Political Connections, Incentives and Innovation: Evidence from ... · innovation is driven by recent studies, such as, Kogan et al. (2012), which shows that innovation is an important

Political Connections, Incentives and Innovation:

Evidence from Contract-Level Data*

Jonathan Brogaard, Matthew Denes and Ran Duchin

April 2015

Abstract

This paper studies the relation between corporate political connections and the allocation, design, and outcomes of government contracts. Using hand-collected data on Federal procurement contracts, we find that connected firms are 10% more likely to win a contract. Connected firms receive larger contracts, with longer durations and weaker incentive structures. Politically-connected firms are also more likely to increase contract amounts and extend deadlines through contract renegotiations. While government contracts enhance firm-level innovation on average, political connections and weak contractual incentives are associated with less innovation, as measured by patents and patent citations. Overall, we show that connections between firms and politicians are associated with distortions in contract allocation and design.

* Contact: Jonathan Brogaard, Foster School of Business, University of Washington, e-mail: [email protected]; Matthew Denes, Foster School of Business, University of Washington, e-mail: [email protected]; Ran Duchin, Foster School of Business, University of Washington, e-mail: [email protected]. We thank seminar participants at the SIFR Conference on Innovation and Entrepreneurship for helpful comments.

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I. Introduction

The financial crisis of 2008-2009 resulted in a wave of government interventions around the world.

Governments initiated tighter regulation, monetary policy interventions, and capital assistance to the

private sector. Economists have debated the influence of government involvement on economic

activity and the private sector since at least Keynes (1936). At the forefront of this debate are the

questions of whether and when government involvement creates value. On the one hand,

government involvement can enhance private sector activity through efficient allocation of capital.

On the other hand, government involvement can crowd out private sector activity and introduce

distortive political favoritism.

In this paper, we provide evidence in this direction by studying the allocation and design of

government procurement contracts. Our main innovation is to use contract-level data to study

detailed contractual agreements awarded by the Federal government to the private sector. Our

empirical analysis seeks to answer three questions. First, how does corporate political activism affect

the allocation of government contracts? Second, what are the contractual features of awarded

contracts, including their incentive structure, duration, and renegotiation terms? Third, do political

activism and contract design impact private sector output?

So far, empirical research has focused mostly on firms’ access to capital. Prior work finds

that politically connected firms have better access to capital (Chiu and Joh (2004), Cull and Xu

(2005), Johnson and Mitton (2003), and Khwaja and Mian (2005)), are more likely to be bailed out

(Faccio, Masulis, and McConnell (2006) and Duchin and Sosyura (2012)), and consequently have

higher values (Roberts (1990), Fisman (2001), and Faccio (2006)). Our contribution lies in

identifying the direct contractual mechanisms that govern the efficacy of both the allocation of

government capital and its subsequent use.

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Our focus on procurement contracts is motivated by several factors. First, these contracts

represent substantial government spending ($203.1 billion a year, which represent 19.3% of the total

annual government expenditure). Second, this setting allows us to observe key information about the

terms of each contract, including its incentive structure, duration, and subsequent renegotiation.

Third, these contracts can be directly linked to individual firms over well-identified time intervals,

generating both within-firm and across-firm variation in government spending.

We collect detailed data on procurement contracts that cover over $1.6 trillion of

government contracts between 2003 and 2010, and match each contract to the recipient firm. We

identify 1,253 firms that received a total of $774 billion in government contracts during our sample

period. After hand-matching these data to Compustat and patent data, our sample contains 299,789

contracts awarded to these firms. On average, our data cover 46.8% of total federal procurement

spending in a year. The average firm receives $147.6 million in a given year, with an average

duration of approximately 8 months.

We measure corporate political activism using firms’ campaign contributions to politicians.

This measure has two important advantages. First, it allows us to compare firms that contributed to

a winning politician with firms that contributed to a losing politician, thus holding constant the

firm’s political activism through campaign contributions. Second, it allows us to separate between

the formation of political connections and contract allocation, thus mitigating simultaneity and

reverse causality concerns. Specifically, we study how contributions to a political campaign affect

contract allocations after the campaign is over and the politician has either won or lost the elections.

We start by showing that, consistent with prior evidence, politically-connected firms are

more likely to receive government procurement contracts. In particular, our estimates suggest that

firms contributing to winning politicians are 10.4% more likely to receive a contract. These effects

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are highly statistically significant and hold after controlling for firm-level characteristics, as well as

unobservable time and industry effects.

It is possible that a firm’s political connections are correlated with some unaccounted for or

unobserved characteristics that increase a firm’s likelihood of receiving government contracts but are

unrelated to political influence. We address this possibility in two ways. First, we construct a placebo

measure of campaign contributions to losing politicians. We find that firms contributing to a losing

politician are not more likely to receive a contract. Second, we exploit cross-sectional variation in the

influence of politicians. Firms contributing to House or Senate committee chairmen of the

Committee on Appropriations or the Committee on the Budget, which play a key role in the

allocation of procurement contracts, further increases the likelihood of receiving a government

contract by 7.9%.

The allocation of contracts to politically-connected firms is consistent with several

hypotheses. One hypothesis is that firms with political connections receive favorable treatment in

the allocation of government contracts. This view would be consistent with theories of the politics

of government ownership and investment (e.g., Shleifer and Vishny, 1994), which suggest that

federal capital is used to accommodate private interests of politicians, such as securing electorate

votes and funding election campaigns. This hypothesis predicts that the value of contracts awarded

to connected firms is likely to trail that of unconnected peers, as predicted in Stigler (1971), Shleifer

and Vishny (1994), and Banerjee (1997).

An alternative hypothesis posits that political connections can mitigate the information

asymmetry between government officials and the firm and result in more informed federal

investment decisions (Downs (1957)). This hypothesis predicts that contracts awarded to politically

connected firms are likely to outperform those awarded to unconnected firms.

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A third possibility is that firms’ political connections do not materially affect the efficacy of

government contracts. Under this view, the terms of procurement contracts mitigate any concerns

about the inefficient allocation or use of Federal capital. For example, these contracts may be

structured in a standardized manner that monitors and incentives the firm through structured payoff

schedules, pay-for-performance, and penalties for low quality or untimely execution.

To investigate these possibilities, we provide novel evidence on the terms of procurement

contracts and their association with political influence. First, we find that connected firms receive

contracts with weaker incentive structures. Firms that contributed to a winning politician receive

about 3.9% fewer incentive contracts. Second, we show that connected firms receive contracts with

larger awards and later completion dates. In particular, politically connected firms receive contracts

that are 15.6% larger at signing, on average. Moreover, they are 11.5% more likely to receive a

longer deadline. Third, we find that, when contracts are renegotiated, politically connected firms are

10.9% to 11.7% more likely to receive increases in the contract award and 10.3% to 11.8% more

likely to receive extensions in contract completion dates.

Taken together, our findings are more consistent with the political favoritism view. They

suggest that concerns about political favoritism are not mitigated, and in fact, exacerbated by the

details of the contractual agreements that accompany government investment.

Our final set of analyses investigates the ex-post outcomes of procurement contracts. Our

research question is twofold. First, we ask whether government contracts spur private sector

innovation. Second, we ask whether conditional on being awarded a contract, political connections

and contract terms affect innovation. Our focus on innovation is motivated by the stated goal of

procurement contracts and government spending to spur innovation (Bayh-Dole Act, 1980). We

measure innovation using the adjusted number of patents and patent citations. These measures are

motivated by Griliches (1990), who finds that patents are a better measure of innovation than

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research and development expenditures, and by Hall, Jaffe, and Trajtenberg (2006), who show that

patent citations are a measure of the value of innovation.

We find that, on average, government spending fosters private sector innovation. Our

estimates indicate that receiving a procurement contract is associated with an increase in the scale

and novelty of innovation, as measured by the adjusted number of patents and patent citations,

respectively. Moreover, based on the attributes of firms awarded Federal contracts, government

investment may help alleviate financial constraints. In the cross-sectional analysis, we find that firms

with lower cash balances, fewer tangible assets, and higher growth options are more likely to receive

government contracts. For example, a decrease of one standard deviation in cash reserves and

tangible assets is associated with an increase of 19.4% and 20.2% in the likelihood of receiving a

government contract, respectively.

However, we find that government investments in politically connected firms lead to less

innovation than investments in unconnected firms. Specifically, firms contributing to winning

politicians are less likely to innovate and these patents are less novel compared to unconnected firms

that also received procurement contracts. We also investigate the link between innovation and

contract design. We find that incentive contracts increase innovation. Winning a procurement

contract that includes incentives increases both the number of patents and their novelty, as

measured by citations, self citations and originality. Since political connections are associated with

fewer incentive contracts, our evidence puts forth contract design as one potential channel through

which political connections distort the allocation of government capital.

