009. Labor laws and innovation. Working paper.

Aggarwal, Rajesh, Felix Meschke, and Tracy Wang, 2012, Corporate Political Donations: Investment or

Agency, Business and Politics 14.

Aghion, P. and J. Tirole, 1994. On the management of innovation, Quarterly Journal of Economics 109,

1185-1209.

Aghion, P. N. Bloom, R. Blundell, R. Griffith, and P. Howitt, 2005. Competition and innovation: An

inverted U relationship. Quarterly Journal of Economics 120, 701-728.

Aghion, P., J. Van Reenen, and L. Zingales, 2013. Innovation and institutional ownership. American

Economic Review 103, 277-304.

Akey, P., 2014. Valuing Changes in Political Networks: Evidence from Campaign Contributions to Close

Congressional Elections. Working paper.

Aldrich, J. H. and D. W. Rohde, 1997. The transition to Republican rule in the House: Implications for

theories of Congressional politics. Political Science Quarterly 112, 541-567.

Alesina, A. and R. Perotti, 1996. Income distribution, political instability, and investment. European

Economic Review 40, 1203-1228.

Amore, M. D. and M. Bennedsen 2013. The value of local political connections in a low-corruption

environment. Journal of Financial Economics 110, 387-402.

Ansolabehere, S. and J. M. Snyder Jr., 1999. Money and institutional power. Texas Law Review 77,

1673-1704.

Ansolabehere, Stephen, J. M. de Figueiredo, and J. M. Snyder Jr., 2003, Why is there so little money in

U.S. Politics?, Journal of Economic Perspectives 17, 105-130.

Ansolabehere, J. M. Snyder Jr., and M. Tripathi, 2002. Are PAC contributions and lobbying linked?

Business and Politics 4.

Armour, J. and D. J. Cumming, 2008. Bankruptcy law and entrepreneurship. American Law and

Economics Review 10, 303-350.

Atanassov, J., 2013. Do hostile takeovers stifle innovation? Evidence from antitakeover legislation and

corporate patenting. Journal of Finance 68, 1097-1131.

Barro, R. J., 1991. Economic growth in a cross-section of countries. Quarterly Journal of Economics

106, 407-443.

Bernanke, B., 1983. Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics

98, 85-106.

30

Bertola, G. and R. Caballero, 1994. Irreversibility and aggregate investment. Review of Economic

Studies 61, 223-246.

Bhattacharya, U., P. Hsu, Tian, H., and Y. Xu, 2014. What affects innovation more: policy or policy

uncertainty? Working paper.

Bloom, N., S. Bond, and J. Van Reenen, 2007. Uncertainty and investment dynamics. Review of

Economic Studies 74, 391-415.

Bombardini, M. and Trebbi, F., 2008. Votes or money? Theory and evidence from the US Congress.

Working paper.

Briffault, R., 2011. Corporations, corruption, and complexity: campaign finance after Citizens United.

Symposium: Citizens United V. Federal Election Commission: Implications for the American Electoral

Process.

Caballero, R., and E. Engel, 1999. Explaining investment dynamics in U.S. manufacturing: a generalized

(S,s) approach. Econometrica 67, 783-826.

Claessems, S., E. Feijen and L. Laeven, 2008. Political connections and preferential access to finance:

the role of campaign contributions. Journal of Financial Economics 88, 554-580.

Coate, S., 2004. Pareto improving campaign finance policy. American Economic Review 94, 628-655.

Coates, J. C., 2012. Corporate politics, governance, and value before and after Citizens United. Journal

of Empirical Legal Studies 9, 657-696.

Cooper, M. J., H. Gulen, and A. V. Ovtchinnikov, 2010. Corporate political contributions and stock

returns. Journal of Finance 65, 687-724.

Cornaggia, J., Y. Mao, X. Tian, and B. Wolfe, 2014, Does banking competition affect innovation?

Journal of Financial Economics, forthcoming.

Duchin, R. and D. Sosyura, 2012. The politics of government investment. Journal of Financial

Economics 106, 24-48.

Edwards, K. and C. Stewart, III, 2006. The value of committee assignments in Congress since 1994.

Working paper.

Faccio, M., 2006, Politically connected firms, American Economic Review 96, 369-386.

Faccio, M., R. W. Masulis, and J. J. McConnell, 2006. Political connections and corporate bailouts.

Journal of Finance 61, 2597–2635.

Faccio, M. and D. C. Parsley, 2009. Sudden deaths: taking stock of geographic ties, Journal of Financial

and Quantitative Analysis 44, 683-718.

Fama E. F. and J. MacBeth, 1973. Risk, return, and equilibrium: empirical tests. Journal of Political

Economy 81, 607-636.

Fan, W. and M. White, 2003. Personal bankruptcy and the level of entrepreneurial activity. Journal of

Law and Economics 46, 543-568.

Fang, V., X. Tian, and S. Tice, 2014. Does stock liquidity enhance or impede firm innovation? Journal

of Finance, forthcoming.

31

Ferguson T. and H. Voth, 2008. Betting on Hitler – the value of political connections in Nazi Germany.

Quarterly Journal of Economics 123, 101-137.

Ferreira, D., G. Manso, and A. Silva, 2014. Incentives to innovate and the decision to go public or

private. Review of Financial Studies 27, 256-300.

Fisman, R., 2001. Estimating the value of political connections. American Economic Review 91, 1095-

1102.

Goldman, E., J. Rocholl, and J. So, 2009. Do politically connected boards affect firm value? Review of

Financial Studies 22, 2331-2360.

Goldman, E., J. Rocholl, and J. So, 2013. Political connections and the allocation of procurement

contracts. Review of Finance 13, 1617-1648.

Grier, K. B. and M. C. Munger, 1991. Committee assignments, constituent preferences, and campaign

contributions. Economic Inquiry 29, 24-43.

Grier, K. B., M.C. Munger, and B. E. Roberts, 1994. The determinants of industry political activity,

1978-1986. American Political Science Review 88, 911-926.

Grilliches, Z., A. Pakes, and B. Hall, 1988. The value of patents as indicators of inventive activity.

Working paper.

