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Political information flow and management guidance

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Abstract

We examine whether politically connected firms play a role in disseminating political information via their management guidance. Using campaign financing activity or the presence of a government affairs office to proxy for firms’ access to political information, we find that politically connected firms are more likely to issue management guidance, and their guidance is more likely to discuss government policies. Further, these relations are attenuated for firms facing high proprietary costs of disclosure. To provide evidence on the source of the political information disclosed through guidance, we examine the timing of when guidance is issued. We find that politically connected firms are more likely to issue guidance and change their government policy–related disclosures prior to the public revelation of government policy decisions. Collectively, these findings suggest that the privileged information firms obtain through their political connections is shared with investors through voluntary disclosures.

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Notes

  1. Wellman (2017) and Ovtchinnikov et al. (2020) document that access to institutional details throughout the legislative and regulatory process leads to more informed investment decisions.

  2. If both the amount of managers’ private information and the level of proprietary costs change simultaneously, the comparative statics in Verrecchia (1990) do not hold. As shown in Kim et al. (2021), when both of these factors increase, the relation with disclosure is inverse U-shaped.

  3. See, for example, Bamber and Cheon (1998), Verrecchia and Weber (2006), and Cao et al. (2018).

  4. A key input into legislators’ policy decisions is the policy research provided by politically connected firms regarding the economic viability of proposed legislation (Wright 1996). By supplying this information to legislators, politically connected firms reinforce ongoing access to policymakers and thus political information (Hillman and Hitt 1999).

  5. As the discussion in conference calls comes from both participants (e.g., analysts) and managers, it is unclear whether their findings primarily relate to questions being asked of firms by participants or managers’ discussion.

  6. Jerke (2010) and Nagy and Painter (2012) provide institutional details on the political intelligence industry.

  7. See also Anantharaman and Zhang (2011), Balakrishnan et al. (2014), Billings et al. (2015), and Guay et al. (2016) for the relation between uncertainty and voluntary disclosure.

  8. For additional evidence on investor uncertainty stemming from governmental actions, see Pástor and Veronesi (2012, 2013), Kelly et al. (2016), Baker et al. (2016), and Hassan et al. (2019).

  9. There are limits imposed on both the amount of money a PAC can solicit and the amount of money a PAC can contribute to a federal election. For example, individuals can contribute up to $5000 per year per corporate-sponsored PAC. Contributions from the corporate-sponsored PAC to candidate campaigns are limited to $5000 per candidate per election. The limits on contributions to House and Senate candidates apply separately to each election in which a candidate participates. In House and Senate races, each primary election, general election, runoff, and special election is considered a separate election. There are no limits, however, on PAC “operating costs,” which includes fundraising activities and electioneering campaigns.

  10. Prior literature maintains that less observable political strategies are complementary to investments in campaign financing (Schuler et al. 2002). However, to the extent that other indirect sources serve as a substitute mechanism for obtaining political information, it would bias us against finding our predicted results.

  11. CBIS was able to provide an electronic dataset beginning in 2011. For the earlier years in our sample, we hand collect data on firms’ government relations data from Washington Representatives, a directory published semiannually by CBIS. We augment the electronic dataset provided by CBIS with our hand-collected data.

  12. We obtain data on political contributions made by firm-sponsored political action committees from the FEC detailed committee and candidate summary contribution files. The FEC does not use company identifiers (i.e., CUSIP, PERMNO, etc.). Therefore, we manually match the FEC data to CRSP/Compustat based on historical company names applicable to that time period (Christensen et al. 2022). If we do not observe contributions for firm i in any of the detailed committee and candidate summary contribution files, we code the number of candidates they support and their level of PAC contributions as zero.

  13. Similar to other recent studies (e.g., Christensen 2016), we use a linear probability model to avoid the incidental parameters problem that can arise when fixed effects are included in maximum likelihood estimators like logit and probit (Greene 2004). In our setting, we avoid the two primary limitations of the linear probability model. First, we are only interested in interpreting the parameter β1 and not predicted values, so we do not require a function that limits predictions to [0,1]. Second, our variable of interest (CONNECTED) is an indicator; therefore, by construction, it is immune to the difficulties in interpreting extreme values of the variable of interest (Wooldridge 2010, pp. 563–564).

