The political acceptability (A) of public policy measures correlates positively with program effectiveness (E) and negatively with program cost (C) and other obstacles to implementation (I) under normal circumstances. Ferguson (1991) observed that the political acceptability of many demand management strategies seemed to correlate negatively with implied program effectiveness. Engineers, economists, and planners each have their own unique professional standards. Increased effectiveness is the primary goal of engineering. Improved efficiency is the generally accepted standard in economics. Process issues are of vital concern in planning. A review of the literature indicates few studies that rate demand management strategies in terms of all four variables of interest (A, E, C, and I) simultaneously. Three relevant studies were identified: one each by an engineer, an economist and a planner. Raw data, regression results, bivariate correlations, and model output reveal that two of the three studies support the Ferguson hypothesis. The other supports a more traditional public policy model. E is the most influential variable in the engineer's data. C is the most influential variable in the economist's data, while I is the most influential variable in the planner's data. These revealing results suggest the subtle manner in which professional training and experience may alter perceptions of transportation policies and programs in professional practice.
Transportation planning is a capital-intensive process. The Federal-Aid Highway Act of 1956 removed gasoline tax revenues from the federal budget and set up a dedicated Highway Trust Fund. The Act further specified that all local return monies from this source must be allocated to the construction of new highway facilities (Weiner 1999). Project evaluation of highway capital investments was relatively simple in those days. Capital costs were fixed and low, at least by later standards. Operating costs were negligible from the government's perspective. Benefits included reduced out-of-pocket travel costs and time. Reduced costs were sufficient to justify most proposed projects, making concerns about the imputed value of travel time irrelevant. In the 1960s, rising land acquisition, highway construction costs, and a diminished potential to shorten travel distances made saving time more important in justifying new highway investments (Walters 1961).
In the 1970s, transportation system management (TSM) came into vogue. Even with savings in travel time properly accounted for, new highway construction became more expensive and difficult to justify, particularly in the face of local citizen opposition. The purpose of TSM is to soften the blow of transportation planning by making it more short-range, user-friendly, and demand-oriented. TSM actions include improved vehicular flow, preferential treatments for high occupancy vehicles, reduced peak period travel, parking management, promotion of alternative modes of transportation, and transit and paratransit service improvements (UMTA 1977). TSM's most ardent admirers predicted its possible demise from political opposition, the projected outcome of institutional inertia, and professional apathy (Gakenheimer and Meyer 1979). However, many TSM strategies have done much better than expected.
In the 1980s, travel demand management (TDM) became the watchword of the day. TDM is the demand side of TSM, making it more suitable for private sector participation. TDM operates even closer to end users such as individuals, households, and firms. TDM strategies include alternative modes and hours of travel, alternative locations for specific activities, as well as economic incentives and institutional arrangements that may be required (Orski 1987).
Due to the wide range of strategies available to deal with traffic problems, the complexity of decisions associated with congestion management has increased in recent years. Sorting through the bewildering array of alternative policies, programs, and projects can be a daunting task. Word of mouth, hearsay evidence, and the occasional case study can only go so far. The more comprehensive the evaluative outlook, the better the implied advisement should be. Comprehensive comparative assessments of traditional planning, TSM, and TDM techniques, however, remain relatively few and far between (Ferguson 2000).
One study by the Urban Mass Transit Association (UMTA) (1977) categorizes TSM measures without identifying any of the potential impacts. Wagner and Gilbert (1978) evaluates TSM measures in terms of effectiveness and cost but omits any discussion of implementation issues. Misch and Margolin (1981) categorizes the "feasibility or prospects" of TSM actions taken in support of ridesharing in a limited fashion. Schonfeld and Chadda (1985) focuses on the effectiveness of travel reduction options with particular emphasis on parking management strategies. Levinson et al. (1987) evaluates the effectiveness of TSM strategies in terms of connected "impact chains." Bhatt and Higgins (1989) focuses mainly on results, measured in terms of mode split (table 1).
Two studies by the Institute of Transportation Engineers (ITE) (1989 and 1997) present information on impacts, costs, and obstacles to implementation associated with a wide range of congestion control and mobility enhancement tools but discuss political acceptability only tangentially. Ferguson (1991) observes that the public acceptability of many TDM strategies correlates negatively with their implied effectiveness. Road and parking pricing are more effective but less popular strategies. However, voluntary efforts to promote alternative modes and hours of travel are more popular but less effective.
Downs (1992), Zupan (1992), and Arnold (1993) evaluate various mixes of congestion control and demand management strategies in terms of four generalized performance measures: effectiveness, cost, ease (or difficulty) of implementation, and political acceptability. Dueker et al. (1998) perform a similar analysis on a smaller number of parking management strategies. Comsis (1993) and the Organisation for Economic Cooperation and Development (OECD) (1994) revert to an earlier emphasis on program effectiveness. McBryan et al. (1996) balances effectiveness, cost, and implementation issues but deliberately excludes political acceptability as "too arbitrary" (Shadoff 1997).
The remainder of this paper focuses on an analysis and evaluation of data derived from Arnold (1993), Downs (1992), and Zupan (1992), hereinafter referenced to as such.
