Exploring the Gender Gap on the GRE Subject Test in Economics

Hirschfeld, Mary; Moore, Robert L.; Brown, Eleanor M.

doi:10.1080/00220485.1995.10844852

Cited by 45 publications

(28 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We find in regard to both subjects that female candidates perform less well, ceteris paribus, than their male counterparts. The result for Economics is not entirely consistent with the findings of Ashworth and Evans (1999) but is in agreement with Hirschfeld, Moore, and Brown (1995), who detected a gender gap in performance using data drawn from the more advanced US Graduate Record Examinations in Economics 21 . The estimated gender effect for Economics in our study is largely mediated through an intercept shift.…”

Section: Discussionsupporting

confidence: 80%

A comparison of A‐level performance in economics and business studies: How much more difficult is economics?

Reilly

Bachan

2005

Education Economics

View full text Add to dashboard Cite

This paper uses ALIS data to compare academic performance in two subjects often viewed as relatively close substitutes for one another at A-level. The important role of GCSE achievement is confirmed for both subjects. There is evidence of strong gender effects and variation in outcomes across Examination Boards. A counterfactual exercise suggests that if the sample of Business Studies candidates had studied Economics nearly 40% of those who obtained a grade C or better in the former subject would not have done so in the latter. The opposite exercise suggests that 12% more Economics candidates would have achieved a grade C or better if they had taken Business Studies. In order to render a Business Studies A-level grade comparable to an Economics one in terms of relative difficulty, we estimate that a downward adjustment of 1.5 UCAS points should be applied to the former subject. This adjustment is lower than that suggested by correction factors based on conventional subject pair analysis for these two subjects.

show abstract

Section: Discussionsupporting

confidence: 80%

A comparison of A‐level performance in economics and business studies: How much more difficult is economics?

Reilly

Bachan

2005

Education Economics

View full text Add to dashboard Cite

show abstract

“…Common wisdom within education is that women do not do as well as men on more quantitative material that is assessed by timeconstrained, multiple-choice tests (Tobias 1992, Walstad andRobson 1997). By the end of the college experience in the US, for example, of 5815 men and 2164 women who elected to take the Graduate Record Exam (GRE) in economics between 1989 and 1993, men averaged 651 and women averaged only 603 on this timeconstrained, multiple-choice test (Hirschfeld, Moore, and Brown 1995). After adjusting for grade point average, Scholastic Aptitude Test (SAT) scores, and math background, Hirschfeld, ef al.…”

Section: The Modelmentioning

confidence: 99%

The Relationship between Multiple Choice and Essay Response Questions in Assessing Economics Understanding

Becker

Johnston

1999

Economic Record

View full text Add to dashboard Cite

Efficiency considerations have led to increased use of multiple‐choice questions to assess economics understanding at the secondary and tertiary levels throughout Australia. A multiple‐choice test would suffice if multiple‐choice and essay questions measure the same dimensions of knowledge, as suggested by least squares estimation of the relationship between these two forms of testing. We show a simultaneous equation bias inherent in least squares estimation of the relationship between these two forms. A two‐stage least squares estimation reveals no relationship, implying that these testing forms measure different dimensions of knowledge. Thus, a single form of testing economics knowledge must be avoided.

show abstract

“…Race and age are controversial variables to include in a learning model. Following Siegfried & Fels (1979) and Hirschfeld, Moore, & Brown (1995), the authors include race, gender, and age variables in the study. A number of model specifications using work experience, international student status, and concurrent hours in various combinations were considered in the preliminary stages of model specification.…”

Section: Data and Modelmentioning

confidence: 99%