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Elections, Uncertainty and Irreversible Investment

Published online by Cambridge University Press:  31 October 2012

Brandice Canes-Wrone  and
Jee-Kwang Park
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Abstract

This article argues that the policy uncertainty generated by elections encourages private actors to delay investments that entail high costs of reversal, creating pre-election declines in the associated sectors. Moreover, this incentive depends on the competitiveness of the race and the policy differences between the major parties/candidates. These arguments are tested using new survey and housing market data from the United States. The survey analysis assesses whether respondents’ perceptions of presidential candidates’ policy differences increased the likelihood that they would delay certain purchases and actions. The housing market analysis examines whether elections are associated with a pre-election decline in economic activity, and whether any such decline depends on electoral competitiveness. The results support the predictions and cannot be explained by existing theories.

Type
Articles
Information
British Journal of Political Science , Volume 44 , Issue 1 , January 2014 , pp. 83 - 106
Copyright
Copyright © Cambridge University Press 2012 

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Footnotes

*

Donald E. Stokes Professor of Public and International Affairs; Professor of Politics, Princeton University (email: bcwrone@princeton.edu) and Assistant Professor of Methodology and American Politics, American University in Cairo. Previous presentations at Caltech, George Mason, Georgetown, Michigan, Princeton, Rutgers, Yale and the 2009 Midwest Political Science Meetings and the 2009 American Political Science Association Meetings have substantially improved this project. We are also grateful to Larry Bartels, Paul Brady, Will Bullock, Peter Buisseret, John Cogan, Hank Farber, Marty Gilens, Jason Kelly, George Krause, Adam Meirowitz, Mike Munger, Tom Romer, Harvey Rosen, Ken Shotts and Erik Snowberg for helpful comments and conversations. An online appendix is available at http://dx.doi.org/10.1017/S00071234.

References

1 Alt, James E. and Chrystal, Alec K., Political Economy (Berkeley, Calif.: Berkeley University Press, 1983)Google Scholar

Drazen, Allan, ‘The Political Business Cycle after 25 Years’, in Ben S. Bernanke and Kenneth Rogoff, eds, NBER Macroeconomics Annual 2000 (Cambridge, Mass.: MIT Press, 2000), pp. 75–138 Google Scholar

Franzese, Robert J., ‘Electoral and Partisan Cycles in Economic Policies and Outcomes’, Annual Review of Political Science, 5 (2002), 369–421 CrossRefGoogle Scholar

Keech, William R., Economic Politics: The Costs of Democracy (New York: Cambridge University Press, 1995)CrossRefGoogle Scholar

2 Alesina, AlbertoLondregan, John and Rosenthal, Howard, ‘A Model of the Political Economy of the United States’, American Political Science Review, 87 (1993), 12–33 CrossRefGoogle Scholar

3 Bernanke, Ben S., ‘Irreversibility, Uncertainty, and Cyclical Investment’, Quarterly Journal of Economics, 98 (1983), 85–106 CrossRefGoogle Scholar

4 Stephen Ansolabehere, ‘Guide to the 2008 Congressional Cooperative Election Study’, Harvard University manuscript, 2009, available at http://projects.iq.harvard.edu/cces/data?dvn_subpage=/faces/study/StudyPage.xhtml?globalId=hdl:1902.1/14003 (accessed 14 December 2011).

5 Cukierman, Alex, ‘The Effects of Uncertainty on Investment under Risk Neutrality with Endogenous Information’, Journal of Political Economy, 88 (1980), 462–75 CrossRefGoogle Scholar

6 Carroll, Christopher D. and Dunn, Wendy E., ‘Unemployment Expectations, Jumping (S,s) Triggers, and Household Balance Sheets’, in Bernanke and Rotemberg, NBER Macroeconomics Annual, pp. 165–230CrossRefGoogle Scholar

Hassler, John, ‘Uncertainty and the Timing of Automobile Purchases’, Scandinavian Journal of Economics, 103 (2001), 351–66 CrossRefGoogle Scholar

Christina D. Romer ‘The Great Crash and the Onset of the Great Depression’, Quarterly Journal of Economics, 105 (1990), 597–624CrossRefGoogle Scholar

7 Ben Ansell suggests that homes are such a large part of individuals’ assets that one's home value influences preferences about social insurance policies. Ben W. Ansell, ‘Bubbling Under: Political Preferences during Asset Bubbles’, University of Minnesota manuscript, 2008.

