Abstract
This study contributes to the debate about tolls’ equity impacts by examining the potential economic costs of tolling for low-income and non-low-income households. Using data from the Puget Sound metropolitan region in Washington State and GIS methods to map driving routes from home to work, we examine car ownership and transportation patterns among low-income and non-low-income households. We follow standard practice of estimating tolls’ potential impact only on households with workers who would drive on tolled and non-tolled facilities. We then redo the analysis including broader groups of households. We find that the degree of regressivity is quite sensitive to the set of households included in the analysis. The results suggest that distributional analyses of tolls should estimate impacts on all households in the relevant region in addition to impacts on just users of roads that are currently tolled or likely to be tolled.
Keywords: tolls, equity, distributional impact, Puget Sound
Introduction and Background
In planning regional transportation systems, policymakers balance overlapping and sometimes competing goals of effectiveness, cost-efficiency, environmental responsibility and social equity. The increasingly popular strategy of tolling drivers on new facilities has implications for all these goals, and is one strategy among others for creating systems that can best move persons and goods through metropolitan areas.
In an era of limited state and local budgets and legislators’ reluctance to support higher fuel taxes or general tax increases, tolls on urban highways and bridges may be an attractive source of funds for construction and maintenance of transportation infrastructure. Urban policy makers are also gradually accepting well known arguments in the transportation research community that well designed tolls can help reduce congestion and air pollution by giving residents incentives to use the highway system more efficiently. The most current federal surface transportation act, the 2005 Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (Public Law 109-59; SAFETEA-LU), gave metropolitan planning organizations greater leeway to use congestion-based tolls as a strategy for reducing total miles driven. New or newly-tolled facilities now operate in California, Texas, Virginia and other states (Bhatt et al. 2008).
Along with tolls’ efficiency and revenue-raising benefits, their impacts on equity have become an important part of the debate about their acceptability and appropriate use. Critics raise concerns that tolls are regressive.– That is, they cost low-income households a higher percentage of their income than middle or upper income households. More broadly, policy makers should consider possible equity impacts of tolls alongside other spatial and transportation topics with implications for low-income residents of metropolitan regions. For instance, for low-income workers who do not live close to employment centers, tolling could exacerbate spatial mismatch (Kain 1992) by further increasing commuting costs. Debates over tolling regimes in Europe – where tolling is more widely used – focus in part on whether tolling constitutes a mechanism of social exclusion whereby lower-income residents are less able to fully participate in normal social activities such as using public roads (Bonsall & Kelly, 2005).
This study contributes to the debate about tolls’ equity impacts by examining the potential economic costs of tolling for low-income and non-low-income households. Using data from the Puget Sound metropolitan region in western Washington State, we examine car ownership and transportation patterns among low-income and non-low-income households. We argue that conclusions about the regressivity of tolls are sensitive to which households are included and the spatial distribution of their residences and workplaces. Although these ideas are recognized in the prior literature, this article makes the conceptual advance of translating them into an empirical analysis.
The analysis improves on past research in two major ways. To our knowledge it is the first to use Geographic Information Systems (GIS) methods to map driving routes from home to work in order to model possible tolling schemes. We determine the extent to which low-income households commute on highway segments that may have tolls in the future and compare how frequently low-income and non-low-income households commute on each segment. Using a simple simulation based on a tolling regime already under public consideration, we confirm earlier findings that the financial costs of tolls are regressively distributed among users of the tolled facility.
Second, prior studies generally examine only drivers who use tolled facilities and occasionally drivers who do not use tolled facilities. By omitting the many low-income households without workers, or with commuters who do not use private vehicles, such studies overstate the effect of tolls on the entire low-income population. We follow standard practice of estimating tolls’ potential impact only on households with workers who would drive on tolled and non-tolled facilities. We then redo the analysis including broader groups of households. We find that the degree of regressivity is quite sensitive to the set of households included in the analysis. These results suggest that distributional analyses of tolls should estimate impacts on all households in the relevant region in addition to impacts on just users of roads that are currently tolled or likely to be tolled. Doing so would accord with standard practice in distributional studies of taxes and income support programs and would offer more insight into how highway tolls may affect equity among all residents of the region.
The remainder of this article has five sections. Section 1 reviews the literature on how tolls affect equity, including what is known about tolls’ impacts on the financial status and driving time of low-income households and on low-income households relative to middle and high income households. Section 2 describes the data set, which contains information about current residential and work locations of each household. It also describes a novel methodology for exploiting this unusual information that uses geographic-specific routing analysis to map current commuting routes. Section 3 first provides data on employment, car ownership, commute mode and commuting routes of low-income and non-low-income households. It then presents estimates of potential tolls’ cost to low-income and non-low-income households. Though results apply only to the Puget Sound region of Washington State, the study’s methods can be applied in any region where suitable data exist. In Section 4 we consider how our conclusions about the distribution of the direct money cost of tolling might be affected by monetizing the time savings resulting from congestion reduction, capturing changes in commuting behavior, or considering the full revenue system. The final section compares the findings to earlier research and discusses the additional information gained when the analysis moves beyond users of tolled facilities to analyze more inclusive sets of households.
Section 1: Literature Review
Equity in transportation has multiple dimensions – income, geographic, modal, gender and still others – and can be examined at the individual, group and geographic level (Weinstein & Sciara 2004, Giuliano, 1994, Taylor, 2004, Ungemah 2007). This study analyzes income equity at the individual and group level, with groups defined by low-income status.
Assessing the income equity of a tolling regime requires analysis at several different levels. An initial question is: what are the regime’s likely financial and time impacts on low-income households? Financial impacts include how much a typical low-income household would spend on tolls per year, the share of income spent on tolls, and how this spending might affect consumption of other goods and services. Time impacts include how much time low-income households would save because of congestion tolls or whether their travel time would increase as they shift to public transportation or to non-tolled but longer or more congested alternative routes.
