Business and Politics (2022), 24, 292–317

doi:10.1017/bap.2022.11

RESEARCH ARTICLE

Economic influence activities and the strategic location

of investment
John M. de Figueiredo1*‡ and Davin Raiha2
1
Duke Law School and Fuqua School of Business, Duke University, Durham, NC 27708 and NBER and 2Department of
Economics & Fitzgerald Institute for Real Estate, University of Notre Dame, Notre Dame, IN 46556
*Corresponding author: Davin Raiha, Email: draiha@nd.edu

Abstract
This article examines the economic influence activities (EIAs) of firms. We argue that firms invest in jobs and
establishments in districts of congressional committee members that have oversight over their businesses and
industries. This investment increases as legislators’ power rises in Congress. Our theory makes three
predictions. First, EIAs by firms will be higher in congressional districts where the legislators have substantial
political influence over the firm, relative to districts where legislators have little influence over the firm.
Second, EIAs will increase with the legislators’ power on the focal committee. Third, when a legislator
exits the committee, EIAs will diminish, but previous investments in the district will remain. We test
these predictions by analyzing the Trinet census of establishments, mapped into the committee structure
of the US Congress, by tracking the investment and employment of firms in each industry in each congres-
sional district over time. Using fixed-effects models, we show the predictions of the theory find substantial
support in the US Senate but not the House. We explore causality by using exogenous exits of politicians by
death and scandals to further complement our analysis, and discuss why EIAs may be less likely to occur and
detect in the House.

Keywords: Economic influence activities; firm political influence; economic geography; firm investment; nonmarket strategy

Introduction
Scholars have documented numerous ways in which firms attempt to influence government policy.
The traditional mechanisms studied in the literature on firm influence include campaign contributions
(through political action committees) and informational lobbying.1 More recently, the literature has
examined the corporate use of philanthropy,2 political connections,3 placement of executives in
government,4 and stakeholder mobilization5 as tools to shape legislators’ views on public policy.
One mechanism that has received less attention in the literature are corporate economic influence
activities (EIAs). EIAs refer to the firm’s use of its operational activities and strategy decisions to affect
the economic environment (usually of the politician’s home district) to influence election outcomes
and/or public policy.6 For instance, in early 2019 leaked internal memos revealed that tech giant
Facebook had lobbied government officials in several countries, promising to locate new data centers
‡
We would like to thank Daniel Blake, Zac Peskowitz, Adam Rooney, Ken Shotts, Brian Silverman, Jesper Sorensen, Mike
Toffel, Hye Young You, as well as audiences at the Academy of Management (AOM) Annual Conference, Danish Research
Unit for Industrial Dynamics (DRUID) Conference, Society for Institutional & Organizational Economics (SIOE)
Conference, and RealPac Research Symposium.
1
de Figueiredo, 2002.
2
Bertrand et al., 2020.
3
Acemoglu et al., 2016; Lester et al., 2008.
4
Hillman et al., 1999.
5
Holburn and Raiha, 2017; Walker, 2012.
6
Raiha, 2018.
© The Author(s), 2022. Published by Cambridge University Press on behalf of V.K. Aggarwal. This is an Open Access article, distributed under the
terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution
and reproduction, provided the original article is properly cited.

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 Business and Politics 293

and employment in countries that agreed to relax their data-privacy laws.7 This mechanism, though
understudied in the literature, can be an important tool that firms have to influence politicians and
align a politician’s interests with the firm’s interests.
In this article, we show how firms strategically increase employment and investment levels in pol-
iticians’ home districts in ways that are consistent with EIAs. We also articulate the mechanism. We
argue that congressional committee membership serves as a conduit through which legislators deliver
favorable public policy to firms that are engaged in EIA in the industries that the committee oversees.
Firms will be more likely to shift their operations to home districts of politicians who serve on those
committees than firms in industries not covered by the same congressional committees in those same
districts.
To examine this question, we develop an intertemporal theory of EIAs in a political district over
time. We argue that once a politician joins a congressional committee, firms in the politician’s district
that are overseen, in a regulatory and legislative sense, by the committee will start to engage in EIA,
creating more jobs and establishments in the district. This new entry and incumbent expansion will
increase monotonically until the legislator exits the committee. At that time, the firm will no longer
increase employment or investment, but will keep its current stock of employees and investment at
the same level, ceasing to increment its economic presence more than other industries.
To test this theory, we use the Trinet census of establishments in the United States from 1981 to
1989 and map these establishments into Senate and House districts across the United States. This data-
set contains information on employees, establishments, and sales by Standard Industrial Classification
(SIC) code, and has been used previously in the economics and management literatures.8 Merging data
from political representation in these districts from 1947 to 1992, we can track the committees that
represent each district and how long that representation has been present. Finally, utilizing previous
work from the finance and political science literatures, we map each congressional committee into
the SIC codes that it oversees, allowing us to connect the Trinet data to a political representation data-
base.9 Using various econometric techniques, we find empirical evidence consistent with our theory in
Senate, but find minimal support in the House. We discuss the potential reasons for these differences
in this article.
An issue that arises is whether the main empirical patterns that appears in the Senate can be
explained by alternative mechanisms—for instance, reverse causality, committee membership selection
effects, or dependent variable selection. We explore and rule out these alternative explanations. In par-
ticular, we exploit exogenous departures of Senate committee members (e.g., for reasons of death or
scandal) to address reverse causality. We control for selection effects by showing that our findings
are robust when the sample is restricted only to districts and legislators who have accumulated com-
mittee experience. We also examine changes in sales as an alternative measure of EIA effectiveness.
Throughout all these tests our results are largely unchanged, further supporting the theoretical mech-
anism around EIAs.
We conclude our analysis with extensions exploring additional heterogeneity in political districts
and heterogeneity across industries. We show evidence that politically safe House districts tend to
have greater EIA than marginal House districts. We also find evidence that is consistent with EIAs
being employed across a wide range of industries—from agriculture, natural resources, and heavy
industries, to hotels, retail, and real estate. Finally, using the length of product development cycles
in different industries, we show that industries that can respond quickly to political events with EIA
are more likely to increase investments. These extensions are meant to be useful points of departure
for future work.
This article makes a number of contributions to the literature. First, it measures the magnitude of
firms’ EIAs of employment and investment in political districts. Second, it develops and tests a
time-series theory of how the EIAs proceed during a legislator’s lifetime of political representation.

7
Cadwalladr and Campbell, 2019.
8
Montgomery and Wernerfelt, 1988; Teece et al., 1994; Liebeskind et al., 1996; Silverman, 1999.
9
Ovtchinnikov and Pantaleoni, 2012; Roberts, 1986.

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 294 John M. de Figueiredo and Davin Raiha

Third, it clarifies the role of congressional committee membership and allows us to further pinpoint
the location and industrial scope of EIAs in a given district. Fourth, it verifies the types of industries
utilizing EIAs, and the key legislators who are the most common targets. We close by discussing what
the theory and empirical work mean for the industrial geography of the United States.
The next section reviews the main relevant literature. The “Theory” section develops the theory. The
“Empirical Strategy” section discusses our data and our empirical strategy. The “Main Results” section
presents our main statistical results. The “Robustness” section examines the robustness of our results
and evaluates alternative explanations. The “Extensions” section explores extensions. The “Conclusion”
follows in the last section.

