- Present Value of Expected Lifetime Productivity, by Age, Gender, and Discount Rate, 1992
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Table B.1 Present Value of Expected Lifetime Productivity, by Age, Gender, and Discount Rate, 1992 Age Males Females Discount Rate 2% 3% 4% 6% 2% 3% 4% 6% Under 1 year $1,174,844 $796,868 $540,496 $266,965 $935,418 $640,454 $438,501 $222,167 1-4 1,207,409 839,078 583,109 302,060 960,870 674,044 472,838 251,248 5-9 1,264,125 917,724 666,246 376,014 1,005,589 736,918 540,030 312,633 10-14 1,329,488 1,013,187 772,138 479,322 1,057,268 813,330 625,674 398,408 15-19 1,383,643 1,102,512 878,501 593,485 1,090,748 875,812 703,230 485,386 20-24 1,392,669 1,150,311 950,129 684,166 1,074,606 891,211 739,115 539,655 25-29 1,339,809 1,138,472 967,391 730,411 1,005,998 854,739 726,223 551,397 30-34 1,234,571 1,074,547 935,265 734,474 903,020 782,758 678,513 531,583 35-39 1,086,645 966,071 858,876 698,385 780,158 688,551 607,701 489,820 40-44 906,233 821,251 744,239 624,795 645,701 579,387 519,884 430,309 45-49 703,411 648,453 597,788 516,717 507,196 462,055 420,932 357,186 50-54 495,242 463,193 433,218 383,990 372,678 344,084 317,684 275,684 55-59 306,461 289,856 274,150 247,882 251,866 235,173 219,586 194,239 60-64 162,855 155,004 147,532 134,871 154,264 145,280 136,819 122,803 65-69 81,016 77,404 73,953 67,993 86,717 82,194 77,907 70,665 70-74 40,266 38,785 37,359 34,865 46,380 44,265 42,247 38,763 75-79 17,167 16,568 15,989 14,985 23,260 22,372 21,517 20,005 80-84 8,412 8,181 7,957 7,543 10,999 10,673 10,357 9,777 85 & over $2,450 $2,421 $2,392 $2,335 $2,659 $2,627 $2,595 $2,534 Source: Rice (1997), personal communication. The values for 3 percent were calculated as the geometric mean of the 2- and 4-percent rates, respectively, for males and females.
- Logistical Regression Results for Past Month Employment
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Table B.2: Logistical Regression Results for Past Month Employment
Dependent Variable = Earnings Past Month > 0 Coefficients and (Asymptotic Standard Errors)Males Females Reduced Model Full Model Reduced Model Full Model Intercept -1.1821**
(0.2567)-1.2499**
(0.2824)-1.5905**
(0.2149)-2.0139 **
(0.2527)Age 0.1909**
(0.0142)0.1381**
(0.0147)0.1863**
(0.0109)0.1648**
(0.0113)Age Squared -0.0028**
(0.0002)-0.0022**
(0.0002)-0.0028**
(0.0001)-0.0025**
(0.0001)Black -0.6930**
(0.0909)-0.4145**
(0.0958)-0.2316**
(0.0688)-0.1860**
(0.0688)Hispanic -0.2712**
(0.1417)0.0657
(0.1584)-0.5269**
(0.0940)-0.2109
(0.0876)Less than High School Graduate 0.1372
(0.1346)0.0851
(0.1099)High School Graduate 0.6499*
(0.1247)0.7874**
(0.0862)Some College 1.0136**
(0.1132)1.0816**
(0.1035)College Graduate 1.1207**
(0.1716)0.8987**
(0.1097)Graduate/Professional School 1.2577**
(0.1497)1.2182**
(0.1214)Married or Living Together As If 0.6314**
(0.0731)-0.3524**
(0.0509)Professional Occupation 0.1610*
(0.0839)0.5482**
(0.0475)Reside in Rural Area -0.0128
(0.0663)-0.0161
(0.0608)Children Under 13 in Household 0.0185
(0.0415)-0.3915**
(0.0259)Alcohol Dependence,
Early Drinking-0.3903**
(0.1705)-0.2741**
(0.1795)-0.2583*
(0.1836)-0.2246
(0.1881)Alcohol Dependence
Ever, Later Drinking0.1365*
(0.0717)0.1800**
(0.0768)0.1192
(0.0774)0.0905
(0.0771)Drug Dependence Ever -0.1326
(0.1887)0.0616**
(0.1910)-0.2975**
(0.1105)-0.2502**
(0.1134)Major Depression Ever -0.1034
(0.0995)0.0959
(0.1054)-0.0083
(0.0562)-0.0955
(0.0590)*Indicates significance at 0.10 level.
