Strategic Issues Facing Transportation, Volume 6: The Effects of Socio-Demographics on Future Travel Demand (2014)

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52 7.1 Defining Key Indicators One of the fundamental uses of scenarios is that, if considered plau- sible, they can help policy makers and other decision makers anticipate and prepare for change. One of the key recommendations from trans- portation planning decision makers, the potential users of Impacts 2050, was that it would be useful to develop a way to monitor key trends in relation to each scenario. The scenario planning literature discusses the utility of identifying early warning signs that can indicate the directions of trends in critical parameters. For example, VMT estimates are “lagging” indicators in the sense that they inform us after travel behaviors have already changed. By contrast, “key” indicators are useful in signaling future changes in travel behavior. For example, a traffic light that turns yellow before it turns red tells us to stop in advance of the red light. Thus, a key indica- tor is simply an early warning sign of future events. We can think of the early warning signs being applied in the same way that leading economic indicators are used to monitor the health of the U.S. economy. In 1995, the Bureau of Economic Analysis of the U.S. Department of Commerce created a private, nongovernmental organization to determine a monthly leading index. The Conference Board publishes a composite Leading Economic Index consisting of 10 indicators designed to predict the activity in the U.S. economy six to nine months in the future. According to Webster’s Dictionary, leading economic indicators are “indicators that change before the economy as a whole changes.” Drawn across sectors that influence economic health, these indicators include average weekly jobless claims for unemploy- ment insurance, building permits for new private housing units, vendor performance (time it takes to deliver orders to industrial companies), and the Standard & Poor’s 500 stock index. Similarly, this study has attempted to consider all of the influencing sectors in Impacts 2050 (e.g., demographics, employment, land use) when identifying early warning signs. Doing so forces the acknowledgement of shifts in trends outside of the transportation-specific domain. The purpose of this exercise is, then, to ask: Which scenarios are we moving toward, and what are the implications? The Impacts 2050 scenarios provide a useful platform for building contingency plans that can be tested against the “what if” projections embedded in the scenarios. For example, are C H A P T E R 7 Key Indicators and Monitoring Approach Chapter 7 Takeaways • Key indicators or early warning signs can indicate the likely directions of trends in key areas that affect transportation. • Economic indicators that change before the economy as a whole changes. • Driving forces that distinguish the four scenarios are economic growth, number of jobs, rate of job loss, rate of job creation, age structure, percentage of foreign born, number of lane miles of road, pro-environment attitudes, and existence of a carbon tax. Critical uncertainties in these variables will affect transportation trends. “No sensible decision will be made any longer without taking into account not only the world as it is, but the world as it will be.” Isaac Asimov, author

Key Indicators and Monitoring Approach 53 contingencies robust and resilient over more than one scenario? If not, can they be adapted to cope with the challenges in the scenarios? Specific early warning signs can be developed on the basis of the key trends set out in the scenarios, supported by appropriate data sources that are monitored regularly. For example, under the Gentle Footprint scenario, potential early warning signs could include shifts in the national environmental agenda due to public attitudes or the political momentum for a carbon tax. Under the Technology Triumphs scenario, early warning signs might be the strength of GDP growth or market penetration of self-driving vehicles. Different users of the scenarios may be more interested in one category of early warning signs than another, depending on their assumptions about critical uncertainties. 7.2 Driving Forces in the Scenarios The research team associated early warning signs with key drivers in the scenarios. To iden- tify the key drivers, the team conducted a cross-impact analysis among the SD model variables (Gausemeier et al. 1998), which include base-condition variables (such as age structure), rates- of-change variables (such as birth rate), and qualitative policy variables (such as introduction of a carbon tax). All of these variables are embedded in the assumptions for each scenario in Impacts 2050, as noted in Table 4.1 in Chapter 4. The impacts of the different model variables on each other were recorded in a cross-impact matrix (or influence matrix), using a scale from 0 (no impact) to 3 (strong impact). In the cross- impact analysis, it is important to assess the direct—not indirect—impacts of the variables. Fig- ure 7-1 presents an extract of the cross-impact analysis matrix; the entire matrix is presented in Appendix E. Figure 7-1 should be read from column to row (note the direction of the arrow). For example, the total population has no direct impact on age structure, so that relationship would be rated 0. By contrast, age structure has a strong direct impact on total population, so it was Effects of Socio-Demographics on Future Travel Demand 1.1 1.2 1.3 1.4 To ta l p op ul at io n Ag e st ru ct ur e Ho us eh ol d st ru ct ur e (m arr ied or no t, k ids or no t) Pe rc en t f or ei gn b or n 1.1 Total population 3 1 2 1.2 Age structure 0 0 2 1.3 Household structure (married or not, kids or not) 0 2 2 1.4 Percent of foreign born in each race group 0 0 0 1.5 Race/ethnicity distribution 0 0 0 3 1.6 Income distribution 0 1 2 1 Cross-Impact Matrix How strong is the DIRECT IMPACT of a column descriptor on the future development of a line descriptor? Please use a scale from 0 to 3: 0: no impact 1: low impact 2: moderate impact 3: strong impact Figure 7-1. Extract of cross-impact analysis matrix.