There are two major hypotheses tested here, one rather precise in nature, the other far less so.
In order to evaluate the possible contribution of professional perspectives to the understanding of complex policy issues such as congestion control or demand management, it is important to know with whom one is dealing. The three studies in question were each authored by a single individual (table 2).
Eugene Arnold is a Senior Research Scientist at the Virginia Transportation Research Council, Virginia Department of Transportation (VDOT) in Charlottesville, Virginia. The Commonwealth of Virginia is one of the most conservative states in the Union. VDOT ranks among the more innovative state transportation agencies, thanks in no small part to its proximity to the nation's capital. Arnold is an active member and national leader in the Institute of Transportation Engineers (ITE). For example, he chaired the committee that prepared the most recent update of Trip Generation (ITE 1997). It should come as no surprise to find that Arnold's structure of the research problem closely parallels that identified in ITE (1989).
Anthony Downs is a Senior Fellow in Economic Studies at the Brookings Institution in Washington, DC. His expertise extends to topics as diverse as democracy, demographics, housing, metropolitan policy, real estate, real estate finance, smart growth, suburban sprawl, and urban policy. This is his first foray into transportation planning in more than a quarter of a century. Downs (1992) expands on the ideas originally set forth in Downs (1962).
Jeffrey Zupan is Senior Fellow for Transportation at the Regional Plan Association in New York City. His research on the relationship between land use and public transportation is well known and respected. Pushkarev and Zupan (1977) showed 1) what type of transit works best, 2) where it works best, 3) which support policies are most effective, and 4) how to estimate transit demand and costs (see Lee 1978). The connection between Zupan (1992) and Pushkarev and Zupan (1977) is clear.
The data used in this analysis are widely available and based on previously published research results. They are reproduced here mainly for the reader's convenience (see tables A-1 to A-3). However, Arnold's original survey data were obtained directly from Arnold for this study. Table A-1 includes more information than was published in the original paper.
Arnold surveyed 85 local, regional, and state transportation agencies in Virginia regarding 53 congestion-reducing measures. The ratings and rankings shown in table A-1 reflect the collective judgment of traffic engineers and transportation planners in Virginia, not necessarily Arnold's personal opinions.
Arnold treats the ordinal scales he uses to measure effectiveness, cost, and implementation as interval scales in his analysis: a testable proposition. Arnold's data are purely quantitative in presentation but largely qualitative in nature. This may reflect a preference among engineers for data and methods that are more objective. Arnold's data include 53 observations and only 4 variables, a case of problem overidentification. This is positive, of course. Engineers prefer larger margins of error.
Arnold's principal research findings include the following.
Downs evaluates 23 congestion-reducing policies using 7 items corresponding to 4 variables (table A-2). Whereas the Arnold data appear purely quantitative, the Downs data appear purely qualitative in nature. Downs uses different semantic scales to describe most of his items. The two cost items share an identical scale.
All of the scales constructed by Downs are ordinal in nature, with the possible exception of the institution required for implementation, which may be categorical. The Downs data are overidentified but not nearly as much as the Arnold data. Downs assigns a non-ambiguous descriptive adjective to each attribute for every policy he considers.
Downs' principal research findings include the following.
Zupan evaluates 22 TDM solutions using 12 items corresponding to 4 variables (table A-3). Zupan is the everyman of performance measurement. He incorporates a little bit of everything in his evaluation matrix, including both words and numbers, scales both absolute and relative, as well as suitable descriptors of measurement variability, non-applicability, and the unknown.
Zupan's 12 items are just barely identified by the 22 observations in his matrix, providing another indication of his tolerance for uncertainty. There is not one item in Zupan's matrix that is ranked consistently using a single semantic scale. The only possible exception is ease of implementation, which still includes two "unknowns" and one "not applicable" rating. Planners may be less technical than engineers and less consistent than economists, but one thing stands clear: they are much more comfortable with uncertainty.
Zupan's principal research findings include the following.
Comparing the methodological results of these three studies would be intriguing but difficult to accomplish. Observed differences between the three databases seem much greater on the surface than any prospective similarities might be. The problem of compatibility must be resolved in order to make any more meaningful comparisons between these studies and their results.
The next objective is to evaluate a model of the following general form:
A= f (E, C, I)
A = (political) acceptability
E = (program) effectiveness
C = (program) costs
I = (obstacles to) implementation
It is hypothesized that (political) acceptability should be
In order to test these hypotheses, it is necessary to convert the data found in tables A-1 to A-3 to standard form. Certain assumptions are required; the most important ones follow.
Given these assumptions, composite variables representing A, E, C, and I may be computed and subsequently analyzed using regression. The initial assignment of values to each semantic scale in the data is straightforward (table 3).
With an initial assignment of semantic values in hand, it is possible to calculate regression equations for each dataset. This does not imply that the results will maximize the potential of any available information, of course. For that to happen, several more questions must be answered.
The Arnold data generally support the traditional public policy model (table B-1):
The Downs data generally support the Ferguson hypothesis (table B-2).
The Zupan data generally support the Ferguson hypothesis (table B-3).