A second question is that of vertical equity. How does tolls’ average burden as a share of income vary as household income rises? If the burden rises, falls or is constant, tolls are respectively progressive, regressive or proportional. Assessing vertical equity requires comparing the financial and time impacts for low-income households to those for middle and high income households. A related issue is whether the payment methods, deposits, and service fees required by the collection technology (commonly an in-vehicle transponder) would disproportionately curtail low-income households’ access to transportation facilities.
Another aspect of income equity, horizontal equity, generally requires that tolls impose similar burdens on households with similar incomes. Assessing horizontal equity requires looking within and across different types of households. Low-income households are heterogeneous and will hence be affected differently by tolling policies. A current low-wage worker who commutes daily via highways will be affected more than a non-worker or someone who currently walks to work.
A final set of concerns around the net distributional effects revolves around the poor as a class or group. To determine whether low-income households generally experience benefit or detriment from a tolling regime, analysts must consider the full policy system, including revenue flows in the absence of a tolling regime and uses of toll revenues.
Facility- and Population-Specific Factors
Answers about how much poor and non-poor persons will pay are necessarily project-and location-specific because they depend on the facilities subject to tolls, whether constant tolls or congestion tolls are imposed, the amount of the toll (and, for congestion tolls, how the amount changes), other relevant attributes of the specific tolling regime, and the demographic characteristics of the region affected by the regime. Analysis must consider population characteristics such as car use, employment and residential patterns. Differences among low-income households in whether they drive, whether and how they commute to work, and how far they live from work imply that the impacts of tolls will not be borne equally.
Tolls will mostly strongly affect car owners. Pucher and Renne (2003) use the 2001 National Household Travel Survey to examine travel patterns of low-income households (incomes below $23,000 in 2006 dollars).1 More than 26 percent of low-income households do not have a car, compared to five percent of households in the next income level and less than two percent of households making more than $114,000 (2006 dollars). Among low-income households that own cars, 65 percent have one, 24 percent have two, and 10 percent have three or more (computed from Pucher and Renne, table 6). Even low-income households with no car report considerable auto use (34 percent of all trips in 2001), usually as passengers in someone else’s car. Low-income households use cars for roughly 75 percent of their trips and public transit for only 4.6 percent of their trips. These figures are consistent with an earlier and frequently cited study by Murakami and Young (1997) which found that 84 percent of low-income households’ trips to work are made in private vehicles.
Employment patterns matter because travel to work is the least discretionary and the most likely to be tolled. Most low-income households contain at least one employed member, but the percentage with workers is lower than for more affluent households (U.S. Census Bureau 2009a). Commuting costs consume a disproportionately high share of low-income workers’ earnings. One estimate suggests that working poor commuters who drive to work spend 8.4 percent of their income on transportation costs, higher than the 4.5 percent of income spent by non-poor driving commuters (Roberto, 2008).
Where poor and non-poor workers live relative to employment opportunities is another consideration. Although residential segregation by race has decreased over the 20th century, segregation by income level is both persistent and increasing (Jargowsky, 1996, Massey, Rothwell & Domina, 2009). Poverty is distributed unevenly over different neighborhoods, with areas of concentrated residential poverty giving rise to worries about spatial mismatch, in which low-income workers live away from employment areas. Nationwide, poverty is increasing fastest in surburban and exurban areas (Kneebone, 2009). The distributional effect of a given toll regime will vary depending on whether it tolls routes between areas of greater or lesser poverty.
Findings on How Tolls Affect the Economic Well-being of the Poor
Prior research suggests that tolling – in general – is regressive, but distributional impacts are rarely the sole focus of studies on tolling. A rigorous assessment of a tolling project’s equity impacts requires complex data and highly sophisticated modeling of households’ potential behavioral responses to a specific tolling regime (Giuliano 1994). Since no study fully meets these requirements, one instead must identify the consensus of the major extant studies. Most tolling research falls into one of two categories: projections of effects of hypothetical tolling regimes or analyses of observed outcomes following enactment of tolls. Table 1 lists previous studies for the U.S. and summarizes their implications or findings about the distributional effects of tolls on low income populations. It does not include studies with no data on income.
Table 1.
Previous Studies’ Findings on Distributional Effects of Tolls in the United States
| Study | Geographic area | Focal tolling regime | Findings on distributional effects or effects on lowest income groups |
|---|---|---|---|
| Small (1983) | San Francisco Bay Area | Hypothetical toll of $1.25–$ 10.00 |
|
| Giuliano (1994) | Los Angeles region | Hypothetical toll of $0.15/mile |
|
| Sullivan (2000, 2002) | Orange County, CA | Observation of SR 91 congestion tolling |
|
| Supernak et al. (2002) | San Diego area | Observation of I-15 congestion tolling |
|
| Safirova et al. (2003) | Northern Virginia | Hypothetical conversion of HOV lanes to tolled and HOT lanes (High Occupancy Transit) |
|
| Burris and Hannay (2004) | Houston area | Observation of HOT lane users and non-users on Katy Freeway |
|
| Safirova et al. (2005) | Washington DC | Hypothetical cordon or link-based tolls |
|
| Franklin (2007) | Seattle area | Hypothetical bridge toll |
|
| Puget Sound Regional Council (2008) | Seattle area | Experiment with variable charge for road use |
|
Small (1983) modeled the equity effects of three hypothetical peak expressway tolls and applied the model to a sample of 118 highway commuters. In his study, when the toll’s financial cost and the value of time savings from less congestion are both counted, the lowest income group ($0–46,000 in 2005 dollars) has the largest absolute losses. Net benefits were inversely related to income for all three tolls, a finding that is echoed in later studies that project possible effects based on current (non-tolled) travel patterns.
Small’s important early study illustrates later conclusions about the literature as a whole, in both substantive findings and methodology. According to Richardson and Bae (1996) and Giuliano (1994), two major stylized facts about the income equity effects of tolls in the United States are 1.) High income drivers tend to benefit because they value their time more than the increased cost of driving, and 2.) Low-income drivers and those who choose to no longer using the tolled routes suffer losses.2Small’s 1983 analysis and many subsequent studies have also relied on samples of current commuters, that is, persons already using the routes to travel by automobile to employment.