**Indicates significance at 0.05 level
- Ordinary Least Squares Regression Results Earnings per Hour
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Table B.3: Ordinary Least Squares Regression Results Earnings per Hour
Dependent Variable = ln (Earnings Past Month/ Hours Worked) Coefficients and (Asymptotic Standard Errors)Males Females Reduced Model Full Model Reduced Model Full Model Intercept 0.0334
(0.1201)-0.0516
(0.1415)0.2809**
(0.1027)0.2719**
(0.1023)Age 0.1138**
(0.0063)0.0810**
(0.0068)0.0985**
(0.0057)0.0686**
(0.0053)Age Squared -0.0012**
(0.0001)-0.0008**
(0.0001)-0.0011**
(0.0001)-0.0007**
(0.0001)Black -0.3160**
(0.0247)-0.1706 **
(0.0245)-0.1623**
(0.0246)-0.0618**
(0.0247)Hispanic -0.2617**
(0.0301)-0.0586*
(0.0325)-0.1737**
(0.0263)-0.0238
(0.0209)Less than High School Graduate 0.2628
(0.0588)0.1006
(0.0632)High School Graduate 0.4407**
(0.0632)0.2531**
(0.0531)Some College 0.5587**
(0.0536)0.4183**
(0.0631)College Graduate 0.7059**
(0.0671)0.5422**
(0.0618)Graduate/Professional School 0.8048 **
(0.0659)0.6524**
(0.0690)Married or Living Together As If 0.1443**
(0.0221)0.0675**
(0.0191)Professional Occupation 0.2755**
(0.0205)0.3471**
(0.0190)Reside in Rural Area -0.1595**
(0.0261)-0.0752**
(0.0247)-0.2261**
(0.0304)-0.1662**
(0.0298)Children Under 13 in Household 0.0458**
(0.0083)0.0324**
(0.0090)-0.0259**
(0.0092)-0.0133
(0.0094)Alcohol Dependence,
Early Drinking-0.1402**
(0.0485)-0.0695
(0.0460)-0.0246
(0.0864)-0.0007
(0.0822)Alcohol Dependence
Ever, Later Drinking-0.0447*
(0.0249)-0.0255
(0.0226)-0.0307
(0.0413)-0.0404
(0.0385)Drug Dependence Ever -0.0990**
(0.0480)-0.0279
(0.0457)0.0417
(0.0426)Major Depression Ever -0.0643**
(0.0294)-0.0695**
(0.0280)-0.0490
(0.0296)-0.0745**
(0.0268)*Indicates significance at 0.10 level.
**Indicates significance at 0.05 level
- Outline for Estimation of Lost Earnings Using the RAND Microsimulation Technique for the Alcohol Dependent Population
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Table B.4: Outline for Estimation of Lost Earnings Using the RAND Microsimulation Technique for the Alcohol Dependent Population
(Note that making estimates for the drug dependent population is directly parallel to the following steps). For the Non-Alcohol Dependent (non-AD) population (but including anyone drug dependent) with earnings in the past month, estimate the following regression:
(1) Ln (Y) = f(Age, Ethnicity, Rural/Urban, Children, Depression, Drug Dependence)
Where Y = earnings in the past month.