Hall, B., A. Jaffe, and M. Trajtenberg, 2001. The NBER patent citation data file: lessons, insights and

methodological tools. Working paper.

Hall, B., A. Jaffe, and M. Trajtenberg, 2005. Market value and patent citations. The RAND Journal of

Economics 36, 16-38.

Hart, D. M., 2001. Why do some firms give? Why do some give a lot?: High-tech PACs, 1977-1996.

Journal of Politics 63, 1230-1249.

Hassler, J. A., 1996. Variations in risk and fluctuations in demand: a theoretical model. Journal of

Economic Dynamics and Control 20, 1115-1143.

He, J., and X. Tian, 2013. The dark side of analyst coverage: the case of innovation. Journal of Financial

Economics 109, 856-878.

Heckman, J. J., 1979. Sample selection bias as a specification error. Econometrica 47, 153-161.

Hirshleifer, D., A. Low, and S. H. Teoh, 2012. Are overconfident CEOs better innovators? Journal of

Finance 67, 1457-1498.

Hojnacki, M. and D. C. Kimball, 2001. PAC contributions and lobbying access in Congressional

committees. Political Research Quarterly 54, 161-180.

Holmstrom, B., 1989. Agency costs and innovation. Journal of Economic Behavior & Organization 12,

305–327.

Jacobson, G. C., 1980. Money in Congressional elections. New Haven: Yale University Press.

Jacobson, G. C., 1985. Money and votes reconsidered: Congressional elections 1972-1982. Public

Choice 47, 7-62.

32

Johnson, S. and T. Mitton, 2003. Cronyism and capital controls: evidence from Malaysia. Journal of

Financial Economics 67, 351-382.

Jones, G. and J. Mckenzie, 2014. Special report: how Italy became a submerging economy. Reuters.

Julio, B. and Y. Yook. 2012. Political uncertainty and corporate investment cycles. Journal of Finance

67, 45-83. Julio, B. and Y. Yook. 2014. Policy uncertainty, irreversibility, and cross-border flows of capital.

Working paper.

Kau, J. B., D. Keenan, and P. H. Rubin, 1982. A general equilibrium model of Congressional voting.

Quarterly Journal of Economics 97, 271-293.

Kortum, S. and J. Lerner, 2000. Assessing the contribution of venture capital to innovation. RAND

Journal of Economics 31, 674-692.

Kroszner, R. S. and T. Stratmann, 1998. Interest-group competition and the organization of Congress:

theory and evidence from financial services’ political action committees. American Economic Review 88,

1163-1187.

Kroszner, R. S. and T. Stratmann, 2000. Congressional committees as reputation-building mechanisms:

repeat PAC giving and seniority on the house banking committee. Business and Politics 2, 35-52.

Kroszner, R. S. and T. Stratmann, 2005. Corporate campaign contributions, repeat giving, and the

rewards to legislator reputation. Journal of Law and Economics 48, 41-71.

Masters, M. F. and G. D. Keim, 1985. Determinants of PAC participation among large corporations.

Journal of Politics 47, 1158-1173.

Milyo, J., D. Primo, and T. Groseclose 2000. Corporate PAC campaign contributions in perspective.

Business and Politics 2, 75-88.

Ovtchinnikov, A. V., 2010. Capital structure decisions: evidence from deregulated industries. Journal of

Financial Economics 95, 249-274.

Ovtchinnikov, A. V., 2013. Merger waves following industry deregulation. Journal of Corporate

Finance 21, 51-76.

Ovtchinnikov, A. V. and E. Pantaleoni, 2012. Individual political contributions and firm performance.

Journal of Financial Economics 105, 367-392.

Pastor, L. and P. Veronesi, 2012. Uncertainty about government policy and stock prices. Journal of

Finance 64, 1219-1264.

Petersen, M., 2009. Estimating standard errors in finance panel sets: comparing approaches. Review of

Financial Studies 22, 435-480.

Pindyck, R. S. and A. Solimano, 1993. Economic instability and aggregate investment. NBER

Macroeconomics Manual 8, 259-318.

Poole, K. and T. Romer, 1985. Patterns of political action committee contributions to the 1980

campaigns for the U.S. House of Representatives. Public Choice 47, 63-112.

33

Romer, P. 1987. Growth based on increasing returns due to specialization. American Economic Review

77, 56-62.

Romer, P. 1990. Endogenous technological change. Journal of Political Economy 98, 71-102.

Romer, T. and J. M. Snyder Jr., 1994. An empirical investigation of the dynamics of PAC contributions.

American Journal of Political Science 38, 745-769.

Seru, A., 2014. Firm boundaries matter: evidence from conglomerates and R&D activity. Journal of

Financial Economics 111, 381-405.

Snyder, J. M. Jr., 1992 Are contributors rational? Untangling strategies of Political Action Committees.

Journal of Political Economy 10, 647-664.

Solow, R. M., 1957. Technical change and the aggregate production function. Review of Economics and

Statistics 39, 312-320.

Spiegel, M. and H. Tookes, 2008. Dynamic competition, innovation and strategic financing. Working

paper.

Stratmann, T., 1991. What do campaign contributions buy? Deciphering causal effects of money and

votes. Southern Economic Journal 57, 606-620.

Stratmann, T., 1998. The market for congressional votes: is timing of contributions everything? Journal

of Law and Economics 41, 85-113.

Stratmann, T., 2002. Can special interests buy congressional votes? Evidence from financial services

legislation. Journal of Law and Economics 45, 345-374.

Stratmann, T., 2005. Some talk: money on politics. A (partial) review of the literature. Public Choice

124, 135-156. Tahoun, Ahmed, 2014, The role of stock ownership by U.S. members of Congress on the market for

political favors, Journal of Financial Economics 111, 86-110.

Tian, X. and T. Wang, 2013. Tolerance for failure and corporate innovation. Review of Financial

Studies, forthcoming.

Wiley, R. E., 1988. “Political” influence at the FCC. Duke Law Journal 1988, 280-285.

Winston, C., 1993. Economic deregulation: days of reckoning for microeconomists. Journal of

Economic Literature 31, 1263-1289.

