Political Incentives and Transportation Funding

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Political Incentives and Transportation Funding
Robert Krol

Robert Krol. “Political Incentives and Transportation Funding.” Mercatus Research, Mercatus Center at George Mason University, Arlington, VA, July 2015.
ABSTRACT This paper examines politicians’ incentives when they decide on the level and allocation of government transportation infrastructure spending. I conclude that transportation infrastructure spending is highly inefficient and often driven by political rather than economic forces. Research shows that transportation project costs are significantly underestimated and traffic flows tend to be overestimated. These errors are large and are not random, suggesting that they are deliberate in order to get projects started, resulting in an inefficient use of funds. Project benefits are concentrated in a state or district, whereas tax costs are spread out nationwide. Legislators embrace inefficient transportation projects because district or state voters do not pay the full cost, and projects proceed even when total costs exceed total benefits. Possible reforms include comparing potential project benefits and costs with those of similar completed projects to assess the accuracy of predicted costs and demand. Reducing the federal government’s role in highway financing would improve outcomes.
JEL codes: H4, H7, R4
Keywords: benefit-cost analysis, forecast bias, federalism, local public goods
Copyright © 2015 by Robert Krol and the Mercatus Center at George Mason University
Release: July 2015
The opinions expressed in Mercatus Research are the authors’ and do not represent official positions of the Mercatus Center or George Mason University.

The transportation sector represents a large part of the US economy, contributing approximately 17 percent to gross domestic product.1 An efficient transportation system is an important part of a strong economy.2 It provides mobility, improving employment opportunities and leisure activities. It also plays a central role in facilitating domestic and international commerce.
Unfortunately, the US transportation system underperforms. Most major cities face serious congestion that results in longer travel times and higher emissions. One estimate is that time wasted sitting in traffic, additional air pollution, and politically driven transportation spending reduce welfare by about $100 billion annually.3 Much of the nation’s highway infrastructure is past its design life, and capacity is insufficient to serve growing areas.4 Reducing highway congestion, providing adequate road capacity, and maintaining existing roads should be top priories for government infrastructure spending. However, despite significant infrastructure expenditures by federal and state governments, congestion and maintenance problems persist.
This paper examines the political economy of US transportation infrastructure decision making. It emphasizes the incentives that influence elected officials when they decide on the level and allocation of government infrastructure spending. First up is a discussion of the biases in project benefit-cost
1. Clifford Winston, “On the Performance of the U.S. Transportation System: Caution Ahead,” Journal of Economic Literature 51, no. 3 (2013): 773–824. 2. There is a large literature evaluating the economic impact of infrastructure spending. See Robert Krol, “Public Infrastructure and Economic Development,” in Handbook of Economic Development, ed. Kuotsai Liou (New York: Marcel Dekker, 1998); Krol, “Infrastructure and Economic Development,” in Encyclopedia of Public Administration and Public Policy, ed. Melvin Dunick and Domonic Bearfield (New York: Taylor & Francis Group, 2014); and Alfredo M. Pereira and Jorge M. Andraz, “On the Effects of Infrastructure Investment,” Journal of Economic Development 38, no. 4 (2013): 1–37. 3. See Winston, “Performance of the U.S. Transportation System,” 774n1. 4. Robert W. Poole Jr., “Interstate 2.0: Modernizing the Interstate Highway System via Toll Finance” (Policy Study No. 425, Reason Foundation, Los Angeles, 2013).

