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The Social Benefits of Higher Education David Bloom, Matthew Hartley, and Henry Rosovsky March 4, 2004.

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Presentation on theme: "The Social Benefits of Higher Education David Bloom, Matthew Hartley, and Henry Rosovsky March 4, 2004."— Presentation transcript:

1 The Social Benefits of Higher Education David Bloom, Matthew Hartley, and Henry Rosovsky March 4, 2004

2 State Spending Per Higher Education Student (in real 2002 dollars) Source: Websites of Chronicle of Higher Education and National Center for Education Statistics; student numbers in 1994, 2001, and 2002 are estimates.

3 The Array of Higher Education Benefits PublicPrivate Economic Social Increased Tax Revenues Greater Productivity Increased Consumption Increased Workforce Flexibility Decreased Reliance on Government Financial Support Higher Salaries and Benefits Employment Higher Savings Levels Improved Working Conditions Personal/Professional Mobility Reduced Crime Rates Increased Charitable Giving/Community Service Increased Quality of Civic Life Social Cohesion/Appreciation of Diversity Improved Ability to Adapt to and Use Technology Improved Health/Life Expectancy Improved Quality of Life for Offspring Better Consumer Decision Making Increased Personal Status More Hobbies, Leisure Activities Source: The Institute for Higher Education Policy, Reaping the Benefits: Defining the Public and Private Value of Going to College, March 1998.

4 Rate of Return A formal way to compare the immediate costs and the subsequent benefits of investment in schooling. The costs include tuition and fees and the income forgone while in school. The benefits include the higher earnings that individuals expect to earn as a result of schooling.

5 Private vs. social, in practice The private rate of return reflects the direct cost of schooling to individuals (i.e., their out-of-pocket cost). The social rate of return reflects the full cost to society of schooling, including any public subsidies. The private rate of return should be based on after-tax income, whereas the social rate of return should be based on pre-tax income, but this is not always done.

6 Typical Estimates of Returns to Education, based on 98 country studies during ( The classic pattern of falling returns to education by level of … education are maintained.) PrivateSocial Primary26.6%18.9% Secondary17.0%13.1% Higher19.0%10.8% Source: G. Psacharopoulos and H. Pastrinos, Returns to Investment in Education: A Further Update, World Bank Policy Research Working Paper 2881, September 2002 (from Table 1).

7 Inferences and concerns…. (from Psacharopoulos and Patrinos 2002) Private returns are higher than social returns where the latter is defined on the basis of private benefits but total (private plus external) costs. This is because of the public subsidization of education and the fact that typical social rate of return estimates are not able to include social benefits. Nevertheless, the degree of public subsidization increases with the level of education, which has regressive income distribution implications. There is a concern in the literature with what might be called social rates of return that include true social benefits, or externalities…. If one could include externalities, then social rates of return may well be higher than private rates of return to education.

8 Constructing Estimates of Private and Social Rates of Return to Education Requires data/assumptions about the lifetime earnings trajectories of individuals with different amounts of schooling. Requires data/assumptions about the out-of-pocket costs of schooling to individuals and to society. Requires data/assumptions about any income spillovers that result from one workers education.

9 Simulation Assumptions Workers earnings with no schooling: $40,000 per year, age 20 through 60 Workers earnings with 1 year of additional schooling: $44,000 per year, age 20 through 60 Private cost of 1 yr of schooling: $5,000 Social cost of 1 yr of schooling: $20,000 Number of workers whose earnings are increased due to one workers schooling increasing by 1 year: $1,000 Amount by which their earnings are increased: $2 per year

10 Lifetime Earnings Trajectory: Effect of One Year of Schooling 40,000 44,000 0 Age: Annual income Foregone income Workers increased earnings

11 Lifetime Earnings Trajectory: Effect of One Year of Schooling, With Direct Costs 40,000 44,000 -5,000 0 Age: Annual income Workers increased earnings Foregone income

12 Lifetime Earnings Trajectory: Effect of One Year of Schooling, With Social Costs 40,000 44, ,000 Age: Annual income Workers increased earnings Foregone income

13 Lifetime Earnings Trajectory: Effect of One Year of Schooling, With Social Costs and Income Spillovers 40,000 44, ,000 46,000 Age: Annual income Income spillovers Workers increased earnings Foregone income

14 Simulated Rates of Return No schooling cost8.5 Private schooling cost = $5, Social schooling cost = $20, Social schooling cost, and income spillover of $2,

15 Estimates of the rate of return to schooling in the US, by gender,1962 and (based on standard human capital earnings functions fit to Current Population Survey data)

16 Notes on estimates of the rate of return to schooling in the US In keeping with standard practice among labor economists, the rate of return to schooling is estimated as the coefficient on years of schooling in an ordinary least squares regression in which the dependent variable is the natural logarithm of the previous years' annual earnings. In addition to years of schooling, the regressors include years of potential work experience (defined as age minus years of schooling minus 6) and its square, and dummy variables for nonwhites and for currently married individuals. Rate of return estimates were constructed separately for males and females for 1962, and for each year during (The education variable is not usable for 1963.) The data are restricted to individuals aged who were employed full time (35 or more hours per week) and year round (50-52 weeks per year). Top-coded earnings figures are adjusted upward by 15 percent of the relevant top code. In addition, we follow the algorithm proposed by Jaeger (1997) for dealing with the change in the CPS education variable that was initiated in 1992 (David A. Jaeger. "Reconciling the old and new census bureau education questions: recommendations for researchers." Journal of Business & Economic Statistics. July Vol. 15, No. 3). The rate of return estimates may be interpreted as the percentage increase in wages that is associated, cet. par., with each additional year of schooling.

