Barbara M. Fraumeni Muskie School of Public Service, USM, Portland, ME & the National Bureau of Economic Research, USA Conference of European Statisticians, UNECE/OECD/Eurostat Task Force on Measuring Sustainable Development Geneva, Switzerland September 24, 2009 Construction of Human Capital Accounts in the Measurement of Sustainable Development Muskie School of Public Service Ph.D. Program in Public Policy

1 Sustainable development as development that meets “…the needs of the present without compromising the ability of future generations to meet their own needs” World Commission on Environment & Development, 1987 Muskie School of Public Service Ph.D. Program in Public Policy

2 Importance of Human Capital Role in satisfying the present and future needs of humankind Not just the impact of humans on natural resources and the environment Muskie School of Public Service Ph.D. Program in Public Policy

3 Context for Constructing Human Capital Accounts OECD consortium Jorgenson-Fraumeni human capital accounts have been constructed for Australia, Canada, China, New Zealand, Norway, Sweden and the United States “New” countries using J-F methodology will facilitate cross-country comparisons Muskie School of Public Service Ph.D. Program in Public Policy

4 Recommend that countries initially estimate only market lifetime income Muskie School of Public Service Ph.D. Program in Public Policy

5 J-F Approach Human capital is measured as lifetime income, e.g., present and future income Muskie School of Public Service Ph.D. Program in Public Policy

6 J-F Approach All of the data listed below is needed for even a market only approach By individual years of age & level of education (highest level attained or enrollment) –Population –Enrollment –Labor compensation –Survival rates (by sex and age only) Muskie School of Public Service Ph.D. Program in Public Policy

7 J-F (1992) “The Output of the Labor Income From contemporary information and data sets, assess the probabilities that persons will go to school, perform market work, and live Future wage rates (labor incomes) are assumed to increase at a specified rate Future labor incomes are discounted Muskie School of Public Service Ph.D. Program in Public Policy

8 J-F (1992) “The Output of the Education Sector” Methodology Backwards recursive Estimates dependent upon those older in the calendar year, e.g., relative future wage rates (labor incomes) come from contemporary relationships Stages dictated by data availability Muskie School of Public Service Ph.D. Program in Public Policy

9 J-F (1992) “The Output of the Education Sector” Five Stages Stage 1: No school or work, ages 0-4 Stage 2: School, but no work, ages 5-15 Stage 3: School and work, ages Stage 4: Work only, ages Stage 5: Retirement, zero income, ages 75 or older Muskie School of Public Service Ph.D. Program in Public Policy

10 J-F (1992) “The Output of the Education Sector” Equation Notation Mi: lifetime market income Nmi: lifetime nonmarket income Ymi: yearly (current) market income Ynmi: yearly (current) nonmarket income G: real rate of growth in labor income R: discount rate Sr: survival rate to one year older s: sex a: age, by single year of age, e.g., age 0, 1, 2,...74, 75+ e: highest level of education attained, by individual level of education from grade 1, 2,..., through at least one year of graduate school older: age + 1, e.g., being one year older Muskie School of Public Service Ph.D. Program in Public Policy

11 J-F (1992) “The Output of the Education Sector” Equations for Ages mi(s,a,E) = ymi(s,a,e) + sr(s,older) * mi(s,older,e) * (1+g)/(1+r) nmi(s,a,e) = ynmi(s,a,e) + sr(s,older) * nmi(s,older,e) * (1+g)/(1+r) Muskie School of Public Service Ph.D. Program in Public Policy

12 J-F (1992) “The Output of the Education Sector” Equations for Ages 0-4 mi(s,a,e) = sr(s,older) * mi(s,older,e) * (1+g)/(1+r) nmi(s,a,e) = sr(s,older) * nmi(s,older,e) * (1+g)/(1+r) Muskie School of Public Service Ph.D. Program in Public Policy

