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24 The Benefits to Taxpayers from Increases in Students’ Educational Attainment of prisoners for that category. We assume that cost per inmate is independent of an inmate’s education level. In making these estimates, we assume that an individual contributes nothing to government revenues and costs while still in school. For example, controlling for demographics, to estimate the effects of graduating from high school rather than dropping out, we subtract the estimated public revenues and costs generated by a high school dropout from age 16 through age 79 from the corresponding estimates of public revenues and costs generated by a high school graduate from age 16 through age 79, assuming that the high school graduate neither contributed to nor drew upon public budgets at ages 17 and 18. Income received in the future is worth less than income received now, because income received now can earn interest over time. Accordingly, we consider the current value of the future benefits to increases in education. Because much of the data are for the year 2002, we discount all dollar amounts to their equivalent value in 2002. That is, we calculate the number of 2002 dollars that are equal in value to the future dollars that will result from an increase in educational attainment. We discount future benefits to 2002 at an annual average rate of 3 percent per year. We assume that, on average, people are indifferent between receiving $1.00 at any time or $1.03 one year later. In calculating the present value of the difference in government revenues and costs associated with an increase in education level, we weight the difference in each year of life by the likelihood that an individual with given age, race/ethnicity, and sex will live to that age (Arias, 2004). The education variables are jointly significant at the 0.001 level in all the regression models used to estimate the effects of education level on tax payments and payments to social support and insurance programs. See Appendix B for details. The education variables are jointly significant in all social program utilization and income models at the 0.05 level or better, except for welfare income, food stamp income, and Unemployment Insurance income for men; hospitalization for women; and SSI income of men and women over 64. For details, see Appendix C. Transformations We used either logarithmic or square-root transformations of the amounts of taxes paid and of social program costs in estimating the models of those amounts. This is customary in analyses of financial variables because the variables are generally rightskewed, as is true in the taxes paid and social program costs we examine here. Only 31 of about 43,000 full-year SIPP observations had declared negative annual income, and none collected negative social program benefits. The large majority of the observations tend to be clustered at a relatively small level, but a few observations are for very large amounts. For example, most families’ federal income taxes are less than $5,000,
Analytic Approach 25 but a very small percentage of families pay more than $50,000 in federal income taxes. The few observations with very large values can dominate the estimation, such that, although the resulting model describes the relationship between the independent variables and the average value of the dependent variable, it does not describe well the relationship between the independent variables and the value of the dependent variable in the typical case. Using logarithmic or square root transformations reduces the influence of the outliers, so that the model more accurately describes the relationships for the large majority of cases. We compared the differences between the fifth and fiftieth and the fiftieth and ninety-fifth percentiles to decide between the logarithmic and square-root transformations. When we use the logarithmic transformations to estimate the taxes that individuals with a given level of education and given characteristics will pay or the social program benefits they will obtain in each year of their life, we take antilogarithms of the projected values to convert the estimates to dollars. In doing so, we obtain projections of the median values of the taxes and benefits. When we use the square-root transformations, we square the projected values to convert the estimates to dollars. The resulting projections are close to the median. In right-skewed distributions such as those examined here, the median is less than the mean. Consequently, our approach generally underestimates the effects of education level on government revenues and costs. Critical Assumptions Throughout this analysis, we assume that the relationships we observe in the SIPP data between individuals’ education levels and government revenues and costs reflect the effects of educational attainment on earnings and, consequently, on tax payments, participation in and benefits from social support programs, and incarceration. There is powerful evidence that increases in educational attainment result in higher earnings, on average. Higher earnings translate directly into higher tax payments. As noted previously, higher earnings both limit eligibility for participation in most social support programs and are inversely related to the benefits to participants in most social support programs. Hence, it is reasonable to assume that educational attainment is related to social program costs. Finally, there is substantial evidence that higher earnings reduce the likelihood of participation in criminal activities. Nonetheless, we cannot exclude the possibility that the observed relationships between education level and social program costs and incarceration may also reflect the influence of other factors that affect both educational attainment and participation in social programs or in crime. To explore the extent to which our results are sensitive to the assumption that the observed relationships reflect the effects of education on public revenues and costs, we
Page 1 and 2: THE ARTS CHILD POLICY CIVIL JUSTICE
Page 3 and 4: The Benefits to Taxpayers from Incr
Page 5: Preface Meeting the educational dem
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Page 11: Figures 3.1. Present Value of Lifet
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Page 25: Abbreviations ADA CPS EITC FTE GED
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Page 39 and 40: Chapter Two Analytic Approach There
Page 41 and 42: Analytic Approach 15 effects of edu
Page 43 and 44: Analytic Approach 17 women employed
Page 45 and 46: Analytic Approach 19 State and Loca
Page 47 and 48: Analytic Approach 21 tion of educat
Page 49: Analytic Approach 23 The difference
Page 53 and 54: Analytic Approach 27 1997 changes g
Page 55 and 56: Chapter Three Payments for Taxes an
Page 57 and 58: Payments for Taxes and Social Progr
Page 67 and 68: Chapter Four Spending on Social Sup
Page 69 and 70: Spending on Social Support Programs
Page 99 and 100: Chapter Seven Educational Attainmen
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Educational Attainment and Public R
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Appendix A Data and Sources Data Th
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Data and Sources 89 Program Benefit
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Appendix B Estimating Tax Payments
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Estimating Tax Payments 93 Federal
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Appendix C Social Program Participa
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Social Program Participation and Co
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Appendix D Incarceration Cost Estim
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References Aos, Steve, Roxanne Lieb
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References 115 Krop, Richard, Steph
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References 117 U.S. Department of J
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