Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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B.2. INDEPENDENT VARIABLES PROJECTIONS B.2.1 Income The following explains the process by which income for the elderly was forecasted by Standard & Poor’s DRI for use in the Oak Ridge National Laboratory (ORNL) projection model. These projections can be seen in Tables B.5 and B.6. B.2.1.1 Standard & Poor’s DRI Tables B.5 and B.6 contain historical estimates and forecasts of the median money income of persons of age 65 and over in 1998 dollars for demographic groups defined: by gender, within age groups 65-69, 70-74, 75-79, 80-84, and 85+, for the U.S. and each of four Census regions, at five-year intervals for the period 1975-2025. Historical annual time series for 1975-1998 were tabulated from the Annual Demographic File of the Current Population Survey. All series were converted to constant dollars using the Census Bureau’s CPIU-X1 deflator. These annual time series show substantial year-to-year variation. To eliminate outliers in the historical data, model-predicted values are shown for historical years in the accompanying spreadsheet. The money income of persons aged 65 and over comes from the following sources (1995 data): Earnings 18% Social Security 42% Retirement/pensions 20% Interest, dividends, rental income 16% Other 5% DRI’s Quarterly Model of the U.S. Economy contains some variables that are related to these components of the money income of the elderly. The forecasting models for regions do not include such direct measures of income but do provide projections of total per capita income, which were used to capture regional differences that affect all demographic groups. GM Project G.6 B - 6 October 2000
Models of median real money income for the U.S. in each of the ten demographic groups defined above were estimated employing the following variables: • Real Social Security payments per person aged 65 and over • Real pension payments per person 65 and over • Real interest income per capita • Real wages (ECI for private wages and salaries deflated with CPIU-X1) All models were estimated in log-log form. Only variables with positive coefficients were retained in the models. Models for each of the ten demographic groups in each region were estimated employing these national variables plus real per capita income of the region. In instances where this combination of variables did not produce a satisfactory model, a simple model relating the income of the demographic group in a region to the income of the corresponding group in the U.S. as a whole was employed. Forecasts through 2010 are based on DRI’s TRENDLONG forecast of November 1999. Projections were extended to 2025 based on the TREND25YEAR forecast of August 1999. GM Project G.6 B - 7 October 2000
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ORNL-6963 Projecting Fatalities in
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4. ANALYSIS OF DATA SETS ..........
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GM Project G.6 vi October 2000
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LIST OF TABLES Table ES-1 Elderly D
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Table A.5.3 Projected Total Fatalit
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NASS National Automotive Sampling S
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Over the past century, the numbers
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Figure ES-1. Projected Active Drive
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Table ES-1. Elderly Driver Fatality
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1. INTRODUCTION America is graying.
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of existing data bases, including h
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various data sources and their abil
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Finally, data sets should contain,
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• Examine data sources to determi
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health care, resulting in longer li
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Age group Table 3.2. Projected Rati
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that individuals will not retire at
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analysis. Distribution of the elder
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More elderly people live in rural a
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assessments of health status) does
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3.2.2 Impacts of Increasing Frailty
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current vision screening tests at d
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Number of Licenses (thousands) 100
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3.3.2 Changes in Driving Habits In
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Transportation Survey (NPTS) data,
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tourist services, and “smart” s
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Specific highway enhancements to re
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3.4.2 Other GM Project Results (Pro
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• As age increased, being struck
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men using mid-1990s data, this gend
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major contributing factor as was dr
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Total (percent) 100 80 60 40 20 0 A
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cohort behavior and conditions for
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4.2.3 Drivers and Driving Distance
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The HRS and the AHEAD studies are c
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these data and weights. Because of
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U.S. Census Bureau (http://www.cens
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5. OVERVIEW OF THE MODELING SYSTEM
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or acquisition costs, of the goods
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institutionalized settings, our pro
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elationships between health and the
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6. THE PROPORTION OF THE ELDERLY PO
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Employment status represents a deri
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statistical relationship exists bet
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more likely to be drivers if there
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Statistics (BLS) projections beyond
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considerably greater proportional c
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Table 6.3 (continued) Western Men W
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Daily Person Trips/Person Figure 7.
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Second, VMT as a consumption item i
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century on which we are unable to o
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The health status effects are posit
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significant one in the final set of
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empirically derived regression coef
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GM Project G.6 7 - 14 October 2000
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Table 7.3. Determinants of Projecte
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8. FATAL CRASH RATES 8.1 MODELING F
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The other fatality risk concept we
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dropped. Since these measures have
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have a regular pattern over the age
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Table 8.2. Total Risk Crash Rate Re
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projection the asymptotic behavior
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percentages of people driving, and
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9.1.1 Computations The projection o
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Figure 9.2, the combination of thes
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Table 9.1. Sensitivity of Total Dri
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material, dampening effect on drive
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Table 9.2. Sensitivity of 2025 Proj
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Age group Table 9.6. Sensitivity of
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9.3.1 The Effect of Time on VMT Pro
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Figure 9.4. Projected VMT, Women (U
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Figure 9.6. Component Contributions
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Step 1: Introduction Step 2: Decisi
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Step 3a: Modifying Income at differ
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Clicking on one of the sheet tabs w
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10. SUMMARY, CONCLUSIONS, AND RECOM
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similarly relied on a regression mo
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Page 164 and 165: population, and a slightly smaller
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Page 168 and 169: projections of VMT may be high. Our
Page 170 and 171: of entry/exit, safety, security, et
Page 172 and 173: Blincoe, Lawrence. 1994. Estimating
Page 174 and 175: College of Commerce and Business Ad
Page 176 and 177: National Association of Development
Page 178 and 179: Suen, S. Ling, C. G. B. Mitchell, a
Page 180 and 181: GLOSSARY Baby boomers Persons born
Page 182 and 183: Table A.1.2. Projections of Vehicle
Page 184 and 185: Table A.1.4. Projections of Vehicle
Page 186 and 187: Table A.2.2. Projections of Drivers
Page 188 and 189: Table A.2.4. Projections of Drivers
Page 190 and 191: Table A.3.2. Elder Driver Fatality
Page 192 and 193: Table A.3.4. Elder Driver Fatality
Page 194 and 195: Table A.4.2. Projected Elderly Driv
Page 196 and 197: Table A.4.4. Projected Elderly Driv
Page 198 and 199: Table A.5.2. Projected Total Fatali
Page 206 and 207: Table B.1. Projections of Non-Insti
Page 208 and 209: Table B.3. Projections of Non-Insti
Page 212 and 213: Table B.5. Male DRI Income Projecti
Page 214 and 215: B.2.1.2. Projection Adjustments to
Page 216 and 217: B.2.5 Seatbelt Use Our projections
Page 218 and 219: C.3. DECREASING GROWTH RATES OF DRI
Page 220: GM Project G.6 C - 4 October 2000 T
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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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