Online Format vs. Live Mode of Instruction: Do Human Capital Differences or Differences in Returns to Human Capital Explain the Differences in Outcomes?

Our paper asks the question: Does mode of instruction format (live or online format) effect test scores in the principles of macroeconomics classes? Our data are from several sections of principles of macroeconomics, some in live format, some in online format, and all taught by the same instructor. We find that test scores for the online format, when corrected for sample selection bias, are four points higher than for the live format, and the difference is statistically significant. One possible explanation for this is that there was slightly higher human capital in the classes that had the online format. A Oaxaca decomposition of this difference in grades was conducted to see how much was due to human capital and how much was due to the differences in the rates of return to human capital. This analysis reveals that 25% of the difference was due to the higher human capital with the remaining 75% due to differences in the returns to human capital. It is possible that for the relatively older student with the appropriate online learning skill set, and with schedule constrains created by family and job, the online format provides them with a more productive learning environment than does the alternative traditional live class format. Also, because our data are limited to the student s academic transcript, we recommend future research include data on learning style characteristics, and the constraints formed by family and job choices.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.