analysis. The next section explains how legislative voting and institutional ­arrangements misallocate transportation funds. The last section highlights transportation policy reforms for a more economically efficient system.
Politicians have limited funds to finance a long list of potential transportation infrastructure projects. Priority should go to projects with the highest net benefits. In principle, analysts are expected to apply an objective benefit-cost analysis to each project to determine its net economic impact. Once this analysis is complete, projects can be ranked. For this approach to work, decision makers must have objective and unbiased estimates of all project benefits and costs.5
Are projects evaluated objectively? The following quotation should worry any taxpayer. In response to the $300 million cost overrun for the Transbay Terminal in San Francisco, Willie Brown, a former Speaker of the California Assembly and former mayor of San Francisco, wrote, “We always knew the initial estimate was way under the real cost. Just like we never had a real cost for the Central Subway or the Bay Bridge or any other massive construction project. So get off it. In the world of civic projects, the first budget is really a down payment. If people knew the real cost from the start, nothing would ever be approved. The idea is to get going. Start digging a hole and make it so big, there’s no alternative to coming up with the money to fill it in.”6
Is San Francisco an outlier, or is it common practice to systematically underestimate project costs and, perhaps, to overestimate project benefits? Researchers have examined transportation projects across many countries and time periods. The results are disturbing.
Bent Flyvbjerg, Mette Skamris Holm, and Søren Buhl compare the actual cost of a transportation project at the time of completion with the estimated cost at the time of the decision to build. They examine 258 transportation projects worth $90 billion built in North America, Europe, and other regions over the last 80 years and find significant cost overruns, suggesting that cost estimates produced large cost errors.7 Table 1 shows project cost overruns.
5. Kenneth A. Small, “Project Evaluation,” in Essays in Transportation Economics and Policy, ed. Jose Gomez-Ibanez, William B. Tye, and Clifford Winston (Washington, DC: The Brookings Institution, 1999). 6. Willie Brown, “When Warriors Travel to China, Ed Lee Will Follow,” SFGate, July 27, 2013, http:// www.sfgate.com/bayarea/williesworld/article/When-Warriors-travel-to-China-Ed-Lee-will-follow -4691101.php. 7. Bent Flyvbjerg, Mette Skamris Holm, and Søren Buhl, “Underestimating Costs in Public Works Projects,” Journal of the American Planning Association 68, no. 3 (2002): 279–95.


Project type

Number of projects

Average cost overrun (%)




Fixed link






All projects



Source: Bent Flyvbjerg, Mette Skamris Holm, and Søren Buhl, “Underestimating Costs in Public Works Projects,” Journal of the American Planning Association 68, no. 3 (2002): 283.

Table 1 shows an average cost overrun of nearly 28 percent. Rail, especially high-speed rail, had the largest cost overrun, almost 45 percent, and roads had the lowest overrun, around 20 percent. There appears to be little difference between the US and European errors. The sample includes cost estimates for projects built before World War II, and the development of computers should have improved modern cost estimates. However, more recent cost estimates show no improvement. The authors conclude that these large and systematic errors were made intentionally to mislead voters.
The same authors also examine the accuracy of traffic flow forecasts using 210 rail and road projects in 14 nations, worth $58 billion total, built from 1969 to 1998.8 They compare the actual traffic in the first year of operation with the original forecast. Table 2 reports their results on the size and distribution of the traffic forecast errors. The forecast error is calculated as the percentage difference between actual and estimated traffic flow. A negative error indicates the forecast exceeded the actual traffic flow and was overly optimistic.




Average error (%)



Percentage of projects with inaccuracies ˃ 20%



Percentage of projects with inaccuracies ˃ 40%



Percentage of projects with inaccuracies ˃ 60%



Source: Bent Flyvbjerg, Mette K. Skamris Holm, and Søren L. Buhl, “Inaccuracy in Traffic Forecasts,” Transport Reviews 26, no. 1 (2006): 11.

8. Bent Flyvbjerg, Mette K. Skamris Holm, and Søren L. Buhl, “Inaccuracy in Traffic Forecasts,” Transport Reviews 26, no. 1 (2006): 1–24.

“To accurately estimate future costs, ridership, and traffic flows, analysts must correctly project economic growth, demographic trends, and inflation. It is no surprise that efforts to forecast costs and traffic flows result in large errors.”