Less is known about observed behavior in response to an actual toll. Three studies – Sullivan (2000, 2002) and Buris and Hannay (2004) – use observational data from enactment of congestion tolls or HOT lanes (high occupancy tolled lanes, which allow single drivers to pay a premium to use high occupancy vehicle lanes). All three find that usage of HOT lanes is positively correlated with income, but none of the studies have sufficient sample size to compare low-income users to others.
Transaction costs and mechanisms can restrict access as well. The paperwork, payment methods and deposits required by transponder programs present a significant obstacle to low-income individuals’ access to tolled facilities because those persons are less likely to have credit cards or bank accounts. Parkany (2005) found income is positively related to transponder ownership, toll road use, and frequency of use. Burris and Hannay (2004) speculate that the costs of purchasing and maintaining a transponder may have made the Houston QuickRide prohibitively expensive for some low-income drivers.
Revenue Use and Alternative Funding Sources
Analysts generally share the view that toll’s negative impacts on low-income drivers could potentially be offset by how the revenues are used (Small 1992a, Santos & Rojey 2004, Safirova et al. 2005, Eliasson & Mattsson 2006). Direct redistribution of revenues on a per-capita basis or according to income can make all users better off and counteract the regressivity of tolls (Small, 1983; Franklin, 2007). While it may be possible, in principle, to redistribute the revenue so that all income groups gain, there are substantial political and administrative obstacles to doing so.
Options to indirectly mitigate tolls’ regressive impacts include using the revenue to finance and subsidize public transportation improvements that disproportionately benefit poor people (Prozzi et al. 2007, Weinstein & Sciara 2004), to support affordable housing options near employment sites, or to give toll credits or exemptions to low-income drivers who must drive solo (Prozzi et al 2007, Weinstein & Sciara 2004). If implemented, these options could have important implications for the overall income equity of tolls. Yet none of them have been implemented in the United States. Under any redistribution plan, some low-income individuals who are constrained to certain tolled routes will be worse off.3
While not redistributing the revenues implies that tolls are likely to be regressive, it is important to recognize the financing a project with tolls will generally impose fewer costs on low-income persons than broad based consumption-oriented taxes such as the gas or sales tax (Schweitzer & Taylor 2008). Since other taxes and fees may well be equally or more regressive than tolls, to fully assess the equity effects of tolling one must compare tolls’ effects to those of an alternate financing method in a no-toll scenario (Franklin 2007, Weinstein & Sciara 2004).
In sum, our review of the literature identified factors needed to evaluate the impact of a tolling regime on low-income populations: a.) car ownership, b.) employment, c.) pre- and post-toll travel patterns, d.) economic impacts due to pricing, e.) impacts due to collection mechanisms and e). larger revenue considerations. Most empirical studies are based on samples of current car-owning, employed commuters and examine one or two of these factors, (usually travel patterns and economic impacts).
Section 2: Data and methodology
Our empirical strategy deals with three of the factors identified above: car ownership, employment and pricing impacts. Using a unique geographic-specific routing analysis, we map current commuting routes and make the assumption that they are the best possible estimate of post-toll travel patterns. We draw on this mapping to estimate the impact of potential tolls on low-income and non-low-income households in the Puget Sound region.4
This method assumes that tolls do not affect current commuting patterns. We make this assumption in view of data limitations. Because generally accepted models of travel behavior imply that tolls induce some drivers to change modes, routes or other relevant behaviors, tolls’ financial costs for both poor and non-poor households will be lower than reported in this study.
Definition of low-income
This analysis defines low-income persons as those living in households with income at or below 200 percent of the official federal poverty line. The official poverty line uses a set of dollar value thresholds that vary by family size and composition, but not geographically. The U.S. Census Bureau annually updates the thresholds for inflation using the Consumer Price Index for All Urban Consumers (CPI-U). In 2009, the official threshold for a family of three was $18,310. For four, it was $22,050.
We examine low-income persons rather than officially poor persons for several reasons. First, critics argue that the federal poverty thresholds are too low, particularly in high-cost areas such as the Puget Sound (Blank, 2008; Pearce & Brooks, 2001). Second, a number of important programs that assist needy persons implicitly acknowledge that the official thresholds are too low by setting their income eligibility thresholds higher than the official poverty line by as much as 300 percent (e.g. Head Start, food stamps, School Lunch Program, State Child Health Insurance Program). Third, the small number of officially poor households in the study’s key data file would lead to imprecise estimates. Last, near-poor households share many characteristics with poor households that contain workers. Research shows that many households that are poor in one year may become near-poor the next year and vice versa as household income fluctuates (Cellini, McKernan, & Ratcliffe, 2008). Because households with workers are those that commute, looking at both poor and near-poor may be more informative
Data
The study uses two data sets. Descriptive information about the low-income population, employment, travel time to work, car ownership, and commute mode comes from the 2007 American Community Survey (ACS) Public Use Microdata Sample files for Washington State’s Puget Sound region: King, Pierce, Snohomish and Kitsap counties. This data set includes 34,106 individuals in 14,911 households. The findings we report use the sample weights, so they are representative of the population at large.
The key data set is the Puget Sound Regional Council’s 2006 Household Activities Survey (HAS). The Council commissioned the HAS to provide information on why households make the choices that they do regarding travel behavior. The survey includes 4,746 households in the Puget Sound region.
Mapping Commuting Routes
One of the HAS’s most valuable features is that it includes both basic demographic information and exact (longitude and latitude) home and work location for nearly all employed respondents. To map workers’ commuting routes, we merged the demographic and latitude-longitude information into a GIS database. We created and applied a mapping algorithm that assigned the most likely route between each home and work pair. We manually checked assigned routes against Google Maps to identify implausible routes, and made hand edits as needed. This route information captured the distribution of commuting trips on both major and minor roads. By combining the route and demographic information, we were able to identify low-income and non-low income workers most likely to use routes that may be tolled in the future.
Limitations of the Household Activities Survey
The HAS has two important limitations. It was designed to accurately represent the distribution of travel modes in the Puget Sound Region, not the distribution of income of users of each mode. Considerable care was taken to oversample transit users. Similar care was not exercised in sampling poor and low-income households and, consequently, they are underrepresented.