Literature
The literature on firms’ attempts to influence public policy is vast. The mainstay of this literature
focuses on campaign contributions and, more recently, on lobbying. The literature on campaign con-
tributions focuses on targets of campaign contributions10 and outputs of those efforts, including leg-
islator effort11 and votes.12 Likewise, the literature on informational lobbying focuses on the targets of
those efforts13 and the effects of lobbying on policy outcomes.14
This article examines an alternative mechanism that firms use to seek favorable public policy—
EIAs. In the management literature, the idea that firms can strategically use operational activities to
influence policy outcomes emanates from the work of Baron,15 who introduced the idea of nonmarket
strategy as a tool available to the firm to influence profitability. From a strategic management perspec-
tive, the literature has been developed by Funk and Hirschman,16 who argue that a firm’s market deci-
sions could be used for political ends.17 More formally, Bonardi and Urbiztondo18 develop a specific
mechanism of EIAs with a theoretical model of asset freezing, where firms time their investment and
employment decisions in a manner favorable to local politicians’ reelection cycles. Raiha examines the
theoretical microfoundations for EIAs in modeling the informational channels through which the eco-
nomic/operational decisions of firms can provide political benefits to politicians and be used as an
influence tool.19 The model shows how a firm’s strategy choices affects the state of the local economy
and, in turn, the evaluations that voters make of the performance of an officeholder. Because of this,
firms can use this capability to extract subsidies and policy favors from incumbent officeholders.
Empirically, many papers have demonstrated that firms in both developed and developing countries
do seem to time their investment and employment decisions in ways that are consistent with electoral
cycles to support of the firms’ favored legislators.20 There is also an emerging literature on the political
geography of firms as it relates to EIAs. Pang et al. examine how firms change their business strategies
in response to changes in representation on the House Transportation Committee.21 They find that
changes in committee membership induced airlines to offer more air routes within or nearby to com-
mittee members’ districts.22 Bisbee and You study the 300 largest manufacturing firms in the United
10
e.g., Bonica, 2016; Snyder, 1990, 1992.
11
Hall and Deardorff, 2006.
12
Ansolabehere et al., 2003.
13
Blanes i Vidal et al., 2012; de Figueiredo and Richter, 2014; de Figueiredo and Cameron, 2019
14
de Figueiredo and Silverman, 2006; Kang, 2016.
15
Baron, 1995.
16
Funk and Hirschman, 2017.
17
A small number of firms are able to obtain what is known as “rifle shot” clauses in legislation. A rifle shot is usually tax
legislation that is written so narrowly, that it benefits one or a very small number of firms. This phenomenon is beyond the
scope of this article. See Zelenak (1989) for a further discussion of rifle shots.
18
Bonardi and Urbiztondo, 2013.
19
Raiha, 2018.
20
Carvalho, 2014; Bertrand et al., 2018; Bandeira-de Mello, 2018; Bonardi and Urbiztondo, 2011.
21
Pang et al., 2020.
22
This is sometimes called the “Trent Lott effect” when Southwest started offering flights to Jackson, MS, when Senator Lott
was the US majority leader, allegedly in the hopes that Lott would “squelch a proposed change in the federal airline ticket tax that
would hurt Southwest” (see Southwest takes mid-sized city experiment to Mississippi. Tampa Bay Times, 31 October, 1997). This

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 Business and Politics 295

States and found that such firms open subsidiaries in congressional districts that are electorally com-
petitive.23 Barber and Blake find that firms locate new establishments in districts that are politically
similar to areas where the firm currently operates.24
In this article, we extend this literature on EIAs to examine a particular supply side mechanism for
public policy—the legislative committee structure of the United States—and how EIAs can utilize com-
mittee membership in the US House and Senate to obtain public policy. Our article complements and
extends previous work by focusing on the committee structure in Congress and developing a direc-
tional, causal, and dynamic theory of firm investments in congressional districts over time.
The literature on committees in the US Congress is also quite extensive.25 Here, we focus on the
dynamics of firms’ investments in different geographies. It has long been known that firms locate
in districts of committee members who are influential. Bingham and Mier26 note merely one
example:27

[S]ome firms that do significant work for the government may choose to locate a facility in a state
or congressional district that is represented by a legislator with particular influence over that com-
pany’s business. For instance, a defense contractor may find it useful to locate a facility in the
district of a member of the Armed Services Committee. Thus, if the contractor needs help in deal-
ing with the defense bureaucracy, it might be advantageous to be a constituent of a member of
Congress with influence among Defense Department officials. (p. 14)

Stories such as this one have been told across a number of heavy industries.28
Despite this work, most studies of the relationship between committees and firms are confined to a
single industry, or one slice of the economy. This limits our understanding of the scope of firm influence.
Moreover, the current literature, while making a substantial contribution to new and expansion of
investment in various political geographies, does not address the dynamics of EIA. Previous papers
usually show a positive relationship between committee representation and firm investment in a dis-
trict, but usually does not describe the evolution of that investment over time, especially if the legislator
exits the committee. Finally, while papers examining investment or employment decisions of firms in
committee member districts show strong correlation between the economic and political variables,
identifying directional effects of the congressional representation and firm investment in a district is
often difficult.29
We attempt to address these shortcomings in the following theoretical and empirical sections of this
article by developing a dynamic theory of EIAs, across the entire US economy, with a specific emphasis
on industry entry, exit, expansion, and contraction in a focal geography, as it relates to a committee
member’s entry, seniority, and exit from a congressional committee. Moreover, as we describe later

effect has been well documented across many airlines with routes to powerful legislators’ rural cities (see US airlines catering to
politicians. Associated Press, 29 October, 1998).
23
Bisbee and You, 2020.
24
Barber and Blake, 2019.
25
Scholars have found a relationship between district employment and committee membership (Weingast and Moran 1983;
Shepsle 1978). In the area of campaign contributions, there is an extensive literature examining the targeting of campaign con-
tributions to legislators on specific committees (e.g., Stratmann, 2005; Grier and Munger, 1991; Romer and Snyder, 1994;
Kroszner and Stratmann, 1998).
26
Bingham and Mier, 1993.
27
Sorenson (1995, 48) describes the naked strategy of firms to gain favorable contracts:

Contractors … understand the game and purposely locate defense plants in the districts of key Congressional com-
mittee members. When Oklahoma’s Senator Robert Kerr, Chairman of the Senate Finance Committee, asked North
American Aviation what Oklahoma would receive for his support, North American responded with two factories,
one in Kerr’s home town of Tulsa and one in the district represented by House Majority Leader Carl Albert.
28
Sorenson, 1995; Hansen et al., 2011. The patterns we describe here are not unique to the United States. Similar analyses can
be found for other countries (e.g., Blake and Moschieri, 2017 and Bandeira-de Mello, 2018).
29
Canayaz (2018), for example, has examined whether the seniority on committees affect stock market valuations of compa-
nies in those representatives’ districts.

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 296 John M. de Figueiredo and Davin Raiha

in this article, we use natural experiments to help our identification and rule out alternative
mechanisms.

Theory
In this section we develop an intertemporal framework of EIAs and draw testable predictions for the
mechanisms regarding the relationship between congressional committee membership and firm activ-
ity (employment and establishments). We use a bit of notation that will later be useful in describing the
empirical model. We begin by assuming two types of actors: firms and politicians. One can think of
these politicians as US senators or representatives. Each politician is assumed to represent a unique
political district d, and serve on at least one committee j. The committee is assumed to have “oversight”
or regulatory jurisdiction over at least one industry i. Thus, each legislator can be characterized by an
unique (d, j, i) triplet. What is particularly important is that a legislator who serves on committee j is
uniquely positioned to provide favorable legislation or regulation to firms in industry i. The legislator
is also assumed to be particularly concerned about reelection (or promotion in the political hierarchy).
Each firm f that participates in industry i, can choose to locate in one or more districts d. Firms are
assumed to seek long-term profits; they can forgo short-term profits to pursue long-term favorable reg-
ulatory environments. With these assumptions, we now turn to understanding the mechanism for EIAs.
At each in point in time t, the firm makes a decision to expand its operations. It can choose to locate
or expand in the most profitable district from an operational standpoint, or the firm can choose to
locate or expand in a district that is less profitable from an operational standpoint, but in a location
that offers long-term political gains that will result in higher long-term profit. The political gains
we envision is the ability to influence the political environment for the industry. The way in which
the firm attempts to influence the political environment is to locate its new facility or expand its
incumbent facility in the district of a politician who has oversight of the industry. This location deci-
sion increases the employment (and in the case of new investment, establishments) of industry i within
district d.
Recall the example in the “Literature” section. A defense contractor has to decide where to locate a
factory to make avionics. In the absence of any political motive, the firm would locate its facility in
California—California being the most efficient location for the facility for a variety of economic rea-
sons (e.g., labor force). However, assume that a senator on the Armed Services Committee represents
Kansas. The advantage to locating in Kansas is that the firm has a chance to influence the Kansas sen-
ator on defense policy. That policy will affect not only the Kansas facility but also the firm’s facilities
throughout the country. The firm is influential because it has created jobs and revenue in the senator’s
district. Hence the firm, in making the location decision, weighs the utility from locating in the most
efficient location in the country against the gains from locating in a suboptimal location from an oper-
ational perspective, but gaining influence over a legislator with policy-making power over the entire
industry by virtue the legislator’s position on the Armed Services Committee.
Banking call centers provide another example. It may be most efficient to locate a banking call cen-
ter in South Dakota because of the state’s labor force, land costs, and geographic proximity to both
coasts. However, in the 1980s, many new banking call centers were relocated to San Antonio,
Texas. Although the location was inferior to South Dakota on many dimensions, the call centers
were located in the district of Henry Gonzalez, the Chairman of the House Banking Committee.
The facilities, and the numerous jobs they created in Gonzalez’s House district, gave the banks access
and influence to a powerful political figure, access that was not granted to these banks before.30
It is useful to point out that firms may face a collective action problem. While it might be collec-
tively rational for firms in an industry to invest in a district, it may not be individually rational for
those firms to ramp up investment unless there is an individual positive net benefit. As the call center
example highlights, firms and industries do seem to overcome that collective action problem. Indeed,
we often observe examples where the largest firms with the highest benefits from favorable industry
legislation enter the politician’s district first.
30
This story was relayed to us by the CEO of a very large US bank.

