Human Capital, Age Structure and Economic Growth Jes

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Human Capital, Age Structure and Economic
Growth Jesús Crespo-Cuaresma and Wolfgang Lutz
World Population Program, International
Institute for Applied Systems Analysis (IIASA)
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Education Matters

• For individual income (Micro)
• For economic development (Macro)
• Whereas at the micro case ... it is established
  beyond any reasonable doubt that there are
  tangible and measurable returns to investment in
  education, such evidence is not as consistent and
  forthcoming in the macro literature
  (Psacharopoulos and Patrinos 2002)
• Findings are dependent on education indicators
  chosen (mean years of schooling, for which age
  groups, distribution by attainment level) and
  quality (consistency across countries and time)
• For health/mortality and fertility
• Indirect effects of education on institutions and
  good governance

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Measuring Formal Education

• Education Flows Policy variable
• (Gross and Net Enrolment by Age, Repetition
  Rates)
• Education Stocks - Change very slowly due to
  great momentum
• Mean years of schooling
• Distribution by highest educational attainment
• Functional literacy (IALS, LAMP)

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Purpose of the Reconstruction

• Assessments of the returns to education at the
  aggregate (national) level require empirical
  information about the educational status of the
  adult population over some time period.
• 2. This information needs to be consistent in
  terms of the definition of educational categories
  across countries and over time.
• 3. Since the effects of educational attainment
  can be expected to differ by age (e.g. the
  education of 20-30 year olds being more important
  for economic growth than that of 65-75 year
  olds) as well as by sex, having full age detail
  for men and women can be considered a great
  asset.
• 4. Only the explicit consideration of distinct
  levels of educational attainment allows for the
  analysis of the relative importance of primary
  versus secondary or tertiary education (and
  different mixes of the three) which should be key
  to all national and international education
  policy plans.

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Previous Work

• Official data from censuses (and some sample
  surveys) as collected by UNESCO are only very
  fragmentary and scattered over time and countries
  and have the problem of changing definitions.
• Barro and Lee (1993, 1993 and 2000) undertook the
  ambitious effort to complement these data with
  the somewhat more consistent time series of
  national school enrollment data at different
  levels using perpetual inventory methods. This
  resulted in a widely used data set that gives
  mean years of schooling of the entire adult
  population 15 and 25 (by sex but without age
  detail) for 107 countries for 1960 to 2000.
• Similar independent efforts have since been made
  by Kyriacou (1991), Lau, Jamison and Louat
  (1991), Lau, Bhalla and Louat (1991), Nehru,
  Swanson and Dubey (1995), De la Fuente and
  Domenech (2001) and by Cohen and Soto (2001)
  which in many cases result in quite different
  estimates of mean years of schooling.
• None of these reconstruction efforts gives the
  desirable age detail combined with the
  distribution over different educational
  attainment groups and accounts for the fact that
  mortality differs by education.

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Our method Demographic multi-state back
projections from 2000

• We need only one reliable data point for each
  country (typically around the year 2000) which
  gives the total population by sex, 5-year age
  groups and the four attainment categories based
  on ISCED no education, primary, secondary and
  tertiary.
• We use population age structures as estimated by
  the UN Population Division in five year intervals
  for all countries in the world since 1950.
• Taking these UN estimates as a basis, our
  reconstruction effort is reduced to estimating
  the proportions in each of the four education
  categories for each 5-year age group of men and
  women.

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Our four education categories for 2000based on
ISCED, from censuses and surveys (mostly DHS)

• No education Never been to school
• Primary Some primary, complete primary,
  incomplete lower secondary
• Secondary Completed lower secondary to
  incomplete first level of tertiary
• Tertiary Completed first level of tertiary or
  higher.

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Sample Table
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When moving back along cohort lines in 5-year
intervals we take advantage of the demographic
identityP(x,t,) P(x5,t5) Deaths /-
Migrants P(x,t,e) P(x5,t5,e) Deaths (,,e)
/- Migrants (,,e) /- EducTransitions (x to
x5, t to t5,e)Necessary Assumptions

• Education differentials in mortality and
  migration
• Dealing with the open interval in highest age
  group
• Age of transition to higher educational
  categories

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Differential Mortality by Education

• We defined mortality differentials in terms of
  life expectancy at age 15 (child mortality is
  more a function of parents education).
• We carried out numerous studies for countries for
  which two reliable subsequent censuses (or public
  use samples of them) were available to assess
  differential mortality (combined with migration)
  along cohort lines.
• Despite all differentials among countries a
  rather general pattern appeared The
  differentials between the lower categories are
  smaller than to the higher.
• Our choice No education Primary 1 year
  difference
• Primary Secondary 2 years difference
• Secondary Tertiary 2 years difference

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Differential Mortality by Education
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Validation of Results(April October 2006)

• The first reconstruction results have been
  checked against much available historical
  evidence on educational attainment distributions,
  in particular
• UIS Attainment Data Set
• Earlier DHS, WFS or other surveys
• OECD and Eurostat education data base
• Numerous individual national statistical agencies
  (in personal communication)
• Whenever there was a significant discrepancy, we
  tried to identify the reason Was it a problem
  with our assumptions (mostly migration) or a
  problem with the other data (mostly definition of
  categories or representativeness of survey) and
  made appropriate adjustments in our data, if
  necessary.

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Exploiting the age dimension of human capital
1) How does the new data compare to the Barro-Lee
dataset in terms of correlation with growth?
(Sala-i-Martin, 1997)2) Do differences in
cohort education help us better explain
differences in income per capita? (Hall and
Jones, 1998)3) What is the role of age-specific
human capital in technology adoption and growth?
(Benhabib and Spiegel, 1994)
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Growth and education a robustness exercise
Are changes in educational attainment a robust
determinant of growth?A stylised growth
regressionHow does the sign, size and
significance of b depend on the set of
conditioning variables?
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Growth and education a robustness exercise
- IIASA/VID dataset versus Barro-Lee- 59
other variables in the pool of Z-variables-
Initial GDP per capita, primary school enrolment
and life expectancy as fixed variables
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Unweighted results

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Weighted results

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The contribution of human capital to income
differences
where f() is a piecewise linear
function based on average returns to schooling
worldwide.
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Income decomposition

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Human capital and technology adoption
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Human capital and technology adoption

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Human capital and technology adoption

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Conclusions
Exploiting the age structure of human capital-
we obtain variables that correlate robustly with
economic growth,- we are better able to explain
differences in income per capita across
countries,- we gain new insights to the nature
of the interaction between education and
technology adoption.
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Paths of ongoing and further research

• Exploiting the age structure of human capital in
  the time dimension panel data analysis.-
  Interaction between gender and education (If you
  educate a man, you educate a person, if you
  educate a woman you educate a family, and a whole
  nation)- Economic growth projections using
  age-structure human capital to improve
  reliability of forecasts.