The average project forecast error reported in the first row indicates rail traffic was overestimated by 51.4 percent. Road forecasts underestimated traffic flows by about 9.5 percent. Forecasting lower road traffic flows may make the construction of roads less attractive. If politicians prefer, for environmental reasons, to get commuters out of their cars and into rail systems, then underestimating road benefits might serve their purpose.
The remaining table entries illustrate the distribution in traffic flow forecast errors. Eighty-four percent of rail traffic forecast errors were greater than 20 percent, and 40 percent of rail projects had forecast errors greater than 60 percent.
Kenneth Button and Zhenhua Chen compare traffic forecasts for 26 US highway projects from 1986 through 2004.9 Because four of these projects were public-private partnerships, they ask whether having a greater private sector role in the evaluation stage of a highway project reduces traffic forecast errors. They find that both types of ownership overestimated actual traffic flows five years into the future. In this study, public-private partnership errors are about half the size of the public forecast errors. However, there are only four public-private partnerships in the sample, insufficient to conclude that public-private partnerships are the solution to forecast bias in transportation project analysis.
Robert Bain examines the traffic flow forecasts of 100 privately financed toll roads, tunnels, and bridges built from 2002 through 2005.10 He finds that the forecasts overestimated traffic flows by an average of 23 percent, which suggests that private forecasters also bias project traffic flow projections. If the group making the projections were risking its own funds, or intended to work on future projects, the projections should be unbiased. Since they are not,

9. Kenneth Button and Zhenhua Chen, “Demand Forecasting Errors and the Ownership of Infrastructure,” Applied Economic Letters 21, no. 7 (2014): 494–96. 10. Robert Bain, “Error and Optimism Bias in Tollroad Traffic Forecasts,” Transportation 36, no. 5 (2009): 469–82; Bain, Toll Road Traffic and Revenue Forecasts: An Interpreter’s Guide (Seville, Spain: Robert Bain Publicaciones Digitales SA, 2009).

there may be an incentive for private project promoters to bias the forecasts in order to speed funding. The bottom line for both investors and taxpayers is the need for skepticism when examining these forecasts.
Forecasting the cost of building and the demand for large transportation projects is difficult. To accurately estimate future costs, ridership, and traffic flows, analysts must correctly project economic growth, demographic trends, and inflation. It is no surprise that efforts to forecast costs and traffic flows result in large errors. If the forecast errors were random and did not persist over time, it would seem that the errors resulted from technical issues and from the general uncertainty associated with trying to predict the future. The strong tendency to underestimate transportation project costs and to overestimate traffic flows, and the persistence of these errors over time, suggests that errors are deliberate. Politicians and special interest groups (construction unions and companies, engineering firms, and bureaucrats) have effectively captured the process. While politicians pretend that the estimates have been done in a scientific way, in reality, they are a “strategic misrepresentation.”11 Transportation expert Martin Wachs draws a similar conclusion. He argues that estimates are presented to the public as scientific and unbiased, but are actually intended to get the project started for political gain.12
How big of an impact do these forecast biases have on transportation infrastructure decisions? Flyvbjerg agrees with Wachs. He concludes that many transportation projects are “financial disasters,” often providing negative returns.13 He describes the outcomes of benefit-cost studies as the “survival of the unfittest,” where projects that should not be built survive by biased analysis. These biases result in a forecast of project viability that significantly exceeds actual project viability.14
Even if systematic errors only increase benefit-cost ratios of viable projects, the result is an inefficient use of limited government funds. There is an opportunity cost associated with using funds on projects with low actual benefit-cost ratios. Shifting funding from projects with low benefit-cost ratios to projects with high benefit-cost ratios will increase efficiency and output. Other projects,
11. See Flyvbjerg, Holm, and Buhl, “Underestimation Costs,” 229n7. 12. Martin Wachs, “Ethics and Advocacy in Forecasting for Public Policy,” Business and Professional Ethics Journal 9, no. 1–2 (1990): 141–57. 13. Bent Flyvbjerg, “Survival of the Unfittest: Why the Worst Infrastructure Gets Built—and What We Can Do about It,” Oxford Review of Economic Policy 25, no. 3 (2009), 344–67, and Flyvbjerg, “Design by Deception: The Politics of Megaproject Approval,” Harvard Design Magazine, no. 22 (Spring/ Summer 2005): 50–59. 14. Bent Flyvbjerg, Nils Bruzelius, and Werner Rothengatter, Megaprojects and Risk: An Anatomy of Ambition (New York: Cambridge University Press, 2003).