Second, the data on income is weak. Household income is reported in broad categories – < $10,000, $10,000–19,999, etc. This crude measure makes it difficult to categorize some households as low-income or not with certainty.
We dealt with this problem with a simple procedure best explained by example. Consider a family of four with two children. Suppose it reported a 2006 income in the $30,000–39,999 range or a lower one. Since 200% of that family’s 2006 poverty line was $40,888, we can unambiguously classify it as low-income. If its reported income was in the $50,000–59,999 range or higher, it is unambiguously non-low-income. If its income fell between $40,000 and $49,999, where we cannot be certain about its low-income status, we assume it was low-income. Though this procedure overestimates the number of low-income families, since the error can never exceed $10,000 we know that all these ambiguous families are not financially well off.
Notwithstanding the HAS’s limitations, its detailed data on home and work location provide a valuable but rarely available resource for understanding the distributional effects of tolls. See (author citation 2009) for a more detailed discussion of the HAS limitations and suggestions for research designs that could produce data even better suited to our questions.
Section 3: Empirical Findings
This section presents new evidence about car ownership, employment and travel patterns.
Low-income and Non-low-income Populations in the Puget Sound Region
In 2007 19.2 percent of all households in the four county Puget Sound region fell below twice the poverty line. The corresponding national rate was 32.8 percent (U.S. Census Bureau, 2009b). The region’s lower rate likely reflects its higher wage rates and better-than-average economic conditions at that time.
Employment, Car Ownership and Commute Mode
Because commuting is the major non-discretionary transportation activity, employment and commute patterns are key for understanding potential impacts of tolls. Persons drive for other reasons, but generally have more flexibility in scheduling and getting to and from non-work activities.
Table 2 shows information on employment and commuting among Puget Sound households below and above twice the poverty line. Eighty-one percent of low-income households and 91 percent of non-low-income households contain at least one worker. Seventy-nine percent of low-income households and 96 percent of non-low-income households own at least one car. On average, a low-income household owns 1.2 cars and a non- low-income household owns 2.0.
Table 2.
Employment, Car Ownership and Commuting Mode by Low-Income Status
| Low-income Households | Non-low-income Households | |
|---|---|---|
| Percent of regional population | 19.2% | 80.8% |
| Characteristics of households | ||
| Contain one or more workers* | 81.0% | 91.4% |
| Mean number of workers* | 1.14 | 1.69 |
| Car ownership (%)* | 78.9% | 96.2% |
| Mean number of cars* | 1.22 | 2.03 |
| Characteristics of workers | ||
| Commuting Mode | ||
| Drives alone* | 62.9% | 72.3% |
| Carpools* | 13.1% | 11.1% |
| Public transportation* | 12.8% | 6.9% |
| Other commute mode* | 11.2% | 9.3% |
| Mean commute time in minutes* | 24.6 | 27.5 |
Significantly different at a 95% confidence interval
Source: Authors’ calculations using American Community Survey data, N=34,106 individuals in 14,911 households, weighted to represent Puget Sound Area population
Workers who currently commute via single occupancy vehicles are likely to be most affected by any new tolling regime. The bottom panel of Table 2 shows commute mode. Driving to work alone is most common, with 63 percent of low-income individuals and 73 percent of non- low-income individuals commuting in this way. Low-income workers are slightly more likely to carpool than non-low-income workers (13.1 vs. 11.1 percent), and more likely to use public transportation (12.8 percent vs. 6.9 percent) or other modes such as walking or biking (11.2 vs. 9.3 percent). On average, low-income persons spend 24.6 minutes commuting, or about two minutes less than non-low-income persons.
Table 2 confirms what other research has demonstrated: low-income persons in the Puget Sound Region are less likely than their non-low-income counterparts to use a personal vehicle to get to work, although considerably more than half still manage to do so. Low-income persons are more likely to commute via public transportation or other modes that would not be subject to tolls. These facts imply that tolling is likely to affect a smaller percentage of low-income persons than non-low-income persons in the region.
Commuting Routes of Low-Income and Non-Low-Income Workers
To assess the impact of different toll scenarios, we focused on 12 segments of the region’s highway system for which tolls have already been discussed or implemented, or that appear to be plausible candidates for congestion tolls. Each segment extends over a different section of the six major highways in King County: I-5, I-405, I-90, SR 520, SR 167, and SR 99. Figure 1 shows all segments, which are described in table 3. They include, for example, the bridges across Lake Washington which connect Seattle and the affluent eastern suburbs (segments 3 and 6). The stretch of I-5 between its junction with I-405 on the north and SR 520 (the northern bridge across the lake) on the south is another (segment 1).
Figure 1. Route Density, All Commuters, Central Puget Sound Region.
Source: Authors’ calculations using the Household Activities Survey
Table 3.
Use of Potentially Tolled Highway Segments by Low-income and Non-low-income Commuters
| Highway segment | Percent of segment users who are low- income | Percent of low- 1 income commuters who use segment | Percent of non- low-income commuters who use segment |
|---|---|---|---|
| 1. I-5 north from SR 520 to I-405 (serves Seattle, northern suburbs) | 8.2 | 8.5 | 14.4 |
| 2. I-405 north from SR 520 to I- 5 (serves Bellevue, Redmond, other eastern suburbs) | 9.1 | 6.1 | 9.1 |
| 3. SR 520 bridge across Lake Washington | 3.2 | 1.0 | 4.5 |
| 4. I-5 between SR 520 and I-90 (serves Seattle) | 8.2 | 9.3 | 15.8 |
| 5. I-405 between SR 520 and I-90 (serves Bellevue, Redmond, other eastern suburbs) | 8.0 | 5.0 | 8.7 |
| 6. I-90 bridge across Lake Washington | 4.6 | 1.3 | 4.0 |
| 7. I-5 south from I-90 to I-405 (serves Seattle, southern suburbs) | 7.1 | 5.1 | 10.0 |
| 8. I-405 south from I-90 to I-5 (serves eastern suburbs | 10.9 | 7.5 | 9.2 |
| 9. I-5 south from I-405 to King County line (connects Seattle and Tacoma) | 12.3 | 5.6 | 6.1 |
| 10. SR 167 south of I-405 junction(connects Seattle and Tacoma) | 13.3 | 7.9 | 7.8 |
| 11. SR 99 from W. Seattle Bridge to tunnel (major Seattle artery) | 21.9 | 2.1 | 1.1 |
| 12. I-90 east of I-405 (connects Puget Sound region to eastern WA) | 3.7 | 1.2 | 4.7 |
| All segments | 9.2 | 31.0 | 46.2 |
Source: Authors’ calculations using weighted Household Activities Survey data. Weighted (unweighted) number of low-income and non-low-income commuters is 180,487 (283) and 1,191,605 (3,023).