Overall, the results in this article document a strong relation between a firm’s political

connections and the allocation, design, and outcomes of government contracts. Contracts won by

politically connected firms are consistent with agency-type inefficiencies from political influence

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predicted in Shleifer and Vishny (1994) and are inconsistent with the efficiency-improving role of

political connections.

Our paper contributes to prior research on government spending. Lerner (1999) shows that

government investment in small firms results in superior growth. Conversely, Cohen, Coval, and

Malloy (2011) use exogenous variation in Senate and House chairmanships and find that shocks to

government spending tend to lower investment and employment. Our results shed new light on

these findings. They suggest that while government spending fosters firms’ innovation on average,

the terms of government contracts awarded to politically connected firms lead to less innovation.

Our paper is also related to the growing literature that studies firm-level innovation. Our focus on

innovation is driven by recent studies, such as, Kogan et al. (2012), which shows that innovation is

an important source of long-term economic growth.

The rest of the paper is organized as follows. Section II describes the data. Section III

provides the empirical evidence on the allocation and design of procurement contracts. Section IV

studies the link between political connections, contracts, and innovation. Section V concludes.

II. Data

The U.S. government commonly is a customer for firms. Contract-level data allows us to

study the relation between political connections, contracting and innovation. This section details

our novel dataset of contracts, which we hand-match to political contributions, patents and financial

variables.

A. Contracting with the U.S. Government

The U.S. government often enters into contracts with firms and individuals. A contract is

initiated when an agency of the federal government determines that it requires a good or service. A

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contracting officer for the agency provides information about the contract on the Federal Business

Opportunities website through a Request For Proposal. Firms have the opportunity to review the

proposal and submit offers for the contract, which are then evaluated by agency employees.

Contracting with the government has been increasingly unified, particularly with the creation of the

Federal Acquisition Regulation in 1984. These regulations provide guidelines for many aspects of

contracts, including bidding, competition and management (Feldman and Keyes, 2011).

The Federal Procurement Data System (FPDS) tracks procurement contracts of the federal

government of the United States. This comprehensive system provides detailed information on

nearly all federal contracts from about 65 different branches, departments and agencies of the

federal government. The U.S. government began providing detailed data on procurement contracts

beginning in 1978, though reporting is often incomplete prior to 2000 (Liebman and Mahoney,

2013). The Federal Funding Accountability and Transparency Act of 2006 led to the creation of the

USAspending.gov website, which provides data from the FPDS starting in 2000. Specifically, the

system reports comprehensive details on any contract with a transaction value of at least $2,500

($25,000 prior to 2004). There are mainly two types of procurement contracts: fixed price and cost

plus. A fixed-price contract is a contract whose price does not vary by the cost of materials or labor.

A cost-plus contract is a contract where the price is determined by the costs incurred by the vendor

plus a certain amount. While the FPDS includes data on classified contracts, it does not contain

records on the U.S. Postal Service and certain legislative and judicial branches.

Firms often contract with the federal government. Table 1 summarizes the contracts

observed in our sample, renegotiation of these contracts and the industrial composition of recipient

firms.

Insert Table 1 About Here

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We restrict our sample to those contracts whose total award is at least $100,000. Panel A explores

contract-level details at initiation. The sample comprises 299,789 contracts to Compustat firms

from 2003-2010. The average initial award of a contract is $1.1 million, with a mean total award of

$2.6 million throughout its duration. A contract typically lasts for over seven months and there is

substantial variation in the length of a contract. Contracts with the government can vary in their

type and about 11.7% of contracts include incentives. These contracts use monetary awards to

incentivize firms to complete contracts timely. Appendix A details contracts with these features.

After initiation, a firm can renegotiate a contract. Panel B of Table 1 details when and how

renegotiation occurs. We observe changes to 49% of contracts and focus on modifications in the

award and length of a contract. Just under 1 million contract level changes occur from 2003-2010

and the average contract has 3 modifications. About 41.4% of award changes are increases and the

mean increase is $1.5 million. Only 13.6% of contracts receive a decrease and the average reduction

is $150,000. Lastly, extensions of contract length are observed 38.7% of the time and, conditional

on receiving a change to its completion date, are 2.2 years on average.

The government contracts with many industries and Panel C highlights the count and size of

contracts by industry. Business equipment, wholesale and manufacturing receive the most contracts

both in terms of the number of contracts and their total value, summarized in the last column of the

table. Business equipment collected $253.1 billion of government spending through 97,986

contracts, while manufacturing won $297.9 billion in 60,118 contracts. Overall, our dataset allows

us to observe $774 billion in contracts awarded to 1,253 firms.

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B. Political Connections

Each election cycle provides firms with the opportunity to contribute to politicians. Firms

allocate funding to candidates running for office in the U.S. Senate or House of Representatives

using political action committees (PACs). In particular, a firm forms a PAC that contributes to a

politician’s election PAC, which finally distributes a firm’s contribution to the politician’s campaign.

While we focus on election PACs, another form of contributions is through leadership PACs.1

Firms can also donate to leadership PACs, which cannot use contributions on direct campaign

expenses.

The Federal Election Commission (FEC) provides detailed data on contributions and

elections. We hand-match contributions from firms to our dataset. We additionally incorporate

election data into our analysis. The FEC provides data on the outcomes of all U.S. Senate and

House elections, including vote tallies by candidate. These data allow us to condition contributions

on election outcomes.

Lastly, we form political connections based on firm-level contributions to candidates

running for election in the Senate or House. A firm is defined as politically connected if it

contributed to a winning politician during the previous election cycle. This connection is specified

as lasting for two years. For example, a firm contributing to a winning politician during the 2005-

2006 election cycle is considered connected to this politician in the following two years in 2007 and

2008. We also explore the robustness of our results by looking at contributions to losing candidates

and to winning politicians that are likely to have relatively greater influence in the allocation of

contracts. A firm is considered as connected to a losing candidate if it contributes to a losing

politician during the previous election cycle. We define those winning politicians likely influencing

contracts as the chairmen of the Committee on Appropriations or the Committee on the Budget in

1 We do not include Super PACs in our data, since it is against the law for contributions to these PACs to be used for a politician’s campaign.

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the Senate or the House. Similar to above, firms are considered connected to committee chairmen if

they contribute to a winning committee chair during the previous election cycle.

C. Measuring Innovation

Innovation is considered an important driver of long-term economic growth (Kogan et al.,

2012). The main proxy for firm-level innovation is patents. While research and development

(R&D) expenditure is a firm’s allocation of capital towards innovative activity, it does not capture

the productive output of its investment. Griliches (1990) demonstrates that patenting activity is a

better measure of research productivity than R&D spending. Further, Hall, Jaffe and Trajtenberg

(2005) highlight that patents alone do not indicate technological breakthroughs. Patent citations are

a proxy of the value of a firm’s innovations.

The United States Patent and Trademark Office (USPTO) issues patents and trademarks, in

addition to providing comprehensive data on these forms of intellectual property. The NBER

dataset, expanded by Kogan et al. (2012), is the source of data on firm-level patent activity in our

sample. The count of patents and patent citations are subject to truncation bias. For the count of

patents, Seru (2014) reports that the average time from application to granting of a patent is two

years. Patent citations are prone to a similar effect, since patents are often not cited until several

years after being granted. To correct for these biases in our sample, both the number of patents and

patent citations are divided by their annual average for a particular patent’s technology class.

Technology class is a grouping of patents that is analogous to an industry classification. These

variables are referred to as adjusted number of patents and adjusted patent citations.

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D. Sample Summary

The sample includes all firms in Compustat between 2003 and 2010, excluding financial

firms (SIC 6000-6999) and regulated utilities (SIC 4900-4999). Table 2 summarizes the firm

characteristics, contracts, political contributions and patent activity of the firms included in our

paper.


We include the following firm characteristics as control variables in the analysis. Size is the natural

log of firm assets. R&D/Assets is research and development expense scaled by assets. Profitability is

measured as earnings before interest, taxes and depreciation over total assets of the firm. Tangibility

is the ratio of net property, plant and equipment to total assets. Book leverage is the book value of

debt over total assets. Cash/Assets is measured as cash and short-term investment divided by total

assets. Market-to-book is the market value of the firm’s equity and its book value of debt relative to

the firm’s assets. HHI is the Herfindahl-Hirschman Index of sales for the industry (at the SIC level).

Profitability, Book leverage and Market-to-book are winsorized at the 1% level in each tail.

The sample consists of 33,091 firm-years, of which 6,121 observations are matched to

contracts. The sample accounts for a yearly average of 45.2% of contracts reported by the federal

government. In the analysis, we focus on the full sample (labeled “All firms”) when examining

whether firms receive contracts and its relation to innovation. We focus on the subsample of firms

receiving contracts (labeled “Firms receiving contracts”), when the outcome is a contract-level

variable, such as its length. This subsample allows us to test how contract-level attributes vary with

political connections. Panel A details firm characteristics. The average R&D investment is 9.2% of

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assets and about 51% of firms invest in R&D. Firms are 32.7% levered and hold about 23.9% of

their assets in cash and short-term investments.