54 The Effects of Socio-Demographics on Future Travel Demand rated 3. This exercise establishes the degree of interconnectedness of all indicators. Figure 7-1 is a matrix, so all variables are included in all columns and all rows. It should also be noted that the scores in the matrix are subjective; they are based on the judgment of the research team. Table 7-1 presents the results of the cross-impact analysis for each model variable in terms of the passivity score (i.e., that variable being influenced by other SD model variables) and activity score (i.e., that variable influencing other SD model variables). The activity and passivity scores are the sums of the scores in the matrix. The table is sorted by activity score, which identifies the most important and least important factors. The higher the activity index of a variable, the more it influences other variables in the model. For example, attitudes favoring clean energy and environmental protection affected a large number Variable Number SD Model Variable Name Activity Score Passivity Score 5.3 Rate of economic growth 38 17 2.2 Rate of job creation 26 12 6.4 Attitudes favoring clean energy and environmental protection 26 9 6.3 Introduction of carbon tax 24 3 1.2 Age structure 22 10 1.4 Percentage foreign-born 21 7 2.1 Number of jobs 21 14 2.3 Rate of job loss 20 6 4.1 Number of lane miles for freeways, arterials, and other highways 19 16 2.4 Rate of job migration within region 16 9 5.1 Telework share 16 14 1.7 Aging rate 14 6 1.8 Workforce participation 14 32 1.1 Birth rate 14 17 3.1 Amount of space that is developed residential, developed other, developable, protected 11 27 3.3 Rate of conversion to/from protected 11 19 6.1 Price of gas 11 3 1.11 Marriage rate 10 8 3.2 Rate of conversion to/from developable 10 25 4.2 Total route miles for rail and bus transit 10 29 1.5 Race/ethnicity distribution 9 4 1.9 Population density (urban, suburban, rural shares) 9 24 1.13 Household formation rate 7 14 5.2 Online shopping share of retail sales 7 12 1.3 Household structure 6 23 1.6 Income distribution 6 22 6.2 Total miles of walk and bike paths 6 4 1.12 Divorce rate 5 5 5.4 Adoption of smartphone or mobile devices with Internet access 5 8 5.5 Market penetration of self-driving vehicles 3 4 1.1 Total population 0 14 Note: Variable numbers (first column) are cross-referenced in Figure 7-2. Table 7-1. Results of cross-impact analysis for each sd model variable.