Figure 1 displays the trip density for all commuters in Puget Sound based on the routes estimated by our mapping procedure. Thicker lines indicate greater numbers of commuters on a given route. Not surprisingly, the most heavily used routes (1, 4 and 7) are segments of the interstate highway (I-5) adjacent to downtown Seattle. Shading within the lines indicates the percentage of users who are low-income. The most heavily-used routes have between 5 and 10 percent low-income drivers. On the two east-west bridges, segment 3 and segment 6, fewer than 5 percent of all commuters are low-income.
Figure 2, derived with the GIS methods described earlier, shows the use of the 12 segments by both low-income and non-low-income commuters. More than two-thirds of low-income commuters use routes that include no segments. Twenty-two percent of low-income commuters use one or two segments; only nine percent use three or more. Tolling all 12 segments, therefore, would increase out-of-pocket expenditures for no more than 31 percent of low-income commuters. Though the modal non-low-income commuter also uses no segments, 32 percent of such commuters’ routes include one or two segments and 14 percent include three or more.
Figure 2. Number of Focal Highway Segments Used by Low-Income and Non-Low-Income Drivers.
Source: Authors’ calculations using weighted Household Activities Survey data.
Column 1 of Table 3 reports the share of each segment’s users that is low-income. The bottom row shows that low-income commuters account for 9.2 percent of all segment users. For segments 9–11, the shares of users with low incomes are three to twelve percentage points higher than 9.2 percent. That is, users of segments 9–11 are relatively more likely to be low-income. The higher share of low-income users is especially pronounced for segment 11. Users of segments 3 and 6 – the bridges across Lake Washington – are much less likely to be low-income. For the other seven segments, the rate of use by low income commuters is within two percentage points of the overall rate.
These findings imply that tolls on segments 3 and 6 would be less regressive than a toll on any other segment. At the other extreme, a toll on segment 11 would be most disproportionately borne by low-income commuters.
The rightmost two columns of Table 3 report the proportion of low income and non-low-income commuters that use each segment. The base for the percentages is total number of low-income or non-low income commuters, including those who use no segments. Hardly any low income commuters use the two bridges (segments 3 and 6) or segment 12.5 Non-low-income commuters are also least likely to drive these four segments. Among low-income commuters only segments 1, 4, 8 and 10 have a rate of use greater than seven percent. No segment attracts more than 9.3 percent of low-income commuters or 15.8 percent of non-low-income drivers.6 Tolling one or two segments would, consequently, impose financial costs on a small fraction of all low-income commuters. Tolling the two bridges, which is most practical and politically feasible, would affect less than three percent of low-income commuters.
Toll Cost Estimates for Low-income and Non-low-income Households
The estimates of low-income and non-low-income households’ use of potentially tolled segments allow us to project the potential annual cost of tolls for both groups and assess whether tolls would cost a disproportionate share of low-income households’ income. We consider two scenarios.7
Scenario 1 assumes that a $1 one-way toll is imposed on all 12 focal segments listed in Table 3. We estimate the annual cost of tolls under this regime for three nested groups of households. The largest group is all households, regardless of whether anyone in a household works, drives a private vehicle to work, or uses a tolled segment. The average number of tolled segments used per day by low-income and non-low-income households is 0.49 and 1.25.
The second group includes only households with at least one person who commutes in a private vehicle, regardless of whether he uses a tolled segment. The average number of tolled segments used per day by low-income and non-low-income households with commuters is 0.84 and 1.48. Many households in the first and second groups would pay no tolls.
The third group is further restricted to households with at least one person who drives a private vehicle on at least one tolled segment. All of these households would pay tolls. The average number of segments driven per day for low-income and non-low-income households in this group is 2.07and 2.78.
Scenario 2 assumes a $2 one-way toll only on one bridge (segment 3).8 We estimate the annual cost of this regime for the small group of households that actually use the bridge.
We compute the annual cost assuming 240 work days per year. In scenario 1 a commuter who drives roundtrip on one segment would pay $1×2 (roundtrip) × 240 = $480 per segment per year. We compare the financial burden of tolls for two illustrative families. One is a family with an income of $15,600, which is the median income among all low-income households. The second’s income is $76,350, which is median income among non-low-income households.9
The upper part of table 4 presents the results for scenario 1. Taken over all households in each group, the average low-income household would pay $235 per year, or $365 less than what a non-low income household would pay. The low-income household would pay 1.5 percent of its income for tolls, compared to 0.8 percent for the non-low-income household. The low-income household’s burden is 1.88 times larger than the non-low-income household’s.
Table 4.
Hypothetical Annual Toll Burdens for Low-income and Non-low-income Households
| Low-income households | Non-low-income households | |||
|---|---|---|---|---|
| Full-system tolling, $l/segment | Annual cost of tolls | Percent of income1 | Annual cost of tolls | Percent of income1 |
| All households | $235 | 1.5 | $600 | 0.8 |
| Commuting households | $403 | 2.6 | $710 | 0.9 |
| Segment commuters | $994 | 6.4 | $1,334 | 1.7 |
| SR 520 bridge one-way toll of $2 | ||||
| All households | $6 | 0.04 | $36 | 0.05 |
| Commuting households | $10 | 0.06 | $43 | 0.06 |
| Segment commuters | $31 | 0.20 | $93 | 0.12 |
| SR 520 commuters | $960 | 62 | $960 | 1.3 |
Uses incomes of $15,600 and $76,350 (the respective median among low income and non-low-income households).
