The increasing importance of empirical analysis in economics highlights the need for efficient ways to bring these skills to the classroom. R Markdown is a new technology that provides a solution by integrating writing, statistical work and computation into a single document. R Markdown benefits students and instructors by streamlining teaching, research, and collaboration. We report on our use of R Markdown in undergraduate teaching, including core courses, electives, and senior theses. We discuss the costs and benefits of adoption, and explain the advantages of R Markdown in teaching reproducibility of empirical work, avoiding time-consuming and error-prone 'cut and paste,' and facilitating a one-stop solution for importing, cleaning, manipulating, visualizing and communicating with data.
This paper compares a nonparametric generalized least squares (NPGLS) estimator to parametric feasible GLS (FGLS) and variants of heteroscedasticity robust standard error estimators (HRSE) in an applied setting. NPGLS consistently estimates the unknown scedastic function and produces more efficient parameter estimates than HRSE. We apply these various approaches for handling heteroscedasticity to data on professor rankings obtained from RateMyProfessors.com. We find that the statistical significance of key variables differs across seven versions of HRSE, leading to different conclusions, and a standard parametric approach to FGLS suffers from misspecification. NPGLS combines the virtues of both of these parametric approaches.
Framing an outcome as a loss causes individuals to expend extra effort to avoid that outcome (Tversky & Kahneman, 1991). Because classroom performance is a function of student effort in search of a higher grade, we seek to use loss aversion to encourage student effort. This field quasi-experiment endows students with all of the points in the course up front, then deducts points for each error throughout the semester. Exploiting 2 course sequences in the business school of a midwestern university, a control for domain-specific knowledge, this study examines the impact of loss aversion when controlling for the student's knowledge in a specific subject. This quasi-experiment indicates that students perform 3Ϫ4 percentage points better when controlling for student ability and domain knowledge (148 subjects). This result is significant at the 1% level in our most robust specification (p ϭ .0020). This result is confirmed by a specification including 4 courses and controlling for student characteristics (217 subjects, p ϭ .0190).
Signatories to the American College and University Presidents' Climate Commitment (ACUPCC) pledge to pursue a path toward carbon neutrality through the choice of a set of Tangible Actions (TAs). The actions can be chosen either because they will lead to reductions or because they are the easiest to achieve. By exploiting the variation in the TAs chosen by colleges, we find evidence for both of these motivations. We find evidence that schools focusing their efforts on improving energy efficiency have achieved swift reductions. Conversely, schools pledging to use green power are generally already utilizing it and therefore do not achieve additional reductions. We conclude with suggestions for improvement in the ACUPCC reporting system that would improve potential for analysis. (JEL Q01, Q40, Q56)
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