Key Indicators and Monitoring Approach 55 of other indicators, so this variable is highly influential on the other variables in the SD model and has an activity score of 26. On the other hand, the higher the passivity index, the more a variable is driven by other variables. Workforce participation is affected by many other variables, so it is considered highly passive, and has a passivity score of 32. Variables with both high activity and high passivity indices, such as rate of economic growth, are strongly interconnected in the system, being driver and driven at the same time. While some scores could be scenario specific, this analysis was conducted in an overarching way, across all scenarios. This analysis was the basis for identifying some SD model variables as key drivers in the scenarios. The four scenarios outline different possible paths to explain how socio-demographics may influence travel demand over the next 30–50 years. While similarities among the scenarios exist, what is important for anticipating and preparing for change are the critical uncertainties, or driving forces, that cause one path to emerge over another. To identify these uncertainties, the research team began with the information about each variable’s activity or passivity. The out- come of this analysis is illustrated in Figure 7-2. Scenario drivers, depicted in the top half of Figure 7-2, are variables that “drive” or influence other variables, so are high on the activity index. The quadrants in which the most important and the least important variables are located are noted as such. (Due to the length of some variable names and limited space in the lower two quadrants of Figure 7-2, only the variable number in Figure 7-1 is referenced. For example, 6.1 in the lower left quadrant refers to the “price of gas” variable.) The future development of these critical uncertainties will strongly affect other variables in the four scenarios. Table 7-2 presents assumptions about how each of these uncertainties will play out in the scenarios. For example, the number of jobs is shown as stable under the Momen- tum scenario and increasing under the Technology Triumphs and Gentle Footprint scenarios, whereas earlier in this report Table 6-7 shows that the percentage in work decreases over time under the Momentum scenario and is mostly stable with the other scenarios. As discussed in Chapter 3, workforce participation is highly influenced by structural forces in the population age distribution. Figure 7-2. Key drivers as outcomes of the cross-impact analysis. Note: Numbers are cross-referenced to the first column in Table 7-1. Key drivers are shown in text. 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 Ac tiv ity In de x Passivity Index Most Important Least Important 1.1 Job creation 1.6 Foreign born 1.5 1.3 1.7 carbon tax # jobs # lane miles Economic growth Age structure Attitudes job loss 2.4 5.1 1.10 3.3 1.9 3.2 3.1 4.2 1.8 1.13 5.2 5.4 1.11 6.1 6.2 1.12 5.5

56 The Effects of Socio-Demographics on Future Travel Demand 7.3 Monitoring Key Indicators or Early Warning Signs Economic growth is both a highly active and a highly passive scenario variable. So while it is a driver in the scenarios, it is not recommended as an early warning sign to be monitored. The drivers related to employment (number of jobs, rate of job loss, and rate of job creation) directly impact economic growth; therefore, they are better relied upon as key indicators. Other key indicators are demographic: age structure and the percentage foreign-born. The remaining key indicators are social or policy variables: attitudes favoring clean energy and environmental protection, and the introduction of a carbon tax. Data sources for monitoring these indicators are described below. It is recommended that trends analysis be conducted for all indicators at least once every two to three years to determine if a region or regions may be moving toward one scenario versus another. A single indicator may not be a reliable measure for the trend. 7.3.1 Economic Indicators: Number of Jobs, Rate of Job Loss, Rate of Job Creation Monthly information relating to jobs at the state or regional level is available from the BLS in the form of labor force and unemployment data from the Local Area Unemployment Statistics program and in the form of nonfarm payroll employment estimates from the Current Employ- ment Statistics program. These data cover 372 metropolitan statistical areas and metropolitan New England city and town areas, plus eight areas in Puerto Rico. 7.3.2 Demographic Indicators: Age Structure, Percentage Foreign-Born Demographic data are available from the American Community Survey, which is an ongoing survey of the U.S. Census Bureau. The survey captures data on age structure, aging, and origin (foreign versus native born). One-year estimates provide detailed data for areas with popula- tions of 65,000 or more. Driving Forces Momentum Technology Triumphs Global Chaos Gentle Footprint Economic growth Steady growth Strong Low Moderate Number of jobs Stable Increasing Decreasing Increasing Rate of job loss Low Zero High Low Rate of job creation Stable High Low High Age structure Population growth, increase in older people Population growth, more young people Slower population growth, fewer older and younger people Population growth, fewer young people Percentage foreign-born Immigration stable Immigration declines Immigration declines Immigration increasing Total number of lane miles Constrained due to funding Private-sector funds No new capacity Carbon tax funds Pro-environment attitudes Gradual shift to high priority Low priority Low in priority Shifts quickly to high priority Carbon tax Gradual introduction No No Yes Table 7-2. How driving forces play out in the scenarios.

