Interactions Between Transportation Capacity, Economic Systems, and Land Use (2012)

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16 C h a p t e r 4 project profiles The case study database has two uses: (1) as a direct source of information on individual project cases, which may be accessed via the T-PICS tool, and (2) as a source of empirical data that can be analyzed by researchers. As a starting point for the latter use, this report presents initial findings from analysis of the database. Table 4.1 shows a profile of the costs and length character- istics of the case studies. It shows values for both range and median (50th percentile, representing middle of the range). Overall, median project costs ranged from around $2 million for small industrial park access roads to more than $5 billion for some major interstate highways (and even higher if mega- projects such as the Oresund Bridge and Boston’s Central Artery/Tunnel are included). Projects also varied from 1 to 244 miles in length. In general, project costs fell into three classes: (1) Low cost— access road projects, which were most commonly in the $1–2 million range and all less than $70 million; (2) Mid-cost— bridge, connector, bypass, and interchange projects, which typi- cally were in the $10–100 million range and all less than $350 million; and (3) High cost—major highways and beltways, which varied from $200 million to more than $3 billion. Table 4.2 presents the median characteristics for additional aspects of the projects: construction time period, cost per mile, and traffic level. It shows that construction time typ- ically was in the range of 3 to 5 years (40–59 months) for the small and medium category projects, but typically rose to 10 or more years in the case of major limited-access highways, beltways, and widening projects. Cost per mile was highest for the bridge and widening projects, presumably because of the more difficult site settings and engineering involved in such projects. Traffic volumes were highest for the highway/ transit passenger intermodal terminals, lowest for the freight facilities (industrial access roads and freight intermodal ter- minals), and in the middle for highway projects. economic Impact Metrics Nature of Impacts To understand the nature of highway economic impacts, it is important to first establish the presence of different impact metrics and their interrelationship. Economic impacts of transportation facilities typically unfold in a sequence, affect- ing different impact metrics and spatial scales over time. Acknowledging these effects, the SHRP 2 case studies (com- pleted in 2010) were restricted to projects that had been com- pleted at least 5 years earlier so that sufficient time would have passed for the impacts to be manifested. In addition, the case studies sought to measure land value and building construc- tion effects at the level of highly localized areas, whereas employment, income, and tax impacts were measured for both local areas and larger areas (ranging from individual munici- palities to multijurisdictional corridors or counties). The case studies confirmed the following typical sequence of impacts. • Transportation impact. A highway project is initiated to affect travel-related costs or accessibility by enabling faster or more reliable travel to and from a particular area or enabling access to a broader set of origin or destination opportunities. The benefitting area may be adjacent to the project or may include areas well beyond the endpoints of the project corridor. There are occasionally adverse impacts on adjacent areas, which tend to be offset by benefits elsewhere. • Land (property) value impact. A transportation improve- ment makes an area more attractive as a place for living, working, or recreation, which results in greater demand for land at the location of the improvement. That improve- ment sometimes leads to an increase in productivity of the location. The greater demand typically leads to higher land values, as reflected in more property sales at higher prices. • Building construction and investment impact. The greater accessibility and value of the location attracts investment in new construction or expansion of housing, commercial Results of Data Tabulation