8 Carroll and Dunn, ‘Unemployment Expectations, Jumping (S,s) Triggers, and Household Balance Sheets’, p. 179.

9 Bittlingmayer, George, ‘Output, Stock Volatility, and Political Uncertainty in a Natural Experiment: Germany, 1880–1940’, Journal of Finance, 53 (1998), 2243–57 CrossRefGoogle Scholar

10 Bittlingmayer, ‘Output, Stock Volatility, and Political Uncertainty in a Natural Experiment’, p. 2245.

11 Bloom, Nicholas, ‘The Impact of Uncertainty Shocks’, Econometrica, 77 (2009), 623–85 Google Scholar

12 Mattozzi, Andrea, ‘Can We Insure Against Political Uncertainty? Evidence from the U.S. Stock Market’, Public Choice, 137 (2008), 43–55 CrossRefGoogle Scholar

13 Alt, James E. and Lowry, Robert C., ‘Divided Government, Fiscal Institutions, and Budget Deficits: Evidence from the States’, American Political Science Review, 88 (1994), 811–28 CrossRefGoogle Scholar

David W. Brady and Craig Volden, Revolving Gridlock, 2nd ed. (Boulder, Colo.: Westview Press, 2005)Google Scholar

Cusack, Thomas R., ‘Partisan Politics and Public Finance: Changes in Public Spending in the Industrialized Democracies, 1955–1989’, Public Choice, 91 (1997), 375–95 CrossRefGoogle Scholar

14 Alarkon, Walter, ‘Ax May Fall on Tax Break for Mortgages’, The Hill, 8 June 2010Google Scholar

15 Albanese, Elizabeth, ‘Texas’ Perry Signs School Finance Bill that Cuts Maximum Property Tax Rate’, Bond Buyer, 6 June 2006, p. 4Google Scholar

16 Herron, Michael C., Lavin, JamesCram, Donald and Silver, Jay, ‘Measurement of Political Effects in the United States Economy: A Study of the 1992 Presidential Election’, Economics & Politics, 11 (1999), 51–81 CrossRefGoogle Scholar

Butler, Michael R. and McNertney, Edward M., ‘Election Returns as a Signal of Changing Regulatory Climate’, Energy Economics, 13 (1991), 48–54 CrossRefGoogle Scholar

17 Kenneth F. Scheve and Matthew J. Slaughter, ‘What Determines Individual Trade-Policy Preferences?’, Journal of International Economics, 54 (2001), 267–92.

18 Garfinkel, Michelle R. and Glazer, Amihai, ‘Does Electoral Uncertainty Cause Economic Fluctuations?’ American Economic Review, Papers and Proceedings of the 106th Annual Meeting of the American Economic Association, 84 (1994), 169–73 Google Scholar

19 Dixit, Avinash K. and Pindyck, Robert S., Investment under Uncertainty (Princeton, N.J.: Princeton University Press, 1994)CrossRefGoogle Scholar

20 Nordhaus, William, ‘The Political Business Cycle’, Review of Economic Studies, 42 (1975), 169–90 CrossRefGoogle Scholar

Persson, Torsten and Tabellini, Guido, Macroeconomic Policy, Credibility, and Politics (Chur, Switzerland: Harwood Academic Publishers, 1990)Google Scholar

Rogoff, Kenneth, ‘Equilibrium Political Business Cycles’, American Economic Review, 80 (1990), 21–36 Google Scholar

21 Schultz, Kenneth A., ‘The Politics of the Political Business Cycle’, British Journal of Political Science, 25 (1995), 79–99 CrossRefGoogle Scholar

22 Price, Simon, ‘Comment on “The Politics of the Political Business Cycle” ’, British Journal of Political Science, 28 (1998), 201–10 CrossRefGoogle Scholar

23 Alt, James E. and Lassen, David Dreyer, ‘Transparency, Political Polarization, and Political Budget Cycles in OECD Countries,’ American Journal of Political Science, 50 (2006), 530–50 CrossRefGoogle Scholar

24 Hibbs, Douglas A. Jr., ‘Political Parties and Macroeconomic Policy’, American Political Science Review, 71 (1977), 1467–87 CrossRefGoogle Scholar

25 Alesina, Londregan and Rosenthal, ‘A Model of the Political Economy of the United States’, pp. 13–14.

26 Bartels, Larry M., Unequal Democracy: The Political Economy of the New Gilded Age (New York: Russell Sage Foundation, 2008)Google Scholar

27 Gerber, Alan S. and Huber, Gregory A., ‘Partisanship and Economic Behavior: Do Partisan Differences in Economic Forecasts Predict Real Economic Behavior?’ American Political Science Review, 103 (2009), 407–26 CrossRefGoogle Scholar

28 See, for instance, the Gallup poll that asks, ‘Looking ahead, how much of a difference would this tax cut (you expected per year) make for you and your family – a big difference, some difference, only a little difference, or no difference at all?’ Gallup/CNN/USA Today poll, February 2001. Retrieved from the iPOLL Databank, The Roper Center for Public Opinion Research, University of Connecticut.