Wright, J. R., 1990. Contributions, lobbying, and committee voting in the U.S. House of Representatives.

The American Political Science Review 84, 417-438.

Zardkoohi, A., 1985. On the political participation of the firm in the electoral process. Southern

Economic Journal 51, 804-817.

34

Appendix A

Variables construction

Variable Definition Source

Innovation measures

Cpatent[t+1, t+3] Natural logarithm of one plus firm i’s total number of citations per patent applied for

in years t+1, t+2 and t+3. The citations of each patent are adjusted for truncation

bias by scaling citations of a given patent by the mean number of citations received

by all patents in that year in the same technological class as the patent.

NBER

Cpatentt+1 Natural logarithm of one plus firm i’s total number of citations per patent applied for

in year t+1, adjusted for truncation bias.

NBER

Effpatent[t+1, t+3] Natural logarithm of one plus firm i’s total number of patents in application years

t+1, t+2 and t+3. Each patent is adjusted for truncation bias, which is corrected by

dividing each patent for each firm-year by the mean number of patents of all firms

for that year in the same technology class as the patent.

NBER

Effpatentt+1 Natural logarithm of one plus firm i’s total number of patents in application year t+1,

adjusted for truncation bias.

NBER

Control variables

R&DAssets R&D expenditure (XRD) divided by book value of total assets (AT) measured at the

end of fiscal year, set to 0 if missing. We adjust for inflation.

COMPUSTAT

ROA Operating income before depreciation (OIBDP) divided by book value of total assets

(AT), measured at the end of fiscal year. We adjust for inflation.

COMPUSTAT

PPEAssets Property, Plant & Equip (PPENT) divided by book value of total assets (AT),

measured at the end of fiscal year. We adjust for inflation.

COMPUSTAT

Leverage Book value of debt (DLTT+DLC) divided by book value of total assets (AT),

measured at the end of fiscal year. We adjust for inflation.

COMPUSTAT

CapexAssets Capital expenditure (CAPX) divided by book value of total assets (AT), measured at

the end of fiscal year. We adjust for inflation and set it to 0 if missing.

COMPUSTAT

HI Herfindahl index in year t constructed based on sales at the 4-digit SIC. COMPUSTAT

HISquare HI x HI COMPUSTAT

TobinQ A firm's market-to-book ratio at the end of fiscal year, calculated as book value of

total assets plus market value of equity minus book value of equity minus balance

sheet deferred taxes (txdb, set to 0 if missing) divided by book value of total assets,

i.e. (AT+CSHOcPRCC_F-CEQ-txdb)/AT.

COMPUSTAT

Size Natural logarithm of book value of total assets (AT) measured at the end of fiscal

year.

COMPUSTAT

Age Firm age. It equals the number of years since its first appearance on the CRSP

database.

CRSP

35

Appendix B

Determinants of firms’ political activity, 1984–2004: First-stage probit model

We use individual firm and industry characteristics shown in the prior literature to be related to the

likelihood of a firm having a political action committee (PAC) to analyze the determinants of firms’

political activity. Relevant prior studies include Masters and Keim (1985), Zardkoohi (1985), Grier,

Munger, and Roberts (1994), Hart (2001). We use firm size, leverage, sales, cash flows, the number of

employees, percentage of industry employees that are unionized, the number of business and geographical

segments, sales concentration, market share, a regulated industry indicator, the amount of government

purchases from an industry, and the number of politically active firms in an industry as the determinants

of firms’ political activity. Consistent with prior studies, we find that larger and more levered firms, with

more sales, more employees, and a higher percentage of unionized employees are more likely to be

politically active. Also, there is an increased likelihood of political activism as the number of business

segments and market share increases and book-to-market and cash flows decrease. Finally, the likelihood

of political activism is positively related to government purchases and to the regulated industry indicator

and negatively related to sales concentration, the number of geographic segments, and the number of

politically active industry firms.

Variable

Probit Model

(1 = active; 0 = not active)

Coefficient

Ln(size) 0.187

(0.005)

a

Ln(sales) 0.136

(0.007)

a

Ln(employees) 0.215

(0.007)

a

No. business segments 0.029

(0.004)

a

No. geographic segments -0.062

(0.005)

a

BM -0.000

(0.000)

a

Leverage 0.274

(0.025)

a

CF -0.026

(0.010)

b

Market share 1.840

(0.312)

a

(Market share)2 -3.206

0.645

a

Herfindahl index -0.342

(0.104)

a

Regulation indicator 0.773

(0.017)

a

Government purchases 0.848

(0.102)

a

No. politically active firms -0.000

(0.000)

a

Pct. employees unionized 2.014

(0.062)

a

Log likelihood -30,662

N 143,274

36

Figure 1

Corporate innovation surrounding the 1994 midterm Congressional election

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate

contributions, contributions from private firms and subsidiaries of foreign firms, as well as contributions from firms

with insufficient data on CRSP/Compustat. The final sample consists of 813,692 political contributions to 5,584 unique

political candidates made by 1,805 unique firms. This figure presents the time-series dynamics of patent applications of

different firms surrounding the 1994 midterm Congressional election. The solid line tracks the treatment firms. The

dashed line tracks the control firms without chair contributions. The dashed line with a triangular marker tracks the

control firms with chair contributions. The shaded area designates the November 1994 election. The portfolio of

treatment firms comprises 126 firms that supported four junior politicians who unexpectedly became committee chairs

in November 1994. The portfolio of control firms with chair contributions comprises 475 firms that supported one or

more existing committee chairs during the 1990 – 1994 period. The portfolio of control firms without chair

contributions comprises 197 firms that did not support existing committee chairs during the 1990 – 1994 period.

