Toward a Human Capital Accounting that Incorporates the Individual Growth Trajectories. M.CIVARDI Univ. Milano-Bicocca. E. ZAVARRONEUniv. IULM (Milano) . R.V.PANSINI Univ. Bocconi (Milano) . Agenda. HC and National Account
Toward a Human Capital Accounting that Incorporates the Individual Growth Trajectories
M.CIVARDI Univ. Milano-Bicocca
E. ZAVARRONEUniv. IULM (Milano)
R.V.PANSINI Univ. Bocconi (Milano)
Human capital strongly contributes to economic growth, to society and on the life standards .
It is not yet systematically measured in NA
Economic capital represents the most important input for economic growth and it is currently evaluated in the National AccountSystems (NA)
1993 UN-SNA: Human Capital is not considered
2008 UN-SNA: Human Capital is only partially
In the SNA 2008 satellite accounts of Tourism, Environment, Health and Unpaid Household Production are considered.
Recent Applications and Studies:
-volume, value, depreciation and asset lifetime data
-intangible, not bought and sold in the market
-formal, informal, vocational, education and training
- Total number of people by age, sex, highest level of educational attainment, participation in the labour force
Several approaches to estimate the quantity or book value of HC have been proposed
Es.:Aproxy for HCoften used is the number of years of education or educational spending.
Alternative approaches to measure nominal investments in education:
(Kendrick, 1976; Malizia, 1998,2009; Collesi,1999; Versace, 2009)
Expected value of future returns
(Jorgenson and Fraumeni, 1989, 1992 and successive modifications: Wei, 2008, 2010; Gu and Wong, 2010, Abraham, 2010)
Use of cost-based measures is problematic when studying consumption or asset allocation decisions because these proxies ignore the cash flow streams that HC can generate.
(Already included in existing education accounts)
(Not included in existing education accounts)
The measure of volume of the Education output (branch 93) refers to the different levels of teaching, distinguished into four main areas:
School system (divided into four levels: pre-primary education; primary education; lower secondary; upper secondary education).
University education (the production of services supplied by universities is split into two CPA classes: Research and Development, for the part related to research, and Education, for the part concerning didactic services).
Subsidiary services to education
For the School system: output method is applied
Measure of output used: the number of pupils.
For each education level changes in quality of services supplied taken into account through the “number of standard pupils” (i.e. pupils per classroom and teaching aids). The quality is therefore measured through input (classrooms and equipment) rather than on the pupils’ achievements.
The Laspeyres volume index for the whole school system is calculated.
For the University education system: quantity
Quantity indicator : the number of students per faculty and/or group of homogenous faculties (18 faculties).
Weights: the cost per student by faculty, using a methodology based on the standard cost per student.
For the University education system: quality
Quality adjustments are outcome-based.
1) The ratio between “regular students” and enrolled students.
2) The reduction of the distance between the average number of years actually spent by student to achieve the degree and the theoretical length.
Same weight assigned to both indicators
The JF model computes expected present value of future labour income for individuals by age, sex and years of education, using data for a given cohort.
The JF model includes:
– both actual market and imputed non-market labour income;
– returns to any additional schooling the individual can be expected to acquire.
Denoting the LLI (value of HC of an individual in year y, of gender s, age a and educational attainment e) by miy,s,a,e is:
It measures HCpc for a given group (by gender, education, age) as the discounted present value of expected LLIpcfor that group.
For individuals obtaining an additional year of schooling by the next year
For individuals remaining at their current educational attainment
ymi : average labour income in the current year (including both market
and nonmarket incomes)
sr,a+1 : probability of a person at age a, surviving to age a+1
senr :school enrollment rate
g : real income growth rate
r : one year period discount rate
t=month of observation; t=1,2,3,…, Tl;
Tl=12 if l=1; Tl=24 if l=2; Tl=36 if l=3
l=year of enrollment
i=interval between t-1 and t
cfujl(i): credits acquired by student j, enrolled at the lth year of degree, in interval i
uhcjl(i): increase of UHC of the student j,enrolled at the lth year of degree, at t respect to t-1.
In our proposal UHC can be estimated through a Latent Curve Model (LCM) (Tucker 1958, Rao 1958).
LCM, used for representing the structure in repeated measures data, is, at its core, a factor analysis model.
In details, we have:
In most factor analysis models the elements in L are freely estimated from the data. However, in latent curve models these elements are often fixed to predetermined values to specify a particular linear o non linear form for the growth process (Curran et al. , 2004).
Data driven choice:GompertzGrowth Curve*
(Browne, 1993; Browne &DuToit, 1991)
*Hypothesis: Given the upper limit α, time t growth rate is proportional to the difference between the maximum achievable level logarithm and the logarithm of the level achieved at time t.
UHC: estimation methods 3
The columns in matrix becomes three: one for each parameter. UHC of the first student is
The parameters can be estimated using software devoted to covariance structures (Mplus, Lisrel, and so on)
In the estimate of the value added of education for the University system the weights proposed by ISTAT researchers for quality adjustments can be replaced by the estimated UHCF (T) of faculty and/or group of homogenous faculties F.
Nl :Number of students enrolled at year l in Faculty F
N :Total number of students enrolled in FacultyF
The difference between the estimated VA before and after the quality adjustments can be considered a measure of the “loss” in the UC production.
With reference to individuals with educational attainment e> upper secondary education, the JF formula can be modified introducing a “learning under use rate” τel where :
It is well know that the Social Accounting Matrix (SAM) is a flexible schema that include data related to the production side, data related to the income distribution and to consumption expenditure. These characteristics allow to consider the SAM not only as a database and as an accounting tool, but also, in a wider sense, as a macroeconomic simulation model.
We propose a SAM specifically designed for the HC Accounting.
Under right hypothesis , the SAM can be used as a Leontief linear model and its solution brings to a matrix of multipliers which allows assessing the effects of changes of some of the variables (exogenous) on the others (endogenous) of the system.
Structural analyses of HC generation and accumulation become therefore possible.