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Mitigating political uncertainty

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Abstract

This study examines whether firms that establish political connections gain differential access to relevant information over legislative developments, thereby mitigating the negative consequences of uncertainty. I find that political connections (partially) offset the negative relation between investment and political uncertainty documented in prior research. My results do not appear to be driven by connected firms pursuing investments that are insensitive to uncertainty. I perform additional tests to address concerns over correlated omitted variables. First, I identify a setting around a tax policy development designed to provide new investment incentives to firms. In this setting, I predict and find that reduced information asymmetry for connected firms results in delaying investment in anticipation of future lucrative tax incentives. Second, I perform a falsification test and document that political connections do not mitigate the effects of general economic uncertainty. Finally, I continue to find support for my hypothesis within a propensity matched sample.

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Notes

  1. For example, Northrop Chief Executive Wes Bush, head of one of the most politically active firms in Washington, acknowledged that delays in the passage of a military spending bill would produce general uncertainty. However, he stood by a forecast of upward earnings growth, announcing that the public would likely see another temporary extension of the spending bill (Hodge and Fitzgerald 2011).

  2. See section 3.2 for a detailed discussion of the empirical measures I rely on to identify politically connected firms.

  3. Individual employees, officers, and shareholders can contribute up to $5000 to the corporate-sponsored PAC. The corporate-sponsored PAC can contribute up to $5000 to an individual candidate’s campaign and up to $15,000 to the national party committee per election. In addition to making contributions, PACs may support (or oppose) candidates by making independent expenditures. Independent expenditures are not contributions, and therefore are not captured in the FEC data I rely on.

  4. Political contributions are regulated by the FEC. As required by The Act, any contribution of $200 or above is publicly available on the FEC website starting with the 1979–1980 election cycle (http://www.fec.gov). I begin my analysis in 1991 and end in 2011 because that is when I have available PAC-GVKEY links. I would like to thank Dane Christensen for making the PAC-GVKEY links available to me.

  5. Cooper et al. (2010) capture multi-period contributions, a necessary component of forming meaningful relationships with policymakers (Hillman and Hitt 1999; Brown et al. 2015, 2016).

  6. I obtain firm headquarters data from Compustat.

  7. I perform two sets of analyses to further control for macroeconomic conditions beyond the baseline specification from Julio and Yook (2012). First, I include additional macroeconomic factors in my multivariate analysis (i.e., term spread, TERM, the default spread, DEF, and the risk-free rate, TBILL) identified in prior research (Fama and French 1989). Second, I replace POL_UNCERTAIN with an indicator variable equal to one in the period leading up to a national election, ELEC _ UNCERTAIN. Elections are exogenously determined, recurring events in the United States that represent heightened periods of uncertainty and thus provide a natural experimental framework that helps address endogeneity concerns related to variable measurement and reverse causality (Julio and Yook 2012, Garfinkel and Glazer 1994, Nordhaus 1975). My results are not sensitive to either of these alternative specifications. Finally, my inferences remain unchanged if I exclude year fixed effects or exclude macroeconomic controls.

  8. When calculating the drop in investment for non-connected firms during high-uncertainty periods, I consider the 75th percentile of POL_UNCERTAIN to represent high-uncertainty periods. The 5.5 % stated above is calculated as follows: the coefficient on POL_UNCERTAIN = 0.0008 (Table 3, column (1)) is multiplied by the 75th percentile of POL_UNCERTAIN = 1.1946 (Table 1, Panel B). I then divide by the mean investment rate for low-uncertainty periods =0.0173 (untabulated), where low uncertainty is defined as the bottom quintile of POL_UNCERTAIN over my sample period. That is, [(0.0008*1.1946)/0.0173].

  9. The 8.1 % stated above is calculated as follows: [(5.7 %–6.2 %)/6.2 %], where 5.7 % represents the absolute drop in investment rates for connected firms and 6.2 % represents the absolute drop in investment rates for non-connected firms. The value of 5.7 % is calculated as follows: the coefficient on POL_UNCERTAIN = 0.0009 (Table 3, column (1)) is multiplied by the 75th percentile of POL_UNCERTAIN = 1.1946 (Table 1, Panel B). I then subtract the mean value of CONNECTED Candidate = 0.3617 (Table 1, Panel B), multiplied by the 75th percentile of POL_UNCERTAIN = 1.1946, multiplied by the coefficient on the interaction of POL_UNCERTAIN and CONNECTED Candidate = 0.0002 (Table 3, column (1)). I then divide this number by the mean investment rate during low-uncertainty periods =0.0168, (untabulated), where low uncertainty is defined as the bottom quintile of POL_UNCERTAIN over my sample period. That is, [((0.0009*1.1946) – (0.3617*1.1946*0.0002))/0.0173]. The value of 6.2 % is calculated as follows: the coefficient on POL_UNCERTAIN = 0.0009 (Table 3, column (1)) is multiplied by the 75th percentile of POL_UNCERTAIN = 1.1946. I then divide by the mean investment rate for low-uncertainty periods =0.0173. That is, [(0.0009*1.1946)/0.0173].