Dueker et al. (1998) evaluate 10 parking management strategies in terms of 3 scopes or applications (temporal, functional, and spatial), 2 benefits or impacts (effectiveness and efficiency), ease of administration and political feasibility. A simple regression reveals that political feasibility is negatively correlated with effectiveness, lending support to the Ferguson hypothesis. No measure of cost was provided, and ease of administration was not correlated with political feasibility among these data.
Booz-Allen & Hamilton (2000) evaluate 32 TDM strategies loosely based on OECD (1994) in terms of 9 direct travel effects, 12 indirect policy effects or implications, practical feasibility (implementation), and political acceptability. A simple regression reveals that obstacles to implementation are strongly negatively associated, direct travel effects marginally negatively associated, and indirect effects not associated with political acceptability, lending support to the Ferguson hypothesis.
It would seem that the Ferguson hypothesis holds sway over congestion control and demand management strategies in many instances. Program effectiveness is negatively associated with political acceptability according to data derived from four out of five independent studies (table 4). This is a most unfortunate result, at least from a public policy perspective.
For those who prefer simpler explanations, bivariate correlations based on nave assumptions are shown in table 5.
These correlations illustrate that
Engineers seek technical solutions to the problems they face. Economists search for low-cost solutions. Planners search for institutional answers (Mandelbaum 1996; Marshall 1997). The independent variable most highly correlated with political acceptability (A) in this analysis is
These simple bivariate correlations would seem to support the idea that political acceptability is in fact defined to some limited extent with particular professional perspectives kept in mind.
One advantage of the equations shown in tables B-1 to B-3 is that values can be calculated for each of the four variables (A, E, C, and I) and compared across each data set.
Arnold's model output (table C-1) does not vary much from the original data (table A-1). The values in table C-1 are directly comparable with those in tables C-2 and C-3, however, while those in table A-1 are not. Residuals are calculated using the following formula:
SRSD = studentized residual
AP = political acceptability (predicted)
AO = political acceptability (observed)
= standard error of AP
A negative SRSD implies a positive bias in favor of that measure. In Arnold's case, survey respondents reported more examples of new and reconstructed highways in Virginia than the model predicts. Toll roads and high-occupancy vehicles (HOV) lanes produced fewer examples than the model predicts, serving as a confirmation of the model.
TSM measures show greater variability in Arnold's data.
Paratransit services and growth management round out the list of strategies that apparently receive a limited form of preferential treatment in Virginia (Cervero 1997).
Downs finds that demand-side policies are slightly more effective than supply-side policies but more difficult to implement (table C-2). This opposes Arnold's findings.
Downs' data show the following supply-side biases.
Down's data show the following demand-side biases.
Downs is the principal author of a widely read Real Estate Research Corporation report on the costs of sprawl (Altshuler 1977). Downs presumably should favor growth management beyond any limited ability it might have to deal with traffic congestion as an urban problem, based on this previous work.
Zupan's data exhibit the following model biases (table C-3).
None of the biases reported here are particularly consistent, other than that growth management shows up as an outlier in all three models. This identifies growth management as an issue of greater than average political controversy. Few of these biases are statistically significant, and none is influential enough to bias parameter estimates.
In summary (table 6):
Other comparisons are best left to readers to explore on their own. Consider the relatively straightforward issue of parking pricing.
These three authors do not necessarily agree on the individual ratings of parking-pricing approaches any more than they do on how parking pricing should be labeled as a congestion control or demand management strategy.
How are program effectiveness, cost, obstacles to implementation, and political acceptability defined?
How do professional perspectives influence these definitions?
How well is the decisionmaking process actually understood?
Arnold (1993) is an excellent example of how to go about evaluating the political acceptability of transportation policies, programs, or projects. Downs (1992), Zupan (1992), and McBryan et al. (1996) provide useful guidance on additional aspects of program cost, obstacles to implementation, and political acceptability. Many others have contributed to the identification of congestion management strategies and measures of their effectiveness.
Confirmation of the Ferguson (1991) hypothesis leaves one important question unanswered. If the traditional public policy model holds true under most normal circumstances, either the model itself or one of its variable components must be in error in the present case. The model itself is too straightforward to be associated with any kind of specification error. The acceptability variable operates much as expected in relationship to the other variables in the model, and is therefore free from further suspicion as well. The cost variable is moderately suspect, due to its omission in some cases and its unusual performance in yet another.
Effectiveness behaves more like a cost than a benefit in most of the models tested here. One must conclude that the beneficial effects thus measured are associated with some additional indirect costs. Such hidden costs must supercede and override the direct benefits included in the measure's original, more limited definition. Further research should confirm or deny the validity of this conjecture.
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* significant at 0.01 level
** significant at 0.05 level
*** 1.00 < t < 2.00
d.f. = degrees of freedom
HOV = high-occupancy vechicle
NA = not applicable
SOV = single-occupancy vehicle
SRSD = studentized residual
TDM = travel demand management
TMA = transportation management association
TSM = transportation system management
U = data are unavailable
VHT = vehicle hours of travel
VMT = vehicle-miles traveled
* Significant at 95% confidence interval.