Paradox of student gender: a case study of economic education from China

Abstract

In many Chinese universities and colleges, female students outperform male students in social science subjects. This paper presents a case study, which examines gender difference in economic education in a Chinese university. We look at a sample of students from the Chinese university and find that holding constant observed student characteristics, female students on average appear to earn higher scores than male students and the gender difference is primarily driven by low achieving students. We further find that the gender difference in exam scores is not because of female students' ability, family background and other unobservable student characteristics. Instead, it is simply a result of female students exerting more effort than male students. We finally explore a wide range of possible explanations for the gender difference in diligence, but find little support for any of the explanations.

Keywords::

• college student academic performance
• gender difference
• economic education

Jel Classification::

• I21
• I23

Acknowledgements

The authors thank Geoffrey Hodgson, Yupeng Shi, Jue Zhang, Su Zhang and seminar participants at Central University of Finance and Economics and Shandong University, China, for helpful comments. The authors also thank Lingyun Zhu and Lifang Cheng for helping get access to the administrative student data and Ping Liang and Yanchao Ding for excellent research assistance in collecting the data set used in the paper. Jian Zhang gratefully acknowledges financial support from Phase III of the China National 211 projects at Central University of Finance and Economics. This work was also supported by the General Program of National Natural Science Foundation of China (No. 71273289).The authors thank two anonymous referees for comments and suggestions. All errors are our own. Corresponding authors: Jian Zhang, [email protected] and Fangbin Qiao, [email protected]. Address: 39 South College Road, Haidian District Beijing, China 100081.

Notes

^1. There are in fact a number of studies examining the effect of study time on student performance in economics courses (e.g., Schmidt, 1983; Borg, Mason, & Shapiro, 1989; Douglas & Sulock, 1995). The regression results from these studies show that with study time in the regression, male/female differences are not robust and in many cases are not statistically significant.

^2. The Central University of Finance and Economics is indeed the real name of the university, not a pseudonym.

^3. The national economic administration major is a unique programme offered by universities in China, which emphasizes studying the overall aspects and workings of a national economy. The major offers more macroeconomics courses than the economics major. Despite this, the curriculum for the national economic administration major is in fact quite similar to that for the economics major except that in the third and fourth year of their studies, students will take a number of different courses.

^4. If students know that they are participating in a survey conducted by the lecturer, then it is possible that students would over-report study time for exams to create positive impressions with the lecturer, which may benefit themselves later on by, for example, helping obtain a good recommendation letter or an opportunity of working with the lecturer on some research projects.

^5. See Supplemental Online Material for the EAI, BSCI and MCQ questionnaires (sections 1–3).

^6. From these choices, a discounting rate (k) is calculated as , where V is the present value of the delayed reward A at delay D. As k increases, the person discounts the future more steeply. Higher values of k indicate higher levels of impulsiveness and therefore, lower self-discipline. For a detailed description on the discount rate, k, and on how to estimate the value of k for each respondent, see Supplemental Online Material (section 4) and Kirby et al. (1999).

^7. We aggregate the occupations by the parents into senior professional/technical work and other work. Such categorization helps crudely to control for the possible impact of human capital of parents and family background on student performance in our later regression analyses.

^8. Unfortunately, it is not clear why the age of mothers is on average 1.5 years older for female students than for male students. This is perhaps just a statistical fluke.

^9. Correlations between the effort variables and the error term generally result in the coefficient estimation for all the other variables including the female variable being biased. The only exception to this is when the effort variables and the other variables are not correlated. However, this is not true as we later show that female students tend to put more effort into learning than male ones (Table 5).

^10. Due to the space constraint, we do not present the regression results. However, they are available upon request from the authors.

^11. Due to the space constraint, we do not present the results. However, they are available upon request from the authors.

^12. Note that to control for perceived labour market gender discrimination in the effort regressions, we interact the female dummy variable with the dummy variable for perceived labour market gender discrimination (1 means that the student thinks that the labour market discriminates female students and 0, otherwise). This is because the perception of labour market gender discrimination could only affect those female students with such perception. Female students who do not think that there is severe discrimination against females in the Chinese labour market would exert the same level of effort as male students, holding other things constant. Thus, if it is because the perception of labour market gender discrimination motivates female students to exert more effort than male students, then one would observe that the inclusion of the interaction dummy in the effort regressions would greatly reduce the coefficient associated with the female variable, resulting in the estimate of the female variable coefficient being close to zero.

^13. It is interesting to note that consistent with the literature, our results show that students that are more self-disciplined tend to spend more time in preparing for exams (Table 8, row 5 and column 6).

^14. Due to the space constraint, we do not present the results. However, they are available upon request from the authors.