Among commuting households, the average cost is necessarily higher — $403 for the low income household (2.6 percent of income) and $710 (0.9 percent) for the non-low-income household. In absolute terms low-income households pay about $300 less. The low-income household’s burden has increased in relative terms. It is 2.89 times larger than the share paid by the non-low-income household.
For only those households with commuters who actually drive on tolled segments, the average yearly cost is much higher — nearly $1,000 for low-income households and more than $1,300 for non-low-income households. This cost would absorb 6.4 percent of the illustrative low-income household’s income, or 3.76 times higher than the representative non-low-income household’s burden of 1.7 percent.
Devoting 6.4 percent of income to tolls would force significant reductions in other types of expenditures and, hence, substantially reduce the economic well-being of low-income households whose workers commute in private vehicles. In the absence of specific efforts to subsidize low-income users of tolled segments, tolls would likely induce many of them to adopt less costly commuting arrangements.
The burden of tolling all segments would be highly unequal among both low-income and non-low-income households in the Puget Sound region. Low-income and non-low-income users of tolled segments would pay an average of about $1,000 and $1,300 per year. Non-users, of course, would pay nothing.
The lower part of Table 4 presents findings when only the bridge has tolls. The $2 one-way toll would cost the small number of households that use the bridge $960 per year, or 6.2 and 1.3 percent of the illustrative households’ incomes. Spending almost $1,000 on tolls would certainly reduce the economic well-being of low-income users of the bridge and encourage them to seek less costly commuting arrangements. While the financial impact would be large for low-income users of the bridge, for low-income commuters overall the impact would be a negligible $10 (0.06 percent of income) per year since only one percent of them actually use this route (table 3, row 3). Similarly, since less than one in twenty non-low-income commuters would pay tolls on the bridge, the impact among all non-low-income commuters taken together would also be negligible (again, 0.06 percent of income).
Section 4. Extrapolations on Travel Time, Route Choice Changes, and Other Factors Our empirical results and simulated toll scheme show that the distributional effects of tolling differ by the choice of population universe as well as the spatial distribution of route users. In this section we assess how our conclusions would change if we considered three other factors commonly used in judging the equity and policy implications of tolling: time savings from congestion reductions, route or other behavioral changes to commute patterns, and the full revenue system.
Time savings
Whether and how to value any time saved due to reduced congestion is a highly contested – yet important – aspect of deciding the costs, benefits and distributional effects of any transit change. How to assign value to commute time is contested for the “average” commuter (Calfee & Winston, 1998, Brownstone & Small, 2005), and the extant literature provides particularly scant guidance for how to value time for lower- versus higher-income households. However, to ignore travel time is to assume it has no value, which seems unsupportable. For this reason, we calculated time savings for our focal route. Details of the calculations are in Appendix 1.
Any reduction in travel time on our focal segment would necessarily mainly benefit higher-income households, as more of them use the route. One approach is to value commute time at the half the hourly wage rate and assume a substantial reduction in congestion-related delays. Doing so reduces net annual cost of the $2 one-way toll to $866 for the low-income household and to $501 for the high-income household. In fact, any of the common monetizing schemes in the literature would increase the regressivity of the toll, and even small differences in the value of time for low versus high income drivers make the tolling scheme on net regressive even for the full universe of commuting and non-commuting households.
Route changes
The estimates do not take into account that some drivers may change routes, modes, and other relevant behaviors in response to the tolls and the associated costs of accessing tolled highways (need for credit card or bank account, deposits, service fees). To the extent that such changes occur, the financial costs for both low-income and non-low-income households would be lower than reported here and probably distributed differently, though the time costs would likely be higher.
We offer a simple thought experiment to examine how the findings on regressivity might differ if we could adjust for changes in routes and other commuting behaviors induced by tolls. How much would low-income households need to reduce use of tolled routes (or switch to mass transit or carpools, which would not require tolls) so that the share of their income spent on tolls equals that of non-low-income households? A situation of equal shares is usually interpreted as distributionally neutral.
Suppose that in the full-system tolling regime non-low-income households did not reduce use of tolled segments. Then, for all low-income households to pay the same share of their income in tolls as non-low-income households (0.8 percent from row one of table 4), they would need to take 47 percent fewer trips on tolled segments.10 If we confine attention to low-income commuting households or segment commuters, use would need to decline respectively by 65 percent or 73 percent. Since a 47 percent reduction in use (much less 65 or 73 percent) seems unlikely, a full-system tolling regime would almost surely remain regressive in financial terms after low-income households adjust their driving behavior. Moreover, the burden of longer driving times necessitated by using minor roads would partially offset low-income households’ financial savings and move the overall regressivity back towards the initial estimate.11
Revenue
Washington State has one of the most regressive state tax structures in the country, due in large part to the absence of a state income tax. Households with annual income below $20,000 pay an estimated 17.3% of their income in state property, sales, and excise taxes, whereas higher income households (in the fourth quintile) pay only 9.5% of their income in taxes (Davis, Davis et al, 2009). If the revenue raised by tolling would supplant funds from the state general fund, tolls would be more progressive.
Other considerations
Might there be benefits to tolling that favor low income people and thereby reduce the regressivity of an area-wide tolling regime? Recent research shows that poor people in the Tampa, FL area are more likely to live near sources of air pollution (Stuart et al. 2009). In that case, any health benefits from traffic reductions and diversions induced by tolls would tend to disproportionately favor the poor and offset some of adverse financial impacts. Given the site-specific nature of tolls’ financial impacts, variations in air quality, and low income neighborhoods’ proximity to major roads, the applicability of this result to the Puget Sound region and other metropolitan areas must be ascertained on a case-by-case basis. The size of any health benefits is not known.