0

1

2

3

4

5

6

7

8

9

1991

1992

1993

1994

1995

1996

1997

1998

1999

Num

ber

of

pat

ents

Year

37

Table 1

Characteristics of politically active and inactive firms, 1979 – 2004

This table presents formation period summary statistics for various firm characteristics of politically active and politically inactive firms. The subsample of

politically active firms consists of 1,805 unique firms with an established political action committee (PAC) and political contributions data from the Federal Election

Commission (FEC) for the period January 1, 1979 – December 31, 2004. The subsample of politically inactive firms consists of all other firms in the

CRSP/Compustat merged database with nonmissing values of the firm characteristics in this table. The numbers in each cell are time-series averages of yearly cross-

sectional median statistics. N is the average annual number of firms in each subsample. t-test is the t-statistic computed under the null hypothesis that the difference

between politically active and inactive firms is zero. Panel A reports characteristics of politically active and inactive firms. Panel B reports characteristics of

politically active and inactive firms based on NYSE annually ranked size decile breakpoints. All numbers, except Size, Age, Patents, and Patent citations are in

decimal form, i.e. 0.01 is 1%. All variables are fully defined in Appendix A.

38

Table 1

Firms Size Age ROA PPE / Assets Lev Q Herfindahl R&D / Assets CAPX / Assets Patents Patent citations N

Panel A: Comparison of politically active and inactive firms

Politically active 1.289 22.346 0.118 0.338 0.261 1.119 0.154 0.012 0.052 6.106 0.309 740

Inactive 0.068 7.000 0.085 0.200 0.190 1.268 0.193 0.038 0.041 0.752 0.171 5405

t-test (difference) 8.14 21.38 6.99 8.13 8.75 -4.47 -4.96 -11.04 2.90 19.07 9.79

Panel B:Comparison of politically active and inactive firms by size deciles

Small Active 0.030 10.404 0.074 0.232 0.289 1.018 0.203 0.007 0.033 0.320 0.111 29

Inactive 0.018 6.240 0.049 0.188 0.202 1.149 0.204 0.041 0.034 0.193 0.094 2665

t-test 4.95 6.36 2.99 1.65 5.07 -5.42 -0.03 -11.63 -0.14 1.29 0.94

Decile 2 0.080 14.115 0.084 0.235 0.258 1.031 0.195 0.008 0.032 0.560 0.167 30

0.076 6.780 0.089 0.191 0.171 1.269 0.191 0.043 0.042 0.335 0.197 688

0.50 9.30 -0.60 2.18 5.39 -6.77 0.29 -10.48 -2.75 1.66 -1.42

Decile 3 0.136 13.077 0.091 0.249 0.265 1.050 0.172 0.007 0.036 0.525 0.176 35

0.134 7.080 0.104 0.191 0.164 1.326 0.190 0.038 0.044 0.714 0.217 490

0.15 7.67 -1.94 3.17 8.04 -7.27 -1.97 -9.97 -2.08 -1.36 -1.31

Decile 4 0.216 14.442 0.098 0.291 0.246 1.061 0.144 0.008 0.043 0.594 0.192 46

0.217 7.560 0.113 0.194 0.169 1.355 0.177 0.034 0.045 0.960 0.223 401

-0.02 7.29 -2.50 6.36 7.10 -8.78 -3.42 -9.17 -0.69 -2.68 -1.60

Decile 5 0.337 17.423 0.105 0.317 0.255 1.067 0.149 0.008 0.047 1.218 0.179 57

0.34 8.380 0.124 0.209 0.176 1.432 0.178 0.029 0.048 1.079 0.265 333

0.038 9.12 -3.38 6.36 7.98 -9.92 -3.30 -8.37 -0.24 0.42 -4.21

Decile 6 0.526 18.904 0.112 0.344 0.270 1.093 0.130 0.008 0.049 0.928 0.190 70

0.52 9.760 0.130 0.241 0.179 1.458 0.173 0.026 0.052 1.198 0.266 264

0.030 8.84 -3.20 5.04 10.17 -8.97 -5.79 -7.20 -0.69 -1.48 -3.27

Decile 7 0.832 21.481 0.118 0.397 0.276 1.098 0.145 0.008 0.053 2.011 0.248 84

0.84 11.260 0.140 0.268 0.201 1.503 0.173 0.024 0.057 1.562 0.310 213

-0.043 9.92 -4.74 5.92 8.86 -10.28 -3.38 -9.62 -0.83 1.67 -2.71

Decile 8 1.411 24.577 0.119 0.387 0.270 1.122 0.149 0.011 0.054 2.968 0.281 104

1.40 13.460 0.145 0.273 0.195 1.542 0.171 0.023 0.058 2.594 0.333 166

0.067 10.82 -5.17 4.41 9.26 -7.80 -2.54 -8.15 -0.69 0.98 -2.22

Decile 9 2.850 29.519 0.122 0.379 0.273 1.137 0.145 0.012 0.058 5.192 0.356 125

2.73 17.000 0.154 0.267 0.191 1.697 0.171 0.023 0.059 3.158 0.378 121

0.335 12.28 -6.28 4.71 14.10 -8.11 -3.40 -6.59 -0.09 4.84 -0.78

Big 9.433 36.769 0.146 0.345 0.245 1.300 0.166 0.022 0.064 20.947 0.559 160

7.326 22.100 0.162 0.289 0.184 1.861 0.189 0.028 0.065 14.835 0.507 64

1.46 10.66 -2.50 2.07 8.33 -4.82 -2.47 -3.17 -0.16 4.21 1.38

39

Table 2

Measures of firm political activism, 1984 – 2004

This table presents data from the FEC detailed contribution files on political contributions to House, Senate, and Presidential elections. We exclude all noncorporate

contributions, contributions from private firms and subsidiaries of foreign firms, as well as contributions from firms with insufficient data on CRSP/Compustat. The final

sample consists of 813,692 political contributions to 5,584 unique political candidates made by 1,805 unique firms. Individual contributions are combined into ten firm-year

level measures of political activism as described in Eqs. 1 – 6 in section 2.2. Panel A presents the descriptive statistics for each political activism measure. Total in row 1

equals Pcand (Eq. 1) in columns 1 – 6 and Camount (Eq. 2) in columns 7 – 12. Committee in row 2 equals PcandCommittee (Eq. 3) in columns 1 – 6 and CamountCommittee (Eq.

