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Essential or Expedient? COVID-19 and Business Closures in the U.S. States

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To what extent has political pressure or connectedness influenced governors' responses to public health recommendations regarding business closures? We investigate whether campaign contributions from particular industries track governors' designations of those industries as ``essential'' during the COVID-19 pandemic. Analyzing the initial iteration of states' lockdown orders, we find preliminary evidence linking receipt of gubernatorial campaign contributions from industry to an increased likelihood of designating that business area as essential. In other words, governors are more likely to designate a business area as essential if they received campaign contributions from that business area. Our result preliminarily suggests that money in politics plays a role in shaping public health responses, and we recommend further research on this matter.
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Essential or Expedient? COVID-19 and Business
Closures in the U.S. States
Jesse M. Crossonand Srinivas C. Parinandi
April 28, 2021
Journal of Political Institutions and Political Economy, 2021
Abstract
To what extent has political pressure or connectedness influenced governors’ re-
sponses to public health recommendations regarding business closures? We investigate
whether campaign contributions from particular industries track governors’ designa-
tions of those industries as “essential” during the COVID-19 pandemic. Analyzing the
initial iteration of states’ lockdown orders, we find preliminary evidence linking receipt
of gubernatorial campaign contributions from industry to an increased likelihood of
designating that business area as essential. In other words, governors are more likely
to designate a business area as essential if they received campaign contributions from
that business area. Our result preliminarily suggests that money in politics plays a
role in shaping public health responses, and we recommend further research on this
matter.
Keywords: campaign finance; COVID-19; federalism; political economy; business &
politics
We wish to acknowledge Jack Nickelson for providing invaluable help with data collection.
Assistant Professor, Trinity University, Department of Political Science and Program on Urban Studies,
103 Storch Hall, San Antonio, TX, 78212; jcrosson@trinity.edu
Assistant Professor, University of Colorado, Boulder, Department of Political Science, 333 UCB, Boulder,
CO 80309, USA; srinivas.parinandi@colorado.edu
1
Introduction
National disasters, whether in the form of extreme weather events, attacks by hostile
forces or, most recently, the outbreak of a deadly infectious disease, present political
leaders with myriad challenges. In addition to coordinating economic relief and, when
necessary, providing physical protections to citizens, politicians face difficult policy
decisions with respect to the economy and workforce, K-12 education, international
and domestic travel, and more. Amidst the growing threat to public health caused
by the arrival of the novel coronavirus to American shores in early 2019, American
governors in particular found themselves deciding between two undesirable alternatives
as COVID-19 case numbers swelled: risk over-crowding hospitals with COVID patients,
or cripple the economy—during an election year for many—by enacting a statewide
shutdown.
Ultimately, most governors responded to the outbreak of coronavirus by temporar-
ily shutting down the states’ economies. However, due in large part to the federal
government’s decision against a national-level shutdown or program of restrictions,
governors bore the responsibility for deciding which industries were truly “essential”
to the health and well-being of each state’s residents. At least publicly, governors cited
their consultations with public health experts and infectious disease scientists as the
primary drivers for their decisionmaking. However, given the gravity of the decision to
shut down major sectors of the economy—as well as to provide exceptions for particular
subsectors—it is not unreasonable to wonder whether and to what extent industries’
political connections may have influenced governors’ initial shutdown orders. Indeed,
while a variety of studies have underscored the efficiency gains associated with expert-
led policymaking (Alesina and Tabellini,2007;Koo et al.,2020), a long literature in
political science and economics has demonstrated the opportunities for businesses and
other interests to see favorable treatment in response to major governmental interven-
tions in the economy. And, as this literature illustrates, these departures equitable from
1
treatment, particularly in granting “rents” to certain economic interests over others,
leads to a wide variety of inefficiencies and negative outcomes (Tullock,1967;Krueger,
1974;Dougan,1991;Piketty, Saez and Stantcheva,2014).
In this study, we examine whether industries’ political connectedness provide them
insulation from the worst of the COVID-induced economic ramifications, in the form
of “essential status” declarations from the governor’s office. More specifically, we in-
vestigate whether a governor’s prior receipt of campaign fund from a given industry
positively predicts that industry’s designation as “essential” during the initial wave
of COVID-related shutdowns. Ultimately, we find suggestive evidence that industries’
campaign ties do correspond with essential-business designations. While we encourage
future research to further interrogate this relationship, particularly in the context of
shutdown repeals later in spring of 2020, we believe this result provides a concerning
depiction of U.S. states’ coronavirus responses. Despite the counsel that both scientific
and public health experts provided to state governments, and even considering the
cross-state response standardization encouraged by federal-level recommendations, we
nevertheless uncover a correlation between campaign finance and COVID-19 shutdown
orders.
Interest Groups, Political Money, and Protective
Rent-Seeking
Generally speaking, quantitative examinations of interest group politics and campaign
finance have failed to uncover a stable relationship between political donations and
policy outcomes. Indeed, while some macro-level research points to overall advantages
that business and moneyed interests enjoy in mobilization and policy outcomes (e.g.,
Olson 1965,Gilens and Page 2014), dozens of examinations of campaign donations un-
cover little evidence of influence over roll call votes (e.g., Wawro 2001), and even some
2
of the most carefully identified examinations of policy change struggle to tie donations
to favorable policy outcomes (e.g., Fowler, Garro and Spenkuch 2020. In fact, empir-
ical investigations of campaign finance have so consistently failed to identify positive
returns for donations that some studies have gone so far as to suggest that donations
more resemble “consumption” behavior than an investment in influence (Ansolabehere,
De Figueiredo and Snyder Jr,2003).