  14. For evidence on investor uncertainty stemming from governmental actions, see Pástor and Veronesi (2012, 2013), Kelly et al. (2016), and Baker et al. (2016).

  15. All inferences hold if we omit the industry fixed effects (results not tabulated).

  16. If we set all control variables at their means, the probability of issuing guidance for connected (unconnected) firms is 37.1% (30.3%). Thus, the likelihood that connected firms issue guidance is 6.8 percentage points higher (i.e., 37.1%–30.3%), which represents a 22.6% increase in the likelihood of issuing guidance (i.e., 6.5% / 30.3%).

  17. We thank Kyle Peterson for providing the competition measure for our sample firms.

  18. In their sample, the majority of politically connected firm-year observations are concentrated in Southeast Asia, a region with vastly different institutional features than our setting; see Table 1 in Hung et al. (2018).

  19. Our objective is to identify the narrative surrounding the guidance event reported by I/B/E/S. To confirm that the 8-Ks we examine include guidance-related discussion, we randomly select 100 I/B/E/S-8-K matches and find narrative discussion related to guidance in 86 of them. Tightening the window to [−1,1] does not improve the match.

  20. Rather than focus exclusively on the voluntary portion of firms’ 8-Ks (i.e., Items 2.02, 7.01, and 8.01), we gather all 8-Ks around the guidance event and sum all policy-related words included in the entire 8-K. Treating all policy words used within 48 h of a guidance event as voluntary is ideal in our setting because policy words used within any item are arguably voluntary, as guidance may trigger or be triggered by events that lead to filing other 8-K items.

  21. Baker et al. (2016) use several dictionaries related to various categories of economic policy uncertainty to develop an index of economic policy uncertainty (EPU). Following their approach, we use the list of terms provided on their website, www.policyuncertainty.com, to identify policy-related words. This list is reproduced in Appendix B.

  22. This measure is calculated using unscaled policy words to minimize the confounding impact that scaling by total words can induce when the total length of the filing changes due to reasons unrelated to policy words.

  23. This approach has a similar intuition to that of Cohen et al. (2013). They develop a methodology designed to measure the impact of legislation on affected firms (and industries) by mapping the terms used for industry classifications to the language in legislative proposals, under the assumption that most legislative changes tend to apply to entire industries rather than specific firms.

  24. Lobbying reports are filed with the Secretary of the Senate’s Office of Public Records and are available by calendar year beginning in 1998. The Center for Responsive Politics (CRP) maintains the lobbying data, which we manually match to Compustat by company name. The lobbying reports disclose specific bills that firms lobby for.

  25. The full list of lobbying issue codes can be found on the House.gov website: https://lobbyingdisclosure.house.gov/help/default.htm?turl=WordDocuments%2Flobbyingissuecodes.htm. There are a total of 79 issue codes.

  26. For example, from reviewing Lockheed Martin’s 2014 lobbying disclosures, we learn that the firm spent over $14 million on lobbying and lobbied over policies pertaining to defense, the federal budget and appropriations, aviation, and taxes. Among the specific bills that Lockheed Martin targeted were the Howard P. “Buck” McKeon National Defense Authorization Act for Fiscal Year 2015 (H.R.4435), Carl Levin National Defense Authorization Act for Fiscal Year 2015 (S.2410), and the Department of Defense Appropriations Act, 2015 (H.R.4870). This detail was pulled from OpenSecrets.org: https://www.opensecrets.org/lobby/clientsum.php?id=D000000104&year=2014.

  27. https://projects.propublica.org/api-docs/congress-api/

  28. We read the titles and topics of a sample of 250 of the 415 bills in this analysis. Of the 250 bills, 121 are appropriations bills, 26 concern regulation, and 19 concern taxation. The remainder are a mix related to immigration (H1b visas), subsidies, and trade.