perhaps even nontransportation projects in education or health care, may provide higher net benefits.15 If the spending results in taxes being higher than necessary, that will have a negative impact on economic growth.
Elected officials may request benefit-cost analysis of potential projects, yet ignore the results.16 An example of this behavior can be found in the Transportation Investment Generating Economic Recovery (TIGER) program that was part of the 2009 stimulus package. TIGER was designed to be a competitive grant program to finance state and local transportation projects. Each project application included a benefit-cost analysis. Department of Transportation (DOT) staff evaluated and rated each analysis for quality. The staff also rated the likelihood that project benefits would exceed costs. Project selection by the DOT was to be primarily based on expected net benefits rather than on noneconomic factors such as community sustainability or the distribution of funds across states. Anthony Homan, Teresa Adams, and Alex Marach examined 154 applications, 51 of which were funded. After controlling for factors such as the transportation mode and the grant size relative to total cost, the quality of the benefit-cost analysis and the likelihood that benefits exceed costs were not statistically significant determinants of project funding.17 In other words, DOT leadership essentially ignored the benefit-cost analysis and made the awards based on other, possibly political factors.
Many government agencies have forecasting responsibilities. In evaluating a cost or revenue forecast for a transportation project, biased forecasts or errors that persist over time suggest an incentive structure that rewards this behavior. This behavior would not result if a high cost estimate had the same political consequences as a low cost estimate.
Cost or benefit estimates are likely to be influenced by politicians and their appointed administrators. As a result, government analysts face pressure to bias the forecast in a direction that favors the objectives of the elected officials they serve.18 If building a new road is important for reelection, agency
15. The Congressional Budget Office evaluated airport and highway project data from the Federal Aviation Administration and Federal Highway Administration and found negative and positive benefitcost ratios on projects. See CBO, The Economic Effects of Federal Spending on Infrastructure and Other Investments (Washington, DC: CBO, 1998). 16. Anthony C. Homan, Teresa M. Adams, and Alex J. Marach, “A Statistical Analysis of the Role of Benefit-Cost Analysis in Awarding Tiger Grants,” Public Works Management and Policy 19, no. 1 (2014): 37–50. 17. Ibid. 18. This can happen in the private sector as well. Terence Lin finds stock analysts often bias their earnings forecasts upward in exchange for information in “Rationality and Analysts’ Forecast Bias,” Journal of Finance 56, no. 1 (2001): 369–85.