Panel B summarizes contract data by firm-year. Contract indicator equals one if a firm receives

at least one contract during a given year. Award amount is the total amount of awards to a firm in a

particular year. Incentives is the percent of contracts with incentives, as defined in Appendix A.

Length is the average contract length (in years). Award increase is a binary variable, equaling one if a

firm receives an increase in a contract’s award and Extension is an indicator variable, equaling one if a

firm receives an extension in a contract’s completion date. Contracts are awarded to 18.5% of firm-

years in our sample and the average size of contracts awarded in a year is $147.6 million. Firms

receive incentives in about 9.9% of contracts and the average length for a firm’s contracts is over

eight months. Contracts receive award increases 68.5% of the time and extensions in 60% of the

sample of firms receiving contracts.

Political connections are detailed in Panel C. We use contributions from firm PACs to

candidates PACs to proxy for political connections, as described in Section B above. Contributions to

winner is a binary variable, equaling one if a firm contributes to a winning politician in the previous

election cycle. Contributions to loser is a binary variable, equaling one if a firm contributes to a losing

politician in the previous election cycle, and Contributions to winning chairman is a binary variable,

equaling one if a firm contributes to a winning candidate who is the chairman of the Committee on

Appropriations or the Committee on the Budget in the Senate or the House. About 8.1% of firms

contribute to winning politicians and 6.2% of firms donate to losing politicians. Firms contribute to

a winning chairman just under 1% of the time.

Lastly, Panel D summarizes innovation. We measure innovative activity, using number of

patents and patent citations. Additionally, we incorporate self citations and patent originality as

proxies for a patent’s importance. Self citations are defined as a firm’s citations to its own patents

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and proxy for a firm’s internal spillovers. Lastly, a patent’s originality is defined by its citations to a

different technology classes. It is measured as one minus the Herfindahl-Hirschman Index of

citations to technology classes. Specifically, Number of patents is the number of patents awarded to

the firm in a year and Patent citations is the average citations per patent awarded in a year. Self citations

is the average citations to a firm’s own patents per patent awarded in a year and Originality measures

the diversity of citations made by a patent. These measures of innovation are adjusted by dividing

by their annual-technology class average. The mean number of patents in our sample is 6.8 patents

and the adjusted number of patents is 0.19. The distribution of patent counts is skewed right, as the

median firm produces no patents in a year. Patent citations are a measure of a patent’s innovative

impact. The average number of patent citations per year is 0.59 and the adjusted number of patents

is 0.27, with a distribution that is also skewed right. The average firm has 0.19 adjusted self citations

and an adjusted originality of 1.154.

III. Political Connections, Contracts and Renegotiation

Sections 3 and 4 present the main results of the paper. Section 3 studies the allocation of

contracts to firms and its relation to political connections. This section further explores how

connections influence contract-level characteristics, such as incentives and renegotiation. Section 4

considers the relation between contracts and firm-level innovation, highlighting the role of

incentives in these contracts.

A. Contracts

In this section, we examine the firm-level determinants of receiving a contract from the U.S.

federal government, including political connections. We find that firms contributing to politicians

and having weaker balance sheets are more likely to win contracts. Specifically, firms donating to

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winning candidates and those with lower cash balances and fewer tangible assets tend to receive

government spending. We additionally find support that these contracts are awarded to firms with

higher growth opportunities and lower leverage.

Table 3 reports on how receiving a contract is related to political connections and firm-level

characteristics.


The first column details the marginal effects (at the mean) from a probit model, where the

dependent variable equals one if a firm receives a contract from the U.S. federal government. The

model includes all firm-years from 2003-2010 and is specified as:

𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟!" = 𝛼 + 𝛽 ∙ 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑖𝑛𝑛𝑒𝑟!" + 𝛾𝑋!" + 𝜇! + 𝜂!"# + 𝜀!" , (1)

where Contributions to winner equals one if a firm contributes to a politician and 𝑋 is a vector of firm

characteristics including size, profitability, tangibility, book leverage, cash-to-assets, market-to-book

and the Herfindahl-Hirschman Index. Models 1 controls for unobserved, time-invariant industry

heterogeneity (𝜂!"#), in addition to year fixed effects (𝜇!), and standard errors are clustered by firm.

Firms donating to winning politicians are 10.4% more likely to win contracts from the government.

Further, firms with lower cash balances (internal finance) and fewer tangible assets (external finance)

are significantly more likely to receive government funds. We also find that large firms with lower

leverage and those with higher growth opportunities are significantly more likely to be awarded

contracts.

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B. Initial Contract Characteristics

Contract-level observations allow us to explore how characteristics, such as contract type,

are related to political connections and firm attributes. Specifically, we study the contract award at

signing, in addition to incentives and length. We restrict the analysis to those firms who receive a

contract. Columns 2 uses the natural log of contracts awarded in a particular year as the dependent

variable. The model uses ordinary least squares (OLS) and firm fixed effects, rather than industry

fixed effects, and is otherwise similar to Equation (1) in that it includes the same firm-level controls

and year fixed effects (𝜇!). The results show that connected firms win contracts that are 15.6%

larger than firms that do not make political contributions.

One might be concerned that the previous results are driven by small contracts and that the

increase in contract award is not economically meaningful. To address this, we estimate a probit

model as in Equation (1), where the dependent variable is an indicator for above median awards.

The sample is similarly restricted to firms receiving contracts. We find results similar to those for

the continuous measure of awards. Firms donating to winning candidates are 18.3% more likely to

win large awards than firms that do not contribute to winning candidates. Firms with weaker

balance sheets, as proxied by lower cash balances and fewer tangible assets, tend to be awarded large

government contracts. Also, larger firms are more likely to win large contracts.

Each contract with the government has a specified form of pricing. This pricing scheme can

induce firms to complete contracts timely and with high quality by providing monetary rewards to

the recipient firm. In the Appendix, we detail the types of contracts classified as having incentives.

We study the percent of contracts each year with incentives, equaling weighting each contract.

Column 4 of Table 3 details the results on contract-level incentives using a similar specification to

Column 2 (OLS with firm fixed effects). The results show that contracts awarded to firms

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contributing to winning candidates have 3.9% fewer incentives. We find nearly identical results

when we weight our measure of incentives by contract value.

Another important lever that can be analyzed at the contract level is the length of the

contract. Many contracts are completed quickly and small in magnitude. To capture economically

meaningful change to the terms of a contract, we create an indicator equaling one if the average

contract length is above median and if the award is large (defined as above median in Column 3).

All else equal, a longer contract is more favorable for the receiving firm. Using a probit specification

as in Equation (1), we find that politically-connected firms are 11.5% more likely to receive contracts

with a longer time to completion.

Taken together, Table 3 provides evidence on the role of being connected to winning

political candidates. Connected firms are more likely to receive a contract. Beyond influencing the

allocation of contracts, firms use their political connections to receive contracts that tend to have

larger awards, with less incentive-based payoffs and longer completion horizons.

C. Contract Renegotiations

After a contract is signed, it can be renegotiated or altered. Table 1, Panel B, highlights that

renegotiation is frequently observed, with just over 49% of all contracts being changed. We focus

on two prevailing forms of renegotiation between a federal agency and a firm. First, we explore

changes in award size. A contract obligation can be either increased or decreased once it has been

awarded. Second, we study the time to complete a contract.

Table 4 provides results about how renegotiated contracts relate to political connections and

financial variables.


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This table focuses on those characteristics after a contract has been awarded. The probit models in

Columns 1 through 4 are of the form:

𝑅𝑒𝑛𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛!" = 𝛼 + 𝛽 ∙ 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑖𝑛𝑛𝑒𝑟!" + 𝛾𝑋!" + 𝜇! + 𝜂!"# + 𝜀!" , (2)

where Contributions to winner equals one if a firm contributes to a winning politician and 𝑋 is a vector

of firm characteristics including size, profitability, market-to-book and the Herfindahl-Hirschman

Index. These models control for unobserved, time-invariant industry heterogeneity (𝜂!"#), in

addition to year fixed effects (𝜇!). Standard errors are clustered at the by firm. Each model is

estimated on the sample of firms receiving a contract in a given year. Marginal effects at the mean

are reported.

We consider four different renegotiation variables. Award Increase is an indicator variable

equaling one when a firm receives an increase in awarded contracts. Large Award Increase is a binary

variable equaling one if award increase is above median for firms receiving contracts. Extension is an

indicator variable equaling one when a firm receives an extension in the completion date in awarded

contracts and Long Extension is a binary variable equaling one if the extension is above median for

firms receiving contracts.