17 Some limitations of the human capital approach Evidence of a causal link that runs from schooling to income Narrow conception of the benefits of schooling –Neglects the effect of schooling on an individuals welfare, above and beyond any increment to their earnings –Neglects the effect of schooling on social welfare, beyond the income gains to the individuals who receive the schooling

18 Higher Education and Good Governance (cross-country correlations with tertiary enrollment rates; all governance indicators are scored on 6 or 10 point scales with higher values reflecting better ratings) Governance Indicator1990 (114 countries) 1995 (121 countries) Corruption in government Positive and Significant Rule of lawPositive and Significant Bureaucratic qualityPositive and Significant Ethnic tensionsPositive and Significant Repudiation of contracts by government Positive and Significant Risk of expropriationPositive and Significant Source of governance data: International Country Risk Guide (ICRG) data, as compiled and processed by Stephen Knack and the University of Maryland.

19 Variable Definitions (Source: International Country Risk Guide) Corruption in Government -- Lower scores indicate "high government officials are likely to demand special payments" and that "illegal payments are generally expected throughout lower levels of government" in the form of "bribes connected with import and export licenses, exchange controls, tax assessment, police protection, or loans." Rule of Law -- This variable "reflects the degree to which the citizens of a country are willing to accept the established institutions to make and implement laws and adjudicate disputes." Higher scores indicate: "sound political institutions, a strong court system, and provisions for an orderly succession of power." Lower scores indicate: "a tradition of depending on physical force or illegal means to settle claims." Upon changes in government new leaders "may be less likely to accept the obligations of the previous regime." Quality of the Bureaucracy -- High scores indicate "an established mechanism for recruitment and training," "autonomy from political pressure," and "strength and expertise to govern without drastic changes in policy or interruptions in government services" when governments change. Ethnic Tensions -- This variable measures the degree of tension within a country attributable to racial, nationality, or language divisions. Lower ratings are given to countries where racial and nationality tensions are high because opposing groups are intolerant and unwilling to compromise. Higher ratings are given to countries where tensions are minimal, even though such differences may still exist. Risk of Repudiation of Contracts by Government -- This indicator addresses the possibility that foreign businesses, contractors, and consultants face the risk of a modification in a contract taking the form of a repudiation, postponement, or scaling down" due to "an income drop, budget cutbacks, indigenization pressure, a change in government, or a change in government economic and social priorities." Lower scores signify "a greater likelihood that a country will modify or repudiate a contract with a foreign business." Risk of Expropriation of Private Investment -- This variables evaluates the risk "outright confiscation and forced nationalization" of property. Lower ratings "are given to countries where expropriation of private foreign investment is a likely event."

20 Higher Education and Entrepreneurial Activity The Total Entrepreneurship Activity (TEA) Index represents represents the share of adults involved in new firms or start-up activities. Individuals with higher levels of education have higher levels of entrepreneurial activity. This result holds true in a large number of countries (though the data almost entirely cover only developed countries).

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23 Proportion of WWII veterans among men by age in 1964 (Source: tabulated from the Current Population Survey)

24 Proportion of Korean War veterans among men by age in 1964 (Source: tabulated from the Current Population Survey)

25 Proportion of men with college or higher degrees by age in 1964 (Source: tabulated from the Current Population Survey)

26 Proportion of men and women with college or higher degrees by age in 1964 (Source: tabulated from the March 1964 Current Population Survey)

27 Income Spillovers Analysis Study of earnings variation among US workers who are (statistically) comparable in terms of their: –Educational attainment –Years of work experience –Gender –Race –Marital status –Industry –Occupation –State of residence –Full-time, year-round status

28 Main Result of the Income Spillovers Analysis Other things equal, workers earn more when they are located in states that have higher proportions of college graduates. Thus, not only do college graduates have relatively high productivity and earnings, they also appear to enhance the productivity and earnings of those with whom they work.

