The Human Capital Theory was brought to life by Gary S. Becker in 1964 and has ever since been widely used as an economic theory trying to explain the reasoning behind educational choices. The theory states that an individual chooses to study if the benefits of the wage exceed the tuition costs and the opportunity costs of forgone earnings. In the first-best world the decision of education is not dependent on income, since it is assumed that there are no imperfections in the capital market and everyone can borrow freely. In real life this is not the case and many low-income families face liquidity constraints when deciding whether to invest in education.
It is widely established in the literature that family income and education are correlated (Björklund and Salvanes, 2011; among others). But more challenging is it to establish exactly what mechanisms are causing this relationship. Three possibilities are commonly used as explanations:
1) Abilities are correlated through generations
2) Human capital of the parents increase children’s cognitive skills
3) Liquidity constraints that may lead low income families to under invest in education
The policy implications from each of these explanations are different. It is therefore important to delineate the significance of the three separately. This is very demanding in terms of data and the empirical evidence so far has been limited and also very ambiguous. A study by Checchi (2003) uses a panel of macro data of 108 countries to investigate the relationship between the income distribution and enrollment rates. He finds that liquidity constraints may limit access to secondary education. Furthermore, the more public resources that are invested in education the weaker the constraint is. He concludes that there is no support for the hypothesis of talented parents foster talented children.
Another study from 2003 that finds complete opposite results is by Heckman and Carneiro. They use American micro data to investigate the relationship between family income and students in higher education. They find that high-income families have a higher share of children in higher education. But by stratifying their sample by grades they do not find any significant differences between family income and children enrolled in higher education. The grades are still a function of income and they conclude that the disadvantage of being a student from a low-income family is to be found in the earlier years of schooling. In other words, it is not the liquidity constraint that prohibits educational investment of low-income families. They recommend that public educational investments could take form as a preschool program or high quality primary school to students from low-income families.
But can we really use recommendations from studies like the above for policy interventions, when data is limited and results are so ambiguous? The main obstacle when trying to investigate the mechanisms of educational choices is the lack of high quality data. And the subject of educational inequality is not only a question of distributing wealth more equally, but the right policy intervention could also increase productivity and the wealth of society as a whole. Therefore, collecting higher quality of educational data is an important task for future research – and also for the education of future generations.
Becker, GS, 1964, “Human Capital: A Theoretical and Empirical Analysis, with Reference to Education”. Chicago. University of Chicago Press.
Björklund & Salvanes, 2011 “Education and Family Background: Mechanisms and Policies”. Handbook of the Economics of Education.
Checchi, D, 2006, “The Economics of Education: Human Capital, Family Background and Inequality”. Cambridge University Press.
Carneiro PM., Heckman J., 2003 “Human Capital Policy”, IZA Discussion Paper No. 821.