Column (1) evaluates Award Increase. The results show that firms contributing to winning

politicians are 11.7% more likely to receive increases in an award. Column 2 considers Large Award

Increase and shows that connected firms are 10.9% more likely to receive large increases in their

average award. Columns 3 and 4 focus on contract length. Column 3 has Extension as the

dependent variable. The marginal effects show that firms contributing to winning candidates are

10.3% more likely to receive contract extensions. Focusing on Long Extension, Column 4 reports

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that politically-connected firms are 11.8% more likely to receive above average extensions. These

results suggest that, in addition to increasing the chance of receiving a contract, firms contributing to

winning politicians are more likely to receive increases in contracts already awarded and obtain more

time to complete contracts.

D. Robustness Tests

So far we have taken a straightforward view of political connections. Either a firm

contributes to a winning candidate or it does not. If the political connection measure is able to

capture preferential treatment for firms, then we may be able to observe additional cross-sectional

heterogeneity. We examine two possible situations. First, we examine whether there is a different

effect between contributing to winning candidates versus contributing to losing candidates. Second,

we evaluate whether contributions to more powerful candidates, as proxied by the politician being a

chairman on the Committee for Appropriations or the Committee on the Budget, has a differential

impact from contributing to other winning candidates. We find evidence that contributing to a

losing candidate is the equivalent of not contributing at all. The results also show that contributing

to a committee chairman typically doubles the effect of being politically connected.

Instead of repeating the entire analysis in Tables 3 and 4, we select the three indicator

variables from Table 3, Contract Indicator, Large Award, and Long Contract, and the two main variables

in Table 4, Award Increase and Extension. Panel A reports the analysis for contributing to winning

candidates versus contributing to losing candidates. Contributions to winner is a binary variable,

equaling one if a firm contributes to a winning candidate during the previous election cycle, and

Contributions to loser is a binary variable, equaling one if a firm contributes to a losing candidate in the

previous election cycle. In Tables 3 and 4 the benchmark was firms not contributing to the winning

candidate, whereas now the benchmark is firms not contributing at all.

19

The results are documented in Table 5.


Each column in Table 5 Panel A reports the marginal effects at the mean from a probit model,

where the dependent variable equals one of the five indicator variables previously mentioned. The

model is:

𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒!" = 𝛼 + 𝛽! ∙ 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑖𝑛𝑛𝑒𝑟!" + 𝛽! ∙ 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑙𝑜𝑠𝑒𝑟!" +𝛾𝑋!" + 𝜇! + 𝜂!"# + 𝜀!" , (3)

where 𝑋 is a vector of firm characteristics including size, profitability, tangibility, book leverage,

cash-to-assets, market-to-book and the Herfindahl-Hirschman Index. The regression controls for

unobserved, time-invariant industry heterogeneity (𝜂!"#), in addition to year fixed effects (𝜇!), and

standard errors are clustered by firm.

For all five dependent variables, the Contributions to winner coefficient is positive and

statistically significant at the 5% level or higher. At the same time, the Contributions to loser coefficient

is never statistically significant. The effect is not driven by contributing to a candidate, but rather it

is driven by contributions to politicians who win. We find that firms contributing to losing

candidates receive contracts that look no different (along the dimensions we analyze) than firms that

make no political contributions at all. This test also helps to address a possible endogeneity issue.

One concern might be that firms contributing to politicians might be somehow different than firms

that do not donate to candidates. This test provides evidence against this concern.

Next we evaluate whether, among winning candidates, there is heterogeneity in the effect of

contributions, specifically whether contributing to a committee chairman has a differential impact

20

than contributions to a less senior candidate. The dependent variables and regression specification

is identical to Equation (3) except that the independent variable Contributions to loser is substituted

with Contributions to winning chairman, a binary variable equaling one if a firm contributes to a winning

politician who is a chairman on the Appropriations or Budget committees of the Senate or the

House of Representatives.

In each regression, the coefficient on Contributions to winning chairman is nearly the same size as

the coefficient on Contributions to winner. For instance, in the Large Award regression (Column 2),

firms contributing to a winning candidate are 16.1% more likely to receive a large award. In the

same specification, we find that firms contributing to a winning chairman are 20.7% more likely

receive a large award. This effect on contributing to a winning chairman is in addition to the effect

of contributing to a winner. Therefore, contributing to a winner who is a committee chairman has

the combined effect of increasing a firm’s likelihood of receiving a large award by 36.8%.

The results in the other columns are similarly large in economic magnitude. These results

further suggest that political connections is a channel through which contracts can be influenced.

The measure of connectivity is reasonably strong as to capture interesting cross-sectional variation.

E. Contract-Level Analysis

The analysis in the sections above has studied contracts at the firm-year level. The unit of

observation in the data is a contract, which allows us to additionally estimate models at the contract

level. A concern might be that contract characteristics differ by the Federal agency awarding the

contract. By estimating our specification at the contract-level, we can directly address this by

removing time-invariant agency heterogeneity.

We focus on the main variables of interest: contract award, length and incentive at signing, in

addition to award change and extension through renegotiation. Award is the natural log of the initial

21

contract award (in thousands of dollars). Length is the contract length at signing (in years). Incentives

is a binary variable equaling one if a contract has incentives. Award Change is the percent change of

an award after its initial signing, relative to the total award of the contract. Extension is the change in

a contract’s completion date after its initial signing (in years). We now include similar controls

across specifications, using size, profitability, market-to-book and the Herfindahl-Hirschman Index,

as well as year and industry fixed effects.

The results are reported in Table 6.


We largely find that our results are robust to including agency-level fixed effects. Column 1 reports

that firms contributing to winning politicians receive contracts that are 24.1% larger and 18.8%

longer (relative to mean contract length), though we no longer find a significant effect for contracts

with incentives. Through renegotiation, we report that firms contributing to winning candidates

receive 2.2% larger increases in the average award and 8.3% longer extensions (relative to the mean

extension). The conclusion of these findings is that, even controlling for agency-level variation, the

political-connected firms continue to receive preferential contract allocation.

IV. Innovation, Contracts and Incentives

This section studies innovation and firms receiving contracts from the federal government.

A measureable outcome and stated goal of federal contracting is to spur innovation. We find that

firms receiving government contracts tend to experience increases in the scale and novelty of

innovation, as measured by number of patents and citations. One mechanism through which

contracts can stimulate innovation is with incentives, which award firms for the quality and

22

timeliness of performing a contract. We find evidence that these contract-level incentives are

associated with increases in patent production and their citations, consistent with this channel.

Further, firms contributing to winning politicians that receive contracts tend to experience a

decrease in the scale and novelty of innovation. Since politically-connected firms tend to have less

incentive contracts, this provides contract design as a channel through which political connections

distort the allocation of government capital.

Before linking incentives and political connections with innovation, we first evaluate the

relationship in general between government contracts and firm-level innovation. Table 7 highlights

the relation between future patenting activity and government contracts.


The dependent variable the number of patents a firm is awarded in a year divided by its annual-

technology class mean. We analyze patents in 1,2, 3 and 4 years after a firm receives a contract. The

analysis includes all firm-years. For the 1-year patent analysis, this results in 33,091 observations.

The number of observations decreases for longer patent periods as the later years cannot be

included due to their shorter horizon. For the 4-year patent analysis the sample is reduced to 17,083

observations. The model is:

𝑃𝑎𝑡𝑒𝑛𝑡𝑠!" = 𝛼 + 𝛽! ⋅ 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑠 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟!" + 𝛾𝑋!" + +𝜇! + 𝜂! + 𝜀!" , (4)

where Contracts Indicator is an indicator variable equaling one if a firm received a contract and X is a

vector of firm characteristics including size, R&D, profitability, tangibility, book leverage, cash

holdings, market-to-book, and the Herfindahl-Hirschman Index. All of the models control for

23

unobserved, time-invariant firm heterogeneity (𝜂!), in addition to year fixed effects (𝜇!), and clusters

standard errors by firm. The main coefficient of interest is 𝛽!, which captures the association

between receiving a government contract and patent productivity.

Controlling for firm characteristics, we find that firms receiving government contract tend to

have significantly higher levels of patent production. One year after receiving a government

contract, firms tend to experience a 0.149 increase in the adjusted number of patents. This increase

lasts through the following four years and slightly decreases to 0.108 in the fourth year. Not only do

firms receiving government contracts immediately become more innovative, they continue to

increase their patent production into the foreseeable future.

While patent production is a straightforward measure of innovation, patent citations may be

a better proxy for the value of innovation. Table 8 studies the relation between patent citations and

government contracts. The model is the same as in Equation (4) except the dependent variable now

captures patent citation. Specifically, the dependent variable is defined as the average citations per

patent granted, divided by its annual-technology class mean. The results are reported in Table 8.


Controlling for firm characteristics, we find that firms receiving government contracts have more

novel patents, as measured by citations. Similar to the number of patents, we find that evidence that

innovation novelty increases for the four years after receiving a contract.

Tables 9 and 10 study the relation between innovation, political connections and incentives.