Section 5. Discussion
If we restrict attention to only households that drive on potentially tolled routes in the Puget Sound region, we find that tolls would absorb one-sixteenth of the representative low-income household’s income. This significant burden would be 3.77 times larger than that borne by the representative non-low-income household, a substantial degree of regressivity. Narrowing the focus to households that use the one bridge that will almost certainly be tolled gives burdens of 6.2 and 1.3 percent. This raises the regressivity; the low-income household’s burden would be 4.77 times larger than the non-low-income household’s. This pair of findings confirms the consensus from previous research that tolls are regressive –among users of tolled facilities, the portion of income paid in tolls is inversely related to their incomes.
A more nuanced story emerges when one moves beyond the conventional focus on users of tolled facilities to analyze more inclusive sets of households. For all commuting households, regardless of whether they use potentially tolled routes, the ratio of the burdens falls to 2.89. For the broadest population – all households regardless of whether they commute – the ratio falls further to 1.88, or half the ratio when the calculation includes only households that use potentially tolled routes. As the analysis becomes more inclusive, the regressivity shrinks.
By looking beyond users of tolled facilities and including all low-income and non-low income households in the analysis, we further demonstrate that tolls are not borne equally among all low-income households, nor among all non-low-income households. Fully 69 percent of low-income households and 56 percent of non-low-income households would pay no tolls (table 3, bottom row). The 31 and 44 percent who do pay would incur significant burdens averaging 2.6 and 0.9 percent of income. Differences in whether household members drive, whether they need to commute to work, how far they live from work, and the specific roads they drive create these differences.
While such differences may raise equity concerns, on balance we suggest the differences are appropriate. If tolls are to reduce congestion (with less pollution as an accompanying benefit), they must give residents who drive the most heavily trafficked roads and bridges incentives to use them more efficiently, whatever their incomes may be. All pricing mechanisms discriminate between those who desire a good or service and those who do not. Tolls are no different. Generally speaking, economic theory suggests that the broader social goals of poverty reduction and income redistribution are best pursued via tax, income transfer and labor market policies, not by suppressing prices’ function of allocating scarce resources.
The lower panel of table 4 more strongly demonstrates that restricting the analysis to users of a tolled route may present a greatly distorted picture of tolls’ distributional impact on more inclusive populations. Taken over all low-income commuters, the burden of the bridge toll is 0.06 percent of income. For all low-income households, the burden falls to 0.04 percent. Because low-income commuters use the bridge much less often than non-low-income households (1.0 versus 4.5 percent of commuters) and a smaller proportion of low-income households have commuters, a toll on the bridge would be distributed roughly proportionally or even slightly progressively across all households in the region.
This last finding has important implications for the choice of mechanism for financing construction of a new bridge or highway. In the case at hand, financing via tolls on the bridge would essentially be distributionally neutral. Doing so would be more equitable than relying on the typical alternatives – sales and gasoline taxes – which are clearly regressive and would impose significant burdens on low-income households that do not use the bridge (Schweitzer & Taylor 2008). More widespread understanding of the argument that we should compare the equity effects of tolls to the equity of the current system of funding, not a perfectly egalitarian one, might increase the acceptability of tolls.
Are there viable alternatives for reducing congestion that would not put as high a financial burden on poor users of facilities that would otherwise be tolled? Offering incentives and mounting public information campaigns to encourage more biking, walking, carpooling and use of HOV lanes may have a role, but are unlikely to significantly reduce congestion.12 Expanding bus, subway and light rail services may be more promising, but the costs of doing so are largely financed with earmarked taxes, not fares (user fees). Such taxes – typically on retail sales, gasoline or property – are generally regressive and burden the many poor families that do not use these services. Ultimately the most effective way to reduce congestion, in our view, is to directly increase the cost of sole-occupancy driving. Congestion tolls are the most suitable means to do so.
Our general patterns that higher-income households are disproportionately likely to contain highway commuters would probably apply in other metro areas as well. The Puget Sound region may be an extreme case, with a high proportion of high-income households commuting on that particular segment, but the general pattern likely holds.
We suggest that distributional analyses of tolls include all households in the relevant region, not just those that use roads that are currently tolled or likely to be tolled. Doing so would accord with standard practice in distributional studies of taxes and income support programs, which take into account households that pay no taxes or even negative taxes (if they qualify for refundable tax credits that exceed their federal income tax liability), or receive no income transfers. Such an approach would offer more insight into how equity effects differ within income groups as well as between them, and how highway tolls affect region-wide equity.
Acknowledgments
Our research was supported by a grant from the Washington State Department of Transportation. We thank Dan Carlson, Mark Hallenbeck, Matthew Kitchen, Rachel Kleit, Paul Krueger, Kathy Lindquist, Kathleen McKinney, Jamie Strausz-Clark, and participants in the West Coast Poverty Center’s seminar series for helpful comments on the study. We also thank Neil Kilgren of the Puget Sound Regional Council for providing us a copy of the Council’s 2006 Household Activities Survey and the Washington State Transportation Center (TRAC) at the University of Washington for clerical support. The contents do not necessarily reflect the official views or policies of the Washington State Transportation Commission, Washington State Department of Transportation, TRAC, Federal Highway Administration, or Puget Sound Regional Council, or the views of any of their employees.
Appendix A
Whether and how to value any time saved due to reduced congestion is a highly contested – yet important – aspect of deciding the costs, benefits and distributional effects of any transit change. Calculating the value of time requires estimating both how much time will be saved and how that time should be valued. This appendix summarizes the process we used to calculate some rough bounds on the value of time potentially saved due to the toll simulated in the article.
Estimating time saved
Prior experiences suggest that a flat-rate corridor toll such as the one we are modeling may or may not reduce travel time. As such, the lower bound estimate for the value of time saved is zero. To calculate an upper bound, we assume reduced congestion results in time savings. According to Cambridge Systematics (2005), average travel times on the 520 segment containing the bridge exceeded ideal travel times by six minutes during the evening rush hour. A commuter who makes 500 one-way trips per year hence spends approximately 50 hours delayed in traffic. For upper-bound purposes, assume that the toll reduces congestion by up to 50%, saving 25 hours annually.