Estimate with Ordinary Least Squares
For the Alcohol Dependent (AD) population (only if alcohol dependent, whether or not start drinking by 15thbirthday)
(2) Ln (Y) = f(Age, Ethnicity, Rural/Urban, Children, Depression, Drug Dependence, Drink by 15)
Estimate with Ordinary Least Squares
Calculate the smearing coefficient for the non-AD (Sn) and AD (Sa) populations respectively. This is done by saving residuals from the respective regressions and calculating the mean of the anti-logs of the residuals.
Calculate the impact of AD:
Predicted earnings of AD population if NOT AD, given coefficients (Bn) of characteristics X in Regression (1) estimated on the non-AD population: Age, Ethnicity, Rural/Urban, Children, Depression, Drug Dependence
(3) E(Yn) = {exp(X*Bn) * Sn}
Predicted earnings of AD population if AD, given coefficients (Ba) of characteristics X in Regression (2) estimated on the AD population: Age, Ethnicity, Rural/Urban, Children, Depression, Drug Dependence, Drinnk by 15.
(4) E(Ya) = {exp(X*Ba) * Sa}
Estimated dollar impact of AD on the individual:
E(Ya) - E(Yn) = {exp(X*Ba)*Sa} - {exp(X*Bn)*Sn}
Sum these values (using appropriate sampling weights) to develop total population estimates. Adjust estimates for observations with missing data by adjusting the estimates up in proportion to the difference between the total population in the group and the weighted population from the survey.
Approach to Estimation of Losses From Excess Unemployment
A second level of analysis is necessary in order to estimate the costs of excessive unemployment among the AD population. This analysis couples selected regression results from the above analysis with the results from logistical regression analysis of unemployment and AD.
Estimate for the non-AD and AD populations, respectively:
Probability of Employment/Earnings (for males and females, respectively)
Pr(EMn) = f(Age, Ethnicity, Rural/Urban, Children, Depression, Drug Dependence)
Estimate with Logistical Regression in SAS
Calculate the expected earnings if not affected (averaging in the expectancy of being employed and the expected wage if employed) for the AD population. This value for a given individual would be equal to their probability of being employed times their expected earnings if employed, based on the product of the logistical regression prediction of probability of being employed, and the smearing adjusted OLS regression results.
Defining Earned Income (EI) as the average across all individuals, including both those with and without employment (EM) and earnings (Y) in the period being analyzed, then expected Earned Income based on the experience of the non-AD population for a given individual with characteristics X is:
E(EIn) = Pr(EMn) * E(Yn) * Sn
evaluated at their values for X, with the smearing adjustment Sn. expected Earned Income for this AD individual with characteristics X is:
E(EIa) = Pr(EMa) * E(Ya) * Sa
Next, calculate the average and total earnings for the AD population using both of these measures. The loss in Earned Income for the AD population, factoring in both the employed and unemployed is equal to the difference in these two values:
Loss = E(EIa) - E(EIn)
This dollar estimate of total loss in Earned Income can be compared to their predicted earned income E(EIa).
Part of the total loss of Earned Income was estimated above in the analysis of loss of Earnings (Y) for the employed population, and this estimate of total loss can be disaggregated into the loss associated with the impact on Earnings (Y) if employed, and the loss from reduced levels of Employment (EM).
- Productivity Losses for Victims of Crime by Type of Crime, 1992
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Table B.5: Productivity Losses for Victims of Crime by Type of Crime, 1992 Percent Due To: Productivity Losses*
(millions of dollars)Type of Crime Number
(000's)Average Work Days Lost Alcohol Abuse Drug Abuse Alcohol Abuse Drug Abuse Total Alcohol and Drug Rape 141 4.6 22.5 2.4 19 2 21 Assault 5,255 3.7 30.0 5.1 776 132 908 Robbery 1,226 4.4 3.4 27.2 24 195 220 Burglary 4,757 1.7 3.6 30.0 39 323 361 Larceny 20,312 1.7 2.8 29.6 129 1359 1,488 Auto Theft 1,959 2.7 3.5 6.8 25 48 72 Total 33,650 1,012 2,059 3,071 Source: U.S. Department of Justice (1994g), table 3.4, p. 249.