Nevertheless, businesses and other interest groups continue to donate billions of
dollars to candidates at the state and federal levels each election. The persistence of
these donations, then, has led scholars to reconsider the source of value provided by
campaign donations. Most prominently, scholars have found that campaign donations
may enable interest groups (and other political actors) to gain access to congressional
offices and heighten attention to favored policy issues. In their seminal study, for
example, Hall and Wayman (1990) find that donations to members of key committees
are associated with greater participation in relevant committee proceedings. More
recently, Kalla and Broockman (2016) find that citizens who legislators identify as
donors are more likely to secure a meeting with the office than are non-donors.
As Hall and Wayman’s (1990) findings suggest, the policy value of access and atten-
tion is most likely manifest in committee and in the details of broader policy initiatives.
Qualitative evidence in particular provides evidence to this effect, perhaps most fa-
mously in Schattschneider’s (1935) analysis of the Smoot-Hawley Act of 1928. Similar
accounts of tax legislation, such as the Reagan tax cuts of the 1980s, underscore the
how broad legislation frequently includes narrower carve-outs.1More recently, both
the 2009 economic stimulus package and 2010 health care reform presented opportuni-
ties for interests to pursue their individual interests as part of much broader legislative
efforts (see, for example, Jacobs and Skocpol 2010).
1During the push for the Reagan tax cuts, one White House staffer went so far as to remark that ”the
hogs were really feeding. The greed level, the level of opportunism, just got out of control” (Greider,1981).
3
In many regards, the advent of the coronavirus pandemic introduced opportunity
for similar carve-outs for individual industries and interests. At the federal level, for
example, the CARES Act passed through Congress, reportedly full of industry-friendly
provisions for which interests had previously lobbied (Phillips et al.,2020). While such
behavior constitutes classic rent-seeking behavior, political and economic dynamics
following the outbreak of COVID-19 were not confined to the provision of relief packages
and loan money. Indeed, in the initial weeks of the pandemic, governors across the
U.S. faced the difficult decision of whether, when, and how to shut down their states’
businesses, educational facilities, and houses of worship. In doing so, governors weighed
the the possibility of further contagion against the almost certain economic downturn
that would follow a shutdown.
Much as lobbyists used the federal-level coronavirus response package to pursue
pre-COVID political goals, we posit that states’ institution of shutdowns presented an
opportunity for industries to insulate themselves from the worst effects of the economic
shutdown. More specifically, we investigate whether particular industries were able to
leverage their political contacts in order to ensure that their businesses would earn an
“essential” designation, enabling them to remain open in spite of the general COVID-19
shutdown.
In theory, there are certainly reasons why one should expect the logic of “tradi-
tional” log-rolling, rent-seeking, and pork-barrel politics to extend to an industry’s
ability to insulate itself from losses. That is, while rent-seeking behavior is most often
associated with a business’s pursuit of “positive” rents, in the form of specialized tax
treatment or favorable regulation, one can imagine that “negative” protections could
prove just as valuable to a business’s bottom line. In the case of coronavirus shutdowns
specifically, essential business declarations provided industry leaders with an especially
appealing outlet for seeking protections, as states’ declarations were both highly spe-
cific with regard to individual industries and were generally issued by a single actor
within the executive branch.
4
Of course, despite these appealing features, there are also reasons to suspect that
businesses would experience considerably less success in “protective” rent-seeking—
particularly in the case of essential business declarations—than they might enjoy under
more “traditional” circumstances. In the first place, states’ shutdown orders were gen-
erally not cut from full cloth. That is, in crafting their individual declarations, states
likely took cues from federal-level authorities, such as the Centers for Disease Control
(CDC) and the Cybersecurity and Infrastructure Security Agency (CISA). Second, un-
like small provisions within larger relief legislation, many aspects of states’ shutdown
orders were well-publicized and highly salient to the average American. Indeed, not
only did the shutdowns affect the statuses of millions of workers (rather than merely
influencing, say, the top-line tax rate paid by an entire corporation), but these deci-
sions were often controversial and covered extensively by the press. Such high salience
and actor specificity clearly generate a high level of policy traceability (Arnold,1990),
and the economic downturn that was likely to follow shutdown orders undoubtedly left
governors searching for ways to limit traceability. Given that federal guidelines offered
governors a clear, outside actor to whom they could foist responsibility, political con-
nections may well have fallen to the wayside as governors pursue the largely necessary
evil of issuing shutdown orders.
Although these features of shutdown orders may undercut rent-seeking behavior, we
suspect that politically connected industries may nevertheless have fared better in the
wake of coronavirus shutdowns than other industries. Faced with an undeniably trying
political environment, governors may well have sought to salvage what little political
currency they could, opting (in an election year, no less) to protect those industries to
which they were politically connected. More specifically, we expect that industries that
donated to governors are more likely, all else equal, to have been declared as “essential”
than industries who did not.