17 buildings, and/or recreation facilities. That is reflected ini- tially in terms of building permits and later in terms of new or upgraded building structures (which can be measured as square footage or dollars of investment). • Employment, income, and output impacts. Once buildings are occupied, there are commonly measurable increases in population (for residential use) or employment (for com- mercial and other uses). The employment increase reflects an added activity level that can also be viewed in terms of income (wages associated with the employment) or busi- ness activity (measured in terms of value added or total output growth). It is important to note that all of these measures reflect different ways to measure the same eco- nomic growth, so these measures cannot be added. • Tax revenue impacts. The added land value and construc- tion activity lead to increases in local property tax collec- tions, whereas the added wages and associated spending lead to increases in income and sales tax collections. The case studies confirmed two key conclusions pertaining to this sequential list of impact measures. First, impacts unfold over time, so no single project will necessarily show every type of impact at the same time. For that reason, multiple impact measures and an appropriately broad period of observation Table 4.1. Profile of Projects: Median and Range for Cost and Length Project Type Total Costa ($ millions) Length (miles) Lane Miles Access road 2 (1.0–68) 2 (1–3) 4 (2–11) Beltway 601 (205–2,796) 27.5 (3–62) 110 (21–372) Bridge 58 (4–101) 1.1 (0.1–12) 4 (0.2–72) Bypass 31 (11–163) 5.5 (2–11) 20 (5–44) Connector 190 (13–250) 7.7 (1.5–10) 35 (6–58) Interchange 47 (5–348) NA NA Major highway 980 (160–5,042) 142 (5–325) 632 (32–1,300) Widening 1,145 (313–2,060) 24.8 (8–244) 85 (50–740) Freight intermodal 197 (37–415) NA NA Passenger intermodal 74 (4–247) NA NA Note: NA = not available. a Excludes “mega-projects”: Oresund Bridge between Denmark and Sweden ($7.2 billion) and Boston’s Central Artery/Tunnel Project ($17 billion). Table 4.2. Profile of Projects: Construction Period, Cost per Mile, and Traffic Project Type Median Months to Construct Median Cost Per Mile (Millions of 2010 U.S. Dollars) Median Traffic Level (Annual Average Daily Traffic) Access road 57 $1.61 5,502 Beltway 120 $30.68 88,000 Bridge 40 $39.22 23,600 Bypass 46 $5.34 19,774 Connector 66 $21.79 16,910 Interchange 40 $14.05 53,450 Major highway 183 $11.05 46,150 Widening 139 $46.17 24,000 Freight intermodal 47 NAa 10,367 Passenger intermodal 59 NAa 136,000 Total 81 $14.98 23,861b Note: NA = not available. a Mileage is not defined for these types of projects. b Excluding passenger and freight intermodal terminals.

18 may be needed to observe economic development impacts. Second, each of the various forms of impact can have a differ- ent spatial pattern of observation; some may be observed at a neighborhood level, whereas others will be spread over a broader community or regional level. These effects also vary systematically by type of project. For instance, connectors, access roads, and interchanges tend to have localized impacts, whereas intercity routes and bypass projects can have broader impacts with some beneficiaries hundreds of miles away. Incidence of Impact Measures Table 4.3 and Figure 4.1 show the extent to which each element of impacts was observed or measured. They distinguish between qualitative information, such as interview observations of a positive or negative direction of impact, and quantitative data that measured the magnitude of impact over time. In some cases, quantitative measures were available, but only for partic- ular set of buildings or properties that did not represent the full area of impact. Of the 100 projects studied, all had some form of quantitative economic impact indicator available. However, the incidence varied widely among impact measures. These results must be interpreted carefully. The differences among impact measures reflect variation in the availability of data rather than differences in impact occurrence. In general, a change in any one of those impact elements is likely to lead to changes in other impact elements. However, there are some notable differences in data availability. In general, employ- ment change is the measure most likely to be measured because there are widely available employment data sets available at the county, community, and even zip code levels across the United States. For this study, the measure of job change reported as a highway impact was defined to be what- ever level of geography was deemed most relevant for that kind of project, adjusted for case study interview findings regarding the portion of observed impact that could be attributed to the highway project. Information on building permits, property transactions, and investment is more difficult Table 4.3. Availability of Impact Measures by Impact Element and Form of Data (Percentage of Cases) Element of Impact Observed Direction of Impact Some Quantitative Data Full Quantitative Data Jobs 100 100 100 Income * * * Business output * * * Building development (sq. ft.) 74 38 36 Direct private investment ($) 57 30 27 Property values 36 30 6 Property tax revenue 50 36 14 * These measures were calculated from employment changes, using applicable local and industry ratios. Percentage of Cases Figure 4.1. Percentage of cases with qualitative and quantitative impact data.

