Returns to Education

Overall vision and linkages to other research teams

The “Economic Returns to Education” team is identifying and measuring the effects of human capital on economic outcomes, understood in a broad sense to include not only economic growth and private returns to education, but also poverty alleviation and institutional change. Although the focus is on research questions related to economic development, policy issues that affect highly developed countries, such as the effect of differences in quality of education on economic outcomes, are also part of our broad research agenda. Inputs from the “Human Capital Data Lab” and the “Modelling Human Capital” teams are essential to our work, as are interactions with the “Education Policy & Planning” and “Education & Labour Market” teams.

  

Specific work plans for mid-2011 to mid-2013

The following ongoing and planned projects will dominate our research efforts in the next two years:

Heterogeneity, externalities and human capital

At the microeconomic level, the relationship between education, employment and income constitute the basic building blocks for measuring the effects of education on economic development. The affiliation of individuals to various subgroups of the population gives rise to heterogeneities in this relationship, whose measurement and interpretation are part of our research. Methodological frameworks for development policy that have been put forward recently, such as the World Bank’s inclusive growth analytics, emphasize the importance of such heterogeneity for identifying binding constraints in the relationship of human capital accumulation to broad-based economic growth and poverty reduction. Within-country regional differences in returns to education, as well as gender-related gaps, for instance, are particularly relevant for the design of optimal education and labor market policies to promote income growth. 

Within this research framework, we have applied econometric methods to analyze the subnational dynamics of returns to education in Uganda. We use the convergence patterns in returns to education to identify regions with well-functioning labor markets, where the supply of skilled labor matches its demand (in terms of lack of skill-mismatch), as well as regions that are characterized by labor market imperfections.

We are also assessing spatial externalities related to poverty reduction through human capital using subnational data for Kenya. We are building econometric models that explicitly account for the potential existence of spatial spillovers across regions and can thus be used to produce realistic policy simulations.

Explaining heterogeneity in returns to education at the individual and macroeconomic levels implies that the empirical assessment of differences in education quality will play a particularly important role in our further research efforts.

 

Distributional aspects related to human capital

The aggregate level of education crucially shapes social and economic welfare in industrialized countries, where human capital is vital for technology driven, sustainable development. Education is equally crucial in developing countries, where it is essential for escaping poverty. However, educational gaps between various groups exist within nearly all countries, hindering education’s welfare-enhancing effects. Allowing for the distributional dimension of educational attainment in the empirical study of social returns to education provides new insight into the mechanics and channels linking education to economic outcomes, including not only economic growth but also inequality, poverty alleviation, political instability, and democracy.

Other tasks of this broad research project are, first, to cooperate with the “Modelling Human Capital” team in improving the quality of mean years of schooling data as a measure of the average level of education attainment. Second, as an estimator of the degree of inequality in the distribution of education, we are calculating Gini Coefficients of Educational Attainment based on the WIC’s recently reconstructed dataset of populations by age, sex, and levels of education for 138 countries for 1970-2010. Third, these newly constructed measures will be used to assess the role of education inequality (and its demographic dimension) as a determinant of economic growth at the macroeconomic level, as well as other socioeconomic and political variables.

In ongoing work, we are also analyzing the relationship between education, age structure and civil conflict. The relationship between education and conflict appears to be multidimensional and complex. We hence aim at providing a comprehensive macro-level study which integrates distributional and cohort-specific aspects of education. The resulting models should provide quantitative assessments of the likelihood of civil conflict based on projections of educational attainment levels and their degree of inequality for different age groups.

 

Age structure dynamics, human capital and economic growth

At the macroeconomic level, the interaction between age dynamics and human capital accumulation has received very limited analytical attention, partly due to the lack of consistent data on age-structured educational attainment. The Human Capital Data Lab and Modelling Human Capital teams provide us with the necessary information to assess empirically the differential role of education in economic growth episodes, which have been hitherto systematically assessed in terms of “demographic dividend” effects.

The importance of model uncertainty when estimating the effects of education on economic growth has been emphasized in the recent empirical literature. We plan to expand the econometric toolkit related to model uncertainty by developing new model averaging methods for improving the assessment of the effect of human capital on economic development.