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 Business and Politics 297

The aerospace industry’s growth in Alabama is an example where multiple firms within the same
industry have overcome the collective action problem at an industry level to increase employment and
their economic footprint to gain political capital. While Alabama has had an aerospace sector for
decades, recent major growth began with the entry of European aerospace titan Airbus in 2013,
which constructed a new major assembly plant in Mobile.31 It was acknowledged that the move by
Airbus to build an assembly plant well outside its traditional operational geography was to gain polit-
ical allies in the United States even at the expense of operational efficiency. Since the initial Airbus
plant announcement in 2012, other American industry players such as GE Aviation, Lockheed
Martin, Northrop Grumman, and Raytheon Technologies have expanded or located production or
operations in Alabama.32 It is notable that US senator, Richard Shelby, has served on the Senate
Appropriations Committee for more than a decade, as well as on the Appropriations Defense
Subcommittee.
Like the campaign contributions literature, our theory of EIAs does not take a stand as to whether
these investments are requested by the politician—colloquially known in the literature as threats or
“shakedowns”—or whether a firm initiates the investment to contemporaneously or subsequently
influence the legislator. The theory only notes that at least some component of the investment is driven
by political considerations.
What makes EIAs different from campaign contributions, though, is when a legislator exits a
position of power (or retires from congress) campaign contributions cease from the interest
group. However, it is extremely uneconomic for a firm to close a facility when there is turnover
in Congress, given the fixed and sunk cost of setup and operation. This sunk cost raises the cost
of exit; we argue that the firm will not close its current facilities, but it will cease to increase invest-
ment and expansion in that district, shifting its future expansion to districts of new powerful
legislators.
From a research perspective, how we measure campaign contributions and EIAs is also quite dif-
ferent. Campaign contributions are, by definition, a flow variable. By contrast EIAs, measured by
employment or establishments are, by definition, stock variables. This difference also yields different
expected patterns in the relationship between committee membership and firm political influence
activities, namely because stocks accumulate over time while flows do not.
To illustrate our theoretical framework, and contrast it to other forms of political influence, we
present figures 1 and 2. The intertemporal predictions for campaign contributions are illustrated in
figure 1. Before the legislator joins the powerful committee (region A), campaign contributions
are set at some level. While on the committee, the legislator will receive an increasing amount of
contributions as she rises in the committee hierarchy (region B). However, when the legislator exits
the committee, the campaign contributions revert to a lower level (region C).
Our theory of EIAs, however, has different predictions that are described in figure 2. Legislators who
are not on the committee should expect to see firm investment in the industrial sectors governed by the
committee to be standardized at some constant level (or macroeconomically trending level) (region A).
However, in districts where members join the committee, the amount of investment and employment
in those industries overseen by the committee will rise as the seniority of the legislator rises on the
committee (region B). The increase in employment may come from a firm creating a new establish-
ment in the district, or from redistributing its current employees from facilities outside the district
into facilities inside the district. Note, in industries which are not overseen by the industry, the
level of investment and employment in the industry will not rise from the trending level (bottom,
no committee line). As noted earlier, unlike the campaign contribution theory of political influence,
our theory of EIAs predicts that when the legislator exits the committee, the number of jobs and
amount of investment in the district will stay roughly constant (region C). The firm will not exit
the district, but will instead keep the current levels of jobs.

31
Farren and DeHaven, 2019.
32
Underwood, 2016.

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 298 John M. de Figueiredo and Davin Raiha

Figure 1: Committee service and contributions.

Figure 2: Committee service and employment.

In addition to the increasing relationship between accumulated committee service and industry
employment, figure 2 also depicts this relationship as concave—that is, accumulated committee service
is associated with increasing employment, but at a decreasing rate. There are two primary theoretical
reasons that suggest concavity may arise, emanating from work of Bonardi and Urbiztondo33 and
Raiha.34 First, the electoral benefits of EIAs diminish as they grow. Initial boosts to the local economy
and employment improve the electoral popularity of the incumbent, but as employment grows the
incumbent’s electoral fortunes become more certain—thus incremental increases in employment
carry smaller political benefits. Second, once there is a critical mass of employment within an industry,
the incumbent’s interests become aligned with the interests of the firm. This is because voters are
increasingly stakeholders of the firm—either directly as employees, or indirectly through economic
multipliers. As stakeholders with an economic dependence on the firm, their view of the politician
will depend on how well the firm is treated by policy makers. Thus, once employment rises sufficiently,
the incumbent politician is incentivized to have the interests of the firm in mind, even without

33
Bonardi and Urbiztondo, 2013.
34
Raiha, 2018.

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 Business and Politics 299

subsequent firm investment. Relatedly, empirical work finds a curvilinear relationship in the related
area of employment and campaign contributions,35 which further informs our theory.36
Overall, our theory of EIAs argues that firms make operational decisions that consider not only the
economic efficiency of the operations but also their opportunity to gain political influence. Firms will
locate some of their facilities to optimize their operational and political value. This will manifest itself
in the location or expansion of facilities and employment in districts of congressional committee mem-
bers who have oversight of the firm’s industry. Figure 2 encompasses the theoretical predictions. Later
in the section “Robustness” we consider the possibility of reverse causation and other alternative
explanations.

Empirical Strategy
Data
To examine changes in industrial activity at the congressional district level, we aggregate a firm-level
database to the industrial level in each congressional district and analyze how industrial investment
and employment responds to a politician’s committee membership over time. We begin by using
the Trinet Inc. Large Establishment Database—a biennial census of establishments in the United
States from 1981 to 1989—which we will henceforth refer to as the Trinet data. The Trinet data
cover more than 80 percent of all establishments as well as 95 percent of establishments owned by pub-
lic firms in the United States and has been used by scholars of economics and strategic management.37
Because the Trinet data provide addresses for each establishment we are able to map the congressional
district of every establishment in the dataset. Unfortunately, the Trinet data does not include unique
establishment identifiers that are consistent across years, so we are unable to identify changes in
employment by individual establishments. To address this shortcoming in the data, we sum over
the establishments in a given district to determine the total employment, establishments, and sales
for a given SIC industry within each federal congressional district for each of the four survey periods
in the Trinet data. This allows us to observe aggregate firm behavior in industries in congressional dis-
tricts. We believe this aggregation, though not a perfect measurement of each identifiable firm activity
over time, still undergirds a useful test of the theory as it encompasses both new entry and expansion
by incumbents. Moreover, as the call center and aerospace examples indicate, collective action issues
at the industry level, though certainly present, should not eliminate the observable effects at the
industry level.
We combine these economic data with information on congressional districts’ characteristics and,
in particular, information on congressional committee membership from 1947 to 1992.38 As described
in the previous section, we hypothesize membership on any given committee is not necessarily valu-
able to a particular industry—only those committees that oversee the particular industry. For instance,
membership on an energy committee will be more relevant to firms in the energy industry, but less so
for firms in the financial industry. To match SIC industries to the congressional committees that are
relevant to them we use the mappings provided by Ovtchinnikov and Pantaleoni39 and Roberts40 who
map House and Senate committees to SIC industries at the four-digit and two-digit levels, respectively.
Our unit of observation is a (four-digit) SIC code-congressional district in a given year (SIC-CD-Y).
Our data spans the last four survey periods of the Trinet data—1983, 1985, 1987, and 1989, which

35
e.g., Bombardini and Trebbi, 2011.
36
Although we argue that concavity is predicted, one might believe there is a linear or even convex relationship to EIA. The
logic is that as a legislator becomes more senior in the committee, she becomes more powerful, and thus a more attractive target
for favors from firms. We leave it to an empirical analysis to determine if our proposed hypothesis or the alternative posited in
this footnote carries more weight in the data.
37
Montgomery and Wernerfelt, 1988; Teece et al., 1994; Liebeskind et al., 1996; Silverman, 1999.
38
Nelson, Garrison. “Committees in the US Congress, 1947–1992,” obtained from Charles Stewart III’s website (http://web.
mit.edu/17.251/www/data_page.html), accessed May 15, 2018.
39
Ovtchinnikov and Pantaleoni, 2012.
40
Roberts, 1986.