29 Income Spillovers Analysis -- Descriptive Statistics VariableDefinitionMean Whole Sample MaleFemale Hourly WageWeekly earnings divided by usual hours worked per week AgeAge in years WhiteNon-Hispanic white BlackNon-Hispanic black Hispanic OtherOther race/ethnicity Married EducationYears of education Less than high schoolLess than 12 th grade of education High schoolHigh school graduate Some collegeSome college without Bachelors degree College or higherBachelors degree or higher Percentage with college or higher degrees by statePercentage of workers with college or higher degrees in the state Percentage with graduate degrees by statePercentage of workers with graduate degrees in the state Percentage of sample from the 1982 CPS Percentage of sample from the 1992 CPS Percentage of sample from the 2002 CPS Number of observations35,53119,51216,019 NOTE: All observations have been drawn from the 1982, 1992, and 2002 March CPS. Sample includes full-time workers between the ages of 18 and 64. Weekly earnings are in 2002 current dollars, deflated by the CPI.

30 Industry and Occupation Categories Industry Categories 1 "Agriculture & Forestry" 2 "Mining" 3 "Construction" 4 "Manufacturing-Durables" 5 "Manufacturing-Nondurables" 6 "Transportation" 7 "Communication" 8 "Utilities" 9 "Wholesale Trade" 10 "Retail Trade" 11 "Finance, Insurance & Real Estate" 12 "Private Household Service" 13 "Business & Repair" 14 "Personal Service" 15 "Entertainment Service" 16 "Professional Service" 17 "Public Administration" Occupation Categories 1 "Managers & Administrators" 2 "Professional & Technical" 3 "Sales" 4 "Clerical" 5 "Private Household Service" 6 "Service exc Private Household Service" 7 "Craftsmen" 8 "Operatives exc Transport" 9 "Transport Equipment Operatives" 10 "Laborers" 11 "Farmers"

31 Income Spillovers Analysis: Log Wage Regression Results with Percentage with College or Higher Degrees by State, Whole Sample Dependent variable: log (weekly earnings). Variable(1)(2)(3)(4)(5)(6) Constant1.304** (.029) 1.011** (.031) 1.165** (.043) 1.379** (.052) 1.497** (.049) 1.696** (.056) High school.198** (.008).195** (.008).092** (.037).079** (.036).077** (.034).062* (.034) Some college.346** (.009).339** (.009).183** (.041).152** (.040).125** (.038).095** (.038) College or higher.660** (.009).638** (.009).314** (.041).256** (.041).210** (.039).149** (.039) Percentage with college or higher degrees by state1.363** (.051).733** (.124).315* (.172).879** (.116).480** (.161) Percentage with college or higher degrees by state *High school.402** (.137).450** (.135).236* (.128).288** (.127) Percentage with college or higher degrees by state *Some college.593** (.148).678** (.147).368** (.138).450** (.137) Percentage with college or higher degrees by state *College or higher 1.155** (.147) 1.348** (.147).786** (.138).981** (.137) State dummiesNo YesNoYes Industry and occupation dummiesNo Yes Adjusted R-squared NOTE: All reported regressions also include the following independent variables: age and its square, three race/ethnic dummies, a marriage dummy, a female dummy, and two year dummies. Standard errors are in parentheses. Columns 4 and 6 include fifty state dummies. Columns 5 and 6 include 16 industry and 10 occupation dummies. * Statistically significant at the.10 level. ** Statistically significant at the.05 level.

32 Variable(1)(2)(3)(4)(5)(6) Constant1.253** (.040).975** (.043) 1.069** (.057) 1.222** (.070) 1.396** (.065) 1.557** (.075) High school.193** (.011).189** (.011).182** (.049).161** (.048).166** (.046).141** (.045) Some college.319** (.012).311** (.012).220** (.055).180** (.054).180** (.052).136** (.051) College or higher.618** (.012).597** (.012).275** (.056).211** (.056).198** (.053).132** (.053) Percentage with college or higher degrees by state1.299** (.071).887** (.162).743** (.232) 1.040** (.153).837** (.217) Percentage with college or higher degrees by state *High school.038 (.181).118 (.179) (.170).010 (.168) Percentage with college or higher degrees by state *Some college.344* (.200).455** (.199).134 (.188).254 (.186) Percentage with college or higher degrees by state *College or higher 1.126** (.199) 1.331** (.198).760** (.187).969** (.186) State dummiesNo YesNoYes Industry and occupation dummiesNo Yes Adjusted R-squared Income Spillovers Analysis: Log Wage Regression Results with Percentage with College or Higher Degrees by State, year old males Dependent variable: log (weekly earnings). NOTE: All reported regressions also include the following independent variables: age and its square, three race/ethnic dummies, a marriage dummy, and two year dummies. Standard errors are in parentheses. Columns 4 and 6 include fifty state dummies. Columns 5 and 6 include 16 industry and 10 occupation dummies. * Statistically significant at the.10 level. ** Statistically significant at the.05 level.

33 Income Spillovers Analysis: Log Wage Regression Results with Percentage with College or Higher Degrees by State, year old females Dependent variable: log (weekly earnings) NOTE: All reported regressions also include the following independent variables: age and its square, three race/ethnic dummies, a marriage dummy, and two year dummies. Standard errors are in parentheses. Columns 4 and 6 include fifty state dummies. Columns 5 and 6 include 16 industry and 10 occupation dummies. * Statistically significant at the.10 level. ** Statistically significant at the.05 level.


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