  29. Although we have discrete data (naturally binned by days), we chose to use kernel density estimation for two reasons. First, weekends, during which there is little voting and guidance activity, create a large amount of noise in the plot, making the differences in the distributions difficult to interpret without non-day binning. Despite this issue, visual inspection of the daily probability of guidance plots shows a higher (lower) incidence of guidance during the 30 days prior to (after) the final roll call vote for politically connected firms, relative to unconnected firms (untabulated). Second, and most importantly, the kernel density estimation approach provides both an estimated distribution and confidence intervals, which allow for statistical testing of the differences in guidance activity.

  30. Although the K-S test was developed for continuous data, Conover (1972) shows that this test is conservative for discrete data.

  31. Using the methodology developed in Hainmueller (2012), we reweight the observations in the connected and unconnected groups creating two samples with identical mean, variance, and skewness for EPU BETA and SIZE.

  32. Our inferences are similar if we replace BEFORE in equation (3) with a variable that measures the number of days the guidance is issued before (after) the legislation (TIMING). TIMING ranges from −30 to +30 (untabulated).

  33. The inclusion of bill fixed effects in equation (3) effectively controls for calendar time and industry fixed effects.

  34. By contrast, if we replace BEFORE with AFTER, an indicator variable that equals 1 if the firm issued guidance in the 30 days after the final roll call vote and 0 otherwise, and re-estimate column (1), we find that the estimated coefficient on CONNECTED is negative and significant (results not tabulated). This finding suggests that, consistent with the distributional analysis presented in Table 4 and Fig. 1, politically connected firms issue less guidance than politically unconnected firms in the period immediately following the roll call vote.

  35. If we set all control variables at their means, the probability of issuing guidance before the final vote for connected (unconnected) firms is 55.0% (51.2%). Thus, the likelihood that connected firms issue guidance is 3.8 percentage points higher (i.e., 55.0% - 51.2%), which represents a 7.4% increase in the likelihood of issuing guidance (i.e., 3.8% / 51.2%).

  36. As an alternative, we randomly selected a date for each bill in our sample, requiring that the random date not fall in the seven days before or after any final roll call dates for sample bills that affect a similar group of industries. All inferences remain unchanged.

  37. The average coefficient from these 1000 estimations is also statistically different from the coefficient reported in column (1) of Table 5 Panel A (t-statistic = 95.60, two-tailed p < 0.001). Furthermore, of these 1000 iterations, only 5% yield a significantly positive coefficient on CONNECTED at two-tailed p < 0.05.

  38. In alternative placebo tests where we shift the event dates back 60 days (so that the pseudo-event window does not overlap with the actual event window) or forward 60 days, we continue to find no significant differences in disclosure between politically connected and politically unconnected firms (untabulated).

  39. In each chamber (House and Senate), a bill must (1) be introduced, numbered, and assigned to a committee; (2) read, debated, and voted on in committee; and (3) returned to the chamber for debate and vote. Once both houses have voted, any differences in the two bills are voted upon as amendments in reconciliation. Based on the congressional calendar and observations of the function of both houses, we expect the minimum time required for each step to be no less than one week (see https://thinkprogress.org/the-three-day-workweek-d4944a813746/, https://www.newyorker.com/magazine/2010/08/09/the-empty-chamber and https://www.congress.gov/days-in-session).

  40. Note that the main effect of FAST in this specification is subsumed by the bill fixed effects.

  41. This process reweights the observations in the connected and unconnected creating two samples with identical mean and variance. We do not perform entropy balancing for CONNECTEDCandidate as it is a continuous variable.

  42. Note that the subsample used to estimate equation (3) with firm fixed effects is reduced because firms with only one observation are excluded.

  43. See also deHaan (2021) for a discussion of issues associated with the inclusion of firm fixed effects in regression models.