4) in columns 7 – 12. Non-committee in row 3 equals PcandNon-Committee (defined in section 2.2) in columns 1 – 6 and CamountNon-Committee (defined in section 2.2) in column 7

– 12. ΔY+ in row 4 equals ΔPcand+ (Eq. 5) in columns 1 – 6 and ΔCamount+ (Eq. 6) in columns 7 – 12. ΔY- in row 5 equals ΔPcand- (defined in section 2.2) in columns 1 –

6 and ΔCamount- (defined in section 2.2) in columns 7 – 12. Panel B presents election cycle firm political contribution totals for each Congressional committee in our

sample. The FEC data on political contributions is for the period January 1, 1979 – December 31, 2004. Since we require five years of data to compute each measure of

political activism, the variables are computed at the end of October of each year, from 1984 to 2004, resulting in 16,065 firm-year observations for each variable.

Pcand Camount

Variable Mean Min 25th Per Median 75th Per Max Mean Min 25th Per Median 75th Per Max

Panel A: Measures of political activism

Total 84.102 1 14 39 117 844 $226,050 0 $20,948 $73,135 $238,656 $6,293,663

Committee 13.483 0 0 3 18 172 44,082 0 0 5,339 35,911 1,574,725

Non-Committee 70.619 0 11 32 97 791 181,968 0 16,142 58,342 192,408 5,354,005

ΔY+ 0.422 0 0 0 0 15 1,126 0 0 0 0 88,615

ΔY- 0.367 0 0 0 0 15 1,306 0 0 0 0 123,913

Panel B: Political contributions to members of Congressional committees

House Congressional committees

Energy & Commerce 7.623 1 2 4 11 51 $13,578 0 $1,593 $4,787 $14,908 $518,973

Financial Services 5.640 1 1 3 6 62 10,637 0 1,284 3,346 9,436 380,510

Transportation 5.942 1 1 3 7 66 10,511 0 1,399 3,910 10,751 393,013

Armed Services 5.462 1 1 3 6 57 10,216 0 1,292 3,305 9,031 327,122

Agriculture 4.930 1 1 3 6 48 8,423 0 1,292 3,264 8,942 325,162

Natural Resources 4.622 1 1 3 6 40 7,886 0 1,272 3,381 9,019 196,176

Merchant Marine 4.915 1 1 3 6 42 7,335 0 1,295 3,328 8,267 173,512

Senate Congressional committees

Commerce 3.241 1 1 2 4 17 $9,479 0 $1,753 $4,726 $12,044 $133,323

Banking 3.165 1 1 2 4 17 9,008 0 1,673 4,457 10,950 109,078

Agriculture 3.138 1 1 2 4 16 8,945 0 1,747 4,501 11,581 108,511

Armed Services 3.042 1 1 2 4 20 8,872 0 1,673 4,185 10,742 139,483

Energy 3.211 1 1 2 4 17 8,639 0 1,587 4,333 10,987 126,395

Environment 2.717 1 1 2 4 15 7,409 0 1,523 3,875 9,597 97,661

40

Table 3

Political contributions and corporate innovation, 1984 – 2004

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate contributions, contributions

from private firms and subsidiaries of foreign firms, as well as contributions from firms with insufficient data on CRSP/Compustat. The

final sample consists of 813,692 political contributions to 5,584 unique political candidates made by 1,805 unique firms. This table

presents the results of estimating Eq. 7 on the sample of politically active and inactive firms for the period November 1984 – October

2004. Models 1, 2, 5, and 6 present the results for the all firms with available data on CRSP/Compustat and include 123,531 firm-year

observations. Models 3, 4, 7, and 8 present the results for politically active firms only and include 16,065 firm-year observations. We

control for firm self-selection in models 3, 4, 7, and 8 by including the inverse Mills ratio (IMR) from the first-stage probit model of

whether a firm has an established PAC on determinants of PAC participation. The first stage probit results are presented in Appendix B.

Panel A presents the results for the total number of patent applications. Panel B presents the results for the total number of patent

citations. All regressions include industry and year fixed effects. All variables are defined in section 2.2 and Appendix A. Standard

errors are in parentheses and are adjusted for heteroskedasticity and correlated within firms. R2 is the adjusted R-squared in the

regression. R2 controls only is the adjusted R-squared from the model that includes only the control variables but does not include

measures of political activism. N is the total number of firm-year observations. a, b, c, indicates significance at the 1%, 5%, and 10%

level, respectively.

Effpatentt+1 Effpatentt+3

Variable 1 2 3 4 5 6 7 8

Panel A: Number of patents

Pcand/102 0.276 a 0.162 a 0.331 a 0.202 a

(0.036) (0.038) (0.042) (0.045)

Camount/106 0.602 a 0.298 a 0.712 a 0.369 a

(0.099) (0.093) (0.117) (0.107)

R&D / Assets 0.689 a 0.692 a 11.197 a 11.226 a 1.139 a 1.142 a 14.074 a 14.111 a

(0.055) (0.056) (1.791) (1.802) (0.077) (0.078) (2.077) (2.087)

ROA 0.120 a 0.100 a 0.711 a 0.710 a 0.246 a 0.222 a 1.003 a 1.003 a

(0.019) (0.019) (0.266) (0.270) (0.026) (0.026) (0.335) (0.340)

PPE / Assets -0.054 b -0.042 c -0.283 b -0.242 c -0.067 c -0.052 -0.346 b -0.294 c

(0.026) (0.026) (0.128) (0.127) (0.034) (0.034) (0.161) (0.160)

Leverage -0.132 a -0.140 a -0.159 c -0.166 c -0.222 a -0.231 a -0.221 b -0.229 b

(0.017) (0.017) (0.086) (0.087) (0.022) (0.022) (0.106) (0.107)

Capex / Assets 0.394 a 0.382 a 0.511 c 0.475 0.556 a 0.542 a 0.611 c 0.566

(0.045) (0.046) (0.292) (0.291) (0.061) (0.061) (0.366) (0.366)

HI 0.342 a 0.365 a 0.212 0.333 0.527 a 0.555 a 0.497 0.649

(0.098) (0.099) (0.391) (0.391) (0.124) (0.125) (0.467) (0.467)