The model is:

𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒!" = 𝛼 + 𝛽! ∙ 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑠 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟!" + 𝛽! ∙ 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑖𝑛𝑛𝑒𝑟!" +𝛽! ⋅ 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑠 𝑋 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑖𝑛𝑛𝑒𝑟!" +𝛽! ⋅ 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑠 𝑋 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑠!" + 𝛾𝑋!" + 𝜇! + 𝜂! + 𝜀!" , (5)

24

As in tables 7 and 8, we measure innovation using the number of patents or citations. We also

incorporate measures of the importance of citations using self citations and originality. Self citations

is the average number of a citations that a patents receives from patents in the same firm.

Originality is a Herfindahl-Hirschman Index of citations to technology classes and proxies for the

diversity of citations made by a patent. Contributions to winner is an indicator variable equaling one if

a firm contributed to a winning politician in the previous election cycle and Incentives is a binary

variable equaling one if a firm receives contracts with incentives during the current year.

The coefficients 𝛽! captures the association between receiving a contract and innovative

activity, and 𝛽!, which is the association between contributing to a winning candidate and patent

measures. Further, the coefficient 𝛽! is the association between receiving a contract, being

politically-connected and innovation, while 𝛽! captures the relation between contract-level incentives

and firm-level patent activity. Table 9 presents results for patent production. We find a positive and

significant level effect for receiving a contract, while we find a negative and significant association

between contributing to a winning candidate and firm-level patenting. Interestingly, we report that

firms receiving contracts with incentives have significantly higher levels of innovation, while

politically-connected firms receiving contracts tend to produce less patents. These results point to a

channel through which political connections lead to declines in innovative activity. Firms with

connections tend to have lower incentives in their contracts, as shown in Table 3. Consistent with

this channel, these firms have much lower levels of patenting activity.

Table 10 details the association between three measures of citations, political connects and

incentives. The relationship with patent citations is similar but weaker. We find that politically

connected firms tend to produce less novel innovation, as measures by citations, self citations and

originality, while we do not find a level effect for receiving a contract. We report that politically-

25

connected firms receiving contracts tend to have less self citations and lower originality.

Additionally, we find that contract-level incentives are related to increases in all measures of

citations.

V. Conclusion

Using hand-collected data on government contracts won by public firms from 2003 to 2010,

we examine political connections, contract-level characteristics and firm-level innovation. We find

that politically-connected firms are more likely to win contracts and these contracts tend to be larger

with later completion dates. We additionally find that contracts awarded to connected firms have

lower incentives and are favorably renegotiated. In robustness tests, we show that firms connected

to losing candidates do not receive any benefits in the allocation of contracts, while firms connected

to committee chairs tend to receive nearly twice the average effect of being politically connected.

Further, we provide evidence that firms winning contracts innovate less and incentive-based

contracts foster innovation. This suggests that lower contractual incentives is one channel through

which politically-connected firms innovate less.

26

Appendix

Section A of this appendix describes the variables examined in the paper. Section B details the

matching procedure for linking contract data to Compustat firms.

A. Variable definitions

This section defines the main variables of the paper and their construction, providing the Compustat

definition where applicable. U.S federal contract data is from the Federal Procurement Data System

(FPDS) and retrieved from USAspending.gov. Patent data is provided by Kogan et al. (2012), which

builds on the NBER patent data matched to Compustat firms. The underlying patent data is

provided by the United States Patent and Trademark Office (USPTO). Campaign contributions and

election data is from the Federal Election Commission.

Variable Name Description Compustat Definition Contract indicator A binary variable equaling one if a firm

receives contracts from the U.S. federal government in a particular year

Award amount Natural log of contract awards (in millions of dollars) in a given year

Incentive Percent of contracts awarded with incentives, which are defined as those contract whose type is “Fixed Price Incentive”, “Cost Plus Incentive”, “Fixed Price Award Fee”, “Cost Plus Award Fee”, “Fixed Price Level of Effort”, “Fixed Price with Economic Price Adjustment” or “Cost Plus Fixed Fee”

Length Time to complete a contract in years Award Increase An indicator variable equaling one if a

firm receives an award increase after receiving a contract

Extension A binary variable equaling one if a firm receives an extension to complete a contract

27

Variable Name Description Compustat Definition Contributions to winner

An indicator variable equaling one if a firm contributes to an election PAC of a candidate for the U.S. Senate or House winning an election

Contributions to loser A binary variable equaling one if a firm contributes to an election PAC of a candidate for the U.S. Senate or House losing an election

Contributions to winning chairman

An indicator variable equaling one if a firm contributes to an election PAC of a candidate for the U.S. Senate or House winning an election and who is a chairman of the Committee on Appropriations or the Committee on the Budget

Number of patents (adjusted)

Patents awarded in a year divided by its annual-technology class average

Patent citations (adjusted)

Patent citations in a year divided by its annual-technology class average

Self citations (adjusted)

Self citations is the average citations to a firm’s own patents per patent awarded in a year divided by its annual-technology class average

Originality (adjusted)

Originality measures the average diversity of citations made by patents in a year divided by its annual-technology class average

Size Total (book) assets at Profitability Measure of firm profitability using

earnings before interest, taxes and depreciation (EBITDA) over assets

oibdp / at

Tangibility Ratio of net property, plant and equipment to firm size

ppent / at

Book leverage Book value of debt over assets (dlc + dltt) / at Cash / Assets Ratio of cash and short-term investment

to size che / at

Market-to-book Ratio of market value to book value (at – ceq + (prcc_f*csho)) / at R&D / Assets Ratio of R&D expense to assets, where

R&D is set to zero if it is missing xrd / at

HHI Herfindahl-Hirschman Index based on sales for industry, defined at the SIC four-digit level

28

B. Matching contracts to Compustat firms

In this section, we detail the matching procedure to combine U.S. federal government

contracts from FPDS with Compustat. The FPDS data does not contain a unique identifier that can

be matched directly to common unique identifiers, such as GVKEY or PERMNO. The data does

contain the parent company name for each vendor. We use this field to match the FPDS with

Compustat company names based on the following process. For each firm in Compustat, we

compute the Levenshtein distance between the company name in Compustat and each parent

company name in FPDS, after removing punctuation and unneeded characters. The Levenshtein

distance is a method of computing the difference between two strings. This distance is

approximately a count of the number of edits necessary to change one string into the other string.

The Levenshtein ratio is calculated as (1 – L/S), where L is the Levenshtein distance and S is the

length of the longest word. This process computes the Levenshtein distance and ratio for 13,867

Compustat names, each matched with 528,056 parent company names in FPDS. We keep all

matches above a Levenshtein ratio of 0.95 and the next closest match after this cutoff. For each

potential match, we examine whether the match of the Compustat company name with the FPDS

parent company name is appropriate. We determine this based on name similarity, Hoover’s

database (which provides company information by DUNS number) and internet searches. This

leads to 16,138 matches between Compustat company names and FPDS parent company names.

29

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31

Table 1: Contracts

Panel A: Initiation

VariableNumber of

observations Mean Median Minimum MaximumStandard deviation

Initial award 299,789 1.098 0.201 0.000 1,746.286 9.824Total award 299,789 2.582 0.274 0.100 8,016.895 38.122Length 299,789 0.640 0.323 0.000 5.003 0.882Incentives indicator 299,789 0.117 0.000 0.000 1.000 0.321

Panel B: Renegotiation

VariableNumber of


Renegotiation indicator 299,789 0.490 0.000 0.000 1.000 0.500Contract changes 299,789 3.034 0.000 0.000 100.000 6.210Percent award change 299,789 24.617 0.000 -26.361 99.245 37.351Award increase 123,970 1.452 0.199 0.000 20.185 3.800Award decrease 40,709 -0.150 -0.033 -0.867 0.000 0.252Extension 115,799 2.239 2.000 -0.770 6.381 1.931

Panel C: Industry Comparison

IndustryNumber of

contractsAverage contract Minimum Maximum

Standard deviation

Totalcontracts

Consumer nondurables 7,069 1.968 0.100 277.791 5.940 13,915Consumer durables 8,074 2.212 0.100 1451.214 28.871 17,862Manufacturing 60,118 4.956 0.100 4017.785 57.268 297,973Oil, gas and coal 921 16.946 0.100 4571.585 166.603 15,607Chemicals and allied products 1,628 2.593 0.100 153.300 9.979 4,221Business equipment 97,986 2.583 0.100 2651.188 21.949 253,107Telephone and television 6,296 0.861 0.100 332.735 5.066 5,418Wholesale, retail and services 74,561 0.691 0.100 409.128 5.761 51,547Healthcare and drugs 8,581 1.966 0.100 1319.422 34.576 16,867Other 34,555 2.821 0.100 8016.895 64.763 97,494

This table provides summary statistics for contracts from the U.S. federal government to all firms in Compustat from2003–2010, excluding financial firms (SIC 6000-6999) and regulated utilities (SIC 4900-4999). Panel A summarizes thesample of contracts at initiation Panel B details contract renegotiations and Panel C highlights contracts by industry at theFama-French 12-industry level. In Panel A, Initial award is the contract award at signing (in millions of dollars) and Total award is the total contract award (in millions of dollars), including any award changes. Length is the initial length of thecontract (in years). Incentives indicator is a binary variable equaling one if a contract has incentives. In Panel B,Renegotiation indicator equals one if a contract is renegotiated and Contract changes is the number of changes to a contract ifrenegotiation occurs. Percent award change is the total change in the award after signing relative to the total contract award(in percent). Award increase is the increase of a renegotiated contract (in millions of dollars) and Award decrease is thedecrease of a renegotiated contract (in millions of dollars), both conditional on an award increase or decrease. Extension is the change in the completion date of a contract (in years). In Panel C, Average contract is the mean contract total award(in millions of dollars) and Total contracts is the total amount of contracts awarded to the industry (in millions of dollars).The appendix provides additional information on variable definitions (Section A).