  10. The 12.9 % stated above is calculated as: [(5.4 %–6.2 %)/6.2 %], where 5.4 % represents the absolute drop in investment rates for firms connected to home-state candidates and 6.2 % represents the absolute drop in investment rates for firms not connected to home-state candidates. I follow the same procedure outlined in footnote 13 when calculating the percentage drop in investment for connected and non-connected firms, respectively.

  11. The 13.0 % stated above is calculated as: [(6.0 %–6.9 %)/6.0 %], where 6.0 % represents the absolute drop in investment rates for firms connected to ideologically consistent candidates and 6.0 % represents the absolute drop in investment for firms not connected to ideologically consistent candidates. I follow the same procedure outlined in footnote 13 when calculating the percentage drop in investment for connected and non-connected firms, respectively.

  12. The survey can be located at the following web address: https://www.census.gov/econ/aces/xls/2003/ace-03.pdf. Consistent with prior research, I use three-digit NAICS codes for industry groupings.

  13. For ease of interpretation, I report the results of my analyses based on partitioning my sample along high vs. low INV_IRR as described above. My inferences are unchanged if I instead re-estimate my regression using the full sample of firm-quarter observations, but include a three-way interaction between POL_UNCERTAIN, CONNECTED and INV_IRR.

  14. My politically connected sample consists of 35,838 firm-quarter observations. Of these observations, 13,754 firm-quarter observations fall under the High_Financial, Yes_Constituency category, 10,379 firm-quarter observations fall under the High_Financial, No_Constituency category, 4126 firm-quarter observations fall under the Low_Financial, Yes_Constituency category, and 7579 observations fall under the Low_Financial, No_Constituency category.

  15. While Baker et al. (2013) document the significance of tax policy uncertainty as one of the primary drivers of general economic policy uncertainty, the authors do not formally validate the tax-specific index. However, Brown et al. (2016), who rely on the TPU index, evaluate the index with respect to actual tax legislation under consideration by Congress. The authors document correlations between the TPU index and major tax-legislative developments, providing some assurance that the TPU index is capturing actual uncertainty over tax legislation.

  16. See Appendix for detailed variable definitions.

  17. In sensitivity analyses I test whether firm-specific effects are robust to including a control for aggregate PAC participation within a given industry. To measure industry-level PAC participation, I aggregate CONNECTED Candidate by industry group (using the 49 industries identified in Fama and French 1997) to construct an industry-based measure, CONNECTED Industry. I then re-estimate the investment model, including CONNECTED Industry and the interaction of CONNECTED Industry with POL_UNCERTAIN as additional control variables. Regardless of the measure of CONNECTED used, the coefficient on the interaction between POL_UNCERTAIN and CONNECTED remains positive and significant.

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Authors and Affiliations

  1. David Eccles School of Business, University of Utah, 1655 E. Campus Center Drive, Salt Lake City, UT, 84112, USA

    Laura A. Wellman

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Correspondence to Laura A. Wellman.

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This paper is based on my dissertation. I would like to thank Dan Dhaliwal (co-chair), Stephen Hillegeist (co-chair), and my dissertation committee members Jennifer Brown, Amy Hillman, Michael Mikhail, and Beverly Walther for their direction, dedication, and invaluable advice. I appreciate the helpful comments and suggestions from an anonymous reviewer, Russel Lundholm (editor), John Barrick (discussant), Phil Berger, Brian Cadman, Hans Christensen, Dhammika Dharmapala, Atif Ellahie, Mara Faccio, Dave Guenther, Michelle Hanlon, Rachel Hayes, Christian Leuz, Michal Matejka, Marlene Plumlee, Katherine Schipper, Jordan Schoenfeld, Nemit Shroff, Doug Skinner, Steve Stubben, the University of Arizona Tax Readings Group, and participants at Arizona State University, Boston College, Duke University, Massachusetts Institute of Technology, Northwestern University, University of Chicago, University of North Carolina, University of Utah, and the 2015 NTA Annual Meeting on Taxation for their helpful suggestions. I appreciate the financial support of the David Eccles School of Business, University of Utah, the Kellogg School of Management, Northwestern University, the University of Illinois at Chicago, and the W.P. Carey School of Business, Arizona State University.

Appendix: Variable definitions

Appendix: Variable definitions

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Wellman, L.A. Mitigating political uncertainty. Rev Account Stud 22, 217–250 (2017). https://doi.org/10.1007/s11142-016-9380-0

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