HI2 -0.235 c -0.254 b 0.208 0.081 -0.399 b -0.422 a -0.034 -0.192

(0.125) (0.127) (0.471) (0.474) (0.157) (0.159) (0.551) (0.554)

Q 0.026 a 0.027 a 0.030 0.033 0.041 a 0.043 a 0.037 0.040

(0.002) (0.002) (0.022) (0.022) (0.003) (0.003) (0.027) (0.027)

Assets 0.098 a 0.107 a 0.127 a 0.145 a 0.138 a 0.149 a 0.133 a 0.155 a

(0.006) (0.005) (0.027) (0.027) (0.007) (0.007) (0.033) (0.034)

LnAge 0.050 a 0.060 a 0.088 a 0.101 a 0.071 a 0.083 a 0.111 a 0.126 a

(0.006) (0.006) (0.023) (0.023) (0.008) (0.008) (0.030) (0.030)

IMR -0.127 c -0.149 b -0.224 a -0.252 a

(0.065) (0.065) (0.084) (0.084)

R2 0.326 0.316 0.574 0.570 0.357 0.349 0.597 0.594

R2 controls only 0.296 0.296 0.563 0.563 0.333 0.333 0.587 0.587

N 123,531 123,531 16,065 16,065 123,531 123,531 16,065 16,065

41

Table 3 – continued

Cpatentt+1 Cpatentt+3

Variable 1 2 3 4 5 6 7 8

Panel B: Patent citations

Pcand/102 0.035 a 0.028 a 0.070 a

0.060 a

(0.006) (0.007) (0.011)

(0.014)

Camount/106 0.075 a 0.053 a 0.148 a 0.109 a

(0.016) (0.017) (0.030) (0.032)

R&D / Assets 0.500 a 0.500 a 2.270 a 2.274 a 0.937 a 0.938 a 3.952 a 3.963 a

(0.029) (0.029) (0.292) (0.291) (0.053) (0.053) (0.529) (0.528)

ROA 0.097 a 0.094 a 0.169 a 0.169 a 0.210 a 0.205 a 0.287 b 0.287 b

(0.008) (0.008) (0.062) (0.063) (0.016) (0.016) (0.121) (0.123)

PPE / Assets -0.022 a -0.021 b -0.063 b -0.056 c -0.037 b -0.034 b -0.103 -0.087

(0.008) (0.008) (0.032) (0.032) (0.016) (0.016) (0.064) (0.065)

Leverage -0.068 a -0.069 a -0.028 -0.030 -0.145 a -0.147 a -0.075 -0.078 c

(0.006) (0.006) (0.025) (0.025) (0.012) (0.012) (0.047) (0.047)

Capex / Assets 0.141 a 0.140 a 0.025 0.019 0.272 a 0.269 a 0.131 0.118

(0.017) (0.017) (0.077) (0.077) (0.032) (0.032) (0.154) (0.155)

HI 0.108 a 0.111 a 0.200 b 0.220 a 0.219 a 0.225 a 0.490 a 0.535 a

(0.027) (0.027) (0.085) (0.085) (0.053) (0.053) (0.165) (0.165)

HI2 -0.094 b -0.096 a -0.128 -0.149 -0.185 a -0.190 a -0.364 c -0.411 b

(0.033) (0.033) (0.098) (0.098) (0.063) (0.064) (0.189) (0.189)

Q 0.014 a 0.014 a 0.007 0.007 0.027 a 0.027 a 0.016 0.017

(0.001) (0.001) (0.005) (0.005) (0.002) (0.002) (0.011) (0.011)

Assets 0.030 a 0.032 a 0.007 0.010 0.057 a 0.060 a 0.009 0.016

(0.001) (0.001) (0.006) (0.006) (0.002) (0.002) (0.012) (0.012)

LnAge 0.011 a 0.013 a 0.019 a 0.021 a 0.024 a 0.027 a 0.032 a 0.037 a

(0.002) (0.002)

(0.006) (0.006)

(0.004) (0.004) (0.012) (0.012)

IMR -0.057 a -0.061 a

-0.119 a -0.127 a

(0.017) (0.017)

(0.033) (0.034)

R2 0.203 0.202 0.385 0.384 0.275 0.274 0.464 0.462

R2 controls only 0.201 0.201 0.381 0.381 0.272 0.272 0.458 0.458

N 123,531 123,531 16,065 16,065 123,531 123,531 16,065 16,065

42

Table 4

Political contributions to Congressional Committees and corporate innovation, 1984 – 2004

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate contributions, contributions

from private firms and subsidiaries of foreign firms, as well as contributions from firms with insufficient data on CRSP/Compustat. The

final sample consists of 813,692 political contributions to 5,584 unique political candidates made by 1,805 unique firms. This table

presents the results of estimating Eq. 7 various subsamples of firms and political contributions measures. Models 1 – 4 present the

results for the total number of patent applications. Models 5 – 8 present the results for the total number of patent citations. All

regressions include industry and year fixed effects, all control variables from table 3 and control for firm self-selection into the

politically active group with the inverse Mills ratio (IMR). Panel A presents the results for political contributions to members of

influential Congressional committees defined in section 2.2. Panel B presents the results for political contributions to members of

outside committees defined in section 2.2. Panel C presents the results for political contributions to members who join or leave

influential committees. Panel D presents the results for political contributions to members who join or leave influential committees

during election and non-election years. Standard errors are in parentheses and are adjusted for heteroskedasticity and correlated within

firms. a, b, c, indicates significance at the 1%, 5%, and 10% level, respectively.