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Acknowledgements

We thank an anonymous reviewer, Vishal Baloria (discussant), Richard Cazier, Ed deHaan, John Donovan, Elia Ferracuti, Maclean Gaulin, Jeremiah Green, Xiao Han, Mingyi Hung, Zack Kaplan, Russell Lundholm (editor), Jed Neilson, Kyle Peterson, Sarah Rice, Casey Schwab, Nate Sharp, Senyo Tse, Jessica Watkins, Chris Yust, and workshop participants from The Hong Kong University of Science and Technology (HKUST), University of North Texas, Texas A&M University, University of Washington, and the 2019 Dopuch Conference at Washington University in St. Louis for helpful comments and suggestions. This project was started when Morris and Wellman were at University of Utah. We appreciate the financial support from HKUST, University of Utah, University of Oregon, New York University – Shanghai, Kellogg School of Management at Northwestern University, and Smeal College of Business at the Pennsylvania State University. Guidance data are from I/B/E/S.

Author information

Authors and Affiliations

  1. Lundquist College of Business, University of Oregon, Eugene, OR, 97403, USA

    Dane M. Christensen

  2. Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, SAR, China

    Arthur Morris

  3. Department of Accounting Information and Management, Kellogg School of Management at Northwestern University, 2211 Campus Drive, Evanston, IL, 60208, USA

    Beverly R. Walther

  4. Smeal College of Business, Pennsylvania State University, 378 Business Building, University Park, PA, 16802, USA

    Laura A. Wellman

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  1. Dane M. Christensen
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Appendices

Appendix 1

Variable Definitions

Variable Name

Description

GUIDE

An indicator variable set to one in firm-quarters where the firm reports management guidance pertaining to net income, earnings per share, fully reported earnings per share, EBITDA, EBITDA per share, and/or funds from operations, zero otherwise.

POLICY WORDS

The number of policy words per 100 words within 8-Ks that correspond to guidance. Policy words are defined based on Baker et al. (2016). Appendix 2 contains a complete list of the policy words.

HIGH POLICY WORDS

An indicator variable set equal to one for firm-quarters in which the aggregate number of policy words in the firm’s 8-Ks are above the 75th percentile in sample period, zero otherwise.

BEFORE

An indicator variable set equal to one if the firm issued guidance in the 30-day period before the final roll call vote of legislation, zero otherwise.

CONNECTED

An indicator variable set equal to one if the firm reports any Political Action Committee contributions during fiscal year t.

CONNECTEDCandidate

The natural logarithm of 1 plus the number of political candidates (House, Senate, and presidential) that the firm contributed money to over years t-5 to t.

GOV AFFAIRS

An indicator variable set equal to one if the firm had a government affairs office during year t, zero otherwise.

SIZE

The natural logarithm of the firm’s beginning-of-quarter market capitalization.

BTM

The firm’s beginning-of-quarter book-to-market ratio.

LOSS

An indicator variable set equal to one if the firm reports a loss in the current quarter, zero otherwise.

RETURN

The firm’s cumulative daily returns over the 12 months prior to quarter t.

RETVOL

The standard deviation of the firm’s daily stock returns over the 12 months prior to quarter t.

INSTOWN

The percentage of the firm’s shares held by institutional investors at the beginning of quarter t.

FOLLOWED

An indicator variable set equal to one if the firm is followed by analysts in quarter t, zero otherwise.

EPU BETA

The sensitivity of the firm’s daily stock returns to the daily economic policy uncertainty (EPU) index over the prior fiscal quarter.

HIGH COMPETITION

An indicator variable set equal to one if the firm’s level of competition using the Li et al. (2013) measure is above the 75th percentile in sample period, zero otherwise.

FAST

An indicator variable set equal to one if the bill passed in fewer than 56 days, zero otherwise.

TEXTUAL DIFFERENCES

The token frequency–inverse document frequency between the vector of government policy words used in the text of guidance issued in the two weeks prior to the date of the final vote (i.e., treatment) to guidance issued in the 90 days prior to the date the bill is introduced that is also bundled with earnings announcements (i.e., benchmark).