19 to obtain because such data come from municipal or county records, which differ widely in their availability and format for tabulation. Magnitude of economic Impact Direction of Impact Impacts can be interpreted in two ways: (1) by drawing on all qualitative and quantitative information, or (2) by drawing only on quantitative measures. Adopting the first approach, Table 4.4 combines qualitative and quantitative data to show the inci- dence of reported positive economic impacts by project type. Viewing Table 4.4 together with Table 4.3, it is apparent that all 100 cases had measures of job impact, with 85 showing evidence of a positive change in jobs for the impact area, whereas two (both rural bypasses) had a negative change. For all of the other impact elements, between 36% and 74% of the cases had observations regarding the direction of impact, and in all of those cases the reported direction of impact was posi- tive. For remaining cases for which interviewees reported they were unable to provide observation about a particular impact element, we cannot eliminate the possibility that this may have sometimes occurred because there was no change to observe. Adopting the second approach, Table 4.5 shows net change results only for the cases that had full quantitative data. Focus- ing on the most widely available impact metric—employment (job) impact, the results show that 85 of the cases had positive changes and only 2 showed a net negative impact, whereas the remaining 13 showed no net impact. The latter finding includes cases for which there was no evidence of job impact and those in which offsetting negative and positive job impacts were seen. The quantitative results reflect net impacts. Highway proj- ects can cause negative visual, air quality, or noise quality impacts on areas that are directly adjacent to them, while pro- viding access benefits to broader surrounding areas. In some cases, highway projects can also cause localized negative job impacts, as would be the case if a highway construction or Table 4.4. Number of Cases with Reported Positive Direction of Economic Impact (Including Qualitative Observations and Quantitative Data) Project Type Total Cases Job Impact Private Investment Building Construct Property Values Tax Revenue Access road 7 7 4 2 1 3 Beltway 8 8 8 8 2 7 Bridge 10 8 7 7 7 7 Bypass 13 7 6 6 5 8 Connector 8 6 6 6 4 5 Interchange 12 10 6 8 2 4 Major highway 14 14 13 13 10 11 Widening 9 9 1 7 2 1 Freight intermodal 10 9 2 9 1 1 Passenger intermodal 9 7 4 8 2 3 Total 100 85 57 74 36 50 Table 4.5. Quantitative Impact Findings on Direction of Impact (Only for Cases with Full Quantitative Data Available) Dimension of Impact Positive Net Change (%) Negative Net Change (%) No Net Change (%) Change Not Observed (%) Total (%) Impact on jobs 85 2 13 — 100 Impact on building construction 36 0a NA 64 100 Impact on private investment ($) 27 0a NA 73 100 Impact on property values 6 0a NA 94 100 Impact on local tax revenue 14 0a NA 86 100 Note: NA = not available. a Measures reflect the net result of positive and negative impacts.

20 expansion project required the taking of some property with existing commercial activity. However, in nearly all cases, such takings are offset by new activity that occurs somewhere else nearby. The incidence of offsetting impacts is noted in text discussions that are part of the case study database. The availability of impact metrics other than jobs is best described as spotty; in other words, in a majority of cases it was not possible (after the fact) to reliably reconstruct net changes occurring in investment, construction, or tax rev- enues. Another source of data was municipal data on overall community-wide business sales and property tax base. Those measures, when available, tended to show positive and nega- tive changes, although the research team cannot be sure what portion of the changes are attributable to the highway project rather than other factors. Size of Impact Table 4.6 shows the range of impact values found in the case study data set, for various aspects of economic impact. Job impacts are the most commonly measured form of economic impacts because they are easy to understand and provide a reference for analysis and comparison. Other impacts on the economy include growth in personal income and business output, such as property values, private investment, building construction, and property tax revenues. Value added or gross regional product is another impact measure that is commonly used in economic models, but information on that metric was not available for this study. It is notable that job impacts were measured in