Donations provide industries with several important connections to governors. First,
having demonstrated additional “commitment” to a politician’s reelection constituency
5
previously, a member understands that maintaining positive relationship with the in-
dustry will remain crucial in future political endeavors (Fenno,1978). Second, as Hall,
Van Houweling and Furnas (n.d.) underscore, donations frequently serve as a signal
regarding an interest’s intention to partner with and provide legislative subsidies to
the targeted elected official. Given the unforeseeable policy challenges associated with
a global pandemic, governors may wish to rely upon particular industries’ support and
expertise in future legislative efforts.
Taken together, then, our central expectation is that—particular with respect to
initial shutdown orders—industries that contributed to a governor’s most recent cam-
paign will enjoy a higher likelihood than non-donor industries, all else equal. While
this central expectation orients our empirical examinations below, it is worth noting
that we are ambivalent as to whether or not the magnitude of industries’ donations
will influence essential declaration status. That is, while it makes intuitive sense that
governors may feel more obligated to service their largest donors, the “binary” nature
of the declaration order introduces practical challenges for governors who wish to pro-
tect “large” donors differently from “small” ones. Doing so would require governors
to select a de facto “cutoff” for (un)worthy industries, at a time when government
was expected to take quick, decisive action. Moreover, as Hall, Van Houweling and
Furnas (n.d.) argue, the raw magnitude of donation may not even capture the relative
commitment of interests and industries to individual politicians, since similarly sized
donations from large and small industries are likely perceived differently by candidate
recipients. Thus, we focus our primary analyses on whether or not the governor ac-
cepted money from a particular industry. However, in supplemental analyses, we do
examine whether magnitude—whether in the total number of donations or the actual
6
dollar amounts—contributes additional explanatory power.2
Before detailing our data collection and measurement strategies, it is worth pointing
out that COVID-related economic shutdowns present a fairly unique opportunity for
investigating rent-seeking in general and “protective” rent-seeking in particular. First,
nearly every governor felt pressure from public health experts to take decisive action,
all at roughly the same time. That is, the rapid outbreak of the coronavirus shocked the
“status quo” in such a dramatic fashion that inaction was likely not due to a satisfaction
with the contemporaneous state of affairs. Second, the nature of a shutdown itself
generated a policy decision of import to effectively every economic sector conceivable.
As a result, essential-business declarations present an opportunity to examine the (non-
)receipt of governmental protections across a wide range of economic actors. Finally,
the concentrated nature of essential-business declarations removes any ambiguity as to
political target for business interests. Indeed, whereas the U.S.’s separation of powers
system frequently obfuscates which actors are truly “pivotal” on any given political
decision, economic shutdowns and essential business declarations ultimately came down
to decision-making by each state’s governor. Even in states that issued shutdown orders
via public health agencies, the governor played a pivotal role in shaping shutdown
orders. Taken together, then, coronavirus-related shutdowns and essential-business
declarations exhibit a variety of traits desirable for examining beneficial treatment for
some interests compared to others.
Data
To evaluate whether donating industries are any more likely to benefit from governors’
essential business declarations during COVID-related lockdowns, we compiled an orig-
2It is worth noting that our data on magnitudes is also significantly noisier than our data on whether
or not industries contributed at all. This is due to missingness in the underlying industry classification
information from the National Institute on Money in State Politics, discussed below.
7
inal dataset of governors’ lockdown orders, broken down by industry. The National
Governors Association (NGA) has compiled a list of state executive orders concerning
COVID, and using this list to locate text of the state executive orders, we were able
to identify which business sectors in a given state were considered as essential, mean-
ing that businesses were referenced as being exempt from lockdown orders (National
Governors Association,2020).
Being able to identify which business sectors are designated as essential allows us to
investigate factors influencing its designation as such. However, several key cross-state
differences preclude us from comparing business treatment across states, absent further
cleaning and classification. Consequently, our first step was to standardize industry
names across states, ensuring that business sectors referenced in one state accurately
correspond to the same type of industry in another state. That is, we needed to ensure
that an order listing “lodging” as essential in Alaska, for example, and an order listing
“hotels” as essential in Arizona were classified on the same industry in our dataset.
We therefore coded each listed essential industries according to the North American
Industry Classification System (NAICS) subsector, thereby giving us a standardized
view of essential declarations across states. the specificity of the state lockdown orders
permitted us to classify industries across the the 3-digit version of NAICS, leaving us
with 91 unique industry classifications. In an appendix to this paper, we list each of
the subsectors along with an appropriate subsector description as well as corresponding
NAICS subsector and sector numbers.3
After standardizing industries across states, we next organized the dataset so as
3These 91 areas correspond to NAICS subsectors from 111 through 814. This range of NAICS subsectors
corresponds to all potential NAICS subsectors except for those identifying public administration. Public
administration includes the officials (governors and key lieutenants of governors) making the decisions about
which businesses are essential, and we do not want observations from this subsector artificially influencing our
analysis of how governors may be influenced by campaign contributions in deciding whether other subsectors
(of which they are not part) should be deemed essential.