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 300 John M. de Figueiredo and Davin Raiha

yields an unbalanced panel of approximately 440,000 observations. The sample spans 1,195 different
SIC industries and 431 different congressional districts.41

Variables
Our main dependent variables are the number of people employed, and the number of establishments,
aggregated by SIC industry within a congressional district, in a given year. These variables measure a
particular industry’s economic presence and footprint within a congressional district.
As mentioned in the “Theory” section, there are three predictions of our theory, each corresponding
to a region in figure 2. To distinguish between these regions we require two different types of indepen-
dent variables that capture the time accrued after a particular event has occurred. Because these var-
iables measure time accrued and are, as a consequence, (monotonically) nondecreasing we call these
measures duration clocks.
Our main independent variable is a duration clock of the number of two-year terms a particular
congressional district’s representatives have accumulated on congressional committees of relevance
to the given SIC industry. For example, consider the four-digit SIC industry 5144—Poultry and
Poultry Products—for whom the House Agriculture Committee and Senate Agriculture, Nutrition,
and Forestry Committee are the only relevant committees.42 Also consider two particular congressio-
nal districts. Prior to 1983, in Illinois’s 17th district, no House representative had served on the House
Agriculture committee (dating back to 1947), until Rep. Lane Evans served three terms on the com-
mittee between 1982–88. As a result the relevant House duration clock for SIC 5144 in Illinois’s 17th
district begins the sample period with a value of 1 (in 1983), rises to 3 (in 1987), and remains at 3 (in
1989). By contrast, by 1983, Washington’s 5th district Rep. Tom Foley had accumulated ten terms of
service on the House Agriculture Committee (even serving as chair from 1975 to 1981). So the House
relevance duration clock for SIC 5144 in Washington’s 5th district begins the sample period with a
value of 10 (in 1983), rises to 11 (in 1985), and remains constant once Foley left the committee.
Because this particular duration clock measures the accumulated time a district’s representative has
spent while in a committee relevant to the SIC industry, we will refer to this variable as DClockIn.
However, because we also need to capture any different patterns in employment after a district’s
representative has left all committees of relevance, we need a second duration clock. This variable,
which we will refer to as DClockOut, measures the number of terms that have transpired since a par-
ticular district’s representative served on a committee of relevance to the industry. This variable allows
us to separately identify the portion of the committee curve in region C of figure 2, as distinct from the
committee curve in region B, and the noncommittee curve in region C.
Like with the variable DClockIn, this second duration clock is also nondecreasing—even if the dis-
trict’s representative subsequently joins a committee of relevance, DClockOut simply remains constant,
and does not increase. Moreover, DClockOut does not begin to increase until a district’s representative
has left all committees of relevance—so districts whose representatives have never served on a commit-
tee relevant to a particular SIC (i.e., those whose DClockIn = 0) have a DClockOut equal to zero. Like
the variable DClockIn, DClockOut accounts for time transpired since departing relevant committees
incurred from before the sample period (dating back to 1947). For instance, if a district had a repre-
sentative on the House Agriculture committee until 1977 (but not after), then the DClockOut for SIC
5144 in that district would be 3 in 1983, as it would have been three terms since the industry had rep-
resentation on a relevant committee in that district.
Table 1 presents basic descriptive statistics. Because our unit of observation is a four-digit
SIC-congressional district-year (SIC-CD-Y), the average numbers of employees, establishments, and

41
The Trinet data does not include an observation for every four-digit SIC-congressional district combination because many
industries are not present in every congressional district. The observations, therefore, generally have positive employment to be
present in the sample. The sample is unbalanced in part due to incomplete surveying by Trinet Inc., and some establishments
crossing the threshold for inclusion in the survey (i.e., minimum of 20 employees).
42
A notable company in this SIC code would be Tyson Foods Inc.

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 Business and Politics 301

Table 1: Descriptive statistics and sample comparison.

Variable Means St. Dev.

Employees 303.76 1056.20

Establishments 3.26 5.68
Sales (000s dollars) 405.67 1691.46
House Committee Member 0.142 0.349
Senate Committee Member 0.388 0.487
House Duration-Clock In 1.539 2.959
House Duration-Clock Out 1.717 3.729
Senate Duration-Clock In 2.902 3.903
Senate Duration-Clock Out 0.903 1.994

Means St. Dev.

Variable
House Committee Member No Yes No Yes t-Test

Employees 291.91 368.10 1008.47 1332.61 13.52***

Establishments 3.24 3.38 5.65 5.81 5.47***
Sales 399.27 440.31 1660.72 1900.60 5.03***
Observations 370 283 61 169
Means St. Dev.
Variable
Senate Committee Member No Yes No Yes t-Test

Employees 267.52 361.48 848.65 1335.28 25.99***

Establishments 3.11 3.50 5.30 6.21 21.86***
Sales 366.40 467.63 1407.97 2059.48 17.87***
Observations 270 419 171 352
Confidence levels: * = 10%; ** = 5%; *** = 1%.
Note: This table reports the descriptive statistics and comparison for the full sample of SIC-CD-Y observations. In the top portion of the table, the
first column reports the mean value of each key variable, while the second column provides the standard deviations. The middle portion
compares the employment, establishments, and sales of the sample between SIC-CD-Ys whose representative is a House Committee member (on
a relevant committee) or not, and the bottom portion between SIC-CD-Ys either of whose senators is a Senate Committee member (on a relevant
committee) or not. In the middle and bottom portions, the first two columns compare means, the second two columns compare standard
deviations, while the third column is the T-statistic from a test comparing the equality of means between the groups.

sales are quite small, though the standard deviations are quite high. An average SIC-CD-Y has just
more than 300 employees, 3.26 establishments, and approximately $400,000 in sales.43
We see that for about 14 percent of the SIC-CD-Ys the local House member is on a committee of
relevance, while for about 39 percent of the SIC-CD-Ys at least one of the state’s senators is on a com-
mittee of relevance.44 As was mentioned, both duration clocks account for accumulated time spend on/
off committees of relevance from before the sample period, dating back to 1947. This is why the var-
iables DClockIn and DClockOut range from zero to twenty-two.
Table 1 presents a comparison of SIC-CD-Ys whose representative(s) are on committees of rele-
vance and those who are not. Though this simple comparison involves no control variables, we see

43
The SIC-CD with the highest employment (averaged over the sample period) was SIC 8062 (General Medical and Surgical
Hospitals) in Illinois’ 3rd district. The SIC-CD with the highest establishments was SIC 5812 (Eating Places) in California’s 40th
district.
44
The percentage is expected to be higher for Senate committees given that there are two senators for each state, and if a sen-
ator is on a committee of relevance to an industry then the senator is relevant for all the congressional districts in their state.

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 302 John M. de Figueiredo and Davin Raiha

that there is a statistically significant difference in the level of employment, establishments, and sales
between the two groups. Quite clearly, SIC-CD-Ys whose representative(s) are on either a House or
Senate committee of relevance, feature 26.1 percent higher employment, 4.3 percent more establish-
ments, and 10.3 percent higher sales than those who are not.

Empirical Strategy
Table 1 shows greater economic activity in industries whose representatives are on committees of rel-
evance to the industry. Thus, we follow the literature that uses variation in congressional committee
membership as a means of empirical identification (e.g., Bertrand et al., 2020; Fouirnaies and Hall,
2018; Powell and Grimmer, 2016). However, we would like to see whether the relationship holds con-
trolling for factors that affect the local level of economic activity, and whether or not greater accumu-
lated committee service is a predictor of economic activity.
To do this, we estimate the following regression specification:

ln(yidt ) = b0 + b1 DClockInidt + b2 DClockIn2idt

+ b3 DClockOutidt + b4 DClockOutidt
2
(1)
+ a1 i + a2 d + a3 t + e

where yidt is the number of employees or establishments in industry i in district d in year t, while i, δ,
and τ are industry, district, and year fixed effects, respectively.45
This equation not only distinguishes between the sections of the curves in figure 2, but allows for
nonlinearity in the sections. Given our hypothesized relationship, we expect employment to be increas-
ing in DClockIn but at a decreasing rate—thus we expect β1 to be positive and β2 to be negative (but
smaller in magnitude). The theory predicts firms do not systematically alter employment or cease to
make further investments once a representative has departed committees of relevance, thus we expect
the coefficients on DClockOut, β3 and β4 to be statistically insignificant.

Main Results
Our main results are contained in tables 2 and 3. In each table, models A through D as well as models
E through H present four different regression specifications, each leading up to the full specification
from equation 1. Models A and E have DClockIn and DClockOut as the independent variables.
Models B and F include both the linear and quadratic DClockIn variables. Models C and G presents
estimates from the full regression equation (1). All the specifications include congressional district,
four-digit-SIC industry, and year fixed-effects. Models D and H further include control variables
for the Democratic Party vote share, from the most recent presidential election, within the congressio-
nal district, as well as the state unemployment rate. Models A through D have the log of industry
employment as the dependent variable, while models E through H have the log of industry establish-
ments as the dependent variable. Table 2 examines the impact of Senate committees—that is, both
DClockIn and DClockOut measure accumulated service time on (and off) Senate committees.
Table 3 examines the impact of House committees—both DClockIn and DClockOut measure accumu-
lated service time on (and off) House committees. T-statistics are reported in parentheses. Throughout
we use standard errors clustered at the industry-state level.46
Table 2 presents regression results on the impact of Senate committee membership on employment.
The estimates reported in model A show that accumulated service on Senate committees of relevance is
associated with higher industry employment—each additional (two-year) term of committee service
translates into an increase in employment of about 0.84 percent. Model A also shows a modest increase
45
There are a total of approximately 1,620 fixed effects.
46
This clustering is similar to that in Bertrand et al. (2020), examining the impact of congressional committee membership on
corporate philanthropy.