29 Speare, Alden Jr. and Goldscheider, Frances Kobrin, ‘Effects of Marital Status Change on Residential Mobility’, Journal of Marriage and the Family, 49 (1987), 455–64 CrossRefGoogle Scholar

30 Ansolabehere, ‘Guide to the 2008 Congressional Cooperative Election Study’.

31 The weighting does not significantly alter the distribution of respondents in each category. The largest change is a four-percentage point decrease in respondents who perceive the election to make some difference in their income; in all other categories, the weighting causes less than a three-percentage-point shift.

32 For the renovations-tax policy analysis, the coefficient and standard error were 0.124 (0.083) for party members and 0.236 (0.140) for independents. For the renovations-job security analysis, the coefficient and standard error were 0.272 (0.096) for party members and 0.116 (0.139) for independents.

33 Reichert, Alan K., ‘The Impact of Interest Rates, Income, and Employment upon Regional Housing Prices’, Journal of Real Estate Finance and Economics, 3 (1990), 373–91 CrossRefGoogle Scholar

34 For example, Albanese, ‘Texas’ Perry Signs School Finance Bill that Cuts Maximum Property Tax Rate’; ‘Governor Rendell: Record 588,638 Pennsylvanians Received Property Tax/Rent Rebates’.

35 Hughlett, Mike, ‘Job Zones Unveiled: State Designates 325 Communities Eligible for Business Subsidy’, Saint Paul Pioneer Press, 19 December 2003, p. C1Google Scholar

36 Brunori, David, ‘Principles of Tax and Targeted Tax Incentives’, State & Local Government Review, 29 (1997), 50–61 CrossRefGoogle Scholar

37 Ebeid, Michael and Rodden, Jonathan, ‘Economic Geography and Economic Voting: Evidence from the US States’, British Journal of Political Science, 36 (2006), 527–47 CrossRefGoogle Scholar

Taylor, Lori L., ‘Revealed-Preference Measures of School Quality’, in Leanna Stiefel, Amy Ellen Schwartz, Ross Rubenstein and Jeffrey Zabel, eds, Measuring School Performance and Efficiency: Implications for Practice and Research, 2005 Yearbook of the American Education Finance Association (Larchmont, N.Y.: Eye on Education Publishing, 2005), pp. 163–85 Google Scholar

38 Fernando, Ferreira and Gyourko, Joseph, ‘Do Political Parties Matter? Evidence from U.S. Cities’, Quarterly Journal of Economics, 124 (2009), 399–422 Google Scholar

39 These states include AL, AZ, AR, CA, CO, CT, DE, FL, GA, HI, IL, IA, KY, MA, MD, MA, MI, MN, NE, NV, NH, NJ, NY, NC, OH, OK, OR, PA, RI, SC, VT, VA, WA, WV and WI. The data for Vermont span only four years.

40 An MSA is a population centre as defined by the US Office of Management and Budget. MSAs typically encompass multiple counties and/or towns.

41 Glaeser, Edward and Gyourko, Joseph, ‘Housing Dynamics’ (Cambridge, Mass.: NBER Working Paper No. 12787, 2006)CrossRefGoogle Scholar

42 The government collects data on new home sales as part of the Survey of Construction, while the National Association of Realtors provides the data on existing home sales.

43 Stephens, William, Li, Ying, Lekkas, Vassilis, Abraham, JesseCalhoun, Charles and Kimner, Thomas, ‘Conventional Mortgage Home Price Index’, Journal of Housing Research, 6 (1995), 389–418 Google Scholar

44 We have also analysed the almost identical index published by the Office of Housing Enterprise Oversight, and the results are similar. We use the CMHPI because it has larger coverage for some MSAs.

45 A working paper by Justin Wolfers argues that home prices reflect voters’ future economic expectations. We considered potential implications of this interpretation of home prices, particularly the possibility that the findings could be driven by bad economic conditions that weaken voters’ economic expectations. Accordingly, we examined whether the findings held when personal income growth was higher than the median level. The results were robust to examining this subsample. For instance, the coefficient and standard error on Gubernatorial Election Year are −0.354 (0.181) for the fixed-effects estimation of Equation 1. Justin Wolfers, ‘Are Voters Rational? Evidence from Gubernatorial Elections’, Wharton Business School manuscript, 2007.