8
ascertain the timing of industry lockdowns. Beyond the actual text of the lockdowns,
the NGA list provided us with the dates when governors issued their lockdowns. With
this information, we can analyze a respective governor’s potential motivations in des-
ignating any one of 91 potential business areas as essential and examine why some of
these 91 areas are considered essential while others are not. Our data are longitudinal
in the sense that different states issue lockdowns at different times, but given that we
ultimately leverage only within-state variation, they are presently better described as
cross-sectional. That is, by examining within-state variation primarily, we are looking
at a snapshot of each state with a lockdown order (when that state issues its lock-
down order) and investigating why that lockdown includes certain components but not
others.4
Taken together, our unit of analysis is state-NAICS industry subsector declaration-
choice.5Our strategy of leveraging within-policy variation and explicitly making the
components of that policy the focus of our attention is a strategy that has become
increasingly common in political science (Boushey,2016;Kreitzer and Boehmke,2016;
Parinandi,2020). This strategy is desirable here, as it permits us to embrace a level of
4We should emphasize that our data are not in an event history format in that we do not follow a
state over time looking at how its slate of essential declarations changes (the NGA, in our observation,
has not yet compiled such longitudinal information in an easy-to-find format). We also do not include
pre-declaration observations for states because we do not want to make strong assumptions about when
states realistically had the opportunity to issue such declarations (it is possible that different states faced
differential realities on when they could actually issue lockdowns, which would complicate the assumption
that each state theoretically had the opportunity to issue a lockdown at the same time). We do believe that
a full event history analysis of lockdowns and reopenings is an ideal follow-up to this study; however, this
will require additional data collection of follow-up orders by governors.
5Recall that our data are essentially cross-sectional, as we evaluate each state lockdown at a particular
point in time based on when that lockdown was issued. However, in light of the fact that many states issued
lockdowns in different weeks, we include a time variable in our main empirical model.
9
data granularity or specificity that would be lost if we treated all lockdowns as if they
were the same and ignored that each governor could choose different mixes of the 91
business subsectors to designate as essential.6
[Figure 1 about here.]
[Figure 2 about here.]
As our description suggests, our dependent variable, Essential, is binary and re-
ceives a value of 1 if a state governor deems an industry as being essential under their
state’s lockdown order, and 0 otherwise. Figures 1 and 2 provide depictions of our
dependent variable. In Figure 1, we present the total number of industries declared
as “essential” by gubernatorial order. Since we categorize essential declarations using
three-digit NAICS codes, the maximum number of declaration is 91. As the map under-
scores, states exhibit notable cross-sectional variation in essential declarations, despite
guidance from federal agencies. This variation is depicted with greater granularity at
the industry level in Figure 2. Here, industries are depicted on the x axis, while states
are displayed on the y axis. As the black shading indicates, essential status is quite
widely applied in some states and considerably more scarce in others. Moreover, while
some industries achieve essential status across nearly all states, others vary across state
lines.
Our key independent variable, Gubernatorial Campaign Contribution, is binary and
receives a value of 1 if a state’s governor received a campaign contribution from an en-
6To be clear, we analyze essential business declarations in states that issued lockdowns. We do not
examine states (e.g., South Dakota) that never issued lockdowns. We also do not model a governor’s
decision to issue a lockdown declaration separately from that governor’s decision about which industries to
declare essential. Doing so would require us to find a variable that predicts issuing a declaration but does
not predict making an industry essential, and we do not believe it is possible to find such a variable. Instead,
our analysis reveals the factors making an essential declaration more likely within the group of states that
issued lockdown declarations.
10
tity in a given NAICS industry subsector, during their most recent election. We opt
for a binary operationalization of this variable not because we believe large and small
donations function precisely the same way, but rather because we lack the necessary
information information to distinguish between large and small donations in a theo-
retically satisfying fashion. That is to say, the “size” of a donation, in terms of the
signal it sends to a candidate, is very likely relative to the overall resource levels of the
donating interest. That is, as Hall, Van Houweling and Furnas (n.d.) have recently
found, the correlation between donations and subsequent access is not governed by the
absolute size of a donation. Rather, as a costly signal of alignment (and desire for
entering into a future subsidy relationship with the legislator, see Hall and Deardorff
(2006)), donations are perceived as “large” (costly) or “small” (costless) by the receiv-
ing legislator based on the donor’s ability to pay—not the actual monetary value to
the legislator’s campaign. Without more information about each industry’s political
“budget,” it is difficult for us to assess how members perceive the size of donations re-
ceived from the industries in our dataset.7Nevertheless, when we do include absolute
donation amounts in our regressions, our results replicate those of Hall, Van Houwel-
ing and Furnas (n.d.): that is, they retain the expected positive sign but fall below
standard statistical significance.
To create our donations variable, we used gubernatorial campaign finance data
from the National Institute for Money in State Politics (NIMSP) (National Institute
on Money in Politics,2020). NIMSP classifies campaign contributions according to
business sector, using a modified version of the Center for Responsive Politics’s clas-
sification code system. Although both systems are loosely based on both the SIC
7In addition to these theoretical concerns, the missingness in our data (discussed below) gave us practical
pause, in terms of the noisiness of the actual dollar amounts donated by each industry. We feel much more
confident about the ability of our data to capture the existence of industry donations, rather than the overall
amount of donations.
11
and NAICS taxonomies, the names of NIMSP business sectors do not correspond di-
rectly with NAICS industry subsectors. We therefore hand-matched the names of each
NIMSP business sector to the corresponding NAICS subsector by consulting the de-
scriptions of NAICS subsectors (provided by NAICS). As a result, we were able to link
each NIMSP business sector to the NAICS subsector that most closely describes that
business area. We unfortunately were unable to acquire past gubernatorial campaign
finance data for the incumbent governors of several states and must drop these states
from the analysis.89 Additionally, in states for which we have gubernatorial campaign
finance data, NIMSP does not provide values for business areas that do not have con-
tributions; for these states, we therefore code all business areas without contributions
as 0. Our number of observations total 3,458 spread out over 38 states.