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Table 2: Full sample regressions—employees/establishments and Senate committees.

ln(Employment) ln(Establishments)
DV
A B C D E F G H

DClockIn 0.0084*** 0.0153*** 0.0151*** 0.0116*** 0.0046*** 0.0075*** 0.0074*** 0.0049**

(4.89) (5.23) (5.13) (3.77) (4.28) (3.98) (3.93) (2.46)
DClockIn-squared. –0.0006*** –0.0005*** –0.0003 –0.0003** –0.0002** –0.0001
(–3.20) (–2.78) (–1.49) (–2.22) (–1.99) (–0.65)
DClockOut 0.0044** 0.0026 0.0027 0.0021 0.0037 0.0034
(1.98) 0.65 (0.67) (1.61) (1.48) (1.34)
DClockOut-squared. –0.0000 –0.0001 –0.0003 –0.0003
(–0.02) (–0.19) (–1.26) (–1.26)
Pres. Democrat Vote Share –0.0013*** –0.0009***
(–4.35) (–5.00)
State Unemployment 0.0062** –0.0001
(2.07) (–0.05)
District FEs Yes Yes Yes Yes Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes Yes Yes Yes Yes

Business and Politics

F-statistic 12.24 15.51 8.01 7.50 9.34 9.97 5.80 6.64
Observations 441,756 441,756 441,756 371,316 441,756 441,756 441,756 371,316
Confidence levels: * = 10%; ** = 5%; *** = 1% (t-values).
Note: This table presents estimates of eight regression models, of accumulated committee service in the Senate on relevant industry employment and establishments, using the full sample. In specifications A–D the
dependent variable is the log of industry employment, while in specifications E–H the dependent variable is the log of industry establishments. All specifications include industry, district, and year fixed effects. The
F-statistic of the test of the joint significance of all explanatory variables, is reported below each column. T-statistics are reported in parentheses. Standard errors are clustered at the four-digit SIC-state level.

303
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304
John M. de Figueiredo and Davin Raiha
Table 3: Full sample regressions—employees/establishments and House committees.

ln(Employment) ln(Establishments)
DV
A B C D E F G H

DClockIn 0.0009 0.0007 0.0031 0.0038 0.0004 –0.0034*** –0.0025* –0.0024

(0.90) (0.30) (1.25) (1.40) (0.76) (–2.76) (–1.82) (–1.64)
DClockIn-squared. –0.0000 –0.0001 –0.0002 0.0003 0.0002** 0.0002**
(–0.02) (–0.78) (–0.86) (3.22) (2.40) (2.19)
DClockOut –0.0010 –0.0043* –0.0047* –0.0013*** –0.0006 –0.0005
(–1.31) (–1.86) (–1.90) (–3.02) (–0.49) (–0.34)
DClockOut-squared. 0.0002 0.0003 –0.0000 –0.0000
(1.48) (1.56) (–0.24) (–0.18)
Pres. Democrat Vote Share –0.0012*** –0.0008***
(–3.92) (–4.36)
State Unemployment 0.0051* –0.0009
(1.68) (–0.41)
District FEs Yes Yes Yes Yes Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes Yes Yes Yes Yes
F-statistic 1.06 0.20 1.10 3.86 4.59 5.21 3.67 4.96
Observations 431,437 431,437 431,437 363,211 431,437 431,437 431,437 363,211
Confidence levels: * = 10%, ** = 5%, *** = 1% (t-values)
Note: This table presents estimates of eight regression models, of accumulated committee service in the House of Representatives on relevant industry employment and establishments, using the full sample. In
specifications A–D the dependent variable is the log of industry employment, while in specifications E–H the dependent variable is the log of industry establishments. All specifications include industry, district, and year
fixed effects. The F-statistic of the test of the joint significance of all explanatory variables, is reported below each column. T-statistics are reported in parentheses. Standard errors are clustered at the four-digit SIC-state
level.
 Business and Politics 305

in employment following the departure of a relevant Senate committee member. Model B shows that
there appears to be a nonlinearity in the relationship between committee service and employment. The
coefficient on DClockIn is positive and statistically significant, while the coefficient on DClockIn-
squared is of much smaller magnitude, negative, and statistically significant. This indicates that
Senate committee service is associated with increasing employment, but at a decreasing rate—as
hypothesized.47 The magnitudes of the coefficients indicate that employment would peak after thirteen
terms of accumulated Senate committee service. In contrast to model A, the statistical significance of
the coefficient on DClockOut vanishes in models C and D where we account for nonlinearity in the
relationship between our independent variables and employment. The coefficients on DClockIn are
nearly identical to those in model B, while the coefficients on DClockOut are both statistically insig-
nificant. Furthermore, the magnitude of the coefficients (i.e., point estimates) on DClockOut are both
considerably smaller than for DClockIn.48 Overall, these results appear consistent with our hypotheses
that committee service should translate into increased employment (at a decreasing rate), but time
after committee departure produces no significant change in employment.49
Models E through H in table 2, are analogous to models A through D, and present regression results
on the impact of Senate committee membership on the number of establishments. Model E shows that
accumulated service on Senate committees of relevance is associated with a higher number of estab-
lishments, while time accrued postcommittee has no statistically significant association with establish-
ments. The magnitude, however, is smaller than the same coefficient in model A—each additional
term of committee service translates into an increase in establishments of about 0.46 percent.
Model F, further, shows that there appears to be a nonlinearity in the relationship between committee
service and establishments. Once again, the direction and statistical significance of the coefficients on
DClockIn are similar to their counterparts from the regression on employment. Thus, it appears that
Senate committee service is associated with increasing establishments, but at a decreasing rate. The
magnitudes of the coefficients on establishments indicate that establishments would also peak after
approximately thirteen terms of accumulated Senate committee service. The results in models G
and H are qualitatively similar to their counterparts in models C and D.50 The coefficients on
DClockIn are nearly identical to those in model B, while the coefficients on DClockOut are both stat-
istically insignificant. Overall, the results in table 2 indicate that there is a similar relationship between
establishments and Senate committee service, as there was between employment and committee ser-
vice, though the magnitude is smaller for establishments than for employment.51
Table 3 is analogous to table 2—it presents regression results on the impact of House committee
membership on employment and establishments. However, unlike table 2, the regression estimates in
table 3 fail to find the same statistically significant association. The coefficients on DClockIn are not stat-
istically significant, though the direction of the coefficients is as hypothesized—that is, the coefficient on
DClockIn is positive, while the coefficient on DClockIn-squared is of much smaller magnitude and neg-
ative. Further, little changes when DClockOut is included in the regressions—the coefficients on
DClockIn remain statistically insignificant, while the coefficients on DClockOut are, for the most part,
also statistically insignificant.52 Overall, table 3 does not indicate the same association between House
committee service and employment or establishments as we observed in the Senate.

47
The full DClockIn effect does not turn negative until twenty-six terms of service are reached by the legislator. To further
explore the concavity, we run the models using levels in the dependent variables (instead of logs). In results presented in appen-
dix table A1, we obtain the same concave effect in these regressions.
48
Each additional term of committee service translates into an increase in employment of about 1.16 percent for new com-
mittee members, while leveling off to 0.92 percent for the median number of years of committee service.
49
Though we find no statistically significant reduction in employment following the departure of a committee member, our
model does allow for the possibility that firms might reduce their economic footprint in response to a departure—a possibility
articulated by Bertrand et al. (2018).
50
All the results for the Senate are qualitatively unchanged when defense contractors are excluded from the analysis.
51
Expansion of an incumbent firm will most likely be seen in an increase in employment but not a new facility (establish-
ment). New entry by a firm from outside the district should be seen in both an increase in the number of establishments
and an increase in employment.
52
In model E, the coefficient on DClockOut is statistically significant, but its direction is negative.

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 306 John M. de Figueiredo and Davin Raiha

In sum, our main results are consistent with the predictions of our hypotheses for Senate commit-
tees, but not for committees in the House of Representatives. Although we expected to find similar
results in the House and Senate, we can identify a number of institutional reasons why one might
find this difference between the two chambers.
First, on a per representative basis, senators are more powerful than House members. There are only 100
senators compared to 435 House members. Furthermore, congressional committees in the Senate are
smaller than those in the House. This difference in power may make it more attractive for firms to target
key senators compared to House committee members. The more limited power of individual House mem-
bers may make them less ideal targets for (arguably) more expensive influence activities such as EIAs.
Second, senators each serve six-year terms as opposed to the two-year terms served by House mem-
bers. If EIAs are intended to influence and establish a longer-term relationship between an industry
and its representatives, the longer terms of senators allows for less risk for firms in making (more)
permanent economic investments.
Third, the jurisdictions of House members are geographically smaller than the jurisdictions of sen-
ators. This makes senators easier targets for EIAs because the specific location of employment and
establishments does not need to be as accurate as it would need to be in targeting House members’
districts.
Fourth, House districts are subject to periodic redistricting, while state boundaries are not altered.
This geographic uncertainty might also make senators relatively more attractive for location-based
EIAs than House members.
Fifth, in concentrated urban areas, the people who work in a particular congressional district may
not live in the same district, but rather commute from another district. Even if jobs are created in a
specific congressional district, those jobs may instead benefit workers from other districts. This
same leakage is less common across state boundaries.
Together, these reasons provide possible explanations for the difference in the House and Senate
results. However, more work is needed to more fully explore the factors that drive the differences in
the results.