46 Generally the two-party vote concerns Democratic and Republican candidates. However, the percentage is based on the two candidates with the largest vote shares, even in the unusual case in which one of them is a third-party candidate.

47 For instance, if we increase the threshold to an advantage of six or seven percentage points, all of the major results hold.

48 Case, Karl E. and Shiller, Robert J., ‘Is There a Bubble in the Housing Market?’, Brookings Papers on Economic Activity (2003), 299–342CrossRefGoogle Scholar

Poterba, James M., ‘House Price Dynamics: The Role of Tax Policy and Demography’, Brookings Papers on Economic Activity (1991), 143–203CrossRefGoogle Scholar

49 Using the absolute level of real income rather than the percentage change does not alter the main findings, but does reduce the effect of income on housing market performance.

50 For example, Case and Shiller, ‘Is There a Bubble in the Housing Market?’.

51 Mankiw, Gregory N. and Weil, David N., ‘The Baby Boom, the Baby Bust, and the Housing Market’, Regional Science and Urban Economics, 19 (1989), 235–58 CrossRefGoogle ScholarPubMed

52 Kelly E. Grace, ‘US Foreclosures Jumped in March’, Wall Street Journal Online, 15 April 2009.

53 Robert Shiller, Irrational Exuberance, 2nd edn (Princeton: Princeton University Press, 1991)Google Scholar

54 Nickell, Stephen, ‘Biases in Dynamic Models with Fixed Effects’, Econometrica, 49 (1981), 1417–26 CrossRefGoogle Scholar

55 Arellano, Manuel and Bond, Stephen, ‘Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations’, Review of Economic Studies, 58 (1991), 277–97 CrossRefGoogle Scholar

56 Arellano, Manuel and Bover, Olympia, ‘Another Look at the Instrumental Variables Estimation of Error Components Models’, Journal of Econometrics, 68 (1995), 29–51 CrossRefGoogle Scholar

Blundell, Richard and Bond, Stephen, ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’, Journal of Econometrics, 87 (1998), 115–43 CrossRefGoogle Scholar

57 Roodman, David, ‘A Note on the Theme of Too Many Instruments’, Oxford Bulletin of Economics and Statistics, 71 (2009), 135–58 CrossRefGoogle Scholar

58 If the income and unemployment variables are considered exogenous, then the Difference-in-Hansen test for the exogenous instruments suggests that one can reject at p = 0.03 the null that they are exogenous. Once income and unemployment are considered predetermined, the p-value for this test increases to p = 0.76.

59 Roodman, ‘A Note on the Theme of Too Many Instruments’, p. 142, 151.

60 The coefficients are similar in magnitude to those in Table 6 from difference-GMM and are significant at p < 0.05, two-tailed, except one case in which the significance level is p = 0.07, two-tailed.

61 In the model that estimates the average effect of a gubernatorial election year, the p-value from the Difference-in-Hansen test is p = 1.00 and from the Hansen J test is p = 0.982. In the model that estimates the effect of competitiveness, the p-value from both the Difference-in-Hansen and Hansen J tests is p = 1.00.

62 We tried reducing the lag structure so that it included only the second or third lag, and we even collapsed this lag, but the Hansen tests still suggested that the models were overidentifed.

63 For example, Poterba, ‘House Price Dynamics: The Role of Tax Policy and Demography’.

64 Beck, Thorsten and Levine, Ross, ‘Stock Markets, Banks, and Growth: Panel Evidence’, Journal of Banking and Finance, 28 (3), 423–42CrossRefGoogle Scholar

65 The results on the key coefficients are also robust to increasing the number of lags used as instruments, but the Hansen tests recommend against these models.

66 For instance, if the only instrument is the collapsed second or third lag, then the Hansen J-statistic and Difference-in-Hansen test suggest the model is overidentified.

67 Haynes, Stephen E. and Stone, Joe A., ‘Political Parties and the Variable Duration of Business Cycles’, Southern Economic Journal, 60 (1994), 869–85 CrossRefGoogle Scholar

Krause, George A., ‘Electoral Incentives, Political Business Cycles and Macroeconomic Performance: Empirical Evidence from Post-War US Personal Income Growth’, British Journal of Political Science, 35 (2005), 77–101CrossRefGoogle Scholar

Sáez, Lawrence and Sinha, Aseema, ‘Political Cycles, Political Institutions, and Public Expenditure in India, 1980–2000’, British Journal of Political Science, 40 (2009), 91–113 Google Scholar

Treisman, Daniel and Gimpelson, Vladimir, ‘Political Business Cycles and Russian Elections, or the Manipulations of “Chudar” ’, British Journal of Political Science, 31 (2001), 225–46 CrossRefGoogle Scholar

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