13 J-F (1992) “The Output of the Education Sector” More Equation Notation Senr: school enrollment rate Enr: grade level enrolled, by individual level of education, grade 1, 2, through at least one year of graduate school e+1: the next higher level of education completed, from grade 1, 2,..., through at most one year of graduate school Muskie School of Public Service Ph.D. Program in Public Policy

14 J-F (1992) “The Output of the Education Sector” Market Equations for Ages 5-34 mi(s,a,e) = ymi(s,a,e) + sr (s,older) * [senr(s,a,enr) * mi(s,older,e+1) + (1 - senr(s,a,enr)) * mi(s,older,e)] * (1+g)/(1+r) nmi(s,a,e) = ynmi(s,a,e) + sr (s,older) * [senr(s,a,enr) * nmi(s,older,e+1) + (1 - senr(s,a,enr)) * nmi(s,older,e)] * (1+g)/(1+r) Muskie School of Public Service Ph.D. Program in Public Policy

15 Focus on implementation of the Fraumeni simplified method Muskie School of Public Service Ph.D. Program in Public Policy

16 Muskie School of Public Service Ph.D. Program in Public Policy “Human capital accounting is simultaneously one of the easiest and most difficult exercises in empirical economics. It is easy in the sense that the statistical techniques necessary are relatively simple. On the other hand, getting the data right can be massive challenge.” Christian (2009)

17 Categorical Approach Challenges Finding data “Adjusting” the data Making reasonable assumptions Muskie School of Public Service Ph.D. Program in Public Policy

18 Categorical Approach Major Issues School (and work?) years –Match between enrollment and age categories –Progression, including assumptions –Age of enrollment Births Muskie School of Public Service Ph.D. Program in Public Policy

19 Examples of Categorical Approaches Canada In most cases individuals of a certain age are assumed to be enrolled in a specific grade determined by their current educational attainment level Individuals who are older individuals for a particular enrollment level are spread across grades Muskie School of Public Service Ph.D. Program in Public Policy

20 Examples of Categorical Approaches Canada In the U.S., students in a particular pre-college grade are typically of two different ages Muskie School of Public Service Ph.D. Program in Public Policy

21 Examples of Categorical Approaches Norway Used years left to complete education Muskie School of Public Service Ph.D. Program in Public Policy

22 Examples of Categorical Approaches China Have data on initial enrollment Used average probability of advancement to the next education level Labor income determined with Mincer equations Muskie School of Public Service Ph.D. Program in Public Policy

23 Deriving population by individual year of age is critical –B(s,yr) is the number of persons born (of age 0) in this and earlier birth years for those in the category –Pop(s,1,1) is categorical population for age category 1 (ages 0-5) and education category 1 (grade 8 or less completed) –Population(s,a,1) is population by single year of age for education category 1 (grade 8 or less completed) –Sr(s,1) is the average one-year rate of survival of individuals in age category 1 (ages 0-5) B(s,yr)  age 0 = population(s,0,1) Sr(s)*B(s, yr-1)  age 1 = population(s,1,1) Sr(s) 2* B(s, yr-2)  age 2 = population(s,2,1) Sr(s,1 )3* B(s, yr-3)  age 3 = population(s,3,1) Sr(s,1 )4* B(s, yr-4)  age 4 = population(s,4,1) Sr(s,1 )5* B(s, yr-5)  age 5 = population(s,5,1) Muskie School of Public Service Ph.D. Program in Public Policy Births

24 Issues With This Birth Imputation Survival rates are taken from the current year The survival rate from age 0 to age 1 is typically significantly different from later ages survival rates Muskie School of Public Service Ph.D. Program in Public Policy

25 Rose-colored glasses effect in the U.S. Elsewhere? Muskie School of Public Service Ph.D. Program in Public Policy

26 Country Success with J-F The current efforts indicate that J-F can be done with categorical data Data problems are being overcome Looks good moving forward Muskie School of Public Service Ph.D. Program in Public Policy