32

Table 2: Summary Statistics

Panel A: Firm Characteristics

VariableNumber of


Size 33,091 4.799 5.028 -6.908 13.590 2.812R&D/Assets 33,091 0.092 0.002 0.000 1.662 0.233Profitability 33,091 -0.374 0.079 -16.200 0.438 2.004Tangibility 33,091 0.230 0.146 0.000 1.157 0.232Book leverage 33,091 0.327 0.162 0.000 3.692 0.618Cash/Assets 33,091 0.239 0.134 -0.169 1.000 0.260Market-to-book 33,091 5.578 1.659 0.540 174.388 19.685HHI 33,091 0.272 0.209 0.054 1.000 0.195

Panel B: Contracts

VariableNumber of


Contract indicator 33,091 0.185 0.000 0.000 1.000 0.388Award amount 6,121 147.623 2.231 0.000 20,718.504 983.227Incentives 6,121 9.925 0.000 0.000 100.000 23.803Length 6,121 0.737 0.501 0.000 51.860 1.309Award increase 6,121 0.685 1.000 0.000 1.000 0.464Extension 6,121 0.601 1.000 0.000 1.000 0.490

Panel C: Political Connections

VariableNumber of


Contributions to winner 33,091 0.081 0.000 0.000 1.000 0.272Contributions to loser 33,091 0.062 0.000 0.000 1.000 0.240Contributions to winning chairman 33,091 0.009 0.000 0.000 1.000 0.095

Panel D: Innovation

VariableNumber of


Number of patents 33,091 6.776 0.000 0.000 3,838.000 72.725Number of patents (adjusted) 33,091 0.191 0.000 0.000 37.956 0.739Patent citations 33,091 0.589 0.000 0.000 71.000 2.478Patent citations (adjusted) 33,091 0.265 0.000 0.000 47.189 1.048Self citations 33,091 0.068 0.000 0.000 29.615 0.480Self citations (adjusted) 33,091 0.190 0.000 0.000 95.248 1.477Originiality 33,091 0.105 0.000 0.000 0.942 0.222Originality (adjusted) 33,091 1.154 0.000 0.000 173.655 4.994

This table reports summary statistics for firm-level characteristics, contracts, political contributions and innovation for all firms in Compustat from2003–2010, excluding financial firms (SIC 6000-6999) and regulated utilities (SIC 4900-4999). Panel A details firm characteristics, Panel Bsummarizes contracts, Panel C highlights political connections and Panel D reports on innovation. Size is the natural log of firm assets. R&D/Assets is research and development expense scaled by assets. Profitiability is measured as earnings before interest, taxes and depreciation over total assets ofthe firm. Tangibility is the ratio of net property, plant and equipment to total assets. Book leverage is the book value of debt over total assets.Cash/Assets is measured as cash and short-term investment divided by total assets. Market-to-book is the market value of the firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry (at the SIC level). Profitability , Book leverage and Market-to-book are winsorized at the 1% level in each tail. Contract indicator equals one if a firm receives at least one contract during agiven year. Award amount is the total amount of awards to a firm in a particular year. Incentives is the percent of contracts with incentives, as definedin Appendix A. Length is the average contract length (in years). Percent award change is the average percent change in contract award and Extension is the average contract extension. Contributions to winner is a binary variable, equaling one if a firm contributes to a winning politician in the previouselection cycle. Contributions to loser is a binary variable, equaling one if a firm contributes to a losing politician in the previous election cycle, andContributions to winning chairman is a binary variable, equaling one if a firm contributes to a winning candidate who is the chairman of the Committee onAppropriations or the Committee on the Budget in the Senate or the House. Number of patents is the number of patents awarded to the firm in a yearand Patent citations is the average citations per patent awarded in a year. Number of patents (adjusted) represents Number of patents divided by its annual-technology class mean and, similarly, Patent citations (adjusted) represents Patent citations divided by its annual-technology class mean. Self citations is theaverage citations to a firm's own patents per patent awarded in a year and Originality measures the diversity of citations made by a patent. Therespective adjusted variables are divided by their annual-technology class average. The appendix provides additional information on variabledefinitions (Section A).

33

Table 3: Political Connections and Contracts

Dependent variable Contract Indicator Award Large Award Incentives Long Contract

Model (1) (2) (3) (4) (5)

Contributions to winner 0.104*** 0.145*** 0.183*** -3.909** 0.115***(0.015) (0.052) (0.036) (1.537) (0.032)

Size 0.042*** 0.187*** 0.057*** 0.388 0.026***(0.002) (0.041) (0.007) (1.085) (0.006)

Profitability 0.011** 0.021 -0.012 1.390 0.008(0.005) (0.050) (0.040) (1.267) (0.035)

Tangibility -0.191*** 0.232 -0.495*** -3.413 -0.403***(0.025) (0.235) (0.091) (7.359) (0.079)

Book leverage -0.018** -0.004 -0.098 -0.803 -0.074(0.009) (0.050) (0.061) (2.211) (0.059)

Cash/Assets -0.161*** 0.015 -0.282*** -2.939 -0.298***(0.018) (0.121) (0.072) (3.668) (0.062)

Market-to-book 0.002*** 0.019*** 0.004 0.086 -0.001(0.000) (0.004) (0.003) (0.124) (0.003)

HHI 0.024 -0.212 0.129 0.136 0.065(0.024) (0.241) (0.081) (7.432) (0.067)

Sample All firmsFirms receiving

contractsFirms receiving



contractsYear fixed effects Yes Yes Yes Yes YesIndustry fixed effects Yes No Yes No YesFirm fixed effects No Yes No Yes No

Pseudo-R2 0.176 0.056 0.115 0.030 0.090Observations 33,091 6,121 6,121 6,121 6,121

This table examines how the likelihood of a firm receiving a contract from the U.S. federal government is related to politicalconnections and firm characteristics. Contract Indicator , which equals one if a firm receives a contract in a given year. Award Amount is the natural log of total contracts awarded (in millions of dollars) in a particular year and Large Award equals one if theawards in a given year are above median for firms receiving contracts. Incentives is the percent of contracts with incentives in agiven year. Long Contract is an indicator variable equaling one if contract length is above median for firms receiving contracts andLarge Award equals one. Contributions to winner is a binary variable, equaling one if a firm contributes to a winning candidateduring the previous election cycle. Size is the natural log of firm assets. Profitability is measured as earnings before interest, taxesand depreciation over total assets of the firm. Tangibility is the ratio of net property, plant and equipment to total assets. Book leverage is the book value of debt over total assets. Cash/Assets is measured as cash and short-term investment divided by totalassets. Market-to-book is the market value of the firm's equity and its book value of debt relative to the firm's assets. HHI is theHerfindahl-Hirschman Index of Sales for the industry (at the SIC level). Profitability , Book leverage and Market-to-book arewinsorized at the 1% level in each tail. Industries are defined at the Fama-French 12-industry level. All models include yearfixed effects and an intercept term. Probit specifications (models 1, 3 and 5) report marginal effects at the mean. Standard errorsare reported in parentheses and clustered at the firm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

34

Table 4: Renegotiation

Dependent variable Award Increase Large Award Increase Extension Long Extension

Model (1) (2) (3) (4)

Contributions to winner 0.117*** 0.109*** 0.103*** 0.118***(0.027) (0.025) (0.029) (0.030)

Size 0.013*** 0.013*** 0.025*** 0.019***(0.005) (0.005) (0.005) (0.006)

Profitability 0.028 0.038 0.010 0.025(0.026) (0.030) (0.023) (0.026)

Market-to-book 0.003 0.006** 0.003 0.002(0.002) (0.003) (0.002) (0.002)

HHI -0.008 0.006 0.097 0.043(0.061) (0.059) (0.064) (0.063)

SampleFirms receiving




contractsYear fixed effects Yes Yes Yes YesIndustry fixed effects Yes Yes Yes YesR-squared 0.040 0.020 0.064 0.054Observations 6,121 6,121 6,121 6,121