To investigate if the gubernatorial campaign contribution variable is associated with
a governor’s “essential” declaration for a particular industry, we utilize state random-
effects logistic regression. Random effects regression permits us to acknowledge that
states may differ in their propensities to designate businesses as essential while also
allowing us to include slow-moving or time-invariant controls in our analysis (Gelman
and Hill,2007). Controls include the Political Party of a governor. The political
party variable is binary and receives a value of 1 if a governor is affiliated with the
Republican Party and 0 otherwise. We expect Republican governors to be more likely
to designate businesses as essential based on the logic that the Republican Party has
been more opposed than the Democratic Party to utilizing shutdowns in combatting
8These states are Indiana, Missouri, Montana, North Carolina, North Dakota, Utah, Washington, and
West Virginia. This missingness is due to missingness in the underlying industry classifications of campaign
donors.
9These missing states do not exhibit obvious geographic similarities; however, to ensure this missingness
is not correlated with our measured public health activities, we further investigated potential differences
driven by this missingness. Fortunately, a ttest comparison states with and without industry classifications
reveals no significant differences in essential-declaration activity.
12
COVID. In addition to the gubernatorial party variable, we also include the percentage
of a state’s 2016 presidential votes that went to Trump (the Trump Vote variable) as
well as a binary variable capturing whether a state has a Republican Legislature. Both
of these variables capture potential opposition to declaring industries as non-essential,
and we expect ex-ante that they will relate positively with the likelihood of declaring
an industry an essential.
We also include a variable capturing a state’s number of cumulative COVID Cases
as of the end of the week preceding the week of observation for a given state. We
obtained this information from the Coronavirus Resource Center at Johns Hopkins
University, and we expect this variable to relate negatively with the likelihood of an
industry being declared essential on average (Johns Hopkins University,2020).10 We
also include a variable capturing the Week of the year based on the idea that state like-
lihood of issuing a COVID lockdown may be related to how much time has progressed
in the year 2020. Finally, we include a state’s level of Legislative Professionalism to
account for the possibility that greater governmental capacity may better enable states
to respond to the logistical challenges of issuing aggressive lockdowns (Squire,2017).
While state random effects regression constitutes our main workhorse model, we also
estimate a supplementary model utilizing state fixed effects regression with standard
errors clustered within each state.
Results
In table 2, the first two model specifications pertaining to the relationship between the
gubernatorial campaign contribution independent variable and the essential dependent
variable. Specification 1 displays the results of state random effects logistic regression
10While we use raw COVID case numbers in this variable, we substitute a logged version of this variable
in corresponding empirical models in tables 1 and 2. Our key substantive result does not change with the
use of the logged COVID measure.
13
while specification 2 displays the results of state fixed effects logistic regression with
standard errors clustered by state. All controls are functionally time-invariant by state
(based on our analyzing of each state’s decision-making as a snapshot in time), so we
only include the independent variable and state indicator variables in the fixed effects
model.
[Table 1 about here.]
The table above reveals a positive and significant association in both model specifi-
cations 1 and 2 between the gubernatorial campaign contribution variable and whether
a state declares a particular NAICS subsector to be essential, offering preliminary ev-
idence that governors are more likely to consider a business subsector to be essential
if they received campaign contributions linked to that subsector. Turning to the other
variables, we do not find evidence of a meaningful statistical relationship, although the
political party (recall that Republican governors receive a value of 1) and Republican
legislature variables possess expected directionalities in terms of how they influence
essential business declarations.
In figure 3, we plot how the presence of a gubernatorial campaign contribution in a
NAICS subsector influences the probability of an industry within that subsector being
classified as essential.11
[Figure 3 about here.]
Figure 3 depicts an increased slope with regard to how the gubernatorial campaign
contribution variable relates to the essential business declaration dependent variable.
11In the figure, the political party variable is set to its most frequently occurring value (0, or non-
Republican control). The Republican legislature variable is set to 0 (a legislature that is not controlled
by the Republican Party), but the same number of observations feature Republican legislative control as
opposed to do not feature it.
14
In terms of quantifying the estimated influence of the gubernatorial campaign con-
tribution variable on essential business declaration, the presence of a gubernatorial
campaign contribution in a NAICS subsector leads to a roughly 10 percent increase
in the probability that an industry within that subsector will be declared essential.12
While such a percentage might seem small at face value, we emphasize that the mag-
nitude of the prediction has important and substantive implications. Given that there
are 2,474 positive instances of the dependent variable in our data (where a positive
instance, as a reminder, means that an industry in a specific NAICS subsector was
designated as essential), one could speculate that a percentage of 10 suggests that ap-
proximately 247 (2474*0.100) essential declarations were attributable to gubernatorial
campaign contributions. Considering that each and every essential declaration has an
effect on public health as well as economic outcomes (Hsiang et al.,2020), a poten-
tial link between gubernatorial campaign contributions and the decision to declare an
industry as essential is non-negligible.