two differ- ent ways depending on the scale of impact area and source of impact measurement. Most often, job impacts were calcu- lated in terms of the change in total level of business activity occurring in a surrounding study area. However, in some cases they were calculated by observing jobs directly attracted to the immediate project area and then applying economic multipliers to account for broader economic impacts also expected to be occurring elsewhere in the region. The income and business output metrics were calculated on the basis of local ratios for wage/worker or output/worker ratios for the applicable industries and areas. The observed range of impacts varied widely. For instance, nearly half (47%) of the projects accounted for less than 1,000 jobs each, whereas a small fraction (10%) of the proj- ects accounted for more than 20,000 jobs each. As a result, the mean impact was five times larger than the median impact (as shown in the table). Table 4.7 shows how the job impacts varied by project type and setting. In general, the upside range of project job impacts allowed them to be classified into three groups that reflected differences in project scale: (1) Small-scale impact—access road projects that generally supported between 500 and 2,000 jobs; (2) Midscale impact—bridge, connector, bypass, interchange and intermodal projects that had widely variable impacts, sometimes zero but other times 10,000–25,000 jobs; and (3) Large-scale impact—major highways and beltways, which always supported some job growth and sometimes supported job increases of 40,000–50,000 or more. It is also apparent that job impacts were typically of a much smaller scale in rural areas. Rural connector and bridge proj- ects sometimes had zero impact, although only the rural bypass projects had a mix of negative and positive impacts. Projects with No Economic Growth Impact The case studies found that 15 of the 100 projects led to a zero or negative impact on job growth. Table 4.8 provides a break- down of those projects by type. It shows that nearly all were bridges, bypasses, connectors, interchanges, or transfer ter- minals. With the possible exception of intermodal projects, Table 4.6. Ranges and Medians of Economic Impact Measures (For Which Quantitative Data Are Available in the Data Set) Measure of Impact Minimum Maximuma Median Mean Employment (jobs) -48 50,505 1,290 5,782 Income ($ millions) $0 $2,332 $53 $267 Business output ($ millions) $0 $8,830 $142 $840 Building development (thousand sq. ft.) 4.2 50,000 1,003 —b Direct private investment ($ millions) $3.0 $6,300 $300 —b Property values ($ millions) $0.15 $85 $16.0 —b Property tax revenue ($ millions) $0.12 $55 $2.1 —b a Maximum excludes Santan Freeway widening (Arizona), Central Artery/Tunnel Project (Massachusetts), and Route 101 beltway (Arizona), all of which had only rough estimates available for job impact. b Insufficient data.

21 these generally were projects designed more to help manage traffic flow than to generate economic growth. The finding for rural community bypass roads was also to be expected. Past bypass studies conducted for a number of different states have shown that job impacts are either slightly positive or negligible in most bypassed communities. That outcome is attributable to the offsetting positive and negative effects of shifting pass-by traffic out of local communities, which represents a potential loss for some traffic-serving businesses but a potential gain for others that benefit from having improved safety and a more attractive urban environ- ment for local residents and visitors. An important finding is that most of these 15 projects had other forms of positive economic impact despite the lack of positive job impact. This included the following findings: • Eight of the cases had gains in business sales at the county level after the project. • Ten of the cases had growth in local per capita income after project completion. • Six of the cases had documented increases in local property values. Job Impact ratios The case studies had an overall ratio of 7 long-term jobs added per $1 million of highway investment, although the ratio var- ied from less than 2 jobs to nearly 90 long-term jobs per $1 million, depending on the type of project and urban/rural setting. (See Figure 4.2.) The access roads, interchange, and connectors tended to have the highest average ratio of long- term jobs supported per $1 million of highway spending. At the other extreme, the beltway, major highway, and widening projects tended to have the lowest average ratio of long-term job growth per $1 million of highway spending. Table 