Monetizing time
One rule of thumb suggested by Small (1992b) is to value time at half the hourly wage. Converting the annual income assumed for the calculations in table 4 into an hourly rate and dividing by two gives hourly time values of $3.75 for low-income and $18.35 for high-income households. However, both willingness-to-pay and observational studies find that the value placed on time savings in transit for low- versus high-income is much more similar, perhaps 1:2 rather than the almost 1:5 ratio of hourly wage rates in our example. (Calfee & Winston, 1998, Brownstone & Small, 2005).
Valuing time saved
Using these values for time saved and time, we calculate different estimates of the value of time saved. The top panel of Table 1A replicates the simulation results from Table 4, showing the estimated toll costs as a percentage of income for low- and high-income households. The second panel estimates a 50 percent reduction in congestion delay valued at half the hourly wage. Monetizing time at this rate now makes the tolling scheme regressive relative to no toll for all households and makes it more regressive for the three sets of commuting households compared to the first panel.
Table 1A.
Hypothetical Annual Toll Burdens Minus Time Savings
| Low-income households | High-income households | |||
|---|---|---|---|---|
| Annual cost of tolls | Percent of income | Annual cost of tolls | Percent of income | |
| Toll cost only | ||||
| All households | $6 | 0.038 | $36 | 0.047 |
| Commuting households | $10 | 0.064 | $43 | 0.056 |
| Segment commuters | $31 | 0.199 | $93 | 0.122 |
| SR 520 commuters | $960 | 6.154 | $960 | 1.257 |
| Toll cost minus value of time saved by 50% reduction in congestion delay Time valued at half hourly wage | ||||
| All households | $5 | 0.035 | $19 | 0.025 |
| Commuting households | $9 | 0.058 | $22 | 0.029 |
| Segment commuters | $28 | 0.179 | $49 | 0.064 |
| SR 520 commuters | $866 | 5.551 | $501 | 0.656 |
| Toll cost minus value of time saved by 50% reduction in congestion delay Time valued at $18/hour for low- and $20/hour for high-income households | ||||
| All households | $3 | 0.020 | $17 | 0.023 |
| Commuting households | $5 | 0.034 | $21 | 0.027 |
| Segment commuters | $16 | 0.106 | $45 | 0.058 |
| SR 520 commuters | $510 | 3.269 | $460 | 0.602 |
| Toll cost minus value of time saved by 25% reduction in congestion delay Time valued at half hourly wage | ||||
| All households | $6 | 0.037 | $27 | 0.036 |
| Commuting households | $10 | 0.061 | $33 | 0.043 |
| Segment commuters | $30 | 0.189 | $71 | 0.093 |
| SR 520 commuters | $914 | 5.859 | $731 | 0.957 |
The next panel illustrates the sensitivity of results to the wage rates chosen. Valuing time more equally for low-income and high-income households ($18 v. $20) results in the scheme being neutral with respect to income for all households, but still regressive for all definitions of commuters. Finally panel 4 illustrates the sensitivity of the findings to assuming a smaller time savings. Comparing panels 2 and 4 shows that with a 25% rather than 50% reduction in congestion-related delays but time valued at half the hourly wage ($3.75 v. $18.35), the tolling scheme is again distributionally neutral for all households. For commuting households the scheme remains regressive, but less so than in panel 2. For example, for commuting households, in panel 2 the share of income spent by low-income households is 100 percent higher than the share spent by non-low-income households. In panel 4, the difference is 42 percent.
Footnotes
Every study that we reviewed uses an income higher than the official federal poverty line to distinguish poor from non-poor households. Because of small sample size, in some studies the lowest income category extends well into the lower-middle and middle class. Consequently, our discussion of each study uses the terms “poor” and “low-income” as defined by its author, not by the official poverty measure.
Eliasson and Mattsson (2006) similarly conclude that tolls are most likely to be regressive where cars are widely used by both high and low-income individuals and low-income people have few alternatives in their modes of travel and less flexible work schedules. This, they observe, is often the case in American cities. They suggest that tolls may not be regressive in European cities, where transportation options and the residential locations of rich and poor generally differ from the American situation.
Net revenues from London’s cordon toll are spent on improved bus services. Better services may mitigate the toll’s regressivity but do not directly compensate specific low-income drivers who either pay substantial tolls or incur additional time costs.
Since collection mechanisms and other revenue issues can only be evaluated in the context of a specific tolling plan, these factors are beyond the scope of this study.
Note that though only 2.1 percent of low-income commuters use segment 11, even a smaller percentage of non-low-income commuters uses it. This is why the users of segment 11 are disproportionately low-income.
Since only 31 percent of low-income commuters and 46 percent of non-low-income commuters drive on one or more segments, the low use of each segment by both income groups is to be expected. If we restrict the base to the number of low-income or non-low income commuters who use at least one segment, each column’s percentages will increase by the same factor (1/.31 or 1/.46), but their ratios will not change.
Washington and other states are most likely to devote toll revenues fully to the construction, improvement and maintenance of tolled facilities and, if funds suffice, other transportation projects (Franklin 2007, Richardson & Bae 1996, Weinstein & Sciara 2004). Hence, neither scenario incorporates a use of toll revenue that might offset the distributional effects of the toll..
The bridge (SR 520) is approaching the end of its engineered life span. Washington State and King County have agreed to jointly toll it to help finance its replacement. Tolls will continue to be collected on the new bridge. The average one-way toll is currently projected to be $2.16 (Seattle Times 2009).
We derived these values from the 2007 American Community Survey because the income categories in the HAS are too broad to provide useful estimates.
To derive this figure, note that 0.8 percent of the representative low-income household’s income is $125. The ratio of $125 to table 4’s projected cost of $235 is .53. This means usage must fall by 47 percent.
If higher income households did reduce their use of tolled routes, low-income households would need even larger reductions in use to achieve distributional neutrality. For instance, if higher income households took 10 percent fewer trips on tolled segments, low-income users would need to reduce their use by 54, 69 and 75 percent.
These alternative modes are not practical for many trips and, to the extent they do succeed in moving some cars off congested roads, others seem to take their place.
Contributor Information
Robert D. Plotnick, University of Washington
Jennifer Romich, University of Washington.
Jennifer Thacker, Burst for Prosperity.
Matthew Dunbar, University of Washington.
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