43

Table 4

Effpatentt+1

Effpatentt+3 Cpatentt+1

Cpatentt+3

Variable 1 2

3 4 5 6

7 8

Panel A: Contributions to influential Congressional committees

PcandCommittee/102 0.538 a 0.667 a 0.076 b 0.158 a

(0.148) (0.177) (0.031) (0.061)

CamountCommittee/106 0.972 a 1.238 a 0.209 a 0.413 a

(0.343) (0.412) (0.075) (0.141)

R2 0.560 0.560 0.588 0.589 0.350 0.391 0.465 0.468

N 11,323 11,323 11,323 11,323 11,323 11,323 11,323 11,323

Panel B: Contributions to outside committees

PcandNon-Committee/102 -0.965 c -0.788 -0.036 0.009

(0.524) (0.693) (0.174) (0.313)

CamountNon-Committee/106 -1.267 -0.634 0.184 0.775

(1.243) (1.686) (0.506) (0.871)

R2 0.617 0.613 0.646 0.645 0.413 0.411 0.499 0.499

N 994 994 994 994 994 994 994 994

Panel C: Contributions to politicians joining/leaving influential committees

ΔPcand + 0.068 a 0.085 a 0.011 a 0.023 a

(0.014) (0.017) (0.004) (0.006)

ΔPcand - 0.007 0.002 0.001 -0.004

(0.013) (0.016) (0.004) (0.006)

I (ΔPcand + > ΔPcand -) 0.086 a 0.124 a 0.023 a 0.049 a

(0.027) (0.033) (0.009) (0.015)

I (ΔPcand + < ΔPcand -) -0.049 c -0.071 b 0.007 -0.003

(0.027) (0.034) (0.009) (0.015)

R2 0.559 0.554 0.587 0.583 0.389 0.387 0.465 0.464

N 11,323 11,323 11,323 11,323 11,323 11,323 11,323 11,323

Panel D: Contributions to politicians joining/leaving influential committees during election and off-election years

Election year coefficients

ΔPcand + 0.036 0.059 -0.001 0.014

(0.039) (0.049) (0.010) (0.014)

ΔPcand - 0.041 0.025 0.022 0.015

(0.037) (0.047) (0.015) (0.024)

I (ΔPcand + > ΔPcand -) 0.040 0.082 0.013 0.037

(0.046) (0.057) (0.015) (0.025)

I (ΔPcand + < ΔPcand -) -0.050 -0.098 0.030 0.020

(0.053) (0.069) (0.021) (0.031)

Non-election year coefficients

ΔPcand + 0.071 a 0.088 a 0.012 a 0.024 a

(0.013) (0.016) (0.004) (0.006)

ΔPcand - 0.004 -0.000 -0.000 -0.005

(0.013) (0.016) (0.004) (0.006)

I (ΔPcand + > ΔPcand -) 0.107 a 0.144 a 0.027 a 0.053 a

(0.031) (0.038) (0.010) (0.016)

I (ΔPcand + < ΔPcand -) -0.043 -0.060 0.003 -0.008

(0.031) (0.039) (0.010) (0.017)

R2 0.559 0.554 0.587 0.583 0.389 0.387 0.465 0.464

N 11,323 11,323 11,323 11,323 11,323 11,323 11,323 11,323

44

Table 5

Political contributions and corporate innovation: Instrumental variables estimation, 1984 – 2004

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate contributions, contributions from private firms and

subsidiaries of foreign firms, as well as contributions from firms with insufficient data on CRSP/Compustat. The final sample consists of 813,692 political

contributions to 5,584 unique political candidates made by 1,805 unique firms. This table presents the results of instrumental variables (IV) estimation. Panel

A presents the results of the first-stage regression that relates a firm’s choice to contribute to a politician to her margin of victory in the prior election. This

regression is estimated separately every year and for every firm in our sample. Average sensitivity is the average coefficient on the politician’s margin of

victory in the prior election. Average R2 is the average R-squared of all 17,873 firm-year first-stage regressions. Panels B and C present the results of the

second stage regressions that relate the first-stage sensitivity to the margin of victory to subsequent innovation growth. We sort all firms into 20 equal- and

value-weighted portfolios based on their estimated sensitivities to the margin of victory in the first-stage regression. We then regress average portfolio

innovation growth on the average sensitivity to the margin of victory. Columns 1 – 3 present the results for equal-weighted portfolios. Columns 4 – 6 present

the results for value-weighted portfolios. In panel B, innovation growth is measured by the growth in the number of patent applications. In panel C, innovation

growth is measured by the growth in the number of patent citations. Standard errors are in parentheses and are adjusted for heteroskedasticity. a, b, c, indicates

significance at the 1%, 5%, and 10% level, respectively.

Panel A: First-stage regression. Tests for relevant instrument

Average

sensitivity Average R2

Total first-stage

estimates

Positive first-stage

estimates

Negative first-stage

estimates

Positive and significant

first-stage estimates

Negative and significant

first-stage estimates

Variable -0.0295 a

(0.004)_

0.004 17,873 6,434 11,439 297 1,024

Panel B: Second stage regression. Dependent variable - number of patents

EW results VW results

Variable Δ Effpatentt+2 Δ Effpatentt+3

Δ Effpatentt+4 Δ Effpatentt+2

Δ Effpatentt+3 Δ Effpatentt+4

Sensitivity to prior margin of -1.028 a -1.747 b -2.508 a -0.851 a -1.446 b -2.078 a

victory (0.285) (0.623) (0.850) (0.236) (0.517) (0.706)

R2 0.123 0.103 0.120 0.123 0.104 0.120

N 20 20 20 20 20 20

Panel C: Second stage regression. Dependent variable - patent citations

EW results VW results

Variable Δ Cpatentt+2 Δ Cpatentt+3

Δ Cpatentt+4 Δ Cpatentt+2

Δ Cpatentt+3 Δ Cpatentt+4

Sensitivity to prior margin of -0.018 0.011 -0.058 b -0.015 -0.009 -0.048 b

victory (0.020) (0.027) (0.026) (0.016) (0.022) (0.021)

R2 0.006 0.002 0.004 0.006 0.002 0.040

N 20 20 20 20 20 20

45

Table 6

Political contributions and corporate innovation: Difference-in-difference estimation

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate

contributions, contributions from private firms and subsidiaries of foreign firms, as well as contributions from firms

with insufficient data on CRSP/Compustat. The final sample consists of 813,692 political contributions to 5,584 unique

political candidates made by 1,805 unique firms. This table presents the results of the difference-in-difference

estimation. The portfolio of treatment firms comprises 126 firms that supported four junior politicians who

unexpectedly became committee chairs in November 1994. The portfolio of control firms with chair contributions

comprises 475 firms that supported one or more existing committee chairs during the 1990 – 1994 period. The

portfolio of control firms without chair contributions comprises 197 firms that did not support existing committee chairs

during the 1990 – 1994 period. Columns 1 – 3 present the average number of patent applications submitted by the

treatment and the control firms during the pre- and post-1994 period. Columns 4 – 6 presents the average growth in

patent applications submitted by the treatment and the control firms during the pre- and post-1994 period. The bottom

two rows present the difference-in-difference results. T-statistics for the difference-in-difference estimates are in

parentheses.