4.7. Range of Job Impacts by Project Type and Metro/Mixed and Rural Setting (For Which Quantitative Data Are Available in the Data Set) Project Type Metro/Mixed Setting Rural Setting (Cases) Low High (Cases) Low High Access road (2) 478 3,195 (5) 7 680 Beltway (7) 2,106 43,753 — — — Bridge (7) 0 11,771 (3) 0 319 Bypass (5) 0 23,977 (8) -48 1,420 Connector (6) 0 14,578 (2) 0 412 Interchange (12) 0 23,520 — — — Major highway (13) 90 50,505 — — — Widening (6) 1,498 15,484 (2) 3,785 4,080 Freight intermodal (7) 0 13,646 (3) 583 3,236 Passenger intermodal (9) 0 10,035 NA NA NA All project typesa (74) 0 50,505 (23) 48 4,080 Note: NA = not available. a Excludes Santan Freeway widening (Arizona), Central Artery/Tunnel Project (Massachusetts), and Route 101 beltway (Arizona), all of which had only rough estimates available for job impact. Table 4.8. Types of Projects That Yielded Zero or Negative Job Impacts (For Which Quantitative Data Are Available in the Data Set) Project Type Cases with Net Zero Job Impact Cases with Net Negative Job Impact Access road — — Beltway — — Bridge 2 — Bypass 4 2 Connector 2 — Interchange 2 — Major highway — — Widening — — Freight intermodal 1 — Passenger intermodal 2 — Total projects 13 2

22 These systematic differences occurred for some good rea- sons. On the one hand, project types with the highest ratio of long-term job growth per $1 million spent—access roads, interchanges, and connectors—often were built to facilitate specific business location or expansion activities that were contingent on having new access routes, interchanges, or con- nectors built. On the other hand, project types with the lowest ratio of observed job growth per $1 million spent—urban freeway (limited-access highways) and highway-widening projects— often required the addition of costly land acquisition and neighborhood impact mitigation costs. The beneficiaries of the projects were more likely to be those whose trips were based at origins and destinations located beyond the highway project endpoints (thus providing benefits beyond the areas immediately surrounding the highway project). There were also substantial differences in the job genera- tion ratio by urban/rural setting. The ratio of long-term jobs per $1 million spent for projects in a metropolitan (or mixed urban/rural) area was more than three times that occurring in rural areas. Fully 22% of the rural projects but only 14% of the metro/mixed projects had zero job creation, and 50% of the rural projects but only 22% of the urban/mixed projects had 0 to 99 net jobs added. The upside potential was most evident for the metro area projects, of which 66% had a long- term job growth impact exceeding 1,000 jobs. There are many possible explanations for this finding, which will need to be further explored in future research. With differences in densities of population and jobs, one hypothesis is that many of the rural projects serve intercity travel and the beneficiaries are more broadly distributed outside of the proj- ect area. Or it may be that land development and private investment impacts take longer to manifest for rural projects. role of project Motivation As part of the data collection process through interviews, des- ignations were made to classify each project in terms of its purpose. Project motivations were classified into nine major categories. Six categories are related to increasing access. They are improving access to terminals of air, rail, and marine modes; international borders; labor markets; and delivery markets. Two categories are related to economic development: tourism market development and facilitation of industrial site development. The final motivation category is congestion management, which most often represents an attempt to reduce or prevent further degradation in traffic flow condi- tions, rather than enabling positive enhancement compared with past or current conditions. In the case study interviews for each project, both local planning officials and business representatives were asked to identify project motivations, and they were allowed to choose multiple motivations. Findings are reported in Table 4.9. Overall, project motivation was obtained for 97 of the 100 projects: 58 were motivated by an access issue, 65 by an eco- nomic development issue, and 54 by a congestion manage- ment issue. The motivation to mitigate congestion was reported most often for urban highway projects, while the motivation to facilitate site development was reported most often for interchange and access road projects. Figure 4.2. Ratio: Median long-term job impact per $1 million of project cost by project type and setting.