This table explores how political contributons are related to subsequent changes in contracts. Award Increase is an indicatorvariable equaling one when a firm receives an increase in awarded contracts. Large Award Increase is a binary variable equalingone if award increase is above median for firms receiving contracts. Extension is an indicator variable equaling one when afirm receives an extension in the completion date in awarded contracts and Long Extension is a binary variable equaling one ifthe extension is above median for firms receiving contracts. Contributions to winner is a binary variable, equaling one if a firmcontributes to a winning candidate during the previous election cycle. Size is the natural log of firm assets. Profitability ismeasured as earnings before interest, taxes and depreciation over total assets of the firm. Market-to-book is the market valueof the firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry. Profitability and Market-to-book are winsorized at the 1% level in each tail. The sample includes years whenfirms receive contracts. Industries are defined at the Fama-French 12-industry level. All models include year and industryfixed effects and an intercept term. Probit specifications (all models) report marginal effects at the mean. Standard errors arereported in parentheses and clustered at the firm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

35

Table 5: Robustness Tests

Panel A: Losing Candidates

Dependent variable Contract Indicator Large Award Length Award Increase Extension

Model (1) (2) (3) (4) (5)

Contributions to winner 0.110*** 0.152*** 0.152*** 0.107** 0.122***(0.021) (0.052) (0.045) (0.042) (0.047)

Contributions to loser -0.008 0.045 -0.051 0.014 -0.025(0.020) (0.052) (0.043) (0.046) (0.049)

Size 0.042*** 0.057*** 0.027*** 0.013*** 0.025***(0.002) (0.007) (0.006) (0.005) (0.005)

Profitability 0.011** -0.010 0.006 0.028 0.010(0.005) (0.040) (0.035) (0.026) (0.023)

Tangibility -0.191*** -0.499*** -0.400***(0.025) (0.091) (0.079)

Book leverage -0.018** -0.098 -0.074(0.009) (0.061) (0.059)

Cash/Assets -0.160*** -0.281*** -0.297***(0.018) (0.072) (0.062)

Market-to-book 0.002*** 0.004 -0.001 0.003 0.003(0.000) (0.003) (0.003) (0.002) (0.002)

HHI 0.024 0.127 0.067 -0.009 0.098(0.024) (0.081) (0.067) (0.061) (0.064)





contractsYear fixed effects Yes Yes Yes Yes YesIndustry fixed effects Yes Yes Yes Yes YesR-squared 0.176 0.116 0.091 0.040 0.064Observations 33,091 6,121 6,121 6,121 6,121

This table provides robustness tests for studying the link between political connections and contracts. Incentives is the percent of contracts with incentivesin a given year. Panel A includes losing candidates, in addition to contributions from winning politicians. Panel B studies the relation between theallocation of contracts and committee chairmanships for the Appropriations or Budget committees of the Senate or House of Representatives. Contract Indicator , which equals one if a firm receives a contract in a given year. Large Award equals one if the awards in a given year are above median for firmsreceiving contracts. Length is an indicator variable equaling one if contract length is above median for firms receiving contracts. Award Increase is anindicator variable equaling one when a firm receives an increase in awarded contracts. Extension is an indicator variable equaling one when a firm receivesan extension in the completion date in awarded contracts. Contributions to winner is a binary variable, equaling one if a firm contributes to a winningcandidate during the previous election cycle and Contributions to loser is a binary variable, equaling one if a firm contributes to a losing candidate during anelection. Contributions to winning chairman is a binary variable, equaling one if a firm contributes to a winning politician who is a chairman on theAppropriations or Budget committees of the Senate or the House of Representatives. Size is the natural log of firm assets. Profitability is measured asearnings before interest, taxes and depreciation over total assets of the firm. Tangibility is the ratio of net property, plant and equipment to total assets.Book leverage is the book value of debt over total assets. Cash/Assets is measured as cash and short-term investment divided by total assets. Market-to-book is the market value of the firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for theindustry (at the SIC level). Profitability , Book leverage and Market-to-book are winsorized at the 1% level in each tail. Industries are defined at the Fama-French 12-industry level. All models include year and industry fixed effects and an intercept term. Probit specifications (all models) report marginal effectsat the mean. Standard errors are reported in parentheses and clustered at the firm level. ***, **, and * denote significance at 1%, 5%, and 10%,respectively.

36

Table 5 (Continued) Panel B: Committee Chairmanships

Dependent variable Contract Indicator Large Award Length Award Increase Extension

Model (1) (2) (3) (4) (5)

Contributions to winner 0.097*** 0.161*** 0.096*** 0.099*** 0.091***(0.015) (0.037) (0.032) (0.027) (0.030)

Contributions to winning chairman 0.079*** 0.207*** 0.136*** 0.156*** 0.104**(0.025) (0.057) (0.043) (0.059) (0.049)

Size 0.042*** 0.056*** 0.024*** 0.012*** 0.024***(0.002) (0.007) (0.006) (0.005) (0.005)

Profitability 0.012** -0.009 0.010 0.029 0.011(0.005) (0.040) (0.036) (0.026) (0.023)

Tangibility -0.191*** -0.496*** -0.402***(0.025) (0.091) (0.078)

Book leverage -0.017** -0.096 -0.073(0.009) (0.060) (0.058)

Cash/Assets -0.161*** -0.281*** -0.297***(0.018) (0.072) (0.062)

Market-to-book 0.002*** 0.004 -0.001 0.003 0.003(0.000) (0.003) (0.003) (0.002) (0.002)

HHI 0.024 0.127 0.061 -0.010 0.096(0.024) (0.081) (0.067) (0.061) (0.064)





contractsYear fixed effects Yes Yes Yes Yes YesIndustry fixed effects Yes Yes Yes Yes YesR-squared 0.176 0.118 0.093 0.042 0.065Observations 33,091 6,121 6,121 6,121 6,121

37

Table 6: Contract-Level Analysis

Dependent variable Award Length Incentives Award Change Extension

Model (1) (2) (3) (4) (5)

Contributions to winner 0.216*** 0.120*** 0.003 2.193** 0.072*(0.058) (0.044) (0.016) (1.030) (0.040)

Size 0.064*** -0.012 -0.000 -0.767*** -0.005(0.011) (0.010) (0.004) (0.267) (0.009)

Profitability -0.056 -0.070 -0.014 -0.691 0.015(0.039) (0.054) (0.029) (1.855) (0.048)

Market-to-book 0.002 -0.002 0.000 0.183 0.003(0.003) (0.005) (0.003) (0.172) (0.005)

HHI 0.215 -0.089 -0.004 -5.844 -0.073(0.168) (0.198) (0.063) (6.223) (0.285)

Sample All contracts All contracts All contracts All contracts All contractsAgency fixed effects Yes Yes Yes Yes YesYear fixed effects Yes Yes Yes Yes YesIndustry fixed effects Yes Yes Yes Yes YesR-squared 0.331 0.171 0.155 0.131 0.120Observations 299,789 299,789 299,789 299,789 299,789

This table provides robustness tests for studying the link between political connections and contracts by estimating specifications at the contract level. Award is the natural log of the initial contract award (in thousands of dollars). Length is the contract lengthat signing (in years). Incentives is a binary variable equaling one if a contract has incentives. Award Change is the percent changeof an award after its initial signing, relative to the total award of the contract. Extension is the change in a contract's completiondate after its initial signing (in years). Contributions to winner is a binary variable, equaling one if a firm contributes to a winningcandidate during the previous election cycle. Size is the natural log of firm assets. Profitability is measured as earnings beforeinterest, taxes and depreciation over total assets of the firm. Market-to-book is the market value of the firm's equity and its bookvalue of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry (at the SIC level).Profitability and Market-to-book are winsorized at the 1% level in each tail. Industries are defined at the SIC 4-digit level. Allmodels include year fixed effects and an intercept term. Standard errors are reported in parentheses and clustered at the firmlevel. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

38

Table 7: Innovation Scale and Contracts

Dependent variable Patents in 1 year Patents in 2 years Patents in 3 years Patents in 4 yearsModel (1) (2) (3) (4)Contracts indicator 0.149*** 0.138*** 0.131*** 0.108***

(0.024) (0.023) (0.023) (0.021)Size 0.082*** 0.078*** 0.073*** 0.066***

(0.006) (0.006) (0.006) (0.006)R&D/Assets 0.005 0.008 0.005 0.003

(0.017) (0.017) (0.018) (0.017)Profitability -0.018*** -0.019*** -0.017*** -0.014***

(0.002) (0.002) (0.002) (0.002)Tangibility -0.041 -0.036 -0.029 -0.034

(0.049) (0.048) (0.045) (0.042)Book leverage 0.012*** 0.011*** 0.011*** 0.010***

(0.002) (0.002) (0.002) (0.002)Cash/Assets 0.062** 0.065** 0.066*** 0.059**

(0.025) (0.026) (0.025) (0.025)Market-to-book 0.002*** 0.002*** 0.002*** 0.002***