Robustness
Though our main results support our theory that firms use EIAs to influence key congressional com-
mittee members in the Senate, there are several alternative explanations that could instead explain the
observed results. In this section we discuss three possible alternative stories that could account for our
findings. Because our main results indicated no systematic relationship between committee service and
economic activity in the House, our remaining results in this section focus on Senate committees.

Committee assignments chasing economic activity

One alternative explanation is that firms aren’t responding to congressional committee membership
and seniority, but rather the reverse—legislators are joining committees of relevance when they observe
certain industries growing in their districts. Then, once the representative anticipates the industry
growth is finished, she chooses to leave the relevant committees. This story assumes legislators are
making these choices to join as well as leave approximately in conjunction with industry growth com-
mencing or terminating.
Discerning between this alternative explanation and our hypothesis, based on EIA, is challenging
because a legislator’s decision both to join and leave a committee is endogenous. However, there
are circumstances in which a legislator leaves congressional committees not by choice, but rather by
exogenous circumstances—for instance, due to death or scandal.
We use the exogenous departures of senators, as described in figure 3, to test whether the alternative
reverse story holds. Models A and C in table 4 report regression estimates that are analogous to models
C and G in table 2, but have some key differences in the sample. While results in table 2 are obtained
from regressions run on the full sample, the results in table 4 are obtained from regressions only

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 Business and Politics 307

Figure 3: Exogenous committee exit and employment.

Table 4: Robustness regressions—Senate committees.

ln(Employment) ln(Establishments)
DV ln(Sales)
A B C D E

DClockIn 0.0189** 0.0092** 0.0107* 0.0051* 0.0198***

(2.17) (2.15) (1.74) (1.76) (6.44)
DClockIn-squared. –0.0010** –0.0003 –0.0009** –0.0002 –0.0005***
(–2.40) (–1.24) (–3.07) (–1.01) (–2.64)
DClockOut –0.0203 0.0037 –0.0079 0.0039 0.0004
(–1.46) (0.82) (–0.89) (1.39) (0.08)
DClockOut-squared. –0.0011 –0.0002 0.0007 –0.0004 0.0004
(–0.74) (–0.36) (0.75) (–1.48) (0.87)
District FEs Yes Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes Yes
F-statistic 6.66 1.40 4.27 1.61 14.93
Observations 100,911 254,639 100,911 254,639 439,171
Confidence levels: * = 10%; ** = 5%; *** = 1% (t-values).
Note: This table presents estimates of five regression models, of accumulated committee service in the Senate on relevant industry employment
or establishments. Specifications A and C use a subsample of SIC-CD-Ys for whom either (1) DClockOut is zero, due to an exogenous departure,
or (2) DClockOut is positive, and the representative is on committee. Specifications B and D use a subsample of SIC-CD-Ys for whom DClockIn is
nonzero. Specification E uses the full sample. In specifications A and B the dependent variable is the log of industry employment, in
specifications C and D the dependent variable is the log of industry establishments, and in specification E the dependent variable is the log of
industry sales. The F-statistic of the test of the joint significance of all explanatory variables, is reported below each column. T-statistics are
reported in parentheses. Standard errors are clustered at the four-digit SIC-state level.

including SIC-CD-Ys where the senator is currently on a committee of relevance or, if not on a com-
mittee, where the departure was due to an exogenous event. The exogenous departures we consider are
departures due to death, resignation related to scandal, or promotion to a higher office (e.g., vice pres-
ident). In this way, the regressions in table 4 are comparing the increasing and concave curve regions
from B and C of figure 3, against the “depart” (constant) curve from region C of figure 3. The alter-
native explanation would predict the trend in employment and establishments for SIC-CD-Ys that

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 308 John M. de Figueiredo and Davin Raiha

retain committee members of relevance is different from trends for SIC-CD-Ys whose committee
members departed exogenously.
The results presented in models A and C of table 4 are qualitatively similar to our main results.
Accumulated Senate committee service is associated with increases in employment and establishments.
Moreover, the increases to both employment and establishments occur at a decreasing rate. The mag-
nitudes of the coefficients are also similar to our main results.
More important are the coefficient estimates on the DClockOut variables, where the departures are
due only to exogenous events. We see in models A and C that growth in employment and establish-
ments levels off after the departure of the senator from a committee of relevance, even though the
departure is exogenous. This is inconsistent with the alternative explanation. In light of these findings
on exogenous departures, this alternative explanation seems unlikely. Similar to arguments made by
Bertrand et al. (2020), who also examined congressional committee departures in establishing causality
in strategic targeted corporate philanthropy, the fact that growth in employment and establishments
levels off after committee departure suggests that at least part of the initial firm investments are polit-
ically motivated.

Committee member selection

Another alternative explanation for our main results is that it could be driven largely from an under-
lying difference between the districts of congressional committee versus noncommittee members. For
instance, it has been well established that members in Congress self-select into committees that either
have jurisdiction over issues that are key for their constituents and/or reflect the main industries in
their districts.53
Perhaps then the result we obtain is simply a result of a level difference between districts with com-
mittee members and those without? Under this argument, industries in districts with committee rep-
resentation would have naturally higher levels of employment and establishments, and these levels do
not increase with more committee service. Rather, our result may be driven entirely by a move from
DClockIn being zero, to DClockIn being positive.
We examine this potential alternative explanation. First, our results on exogenous departures
(models A and C in table 4) involve only SIC-CD-Ys with at least some accumulated Senate committee
service—that is, for all SIC-CD-Ys in the subsample, DClockIn is strictly positive. Yet the results are
similar in magnitude and statistical significance to our main findings. Second, we estimate the same
regressions as models C and G from table 2, but with the sample restricted only to SIC-CD-Ys that
have accrued at least one term of relevant committee service—that is, DClockIn is at least one.
We present the results in models B and D in table 4. In models B and D we see that the coefficient
on DClockIn is positive and statistically significant, and the coefficient on DClockIn-squared is nega-
tive, and of approximately the same magnitude as in previous estimates—however, now it is statistically
insignificant. As before, the coefficients on DClockOut remain statistically insignificant. We therefore
still observe a positive association between accumulated Senate committee service and both employ-
ment and the number of establishments.54 Overall, we can again see that employment and establish-
ments do increase with accumulated committee service, even conditional on a legislator already having
served on the committee. Hence, the “difference-in-level” argument cannot replace the accumulated
committee experience explanation we provide.

EIA Effectiveness
A final concern that may arise relates to measurement of the dependent variable in our main statistical
analysis. We focus on potential inputs that we believe directly reflect EIA—employment and

53
See Fenno (1973), Mayhew (1974), Shepsle (1978), or Weingast and Moran (1983).
54
Though not reported, the results are qualitatively similar in magnitude and statistically stronger in significance for a regres-
sion with sales as the dependent variable.

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 Business and Politics 309

investment (business creation). But is EIA effective? Are the inputs yielding benefits to the firms that
engage in EIA?
Measuring EIA’s effectiveness is extremely difficult. The effect might manifest itself in one or many
forms, might accrue in various geographies in which the firm competes, and might benefit the firm in
the form of higher prices, lower costs, more quantity, or better future corporate positioning. Given the
heterogeneity of firm and industries engaged in EIA, pinpointing the results of these activities in a
large-scale, cross-industry dataset is challenging.
We can, however, attempt a partial examination of the question in the committee members’ focal
district. The Trinet data records sales by establishment. To the extent that EIA results in “quantity”
benefits in the focal congressional district, we should see an increase in the relevant industry sales
in the district during the legislator’s committee tenure.
To test this possibility, we aggregate the sales data to the four-digit-SIC-congressional-district-year
level and estimate equation (1) with sales being the dependent variable. Model E of table 4 presents
regression results on the impact of Senate committee membership on sales. As with employment
and number of establishments, we find strong evidence that accrued Senate committee service results
in higher sales, and that sales increase at a decreasing rate with more accumulated committee service.
However, in model E both DClockOut coefficients are statistically indistinguishable from zero. These
results indicate that not only do sales increase with accumulated committee service (as with employees
and establishments), sales remain at the same levels following a representative’s departure from com-
mittee. While a rough and partial test, these results of the benefits of EIA are consistent with the pat-
terns of EIA found in the main regression analysis.

