Barbara M. Fraumeni Muskie School of Public Service, USM, Portland, ME

Barbara M. FraumeniMuskie School of Public Service, USM, Portland, ME

& the National Bureau of Economic Research, USA

Conference of European Statisticians, UNECE/OECD/EurostatTask Force on Measuring Sustainable Development

Geneva, Switzerland September 24, 2009

Construction of Human Capital Accountsin the

Measurement of Sustainable Development

Muskie School of Public Service Ph.D. Program in Public Policy

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Sustainable development as development that meets

“…the needs of the present without compromising the ability of future

generations to meet their own needs”

World Commissionon Environment & Development, 1987


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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


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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


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Recommend that countries initially

estimate only market lifetime income


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J-F Approach

Human capital is measured as lifetime income, e.g., present and

future income


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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)


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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


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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


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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 16-34

• Stage 4: Work only, ages 35-74

• Stage 5: Retirement, zero income, ages 75 or older


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Equation Notation

Mi: lifetime market income Nmi: lifetime nonmarket incomeYmi: yearly (current) market incomeYnmi: yearly (current) nonmarket incomeG: real rate of growth in labor incomeR: discount rateSr: survival rate to one year olders: sexa: 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 schoololder: age + 1, e.g., being one year older


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Equations for Ages 35-74

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)


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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)


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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


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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)


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Focus on implementation of the Fraumeni

simplified method


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“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)

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Categorical Approach Challenges

• Finding data

• “Adjusting” the data

• Making reasonable assumptions


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Categorical Approach Major Issues

• School (and work?) years– Match between enrollment and age

categories– Progression, including assumptions– Age of enrollment

• Births


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Examples of Categorical ApproachesCanada

• 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


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Examples of Categorical ApproachesCanada

In the U.S., students in a particular pre-college grade are typically of two

different ages


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Examples of Categorical ApproachesNorway

Used years left to complete education


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Examples of Categorical ApproachesChina

• Have data on initial enrollment

• Used average probability of advancement to the next education level

• Labor income determined with Mincer equations


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• 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)


Births

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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


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Rose-colored glasses effect in the U.S.

Elsewhere?


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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


Barbara M. Fraumeni Muskie School of Public Service, USM, Portland, ME

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