(0.000) (0.000) (0.000) (0.000)HHI 0.217*** 0.183*** 0.164** 0.186*

(0.054) (0.062) (0.078) (0.098)

Sample All firms All firms All firms All firmsYear fixed effects Yes Yes Yes YesFirm fixed effects Yes Yes Yes YesR-squared 0.187 0.185 0.184 0.180Observations 33,091 27,246 21,908 17,083

This table examines the association between number of patents and receiving contracts from the U.S. government. Thedependent variable is defined as Patents in a certain number of years, which is Number of patents divided by its annual-technology class mean and Number of patents is the number of patents awarded to the firm in a year. Contracts indicator is abinary variable, equaling one if a firm receives a contract in a given year. Size is the natural log of firm assets. R&D/Assets is R&D expense over total assets, where missing R&D is set to zero. Profitability is measured as earnings before interest, taxes and depreciation over total assets of the firm. Tangibility is the ratio of net property, plant and equipment to total assets.Book leverage is the book value of debt over total assets. Cash/Assets is measured as cash and short-term investment dividedby total assets. Market-to-book is the market value of the firm's equity and its book value of debt relative to the firm's assets.HHI is the Herfindahl-Hirschman Index of Sales for the industry. Profitability , Book leverage and Market-to-book are winsorizedat the 1% level in each tail. All models include year and firm fixed effects and an intercept term. Standard errors are reportedin parentheses and clustered at the firm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

39

Table 8: Innovation Novelty and Contracts

Dependent variable Citations in 1 year Citations in 2 years Citations in 3 years Citations in 4 yearsModel (1) (2) (3) (4)Contracts indicator 0.082*** 0.085*** 0.080*** 0.088**

(0.027) (0.027) (0.026) (0.035)Size 0.062*** 0.058*** 0.053*** 0.048***

(0.003) (0.004) (0.003) (0.005)R&D/Assets 0.046 0.040 0.037 0.046

(0.035) (0.035) (0.042) (0.045)Profitability -0.006*** -0.008*** -0.008*** -0.006**

(0.002) (0.002) (0.002) (0.002)Tangibility 0.073* 0.080** 0.099** 0.064*

(0.040) (0.041) (0.042) (0.036)Book leverage 0.005 0.004 0.004 0.005

(0.003) (0.003) (0.004) (0.004)Cash/Assets 0.319*** 0.251*** 0.242*** 0.210***

(0.036) (0.032) (0.034) (0.037)Market-to-book 0.001*** 0.001*** 0.001*** 0.001***

(0.000) (0.000) (0.000) (0.000)HHI 0.209** 0.123 -0.062 0.118

(0.092) (0.099) (0.124) (0.134)

Sample All firms All firms All firms All firmsYear fixed effects Yes Yes Yes YesFirm fixed effects Yes Yes Yes YesR-squared 0.083 0.085 0.077 0.067Observations 33,091 27,246 21,908 17,083

This table examines the association between patent citations and receiving contracts from the U.S. government. Thedependent variable is defined as Citations in a certain number of years, which is Patent citations divided by its annual-technology class mean and Patent citations is the citations per patent awarded. Contracts indicator is a binary variable, equalingone if a firm receives a contract in a given year. Size is the natural log of firm assets R&D/Assets is R&D expense over totalassets, where missing R&D is set to zero. Profitability is measured as earnings before interest, taxes and depreciation overtotal assets of the firm. Tangibility is the ratio of net property, plant and equipment to total assets. Book leverage is the bookvalue of debt over total assets. Cash/Assets is measured as cash and short-term investment divided by total assets. Market-to-book is the market value of the firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry. Profitability, Book leverage and Market-to-book are winsorized at the 1% level in eachtail. All models include year and firm fixed effects and an intercept term. Standard errors are reported in parentheses andclustered at the firm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

40

Table 9: Patents, Political Connections, and Incentives

Dependent variable Patents in 1-4 years Patents in 1-2 years Patents in 3-4 yearsModel (1) (2) (5)Contracts indicator 0.028** 0.022* 0.042**

(0.012) (0.013) (0.021)Contributions to winner -0.190*** -0.183*** -0.186***

(0.037) (0.042) (0.045)Contracts X Contributions to winner -0.204*** -0.188*** -0.260***

(0.057) (0.060) (0.079)Contracts X Incentives 0.114*** 0.108*** 0.120***

(0.030) (0.037) (0.042)Size 0.010*** 0.012*** 0.015***

(0.004) (0.004) (0.005)R&D/Assets -0.005 -0.011 0.008

(0.007) (0.008) (0.009)Profitability -0.002* -0.003** -0.002

(0.001) (0.001) (0.001)Tangibility 0.035* 0.043* 0.053**

(0.019) (0.022) (0.025)Book leverage 0.005*** 0.006*** 0.003**

(0.001) (0.001) (0.002)Cash/Assets 0.021 0.024 0.050***

(0.017) (0.021) (0.018)Market-to-book -0.000 -0.000 -0.000

(0.000) (0.000) (0.000)HHI 0.154** 0.168*** 0.170*

(0.061) (0.061) (0.088)

Sample All firms All firms All firmsYear fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR-squared 0.100 0.082 0.097Observations 33,093 33,092 21,909

This table examines the association between number of patents and receiving contracts from the U.S. government. Thedependent variable is defined as Patents in a certain number of years, which is Number of patents divided by its annual-technology class mean and Number of patents is the number of patents awarded to the firm in a year, averaged over thenumber of years specified. Contracts indicator is a binary variable, equaling one if a firm receives a contract in a given year. Contributions to winner is a binary variable, equaling one if a firm contributes to a winning candidate during the previouselection cycle. Incentives is an indicator variable equaling one if a firm receives a contract with incentives in a given year.Size is the natural log of firm assets. R&D/Assets is R&D expense over total assets, where missing R&D is set to zero.Profitability is measured as earnings before interest, taxes and depreciation over total assets of the firm. Tangibility is theratio of net property, plant and equipment to total assets. Book leverage is the book value of debt over total assets.Cash/Assets is measured as cash and short-term investment divided by total assets. Market-to-book is the market value ofthe firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry. Profitability , Book leverage and Market-to-book are winsorized at the 1% level in each tail. All modelsinclude year and firm fixed effects and an intercept term. Standard errors are reported in parentheses and clustered at thefirm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

41

Table 10: Citations, Political Connections, and Incentives

Dependent variable Citaitons in 1-4 yearsSelf citaitonsin 1-4 years

Originalityin 1-4 years

Model (1) (3) (5)Contracts indicator -0.010 0.001 -0.048

(0.018) (0.021) (0.058)Contributions to winner -0.099*** -0.043 -0.248***

(0.029) (0.035) (0.083)Contracts X Contributions to winner -0.017 -0.076** -0.318***

(0.040) (0.038) (0.117)Contracts X Incentives 0.086*** 0.092*** 0.203**

(0.029) (0.028) (0.081)Size 0.000 0.006 0.015

(0.005) (0.011) (0.017)R&D/Assets -0.007 -0.022 -0.001

(0.018) (0.041) (0.044)Profitability 0.000 -0.000 0.002

(0.001) (0.003) (0.005)Tangibility 0.070** 0.009 0.155*

(0.028) (0.044) (0.088)Book leverage 0.005*** 0.008*** 0.015***

(0.002) (0.003) (0.006)Cash/Assets 0.073*** 0.035 0.290***

(0.021) (0.027) (0.074)Market-to-book -0.000** -0.000* -0.001

(0.000) (0.000) (0.000)HHI 0.198** 0.300 0.522**

(0.088) (0.219) (0.226)

Sample All firms All firms All firmsYear fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR-squared 0.083 0.021 0.080Observations 33,093 33,093 33,093

This table examines the association between number of patents and receiving contracts from the U.S. government.Citations is Patent citations divided by its annual-technology class mean and Patent citations is the citations per patentawarded. Self citations is the average citations to a firm's own patents per patent awarded in a year and Originality measures the diversity of citations made by a patent. The respective adjusted variables are divided by their annual-technology class average. Contracts indicator is a binary variable, equaling one if a firm receives a contract in a given year.Size is the natural log of firm assets. R&D/Assets is R&D expense over total assets, where missing R&D is set to zero.Profitability is measured as earnings before interest, taxes and depreciation over total assets of the firm. Tangibility is theratio of net property, plant and equipment to total assets. Book leverage is the book value of debt over total assets.Cash/Assets is measured as cash and short-term investment divided by total assets. Market-to-book is the market value ofthe firm's equity and its book value of debt relative to the firm's assets. HHI is the Herfindahl-Hirschman Index of Sales for the industry. Profitability , Book leverage and Market-to-book are winsorized at the 1% level in each tail. All modelsinclude year and firm fixed effects and an intercept term. Standard errors are reported in parentheses and clustered at thefirm level. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

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Political Connections, Incentives and Innovation: Evidence from ... · innovation is driven by recent studies, such as, Kogan et al. (2012), which shows that innovation is an important