While these results provide evidence consistent with the notion that governors were
sensitive to political donations as they rendered essential business declarations, the
nature of these considerations remains unclear in a variety of regards. In particular,
as previous research has underscored (e.g., Bonica 2016), interest groups differ dra-
matically in their broader political donation strategies. Whereas some interests signal
their ideological or policy alignment with a party by donating almost exclusively to
one party or the other, others adopt an “access-oriented” or “hedging” strategy, giving
large sums to both parties. Given that this strategy is especially prevalent among busi-
ness interests, the business-declaration application in this study offers an opportunity
to examine whether governors appear to reward loyal partisan industries—or whether
12The calculated values are actually 0.665 in the presence of a gubernatorial campaign contribution versus
0.555 in the absence of such a contribution. The difference between these two values is 0.100, which multiplied
by 100 yields 10 percent.
15
they merely favor the politically active over the politically inactive.
We examine this mechanism in the third and fourth models of Table 2. In the
third model, we substitute our binary donations-to-governor variable for a variable
that captures whether an industry donated only to the governor’s campaign—and
not the opponent. Given this operationalization, the variable measures both political
activity and loyalty to the governor in particular. As the results depict, governors
do not appear to reward interests for exclusivity of support. Indeed, the coefficient
on the Only Contributed to Governor variable is both substantively and statistically
insignificant. In the fourth and final model, we adopt a complementary approach, this
time including two two binary contribution variables. The first variable is identical to
our original variable in the first two models, capturing whether or not the governor
received a donation from a given industry. The second variable, however, captures
whether or not the governor’s opponent received donations from a given industry. As
the final model illustrates, the governor does not appear to distinguish much between
“his” or “her” supporters and the politically active more broadly. To be sure, donating
to the governor’s campaign is a stronger predictor when both variables are included in
the same model. But it does not appear that the governor is punishing industries if
they have donated to the opponent.13 14
As another check on the results discussed in table 2, we utilize the same model
specifications from that table but divide our sample into two groups depending on
whether states reference federal critical infrastructure (CISA) guidance in their orders
or not. Federal CISA guidance is meant to encourage a standardized COVID response
13It is worth noting here that we did not uncover any interests that gave only to the opponent. Instead,
our results are driven by industries who were “loyal” to the governor, compared to those were political active
or inactive overall.
14We thank an anonymous reviewer for suggesting this supplemental analysis. We believe the last several
models add useful nuance to the empirical results we present.
16
among the individual states by highlighting industries that the federal government
believes is absolutely necessary to keep open, and it is possible that the influence
of gubernatorial campaign contributions may be blunted in states where CISA was
referenced in lockdown orders. Table 3 shows results from replicating the random and
fixed effects analyses on the two groups of states that do and do not reference CISA.
[Table 2 about here.]
Table 3 shows that the gubernatorial campaign contribution variable influences
whether a governor deems an industry to be essential in both the states referenc-
ing CISA guidance as well as those not referencing such guidance. Interestingly, the
coefficient value associated with gubernatorial campaign contribution is larger with
respect to the sample of states that do not reference CISA compared to the sample
of states that reference CISA, suggesting the possibility that the encouragement of
standardization provided by CISA guidance may ameliorate the influence of campaign
contributions on essential business declarations.15 Table 3 also provides some defense
against the possibility that our results are an artifact of businesses commonly deemed
to be “essential” simply contributing more to campaigns than businesses not commonly
deemed to be essential. If our result was purely an artifact of businesses commonly
perceived as essential contributing more than other businesses, we would expect to see
an association between the gubernatorial campaign contribution variable and essential
business declaration in the sample of states referencing CISA (since CISA enumerates
a list of business areas that are commonly considered essential) but not see an associ-
ation between the gubernatorial campaign contribution variable and essential business
declaration in the sample of states not referencing CISA. The fact that an effect exists
15A test for equality with respect to whether the gubernatorial campaign contribution variable has the
same influence on essential business declarations across both CISA samples is not supported, suggesting that
the influence of the gubernatorial campaign contribution variable is different quantitatively in the sample of
states referencing CISA compared to the sample of states that do not reference CISA.
17
across both samples suggests that campaign contributions germanely influence essential
business declarations.16
Conclusion
In this paper, we conduct an exploratory investigation into whether the giving of gu-
bernatorial campaign contributions influences governors’ decisions to declare business
areas as essential when responding to the ongoing COVID-19 pandemic. The question
not only has pertinence to the immediate issue of how money in politics may play a
role in shaping the course of public health policy but also relates to the broader matter
of how money in politics shapes executive action more generally. Our cross-sectional
study of initial executive state lockdown orders provides preliminary evidence that
receiving campaign contributions from a particular NAICS business subsector makes
governors more likely to declare that subsector as being essential in their orders. The es-
timated magnitude of the influence is appreciable, and the influence persists regardless
of whether a state references federal CISA guidelines in its policy-making, suggesting
that the result is not an artifact of states not following CISA guidance.
We issue some notes of caution in interpreting the result. First, while we find
evidence that gubernatorial campaign contributions influence essential business decla-
rations, this does not automatically imply that particular business sectors are “buying”
policy concessions from governors. It could be the case, for example, that governors
are proactively taking contributions into consideration when setting policy without
overt prodding from specific industries. Second, we recommend that observers not
immediately assume that governors are undermining or acting antithetical to public
16We utilize other operationalizations of campaign contribution influence in addition to the gubernatorial
campaign contribution variable, including the percentage of total receipts coming from a given industry as
well as the percentage of total contributions coming from a given industry. Results using these variables are
less consistent in terms of predicting essential business declarations.
