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We would like to thank Andrew Feltenstein, Eric Clifton, Shanta Devarajan, Eric Thorbecke, Gerard Glomm, Kenneth Judd, Fabio Canova, and participants at the CEPR conference on Dynamic Aspects of Public Expenditures in Copenhagen for their comments. We would also like to thank George Korniotis for his research assistance.
For cross-country studies that emphasize the importance of schooling for economic growth see Barro and Lee (2000), Hanushek (1996), and Bosworth and Collins (1996). Some examples of studies that focus on connection between schooling, productivity, and earnings include Card and Krueger (1992) for the US and Behrman and Birdsall (1983) for Brazil.
In a cross country study, Judson (1998) finds that countries whose allocations are inefficient gain little in output and growth from their investments in education.
While country circumstances differ, in general in economies with less than universal basic education, most studies find that the rates of return to education are greatest for primary, followed by secondary and tertiary education (Psacharopolous (1993), World Bank (1995)).
The 17 groups represent agents with no schooling up to a maximum of 16 years of primary, secondary and tertiary education.
Ghana is used as an illustrative case and similar results can be derived by applying the model to other countries with less than universal basic education. Some parameters used in the simulations were estimated for other developing countries with characteristics similar to Ghana.
Years of schooling attainment of adults in Ghana were obtained from Barro (2000). Enrollment rates for children at different levels of education were taken from Blunch and Verner (2000).
While this assumption may be unrealistic for developing countries, particularly in sub-Saharan Africa, it is adopted for analytical tractability.
This assumption rules out the widely observed phenomenon of grade repetition in developing countries, but is adopted for analytic tractability.
Note that this is the budget constraint faced by households for i = 23,…29 and i = 45,…50. The relevant schooling decisions are made between the ages of 30 and 44, the time at which the child first starts school to the point when the child can quit school permanently.
We ignore the distortionary impact of financing higher public spending through taxes on capital or labor to in order to focus on the effects of alternative composition of government spending on demand for schooling. Moreover, Burgess and Stern (1993) note that developing countries typically rely more heavily on indirect taxes than do developed countries.
While it may be more realistic to assume that the same school fees are paid for full-time and part-time schooling, for analytical tractability, we assume that overall schooling costs are lower with part time schooling.
Canagarajah and Coulombe (2000) note that, on average, children in Ghana earn one sixth of what adults earn.
Note that the level of subsidy provided can vary with the age of the parent and the commensurate level of education of the child. Since schooling costs net of the subsidy vary across education levels, different cohorts face different environments.
These costs include school fees, books and other related education materials such as school uniforms.
This functional form is used widely both in the empirical literature and the literature on human capital accumulation. See Ben-Porath (1967) and Heckman (1999), Heckman, Lochner and Taber (1998). Glomm and Ravikumar (1998) introduce a school quality argument in the human capital accumulation equation.
The solution to the dynamic schooling problem is as follows. Working backward from T, the end of the lifecycle, the value of going to school for an additional year and the value of stopping schooling and entering the labor market can be characterized using backward recursions.
The present model implicitly assumes that the labor markets for the four types of labor are segmented. In many developing countries, it is not unusual to find underemployment, whereby more educated workers decide to enter the market for less skilled activities. While this would result in a greater “crowding out” of unskilled workers and, hence a larger differential between skilled and unskilled wages, the basic thrust of our results will continue to hold.
See Deaton (1991) for a discussion of restrictions on the subjective discount factor in economies with infinitely lived agents.
Recent empirical evidence on the value of β suggests that a subjective discount factor greater than unity is plausible (Hurd 1989).
Since education costs are fixed, we assume that when government increases its contribution to education parents pay less in form of education expenses.
In contrast with the baseline, unskilled parents do not have to deaccumulate assets (borrow) earlier in life to finance schooling for their child.
These transfers can take the form of transfers-in-kind (school lunches for the poor or directed subsidies for education in the form of free textbooks or school fees) or transfers in cash (social security and welfare payments).