Extensions
In this section, we explore extensions to our theory and main results. Although the results in this sec-
tion are not definitive, they do establish what we believe are interesting facts that can be extended by
future work.

Political heterogeneity
In the main section of the paper, we include district fixed-effects in all regressions to control for time-
invariant district heterogeneity. However, the political competitiveness of a district may change over
time and cause different EIA intensity by firms. The recent literature does not contain a single predic-
tion as to how electoral competitiveness might affect EIA intensity. On one hand, tight political con-
tests might result in higher job creation rates55 and, on the other hand, wide election margins by a
politician might attract private investment to a legislator who might be a long-term attractive ally.56
To examine the role of vote margin on EIA, we estimate models that include an election vote mar-
gin variable as a measure of safety. In particular, we add Vote Margin to the employment specification
in equation (1), both directly and interacted with the DClockIn variables.57
The results including Vote Margin are presented in table 5. In the Senate models A and B we see
that our original findings are unchanged by the inclusion of vote margins. The coefficient for DClockIn
is still positive, statistically significant, and of a similar magnitude to our main results in table 2. By
itself, the coefficient on Vote Margin is positive and statistically significant. The positive magnitude
indicates that states with higher average win margins exhibit higher employment. In model B we
find no coefficients on the interaction terms between Vote Margin and DClockIn (or its square) are
statistically significant. Overall, inclusion of Vote Margin has little effect on our original Senate results.

55
Bertrand et al., 2018; Raiha, 2018; Bisbee and You, 2020.
56
Berry and Fowler, 2018; Romer and Snyder, 1994.
57
We define Vote Margin in table 5 as the difference between the percentage of votes obtained by the election winner over the
second place candidate. For House races, the win margin is the simple difference in percentage votes between the top two vote
receiving candidates in the previous election. However, because states have two senators, we the average win margins from the
previous elections for each Senate seat as our win margin measure.

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 310 John M. de Figueiredo and Davin Raiha

Table 5: Win margins results—Senate and House committees.

ln(Employment)
DV
Senate House
Committees
A B C D

DClockIn 0.0150*** 0.0139*** 0.0039 0.0038

(5.09) (3.03) (1.44) (1.41)
DClockIn-squared. –0.0005*** –0.0004 –0.0002 –0.0002
(–2.71) (–1.51) (–1.86) (–0.87)
DClockOut 0.0027 0.0027 –0.0048* –0.0050**
(0.69) (0.69) (–1.93) (–1.99)
DClockOut-squared. –0.0000 –0.0000 0.0003 0.0003
(–0.01) (–0.01) (1.59) (1.62)
Vote Margin 0.0013*** 0.0013*** 0.0008*** 0.0003
(4.06) (3.37) (4.78) (1.42)
Vote Margin×DClockIn 0.0001 0.0003***
(0.32) (4.05)
Vote Margin×DClockIn − squared. –0.0000 –0.00001**
(–0.43) (–2.47)
Pres. Democrat Vote Share –0.0047*** –0.0047*** –0.0075*** –0.0075***
(–7.77) (–7.77) (–10.54) (–10.55)
State Unemployment 0.0024 0.0023 0.0067** 0.0069**
(0.85) (0.84) (2.21) (2.28)
District FEs Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes
F-statistic 14.79 11.54 19.25 17.65
Observations 441,756 441,756 363,211 363,211
Confidence levels: * = 10%; ** = 5%; *** = 1% (t-values).
Note: This table presents estimates of four regression models, of accumulated committee service in the Senate and House on relevant industry
employment, using the full sample. In all models the dependent variable is the log of industry employment. In models A and B the committee
service is in the Senate, while in models C and D the committee service is in the House. All models include industry, district, and year fixed
effects. The F-statistic of the test of the joint significance of all explanatory variables, is reported below each column. T-statistics are reported in
parentheses. Standard errors are clustered at the four-digit SIC-state level.

Models C and D in table 5 show, however, the win margin results are not the same in the House. In
models C and D, the direct effects of DClockIn and its square continue to be statistically insignificant,
as in the original results. In addition, in model C, the direct effect of Vote Margin is positive and stat-
istically significant, as in the Senate models in the table. But in model D, we find that the interactions
between win margin and DClockIn are both statistically significant. This finding, in model D, provides
the first glimpse of a result in the use of EIA for House committees, and indicates that increased com-
mittee seniority is associated with increased employment only in electorally safe House districts. The
result is worth further future investigation.

Industry heterogeneity: Breadth of industries

While our main findings indicate that firms appear to engage in EIA toward relevant Senate committee
members, we examine here the extent of EIA across industries. To do this, we run regressions with

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 Business and Politics 311

separate DClockIn variables for each two-digit SIC industry. We estimate the following regression
specification:

ln(yidt ) = b0 + b1 DClockInidt
SIC=01
+ . . . + b97 DClockInidt
SIC=97
(2)
+ b3 DClockOutidt + a1 i + a2 d + a3 t + e

where yidt is employment, and i, δ, and τ are defined as before. DClockInidt SIC=X
is the industry-specific
DClockIn variable. Equation (2) allows us to measure the importance of Senate committee seniority
differently across different industries. We use two-digit SIC industries because this involves only
seventy-four DClockIn coefficients.58 Also, to simplify the analysis and reduce the number of coeffi-
cient estimates, we do not include squared terms.
We estimate a total of seventy-four two-digit industry coefficients.59 A total of nineteen of the
industry DClockIn coefficients are positive and statistically significant, while twenty-nine of them
are positive but not statistically significant. Another twenty coefficients are negative and not statistically
significant, and six of them are negative and statistically significant. Consistent with our earlier find-
ings, the coefficients on the DClockOut variables are not statistically significant. The binomial proba-
bility of at least nineteen coefficients being statistically significant by chance is less than 0.1 percent,
even at a 10 percent significance level.
Table A2, in the appendix, presents the coefficient estimates from the industries that had positive
and statistically significant coefficients. A broad variety of industries are represented ranging from agri-
culture to oil and gas, to eating and drinking places, industrial machinery and equipment, as well as
hotels and other lodging. The scope of industries ranges from heavy industries studied by other
researchers,60 to more service-oriented industries often overlooked in the literature.

Industry heterogeneity: Responsiveness of investment

In a final extension, we explore the responsiveness of investment entry to committee membership and
seniority. While some industries may face significant lag times and start-up costs when locating new
establishments, or reorienting operations and employment, others might have varying degrees of flex-
ibility and ease in creating new operations.
We attempt to quantify the ease with which firms in an industry can make new investments in EIA,
by examining one potential measure of operational flexibility and time horizon—the length of the
product development cycle (PDC). The PDC refers to the industry average time it takes to develop
and bring new products to market.61 Some industries, such as chemicals and pharmaceuticals, face
long PDCs as it typically takes years of development, research, and operational readjustment to create
new products. By contrast other industries, such as retail or printing and publishing, can develop and
reorient operations to develop new products in a shorter period of time. As such an industry’s typical
PDC is likely correlated with its ease of operational flexibility.
In the context of EIAs, we believe that firms in industries with greater operational flexibility and
ease of entry are more likely to employ EIAs. To test this, we employ a dataset of average PDCs at
the industry level developed by National Academy of Engineering62 and Bushman et al.63 We define
the variable Short PDC as those industries with a PDC of less than four years.64 While the set of indus-
tries measured by this dataset is not exhaustive, it covers more than one-third of our samples’
district-industry-year observations.
58
By contrast, performing the same analysis with four-digit SIC industries would require 1,189 coefficients.
59
Seven industries were dropped due to having no relevant Senate committees.
60
Bingham and Mier, 1993; Sorenson, 1995; Hansen et al., 2011.
61
National Academy of Engineering, 1992; Bushman et al., 1996.
62
National Academy of Engineering, 1992.
63
Bushman et al., 1996.
64
This dataset and measure has been used by several papers in the finance and accounting literatures (e.g., Erkens, 2011; Raiha,
2019).

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 312 John M. de Figueiredo and Davin Raiha

Table 6: Industry heterogeneity table—Senate committees.

ln(Employment)
DV
A B C D

DClockIn 0.0055 0.0209*** 0.0142 0.0143

(1.14) (2.34) (1.57) (1.57)
DClockIn-squared. –0.0012** –0.0007 –0.0007
(–2.02) (–1.18) (–1.19)
DClockOut 0.0198** 0.0221 0.0223
(2.38) (1.42) (1.42)
DClockOut-squared. –0.0006 –0.0006
(–0.40) (–0.40)
DClockIn×Short PDC 0.0131*** 0.0246*** 0.0248*** 0.0248***
(4.16) (4.90) (4.95) (4.95)
DClockIn-squared.×Short PDC –0.0009*** –0.0008*** –0.0008***
(–3.61) (–3.05) (–3.03)
DClockOut×Short PDC 0.0081** 0.0049 0.0051
(2.20) (0.63) (0.66)
DClockOut-squared.×Short PDC –0.0000 –0.0000
(–0.05) (–0.06)
Pres. Democrat Vote Share –0.0045***
(–4.33)
State Unemployment 0.0028
(0.57)
District FEs Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes
F-statistic 5.82 7.27 4.23 5.41
Observations 163,876 163,876 163,876 163,876
Confidence levels: * = 10%; ** = 5%; *** = 1% (t-values).
Note: This table presents estimates of four regression models, of accumulated committee service in the Senate on relevant industry employment,
using the subsample of industries for which the product development cycle variable is available. In all models the dependent variable is the log
of industry employment. All models include industry, district, and year fixed effects. The F-statistic of the test of the joint significance of all
explanatory variables, is reported below each column. T-statistics are reported in parentheses. Standard errors are clustered at the four-digit
SIC-state level.