Appendix 2

Policy Term List from Economic Policy Uncertainty Websitea

Category

Term Sets

Entitlement Programs

entitlement program, entitlement spending, government entitlements, social security, Medicaid, Medicare, government welfare, welfare reform, unemployment insurance, unemployment benefits, food stamps, afdc, tanf, wic program, disability insurance, part d, oasdi, Supplemental Nutrition Assistance Program, Earned Income Tax Credit, EITC, head start program, public assistance, government subsidized housing

Financial Regulation

banking supervision, glass-steagall, tarp, bank supervision, thrift supervision, dodd-frank, financial reform, commodity futures trading commission, cftc, house financial services committee, basel, capital requirement, Volcker rule, bank stress test, securities and exchange commission, sec, deposit insurance, fdic, fslic, ots, occ, firrea, truth in lending

Fiscal Policy and Government Spending

government spending, federal budget, budget battle, balanced budget, defense spending, military spending, entitlement spending, fiscal stimulus, budget deficit, federal debt, national debt, Gramm-Rudman, debt ceiling, fiscal footing, government deficits, balance the budget

Health Care

health care, Medicaid, Medicare, health insurance, malpractice tort reform, malpractice reform, prescription drugs, drug policy, food and drug administration, FDA, medical malpractice, prescription drug act, medical insurance reform, medical liability, part d, affordable care act, Obamacare

Monetary Policy

federal reserve, the fed, money supply, open market operations, quantitative easing, monetary policy, fed funds rate, overnight lending rate, Bernanke, Volcker, Greenspan, central bank, interest rates, fed chairman, fed chair, lender of last resort, discount window, European Central Bank, ECB, Bank of England, Bank of Japan, BOJ, Bank of China, Bundesbank, Bank of France, Bank of Italy

National Security

national security, war, military conflict, terrorism, terror, 9/11, defense spending, military spending, police action, armed forces, base closure, military procurement, saber rattling, naval blockade, military embargo, no-fly zone, military invasion

Regulation

regulation, banking supervision, glass-steagall, tarp, bank supervision, thrift supervision, dodd-frank, financial reform, commodity futures trading commission, cftc, house financial services committee, basel, capital requirement, Volcker rule, bank stress test, securities and exchange commission, sec, deposit insurance, fdic, fslic, ots, occ, firrea, truth in lending, union rights, card check, collective bargaining law, national labor relations board, nlrb, minimum wage, living wage, right to work, closed shop, wages and hours, workers compensation, advance notice requirement, affirmative action, at-will employment, overtime requirements, trade adjustment assistance, davis-bacon, equal employment opportunity, eeo, osha, antitrust, competition policy, merger policy, monopoly, patent, copyright, federal trade commission, ftc, unfair business practice, cartel, competition law, price fixing, class action, healthcare lawsuit, tort reform, tort policy, punitive damages, medical malpractice, energy policy, energy tax, carbon tax, cap and trade, cap and tax, drilling restrictions, offshore drilling, pollution controls, environmental restrictions, clean air act, clean water act, environmental protection agency, epa, immigration policy

Sovereign Debt, Currency Crises

sovereign debt, currency crisis, currency crash, currency devaluation, currency revaluation, currency manipulation, euro crisis, Eurozone crisis, european financial crisis, european debt, asian financial crisis, asian crisis, Russian financial crisis, Russian crisis, exchange rate

Taxes

taxes, tax, taxation, taxed

Trade Policy

import tariffs, import duty, import barrier, government subsidies, government subsidy, wto, world trade organization, trade treaty, trade agreement, trade policy, trade act, doha round, uruguay round, gatt, dumping

  1. aThe categories listed and the terms within each category are taken from https://www.policyuncertainty.com/categorical_terms.html

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Christensen, D.M., Morris, A., Walther, B.R. et al. Political information flow and management guidance. Rev Account Stud 28, 1466–1499 (2023). https://doi.org/10.1007/s11142-022-09671-7

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  • DOI: https://doi.org/10.1007/s11142-022-09671-7

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