18
health interests. Some industries that contributed to gubernatorial campaigns may
actually be essential from the vantage point of providing necessary goods and services
to the general public, and insofar as governors are making decisions with this vantage
point in mind, they are not necessarily detracting from public health concerns while
setting COVID-related policy. Our goal here is not to disparage state COVID lock-
down responses but rather to start an intellectual conversation about how the pervasive
presence of money in politics could impact those responses, and our preliminary results
here suggest that this is an intellectual conversation worth continuing.
There are several potential extensions to this project that would serve to advance
this intellectual conversation. Our analysis is cross-sectional and focuses on initial state
lockdown essential business declarations. Once data on phases of reopening and (if ap-
plicable) reclosing across the states is fully and systematically available, researchers
could analyze our initial essential business declarations as part of a larger spectrum
of COVID-related business area activity and evaluate (1) whether gubernatorial cam-
paign contributions also influence reopening and reclosing decisions, and (2) whether
the influence of gubernatorial campaign contributions has been more pronounced at
certain stages of the COVID crisis compared to others (for example, it may be the case
that contributions are more influential later on compared to during the initial states’
responses).
Another future extension involves applying our analysis to state legislators in ad-
dition to governors. While the emergency authority bestowed upon governors served
as a springboard for governors to issue COVID-related lockdown orders, many state
legislators have utilized their position to criticize executive branch-driven lockdown re-
sponses. For example, in commenting about Michigan Governor Gretchen Whitmer’s
response to COVID, Lee Chatfield, the Speaker of Michigan’s House of Representa-
tives, remarked that “we can prioritize public health, yet still be responsible in how we
battle COVID-19” (Tribou, Doug,2020). As House Speaker, Chatfield is in a position
to apply pressure on Whitmer and potentially influence how the state’s lockdown order
19
has changed over time. Did campaign contributions to Chatfield and other legislators
influence their own strategies in responding to the Governor’s lockdown order?
A final extension relates to examining how businesses may influence state COVID
responses in non-pecuniary ways. Our focus here has been on examining financial influ-
ence, but businesses and business-affiliated interest groups can also protest, use social
media to marshal public opinion, and even issue challenges in the legal system.17 All of
these methods of outreach can influence gubernatorial policy responses to COVID, and
analyzing them together alongside contributions might give researchers a better under-
standing of which outreach method may be most effective at influencing gubernatorial
action. Moreover, it should be emphasized that we should not expect that all business
areas would be opposed to lockdowns a priori. Some businesses may indicate support
for lockdowns, and understanding the dynamics of which business areas support versus
do not support lockdowns would give us greater insight into the economic foundations
of COVID-related lockdown support and opposition.
Ultimately, we find a link between gubernatorial campaign contributions and es-
sential business declarations in initial COVID lockdown orders. Given the ongoing
challenges of combatting COVID (as well as the ongoing stresses placed on state gov-
ernments to continue issuing policy pronouncements to do so), our research may shed
light on state-level COVID policy moving forward as well as provide clues as to how
responses to other public crises might be crafted in the future.
17The use of legal challenges to influence lockdown orders is particularly interesting, as these challenges
could impact governors’ willingness to enforce lockdown orders, which may have downstream implications
on what may be reclassified as essential.
20
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Appendix
NAICS Subsectors in Analysis
Sector Number Subsector Number Subsector Description
11 111 Crop Production
11 112 Animal Production
11 113 Forestry and Logging
11 114 Fishing, Hunting, and Trapping
11 115 Support for Ag and Forestry
21 211 Oil and Gas Extraction
21 212 Mining
21 213 Support Activities for Mining
22 221 Utilities
23 236 Construction of Buildings
23 237 Heavy and Civil Engineering Construction
23 238 Specialty Trade Contractors
31 311 Food Manufacturing
31 312 Beverage and Tobacco Manufacturing
31 313 Textile Mills
31 314 Textile Product Mills
31 315 Apparel Manufacturing
31 316 Leather and Allied Product Manufacturing
32 321 Wood Product Manufacturing
32 322 Paper Manufacturing
32 323 Printing and Related Support Activities
32 324 Petroleum and Coal Products Manufacturing
32 325 Chemical Manufacturing
32 326 Plastics and Rubber Products Manufacturing
32 327 Nonmetallic Mineral Product Manufacturing
33 331 Primary Metal Manufacturing
33 332 Fabricated Metal Product Manufacturing
33 333 Machinery Manufacturing
33 334 Computer and Electronic Product Manufacturing
33 335 Electric Equip., Appliance, Component Manufacture
33 336 Transporation Equipment Manufacturing
33 337 Furniture and Related Product Manufacturing
33 339 Miscellaneous Manufacturing
42 423 Merchant Wholesalers, Durable Goods
42 424 Merchant Wholesalers, Nondurable Goods
42 425 Wholesale Markets and Agents and Brokers
44 441 Motor Vehicles and Parts Dealers
Continued on next page
25
Table 1 – continued from previous page