To test our hypothesis, we add interactions to the specification in equation (1), so that the DClockIn
and DClockOut variables appear on their own, as well as interacted with Short PDC.
The results are presented in table 6. Models A through D are analogous to the same labeled models
in table 2. In all models we find that the coefficient on the interaction between DClockIn and Short
PDC is positive and statistically significant, which indicates that committee membership and seniority
is associated with greater employment particularly for industries with short PDCs. Furthermore, the
interaction between Short PDC and the squared DClockIn is also statistically significant and consistent
with our main results. We generally find little evidence of any statistically significant interaction
between Short PDC and DClockOut. In models C and D we also observe no significant association
between DClockIn and employment for industries with long PDCs. The direct effect of the clock var-
iables maintain their original signs, though at times do not reach statistical significance, perhaps
because of the lower power of the tests with the fewer observations. These findings suggest that
firms with potentially greater operational flexibility and ease of entry are more likely to engage in
EIA. These results, and the role of different kinds of fixed and sunk costs, do warrant further

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 Business and Politics 313

investigation in the future, as the coefficients of the DClockOut parameters do meander in and out of
statistical significance.

Conclusion
In this article we have attempted to empirically analyze the use of EIAs by firms to influence key rel-
evant congressional committee members in the US Congress. We find evidence that firms increase
employment and establishments in the states of senators who are on committees that oversee or
have some authority over the industries of the firms. As a senator accumulates greater committee
seniority, firms increase employment and the number of establishments over time at a decreasing
rate. However, once the senator leaves the committee of relevance, firms appear to attenuate their
expansion and hold constant their economic footprint. Although we explore the same relationship
in the House of Representatives, we find limited evidence of any systematic relationship in that venue.
We also explore a number of alternative explanations for our main findings. Using exogenous
departures of Senate committee members, we show that reverse causality in the relationship between
committee service and economic activity is unlikely. By studying subsamples of districts that have at
least some committee representation we also demonstrate committee member selection effects are
unlikely to explain our findings. Finally, utilizing data on establishment sales, we show that the EIA
output measures demonstrate results consistent with EIA input measures.
Further, we extend our analysis by examining the effect of heterogeneity in electoral margins and
heterogeneity in industries on the use of EIAs. We find greater EIA in House districts with electorally
safe seats, but find no such effect in the Senate. In addition, we find data consistent with the systematic
use of EIAs in a range of industries, but differences in EIA utilization based on different product devel-
opment cycles across industries. We believe these extensions will be useful points of departure for
future research.
Our findings contribute to the literature on firms’ political influence. EIA is often employed but not
well understood. Even though the US Congress has been the subject of many studies on the use of
lobbying and campaign contributions to influence key members of congress, our study is one of the
first large-scale studies of the use of EIAs in the context of the US Congress and economy for a
broad set of industries. This allows us to explore the cross-industry and time-series variation in the
use of EIAs. It highlights one more tool in the firm’s arsenal of political influence and non-market
strategy.
The findings here open and suggest a variety of avenues for future research. Three emanate directly
from our work. First, data limitations—and in particular the lack of unique establishment identifiers
that are consistent across establishments across time—preclude us from analyzing individual firm
behavior. It necessitated that we aggregate the unit of observation to the SIC-CD-Y level. New and
more detailed datasets that overcome these data limitations should allow future work to test this theory
in a more detailed and nuanced way. Second, future work might explore the reasons for leveling, as
opposed to withdrawal, of investment after politicians leave committees. We argue this is because of
the cost of investment withdrawal. An additional contributory factor may be that firms have achieved
agglomeration economies in their industries and thus find higher operational efficiencies ex post rel-
ative to the ex ante investment decision. A parsing through this effect might yield interesting results.
Third, future work could examine the substitution effect between EIAs and other forms of political
influence such as campaign contributions and lobbying. While Raiha examined this question theoret-
ically,65 there are few empirical studies of EIAs that have examined how different forms of influence
interact as substitutes or complements.66 Future research could empirically examine under what con-
ditions different forms of political influence would be most effective and, in doing so, potentially con-
tribute new insights into the long-standing literatures on campaign contributions and lobbying.

65
Raiha, 2018.
66
See Bombardini and Trebbi, 2011 for an example.

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 314 John M. de Figueiredo and Davin Raiha

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 316 John M. de Figueiredo and Davin Raiha

Appendix A

Table A1 Unlogged DV table—Senate and House committees.

Employment Establishments
DV
Committees Senate House Senate House
A B C D

DClockIn 4.6649** 1.0457 0.0434*** –0.0148*

(2.02) (0.49) (3.27) (–1.67)
DClockIn-squared. –0.0258 –0.0444 –0.0014* 0.0010*
(–0.15) (–0.29) (–1.86) (1.70)
DClockOut –2.6750 –2.3185 0.0299 –0.0000
(–0.77) (–1.28) (1.63) (–0.00)
DClockOut-squared. 0.5611 0.1114 –0.0018 –0.0006
(1.20) (0.93) (–1.10) (–1.09)
Pres. Democrat Vote Share –1.4739*** –1.4978*** –0.0130*** –0.0139***
(–3.21) (–3.23) (–3.15) (–3.39)
State Unemployment 2.5914 2.0252 0.0018 –0.0052
(1.45) (1.13) (0.10) (–0.28)
District FEs Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes
F-statistic 3.99 2.40 4.57 5.10
Observations 441,766 431,447 441,766 431,447
Confidence levels: * = 10%; ** = 5%; *** = 1% (t-values).
Note: This table presents estimates of four regression models, of accumulated committee service in the Senate and House on relevant industry
employment or establishments, using the full sample. In models A–B the dependent variable is the level of industry employment, while in
models C–D the dependent variable is the level of industry establishments. In models A and C the committee service is in the Senate, while in
models B and D the committee service is in the House. All models include industry, district, and year fixed effects. The F-statistic of the test of the
joint significance of all explanatory variables, is reported below each column. T-statistics are reported in parentheses. Standard errors are
clustered at the four-digit SIC-state level.

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 Business and Politics 317

Table A2: Senate committees and significant SIC industries.

Coef.
DV = log(Employees) (t-stat)

SIC-01 (Agricultural Production - Crops) 0.0596***

(5.24)
SIC-02 (Agricultural Production - Livestock) 0.0579***
(3.81)
SIC-07 (Agricultural Services) 0.0490**
(2.18)
SIC-13 (Oil & Gas Extraction) 0.1550***
(4.72)
SIC-14 (Nonmetallic Minerals) 0.0460**
(2.34)
SIC-21 (Tobacco Products) 0.0991*
(1.65)
SIC-27 (Printing & Publishing) 0.0218***
(2.58)
SIC-34 (Fabricated Metal Products) 0.0222***
(2.82)
SIC-35 (Industrial Machinery & Equipment) 0.0391***
(3.62)
SIC-45 (Transportation by Air) 0.0166**
(2.36)
SIC-52 (Building Materials) 0.0273*
(1.91)
SIC-53 (General Merchandise Stores) 0.0522**
(2.55)
SIC-58 (Eating & Drinking Places) 0.0561***
(2.84)
SIC-59 (Miscellaneous Retail) 0.0164*
(1.80)
SIC-64 (Insurance Agents, Brokers, & Service) 0.0339*
(1.91)
SIC-65 (Real Estate) 0.0282***
(4.20)
SIC-70 (Hotels & Other Lodging) 0.0892***
(3.99)
SIC-80 (Health Services) 0.0172*
(1.91)
SIC-83 (Social Services) 0.0317*
(1.75)
Confidence levels: * = 10%; ** = 5%; *** = 1%.
Note: This table presents the positive and significant coefficient estimates of a regression that includes a separate DClockIn measure of
committee service, for each two-digit SIC industry. The dependent variable is the log of industry employment. The regression includes industry,
district, and year fixed effects. T-statistics are reported in parentheses. Standard errors are clustered at the four-digit SIC-state level.

Cite this article: de Figueiredo JM, Raiha D (2022). Economic influence activities and the strategic location of investment.
Business and Politics 24, 292–317. https://doi.org/10.1017/bap.2022.11

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