Note: Components may not sum to totals because of rounding.
*Based on average value of productivity of $133 per day.
- Number of Incarcerations and Person Years Served Due to AOD by Type of Offense and Sex, 1992
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Table B.6: Number of Incarcerations and Person Years Served Due to AOD by Type of Offense and Sex, 1992 Incarcerations Due to Offense In: Percent Offenses Due To: Person Years Served for Crime in 1992 Due to: Alcohol Abuse Drug Abuse Type of Offense Fed. and State Prison Local Jails Total Alc. Abuse Drug Abuse Males Fe-
malesMales Fe-
malesHomicide 86,685 12,370 99,055 30.0 15.8 27,725 1,991 14,602 1,049 Assault 67,082 31,808 98,891 30.0 5.1 27,679 1,988 4,706 338 Sexual Assault 76,081 15,021 91,102 22.5 5.1 20,395 102 4,623 23 Robbery 127,892 29,599 157,491 3.4 27.2 4,996 359 39,967 2,870 Burglary 100,183 47,270 147,454 3.6 30.0 4,953 356 41,272 2,964 Larceny - Theft 40,089 34,901 74,989 2.8 29.6 1,959 141 20,710 1,487 Auto Theft 18,076 12,370 30,446 3.5 6.8 994 71 1,932 139 Drug Laws 217,594 101,609 319,203 0 100.0 0 0 297,817 21,387 Receiving Stolen Property 11,890 10,603 22,493 0 15.1 0 0 3,169 228 Drunk Driving 0 38,877 38,877 100.0 0 36,272 2,605 0 0 Liquor Laws 0 0 0 100.0 0 0 0 0 0 Drunkenness, Vice, Vagrancy 0 7,510 7,510 90.0 0 6,306 453 0 0 Total 883,656 441,780 1,325,436 131,280 8,065 428,797 30,484 Source: U.S. Department of Justice (1994g), tables 6.17, 6.21, and 6.29; pp. 591-600.
Note: Components may not sum to totals because of rounding.
- Productivity Losses Due to Incarceration by Type of Offense and Sex, 1992 (millions of dollars)
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Table B.7: Productivity Losses Due to Incarceration by Type of Offense and Sex, 1992 (millions of dollars) Type of Offense Alcohol Abuse Drug Abuse Total Total Males Females Total Males Females Males Females Total Homicide 1,158 1,105 53 610 582 28 1,687 81 1,768 Assault 1,156 1,103 53 197 188 9 1,291 62 1,353 Sexual Assault 816 813 3 185 184 1 997 3 1,001 Robbery 209 199 10 1,670 1,593 77 1,792 86 1,879 Burglary 207 197 10 1,724 1,645 79 1,843 89 1,931 Larceny - Theft 82 78 4 865 826 40 904 43 947 Auto Theft 42 40 2 81 77 4 117 6 122 Drug Laws 0 0 0 12,443 11,872 571 11,872 571 12,443 Receiving Stolen Property 0 0 0 132 126 6 126 6 132 Drunk Driving 1,515 1,446 7,076 0 0 0 1,446 70 1,515 Liquor Laws 0 0 0 0 0 0 0 0 0 Drunkenness, Vice, Vagrancy 263 251 12 0 0 0 251 12 263 Total 5,449 5,233 215 17,907 17,093 814 22,326 1,030 23,356 Source: U.S. Department of Justice (1994g), tables 6.17, 6.21, and 6.29; pp. 591-600.
Note: Components may not sum to totals because of rounding.