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Elections, Uncertainty and Irreversible Investment

  • Brandice Canes-Wrone and Jee-Kwang Park
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.

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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.

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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.

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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

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

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)

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

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

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

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. C1

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

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

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

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

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)

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

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), 299–342

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

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

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)

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

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

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

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

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

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), 423–42

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

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), 77–101

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

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

* Donald E. Stokes Professor of Public and International Affairs; Professor of Politics, Princeton University (email: ) 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.

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British Journal of Political Science
  • ISSN: 0007-1234
  • EISSN: 1469-2112
  • URL: /core/journals/british-journal-of-political-science
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