Academic journal article Journal of Economics and Economic Education Research

Student Choice of Effort in Principles of Macroeconomics

Academic journal article Journal of Economics and Economic Education Research

Student Choice of Effort in Principles of Macroeconomics

Article excerpt

INTRODUCTION AND LITERATURE REVIEW

Student effort is recognized as an important input in education production function. Although effort is essential in the theoretical modeling of education production, the direct treatment of effort has been limited both theoretically and empirically. Student effort has been modeled in the literature by McKenzie and Staaf [1974], Wetzel [1977], Becker [1982], Becker and Rosen [1992] and Krohn and O'Connor [2005]. A limitation found in these previous studies are they are focused principally on modeling educational production and give effort limited attention and tend not to empirically estimate effort at all.

Empirical estimations are limited to studies by Wetzel [1977] and Krohn and O'Connor [2005]. These empirical studies offer limited explanations of the connection of the regressors to the utility function of the student or to the educational production function. Wetzel [1977] estimates regressions where the dependent variables are indirect measures of effort. Wetzel constructs three McKenzie and Staaf [1974] styled effort variables by dividing gain in TUCE scores by three different aptitude scores based on the SAT as a proxy for effort. Wetzel uses end of semester TUCE score and never directly observes student effort. In the Wetzel study, explanatory variables are limited to student grade expectation and hours worked as predictors of student effort. Wetzel finds student work hours has a negative impact on effort and grade expectation has a positive impact on effort. More recently, Krohn and O'Connor [2005] estimate student effort with an actual observation of effort rather than a McKenzie-Staaf proxy. However, the independent variables used to estimate effort are limited to a small vector of human capital measures, GPA, SAT and previous classes in economics. Other regressors include the pretest score and a dummy variable for gender. Krohn and O'Connor find students with higher ability study more. They also find evidence that females may in fact put forth more effort and that higher exam scores earlier in the semester lead to less effort exerted later in the semester. Overall, the collective right hand specification in this literature is thin and the development of this topic has been limited.

The purpose of this paper is to add to the existing literature by developing a more thorough specification of the model structure and provide an explicit connection of the empirical estimation of effort to the underlying student utility function. The resulting model provides a more complete perspective on the vectors determining student choice of effort. The empirical model will then be estimated using a richer list of explanatory variables than has previously appeared in the literature. The results will be used to calculate both marginal effects on post test scoring and the actual learning differentials implicitly observed in our data. This approach provides a fuller presentation of the determinants of effort in both theoretical utility maximization and the observed impact of effort determinants on learning.

MODEL

The model of student choice implicit in the literature is a tradeoff between the utility of the student's post test score (S1) and the disutility of the student's effort (E), the student's utility/disutility tradeoff. While this literature poses the problem as a utility/disutility tradeoff, the disutility of effort is a surrogate for the opportunity cost of effort in addition to any unpleasant aspect of the work itself. The disutility of effort is net of any pleasant aspect to the work itself. A student's post test score depends on the student's pretest score (S0), the rate of depreciation (d) of pretest understanding and the student's gain (G) from effort. Equation 1 shows this relationship.

1. S1 = G + (1 - d)S0

While we note the rate d above, we have no reliable measure of how knowledge prior to day one of the class depreciates across students. …

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