Sector Number Subsector Number Subsector Description
44 442 Furniture and Home Furnishings Stores
44 443 Electronics and Appliance Stores
44 444 Building Material, Garden Supplies Dealers
44 445 Food and Beverage Stores
44 446 Health and Personal Care Stores
44 447 Gasoline Stations
44 448 Clothing and Clothing Accessories Stores
45 451 Sport Goods, Hobby, Book, and Music Stores
45 452 General Merchandise Stores
45 453 Miscellaneous Store Retailers
45 454 Nonstore Retailers
48 481 Air Transportation
48 482 Rail Transportation
48 483 Water Transportation
48 484 Truck Transportation
48 485 Transit and Ground Passenger Transportation
48 486 Pipeline Transporation
48 487 Scenic and Sightseeing Transportation
48 488 Support Activities for Transportation
49 491 Postal Service
49 492 Couriers and Messengers
49 493 Warehousing and Storage
51 511 Publishing Industries
51 512 Motion Picture, Sound Recording Industries
51 515 Broadcasting
51 517 Telecommunications
51 518 Data Processing, Hosting, and Related Services
51 519 Other Information Services
52 521 Monetary Authorities, Central Bank
52 522 Credit Intermediation and Related Activities
52 523 Securities, Commodity Contracts, Investments
52 524 Insurance Carriers and Related Activities
52 525 Funds, Trusts, Other Financial Vehicles
53 531 Real Estate
53 532 Rental and Leasing Services
53 533 Lessors of Nonfinancial Intangible Assets
54 541 Professional, Scientific, Technical Services
55 551 Management of Companies and Other Enterprises
56 561 Administrative and Support Services
56 562 Waste Management and Remediation Services
61 611 Educational Services
62 621 Ambulatory Health Care Services
Continued on next page
26
Table 1 – continued from previous page
Sector Number Subsector Number Subsector Description
62 622 Hospitals
62 623 Nursing and Residential Care Facilities
62 624 Social Assistance
71 711 Performing Arts, Spectator Sports, and Related
71 712 Museums, Historical Sites, and Similar Institutions
71 713 Amusement, Gambling, and Recreation Industries
72 721 Accommodation
72 722 Food Services and Drinking Places
81 811 Repair and Maintenance
81 812 Personal and Laundry Services
81 813 Relig., Grantmaking, Civic, Professional Orgs.
81 814 Private Households
27
Figure 1: Number of essential business declaration by state
States depicted in gray did not issue any kind of shutdown order during
the period of study.
28
Figure 2: Number of essential business declaration by state
States depicted in white did not issue any kind of shutdown order during
the period of study.
29
Figure 3: Gubernatorial Campaign Contributions on Essential Business Declarations
.4 .5 .6 .7 .8 .9
Essential Business Declaration Probability
0 1
Presence of Gubernatorial Campaign Contribution in NAICS Subsector
Predictive Margins with 95% CIs
30
Table 2: Gubernatorial Campaign Contributions and Essential Business Declarations
Variable Random EffectsFixed Effects Random Effects Random Effects
w/Clustering
Gubernatorial Campaign Contribution 0.669*** 0.686*** 0.425***
(0.102) (0.089) (0.652)
Only Contributed to Governor 0.103
(0.137)
Contribution to Opponent 0.366**
(0.154)
Political Party of Governor 0.527 0.490 0.577
(0.649) (0.639) (0.652)
Trump Vote -0.003 -0.007 -0.004
(0.058) (0.057) (0.058)
Republican Legislature 0.615 0.707 0.569
(1.087) (1.070) (1.091)
COVID Cases 0.0001 0.0001 0.0001
(0.0003) (0.0003) (0.0003)
Week -0.347 -0.360 -0.336
(0.549) (0.540) (0.551)
Legislative Professionalism 0.849 0.871 0.786
(3.413) (3.356) (3.423)
Observations 3458345834583458
***<0.01;**<0.05; and ***<0.10 with respect to critical thresholds.
In the random effects specification, a likelihood ratio test of the
proposition that ρequals 0 is rejected. The test statistic value is
1081.02 with a corresponding probability of being greater than or
equal to the test statistic of 0.000.
3458 refers to the number of observations including zero values for
the dependent variable. 2474 refers to the number of positive values
(essential business declarations) for the dependent variable.
31
Table 3: Gubernatorial Campaign Contributions and Essential Business Declarations based
on CISA Reference
Variable REFE w/Clustering RE∧∧ FE w/Clustering
Gubernatorial Campaign Contribution 0.636*** 0.605*** 0.731*** 0.741***
(0.154) (0.170) (0.137) (0.088)
Political Party of Governor -0.373 0.676
(0.421) (1.375)
Trump Vote 0.028 -0.050
(0.031) (0.170)
Republican Legislature 0.512 0.359
(0.715) (3.084)
COVID Cases 0.0002 0.00002
(0.0002) (0.00009)
Week -0.752* -0.010
(0.394) (0.977)
Legislative Professionalism 0.631 -2.417
(1.988) (10.790)
Referencing CISA Not Referencing CISA
Observations 2002 (1747)2002 (1747)1456 (727)†† 1456 (727)††
***<0.01;**<0.05; and ***<0.10 with respect to critical thresholds.
In the random effects specification, a likelihood ratio test of the
proposition that ρequals 0 is rejected. The test statistic value is
59.75 with a corresponding probability of being greater than or equal
to the test statistic of 0.000.
2002 refers to the number of observations including zero values for
the dependent variable. 1747 refers to the number of positive values
(essential business declarations) for the dependent variable.
∧∧ In the random effects specification, a likelihood ratio test of the
proposition that ρequals 0 is rejected. The test statistic value is
405.98 with a corresponding probability of being greater than or
equal to the test statistic of 0.000.
†† 1456 refers to the number of observations including zero values for
the dependent variable. 727 refers to the number of positive values
(essential business declarations) for the dependent variable.
32
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Article
Full-text available
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