Key Indicators and Monitoring Approach 57 7.3.3 Transport Supply Indicators: Total Number of Lane Miles for Freeways, Arterials, Other Highways, and Transit It is assumed that state and local transportation agencies will have the necessary database to monitor this indicator. 7.3.4 Social and Policy Indicators: Attitudes Favoring Clean Energy and Environmental Protection and the Introduction of a Carbon Tax Ongoing national surveys provide insights into the trends in America’s environmental atti- tudes, such as the Pew Research Center and Gallup. However, these survey results are rarely disseminated at the regional or state level. It is recommended that questions on environmental attitudes be added to existing local, regional, or state surveys. For trend purposes, it would be important to ensure that question wording and sampling methodology are consistent between survey iterations. The report for NCHRP Project 20-07/Task 260, Putting Customer Research into Practice: Guidelines for Conducting, Reporting, and Using Customer Surveys Related to Highway Maintenance Operations, provides useful guidance (Zmud 2012). The introduction of a carbon tax is a qualitative variable that is binary (yes, no). If no, it can be monitored in terms of how close a state may be to enacting low-carbon legislation. 7.4 Identifying Indicators Using Impacts 2050 In addition to the identified indicators, other candidates for inclusion in the indicator list may be SD model variables that show strong variation in outcomes across scenarios for a particular region. A state DOT or MPO would have to run Impacts 2050 to determine which variables would be most informative. For the regions studied, candidate variables may be percentage of car-sharing and percentage of low-income population. For example, Table 7-3 illustrates the variation across regions and across scenarios for percentage of car-sharing. Since the trajectory of percentage of car-sharing is so different for each scenario, the moni- tored trend for percentage of car-sharing would be one indication of the direction in which society may be heading. 7.5 Monitoring the Future with Impacts 2050 Currently, the Impacts 2050 base data are U.S. Census data from 2000. Some state DOTs or MPOs may have data at finer resolution or more recent data that can be used to calibrate the simulations in future years. The tool is calibrated to 2010 Census data. With regular use, it can Scenario Atlanta Boston Detroit Houston Seattle Momentum 5–25% decrease 5–25% decrease 5–25% decrease –5% to 5% change 5–25% decrease Technology Triumphs >25% decrease >25% decrease >25% decrease 5–25% decrease >25% decrease Global Chaos >25% increase >25% increase >25% increase >25% increase >25% increase Gentle Footprint 5–25% increase 5–25% increase 5–25% increase 5–25% increase 5–25% increase # of Different Outcomes 4 4 4 4 4 Table 7-3. Trajectory results for percentage of car-sharing from Table 6-7.

58 The Effects of Socio-Demographics on Future Travel Demand be progressively calibrated for successive years—2015, 2020, etc., which will refine the simulated long-term outcomes and allow for monitoring the indicators over time. Thus, transportation agencies that regularly use Impacts 2050 will in some way be “reinventing” it. This tool was designed with reinvention in mind. Its generic character enables an agency to get it up and running quickly. It is likely and acceptable that some transportation agencies may adopt some components of Impacts 2050 and change or reject others. It is hoped that in doing so agencies will “fix” Impacts 2050 to better meet their needs. Reinvention was a key element in the diffusion and use of the Census Bureau’s Topologically Integrated Geographic Encoding and Referencing (TIGER) system when it was first introduced. The primary strategic issues or decisions facing a transportation agency provide the focus for implementing Impacts 2050. With this new information Impacts 2050 provides, transportation agencies must be prepared to implement change, as is discussed in the next chapter.