Effpatentt+1 Δ Effpatentt+1

Firms Pre-1994 Post-1994 Difference Pre-1994 Post-1994 Difference

(1)Treatment firms 4.851 7.152 2.301 0.120 0.670 0.550

(2) Control firms with chair contributions 8.172 8.309 0.137 0.014 -0.287 -0.301

(3) Control firms w / o chair contributions 1.850 1.938 0.087 0.061 -0.024 -0.085

DiD (1 – 2) 2.164

(2.21)

0.851

(4.06)

DiD (1 – 3) 2.214

(2.39)

0.635

(3.21)

46

Table 7

Difference-in-difference estimation: Regression results

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate

contributions, contributions from private firms and subsidiaries of foreign firms, as well as contributions from firms

with insufficient data on CRSP/Compustat. The final sample consists of 813,692 political contributions to 5,584

unique political candidates made by 1,805 unique firms. This table presents the results of estimating Eq. 9. The

portfolio of treatment firms comprises 126 firms that supported four junior politicians who unexpectedly became

committee chairs in November 1994. The portfolio of control firms with chair contributions comprises 475 firms that

supported one or more existing committee chairs during the 1990 – 1994 period. The portfolio of control firms

without chair contributions comprises 197 firms that did not support existing committee chairs during the 1990 –

1994 period. Panel A presents the results for the treatment firms and the control firms with chair contributions.

Panel B present the results for the treatment firms and the control firms without chair contribution. I(post-1994) is an

indicator variable set to one for years 1995 – 1999 and zero otherwise. I(Year 1995) is an indicator variable set to

one for 1995 and zero otherwise. I(Year 1996) is an indicator variable set to one for 1996 and zero otherwise.

I(Post-1996) is an indicator variable set to one for years 1997-1999 and zero otherwise. Standard errors are in

parentheses and are adjusted for heteroskedasticity and correlated at the firm-post-event level. a, b, c, indicates

significance at the 1%, 5%, and 10% level, respectively.

Variable Effpatentt+1 Effpatentt+1

Δ Effpatentt+1 Δ Effpatentt+1

Panel A: Treatment vs. control firms with chair contributions

I(Post-1994) -5.491 a -5.387 a -1.594 a -1.585 a

(1.116) (1.117) (0.471) (0.489)

Treatment firm × I(Post-1994) 2.342 b 0.832 a

(0.914) (0.240)

Treatment firm × I(Year 1995) 0.036 0.066

(0.818) (0.459)

Treatment firm × I(Year 1996) 1.044 0.413

(0.844) (0.433)

Treatment firm × I(Post-1996) 2.905 a 0.967 a

(1.021) (0.364)

N 4,354 4,935 4,354 4,935

R2 0.917 0.919 0.169 0.167

Panel B: Treatment vs. control firms w / o chair contributions

I(Post-1994) -1.221 -1.382 b -0.641 b -0.663 a

(0.741) (0.764) (0.317) (0.337)

Treatment firm × I(Post-1994) 1.672 b 0.540 a

(0.761) (0.162)

Treatment firm × I(Year 1995) 0.089 0.299

(0.691) (0.414)

Treatment firm × I(Year 1996) 0.518 0.463

(0.765) (0.373)

Treatment firm × I(Post-1996) 2.203 a 0.560 b

(0.840) (0.261)

N 2,245 2,560 2,245 2,886

R2 0.890 0.906 0.173 0.189

47

Table 8

Political contributions and subsequent legislative dynamics, 1984 – 2004

The political contributions data are from the FEC detailed contribution files. We exclude all noncorporate

contributions, contributions from private firms and subsidiaries of foreign firms, as well as contributions from firms

with insufficient data on CRSP/Compustat. The final sample consists of 813,692 political contributions to 5,584

unique political candidates made by 1,805 unique firms. This table presents the results of the probit regression that

relates the year of industry deregulation to the inflow of new innovator firms into each industry during the prior

year. The deregulatory legislation and the year of the passage of each legislation are listed in panel B. New

innovators are defined as firms that begin innovating in an industry and innovate more often that the average

industry firm. Politically active are firms with an established political action committee (PAC). Standard errors are

in parentheses and are adjusted for heteroskedasticity. a, b, c, indicates significance at the 1%, 5%, and 10% level,

respectively.

Panel A: Major deregulatory initiatives

Year Deregulatory Initiative

Petroleum and Natural Gas

1989 Natural Gas Wellhead Decontrol Act

1992 FERC Order 636

Telecommunications

1988 Proposed rules on price caps (FCC)

1996 Telecommunications Act

Utilities

1988 Proposed rules on natural gas and electricity (FERC)

1992 Energy Policy Act

1996 FERC order 888

1999 FERC order 2000

Transportation

1986 Trading of airport landing rights

1987 Sale of Conrail

1993 Negotiated Rates Act

1994 Trucking Industry and Regulatory Reform Act

1995 ICC Termination Act

Panel B: Regression results

All deregulation Congressional Acts Executive orders

Variable 1 2 3 4 5 6

New innovator 0.002 0.004 b -0.001

(0.002) (0.002) (0.005)

New innovator × Politically active 0.006 b 0.008 a 0.001

(0.002) (0.002) (0.007)

New innovator × Politically inactive -0.004 -0.004 -0.004

(0.007) (0.007) (0.008)

Log likelihood -233.78 -232.95 -122.82 -122.01 -135.61 -135.51

Pseudo R2 0.001 0.004 0.004 0.010 0.000 0.001

N 8,812 8,812 8,812 8,812 8,812 8,812