Demography and Human Capital

a) Research Plan

The objectives of this project will be to more fully understand the dynamics of an improving educational composition of the population and demonstrate the long-term implications of nearterm investments in education.

Recently developed methods for projections of the population by level of education, originally a by-product of other IIASA work in population projections, have enabled the POP Program to establish a significant niche in the international community. In addition to poverty eradication, global education is now at the top of international development efforts as defined by the UN Millennium Development Goals (MDG) and new World Bank priorities. However, as was described in the Background section, neither experts in education statistics nor education economists have been able to produce appropriate methods for showing how the educational composition of the adult population changes as a consequence of specific school enrollment rates.

The demographic methods of multi-state projections, however, provide a perfect tool for this purpose. This is especially true given that education is something typically acquired early in life and thus trends in educational attainment follow strong cohort patterns. Multi-state methods were developed at IIASA in the 1970s and 1980s, but have generally not spread beyond a relatively small number of methodologically sophisticated demographers. This limited use is now poised to expand. IIASA’s methods for education projections have recently received considerable attention from the World Bank and UNESCO. UNESCO has decided to abandon its earlier method of literacy forecasting and adopt the IIASA methodology instead. As for the projection of the full distribution of educational attainment (no education, primary, secondary and tertiary) it is now likely that with funding from the World Bank and from UNESCO and in collaboration with the Washington-based Academy of Educational Development (AED), educational attainment projections for most countries in the world will be carried out.

Over the planning horizon this project will be carried out in three steps.

There will be significant synergies with the projections carried out within the Dynamics of Global Aging project, but the education projections will be deterministic (using alternative education scenarios, compared with the stochastic population forecasts). There is thus the challenging task of making the two sets of national level projections compatible. As described above, it is planned to produce these education projections in close collaboration with UNESCO, the World Bank and AED. It is already planned to publish the results in UNESCO’s 2007 Education for All report, which is likely to be very influential in the context of the MDG mid-term assessment.

Currently, there are two parallel activities in this field.

Education Back and Forward Projections (1970-2050): The first will try to improve the assumptions for the scenarios about future school enrollment changes and educational transition 138 probabilities from one state of attainment to the next. A serious and policy-relevant effort in education forecasting requires more than the application of existing multi-state models. These methods need educational transition rates as input variables, but deriving realistic values for these transition rates is far from trivial. Some major issues are: How fast can educational systems expand when you start at a low level? How quickly can the teachers be trained? How fast can the schools be built? What are the repetition rates? Within IIASA such issues have been addressed in the context of recent PDE (population-development-environment) case studies on Mozambique and Namibia. This involves separate demographic models for the population of teachers and students, which distinguish individual year school grades, in addition to the multistate model of the adult population. Similarly sophisticated analyses will be needed for applications to other countries.

Human Capital and Economy: A parallel activity during the current timeframe concerns the use the estimated data for the period 1960–2005 to carry out new econometric and other analyses about the impact of improving human capital by age on economic growth and poverty reduction in individual countries. As discussed above it can be expected that this more specific human capital database will bring new insights to this often highly contentious topic. Given that we will not only have the past time series of human capital by age and sex but also projections, the patterns of influence of human capital on economic growth estimated for the past may then be applied to the future (together with other information and assumptions) to make long-term economic growth projections.

It is too early to say if a new set of human capital projections will be performed as part of the current round of population projections. This will be decided after a review of the progress made.

Another potentially important user of this new analysis will be the IPCC, where Working Group 2 on the vulnerability to climate change is looking for quantitative ways of forecasting the resilience of specific populations. Since education is a prime source of empowerment for both individuals and societies, forecasts of human capital formation may serve this function well. It is a prime candidate for getting an analytic and quantitative handle on the difficult question of future vulnerability. This topic clearly requires further research, some of which may well be conducted in the context of population-development-environment case studies.

b) Previous population projections

• Future Human Capital: Population Projections by Level of Education by 13 World regions

Population projections by level of education have been applied by the IIASA World Population Program to many settings:

• Mauritius PDE Study (Lutz et al. 1994)
  
• Cape Verde PDE Study (Wils 1996)
  
• Cape Verde, Sudan, and Tunisia (Goujon and Wils, 1996)
  
• North Africa (Yousif et al., 1996)
  
• Jordan, Lebanon, Syria, West Bank and Gaza Strip (Goujon, 1997)
  
• Yucatan Peninsula (Goujon et al., 2000, pp.141-172)
  
• The World's Changing Human Capital Stock: Multi-State Population Projections by Educational Attainment (Lutz and Goujon, 2001)
  
• China by rural and urban area (Cao 2002, Cao 2000), by region (Toth et al., 2003).
  
• Indian States (Goujon and McNay, 2003)
  
• Goujon &,Lutz. 2004. Future human capital: Population projections by level of education. Pages 121-157 in W. Lutz, W.C. Sanderson, and S. Scherbov (eds.)
  
• Sanderson, 2004. Interactions between education and HIV: Demographic examples from Botswana. Pages 227-263 in W. Lutz, W.C. Sanderson, and S. Scherbov (eds.)
  
• Lutz, 2006. Global demographic trends, education, and health. Pages 252-268 in Interactions Between Global Change and Human Health. Vatican City: The Pontifical Academy of Sciences, Scripta Varia 106
  
• Educational Attainment in Brazil (Figoli, 2006)
• Southeast Asia (Goujon and KC, 2008. The past and future of human capital in Southeast Asia: from 1970 to 2030, Asian Population Studies 4(1), pp.31-56)

Responsible for this page: Suchitra Subramanian
Last updated: 19 Sep 2011