An Overview of the Literature Measuring Education Cost Differentials

Abstract

Educational cost functions are used to estimate the additional costs associated with certain student and school district characteristics. Scholars and policymakers often assert that the results of cost function analysis can be used to distribute state aid more equitably among school districts. This technique, commonly referred to as “costing out,” allows state aid to vary with characteristics over which school officials have no control, with weights equal to coefficient estimates. This article reviews the literature that estimates educational cost functions and attempts to facilitate the comparison between the studies' methodologies and results.

Notes

Scholars have also estimated education cost functions in countries other than the United States. For example, readers are encouraged to review Nguyen-Hoang (in press), which estimates an educational cost function in Vietnam. The estimates for Vietnam presented in this study are within the range of estimates produced for states within the United States.

With scholars’ focus on the distribution of state aid and the difficulty in tracing those dollars to the school level, school districts are the primary units of analysis for cost function studies, and school district characteristics are considered in determining appropriate adjustments in aid formulas.

A few states use a “power-equalizing” formula for their basic operating aid, which is a type of matching grant. In these states, the rate at which the state matches education spending varies across districts and often lowers the price of education in low-wealth districts (Yinger, 2004). The higher the rate at which a tax dollar is matched, the lower the tax price of education.

Categorical aid is designated for a specific purpose and comes with restrictions on its use.

Note that Duncombe, Lukemeyer, and Yinger (2008) and Reschovsky and Imazeki (2001) examined multiple states. Each state's estimation is referred to as a “part” of the study for distinction purposes.

^aThe study also includes instruments for the salary variables; see Table 5.

Two-stage least squares is the estimation strategy used to address endogeneity; it uses variation in instruments to identify the relationship between the endogenous variable and the dependent variable. For the case of student performance in the cost equation, an ideal instrument is a variable that is correlated with student performance in the district and uncorrelated with the district's spending.

^aThe study also includes instruments for the performance measures; see Table 3.

Hoxby (2000) found that competition from private schools is related to increases in performance within neighboring public schools, which can be interpreted as an efficiency argument.

For example, note the relatively small difference between the estimated coefficients on student performance in Imazeki (2008) and Duncombe et al. (2008) part 4.

**The variable is significant at the 5% level.

**The variable is significant at the 5% level.

**The variable is significant at the 5% level.

**The variable is significant at the 5% level.

**The variable is significant at the 5% level.

**The variable is significant at the 5% level.

Measures of inefficiency include both waste and spending on other educational objectives not included in the study. For example, if the student performance measures include scores on mathematics and English exams and graduation rates, spending on science and music programs is treated as inefficient spending. More efficient districts may waste less, or they may provide fewer educational programs.