23 Figure 4.3 shows how the project motivations varied by setting. Many projects had more than one motivation, so the sum is not 100%. Focusing just on the highway projects (excluding intermodal terminals), the chart shows that the most common project motivation in rural and metro areas was congestion mitigation. Site access and delivery market access were the next two most common reasons in metro/ Table 4.9. Project Motivation by Project Type Category of Motivation Highway Projects Freight Intermodal Passenger Intermodal Total Enhance access Improve access to airports 18 2 0 20 Improve access to rail 4 6 0 10 Improve access to international border 2 1 0 3 Improve access to marine port 7 2 0 9 Improve labor market access 26 0 4 30 Improve delivery market access 29 3 0 32 Any of the above 58a Promote economic development Facilitate site development 42 2 8 52 Facilitate tourism 26 0 0 26 Any of the above 65a Reduce congestion Mitigate congestion 47 0 7 54 All projects 78b 10b 9b 97b a The reported numbers for “any of the above” are less than the sum of the preceding lines because some projects had multiple motivations. b The reported numbers for “all projects” are less than the sum of the preceding lines because some projects had multiple motivations. Figure 4.3. Project motivations (percentage of highway cases with each motivation). mixed and rural settings, whereas tourism was an important motivator in rural areas and labor market access also was key in metro/mixed areas. Figure 4.4 shows how project motivations also varied by type of project. Not surprisingly, access considerations were the strongest motivations for the major highways and freight intermodal projects. Congestion mitigation motivations were

24 Table 4.10. Incidence of Nontransportation Factors Affecting Job Growth Nontransportation Factors Incidence (%) Positive local factors Available infrastructure (sewer, water, telecom) 33 Land use management 45 Financial incentives/business climate 46 Negative local factors Lack of infrastructure (sewer, water, telecom) 10 Lack of land use management 6 Lack of financial incentives/negative business climate 5 All projects 100 strongest for the bridge and beltway projects, and economic development motivations were strongest for the access road and passenger intermodal projects. role of Nonhighway Factors The economic impact was often supported by nontrans- portation factors, most commonly as the presence of other infrastructure investments, land use policies, or business development incentive programs. In some cases, the synergy among multiple factors created a positive economic develop- ment climate that led to additional job creation. In other cases, a lack of complementary infrastructure and supportive policies diminished job impacts. Table 4.10 shows the fre- quency with which these various nontransportation factors were cited in case study interviews as matters affecting the long-term job growth impacts of highway projects. Table 4.11 shows how the job growth impact of highway projects varies, depending on the presence of positive or neg- ative local factors. It indicates that greater long-term job Figure 4.4. Project motivation by project type. growth was reported for highway projects with positive local factors than occurred with projects lacking such supportive factors. The median job creation was slightly more than 180 for projects for which a lack of complementary infrastructure or policies inhibited economic development, compared with more than 1,420 for projects for which supportive factors were reported. Projects that cited both positive and negative policies included a wide range of job impacts, which resulted in a median of 1,050 jobs. The influence that local factors can have on economic out- comes is even more apparent when grouped by level of eco- nomic distress, as shown in Figure 4.5. Nondistressed areas with positive local factors had higher median ratios of jobs per $1 million than did distressed areas. Taken together, these tables and figures illustrate the mag- nitude of long-term economic activity growth that typically follows highway-related projects and the ways in which proj- ect types and settings interact to affect those outcomes. A fur- ther effort to establish these relationships is presented via statistical analysis in Chapter 5.

25 Table 4.11. Effects of Nontransportation Factors on Magnitude of Job Growth Nontransportation Factors Number of Cases Total Job Impact (all projects) Median Job Impact (per project) Mean Job Impact (per project) Positive 57 271,362 1,420 4,761 Negative 8 11,757 183 1,470 Mixed positive and negative 8 19,625 1,050 2,453 Not reported 23 207,627 808 9,027 Total 96 510,371 1,269a 5,316a Note: This table excludes Santan Freeway widening (Arizona), Central Artery/Tunnel Project (Massachusetts), and Route 101 beltway (Arizona), which had only rough estimates available for job impact. It also excludes Interstate 26. That project reported nearly 31,000 jobs, yet local officials reported that the project never reached its full potential because of lack of adequate infra- structure and land use management. a The median of 1,269 and mean of 5,316 reported here differ from the median of 1,290 and mean of 5,782 reported in Table 4.6 because the Interstate 26 project was excluded from this table. Figure 4.5. Effect of nontransportation factors on job growth by local economic condition (distress level).