pressure will bias benefit-cost estimates in a way that puts the project in a more economically favorable light. The analyst’s pay and future job opportunities may depend on how closely they play along. While the analyst’s professional reputation serves as a check on this process, it appears that political pressures dominate. Elected officials are likely to reward analysts whose estimates make it easier to carry out official agendas.19
Large, biased benefit-cost projection errors make the outcome of infrastructure projects highly uncertain. Projects that appear to be economically viable when proposed often turn out poorly. Ultimately, the impact of an infrastructure project depends on its net rate of return. Research on the rate of return associated with infrastructure investment provides a range of estimates from 0 to as high as 100 percent.20 As a result, it is difficult to know what the average rate of return is on infrastructure investment. Furthermore, it is not clear what the actual rate of return will be on an individual project. The finding that infrastructure spending is often unrelated to output and productivity does not mean there are no economically worthwhile transportation projects to be built, but the political incentives lead to less funding for worthwhile projects while other projects with lower net benefits get funded instead.
We probably cannot eliminate the political pressure to bias projections in the direction that interest groups and politicians prefer. The payoff to politicians and interest groups is too high to ignore. However, certain reforms could improve benefit-cost estimates for transportation infrastructure projects.
19. Robert Krol, “Evaluating State Revenue Forecasting under a Flexible Loss Function,” International Journal of Forecasting 29, no. 2 (2013): 282–89, and Krol, “Forecast Bias of Government Agencies,” Cato Journal 34, no. 1 (2014): 99–112. 20. For survey papers, see Krol, “Public Infrastructure and Economic Development”; Krol, “Infrastructure and Economic Development”; Pereira and Andraz, “On the Effects of Infrastructure Investment.” See also David Aschauer, “Is Public Expenditure Productive?,” Journal of Monetary Economics 23 (1989): 177–200; Alicia Munnell, “How Does Public Infrastructure Affect Regional Economic Performance?,” New England Economic Review (September/October 1990): 11–32; Douglas Holtz-Eakin, “Public-Sector Capital and the Productivity Puzzle,” Review of Economics and Statistics 76 (1994): 12–21; Robert Krol, “Public Infrastructure and State Economic Development,” Economic Development Quarterly 9, no. 4 (1995): 331–38; Chad Shirley and Clifford Winston, “Firm Inventory Behavior and the Returns from Highway Infrastructure Investments,” Journal of Urban Economics 55 (2004): 398–415; Sarantis Kalyvitis and Eugenia Vella, “Public Capital Maintenance, Decentralization, and U.S. Productivity Growth,” Public Finance Quarterly 39, no. 6 (2011): 784–809; and Alfredo Pereira and Jorge Andraz, “On the Regional Incidence of Highway Investments in the USA,” Public Finance Quarterly 48, no. 3 (2012): 819–38.

First, any benefit-cost calculation should be subject to outside peer review.21 A group of specialists at universities not directly involved with the project could be selected to review the analysis. While it is likely to be difficult to find completely objective reviewers, an independent review can provide some perspective, possibly improving results. This approach appears to have improved the revenue forecasts of the Congressional Budget Office and of budget agencies outside the United States.22
Second, Flyvbjerg suggests it would make sense, where possible, to compare estimates of costs and traffic flows with outcomes of completed projects of comparable size under similar economic and demographic conditions.23 If there is a sample of comparable projects, a distribution of outcomes can be ­constructed. Then, the estimates for the new project can be compared with previous outcomes of similar projects. This process could help establish the degree of confidence taxpayers should have in the estimates. Making this information public would provide taxpayers with a basis to decide whether the projections seem reasonable.
Third, each benefit and cost estimate should be calculated using a range of possible assumptions. For example, what happens to the benefit-cost ratio when using an alternative discount rate? How sensitive are the estimates to variations in key assumptions, such as economic growth and inflation rates? Are the estimates robust?24
Finally, to generate the right incentives, tie an analyst’s salary or bonuses to the accuracy of his or her cost and benefit projections. Compensation for outside reviewers should also be tied to accuracy.
Transportation infrastructure in the United States is financed using federal, state, and local funds. A major source of funding is the federal Highway Trust Fund, whose funding and spending priorities Congress reauthorizes every five years, although political wrangling can delay the reauthorization. The Federal Highway Administration divides the total funding of a highway authorization bill among the states based on a formula contained in the legislation.25
21. See Small, “Project Evaluation,” 168n5. 22. See Krol, “Forecast Bias of Government Agencies,” 99–112n17. 23. Flyvbjerg, “Survival of the Unfittest.” 24. See Small, “Project Evaluation,” 158–60n5. 25. Todd M. Nesbit and Steven F. Kreft, “Federal Grants, Earmarked Revenues, and Budget CrowdOut: State Highway Funding,” Public Budgeting & Finance 29, no. 2 (2009): 94–110.

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Political Incentives and Transportation Funding