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Journal of Comparative Economics

journal homepage: www.elsevier.com/locate/jce

Competitive experience and gender difference in risk preference,trust preference and academic performance: Evidence from Gaokaoin China

Yi Lua, Xinzheng Shi⁎,a, Songfa Zhongb

a School of Economics and Management, Tsinghua University, 100084, Beijing, ChinabDepartment of Economics, National University of Singapore, 1 Arts Link, 117570, Singapore

A R T I C L E I N F O

Keywords:CompetitionRisk preferenceTrustGender difference

JEL classification:D03J16

A B S T R A C T

This study examines whether and how competitive experience affects gender difference in thepreferences for risk and trust as well as academic performance. By utilizing the provincial dif-ferences in college admission rates as an indicator of competitive experience for students, weassess its relationship with gender difference in risk preference, trust preference, and academicperformance. We find that females from provinces with lower college admission rates are morerisk averse and less trustful, and perform better in more competitive environment, compared withtheir male counterparts. Our study suggests that observed gender differences may partially reflectthe effects of schooling environment rather than inherent gender traits.

1. Introduction

Despite significant educational advancements for women, a substantial gender gap in labor market outcomes, such as womenbeing under-represented in high-paying jobs and high-level occupations, has been commonly observed and extensively recognized(Bertrand, 2011). At the mean time, numerous studies from both behavioral and experimental economics have consistently found thatmen and women differ in a wide range of economic preferences (Croson and Gneezy, 2009). For example, compared with men,women are more risk averse (e.g., Eckel and Grossman, 2008; Dohmen et al., 2011), dislike competitive environments more (Gneezyet al., 2003; Niederle and Vesterlund, 2007; Flory et al., 2015), and are more situationally specific in social preferences (Croson andGneezy, 2009). In addition, gender differences in economic preferences have been suggested to explain gender differences in labormarket outcomes (Buser et al., 2014).

Recent studies have investigated the role of socialization in the observed gender differences. Gneezy et al. (2009) show that menopt to compete at roughly twice the rate as women in the patriarchal society of Maasai in Tanzania, which is similar to what isobserved in standard industrial societies (Niederle and Vesterlund, 2007). By contrast, women often prefer a competitive environ-ment than men in the matrilineal society of Khasi in India. This cultural reversal has been shown to develop through socializationfrom the young age (Andersen et al., 2013). A similar reversal is also observed for gender difference in spatial ability (Hoffman et al.,2011). Alesina et al. (2013) examine the historical origins of gender roles, and suggest that societies that traditionally practicedplough agriculture currently exhibit unequal gender norms. Booth and Nolen (2012a,b) observe that gender differences in riskattitude and competitiveness depend on whether the girls have attended a single-gender or mixed-gender school, suggesting theimportance of schooling environment in shaping gender differences. In this regard, Bertrand (2011) observes,

https://doi.org/10.1016/j.jce.2018.05.002Received 18 July 2017; Received in revised form 10 May 2018; Accepted 21 May 2018

⁎ Corresponding author.E-mail address: [email protected] (X. Shi).

Journal of Comparative Economics 46 (2018) 1388–1410

Available online 05 June 20180147-5967/ © 2018 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved.

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“is it also the case that women’s attitudes towards, say, risk or other-regarding preferences, have been converging over timetowards men’s? This would certainly fit with the view that the gender differences in preferences are not hard-wired but rather areflection of environmental influences, and warrant more research on the specific changes in the home or schooling environmentsthat might have triggered the convergence in attitudes.”

To further understand the role of socialization, the current study investigates the effects of competitive experience in school inshaping gender differences. In their seminal paper, Nolen-Hoeksema and Girgus (1994) observe that there are no gender differencesin depression rates in prepubescent children, but girls and women are about twice as likely to be depressed as boys and men after theage of 15. They propose a hypothesis that compared with males, stress experience leads females to display considerable withdrawaland internalizing behaviors. This hypothesis has been used extensively to explain the observation that females under stress are highlyvulnerable to depression while male under stress are more likely to develop alcohol dependence. Following this hypothesis, it is likelythat stressful experience leads females to become more risk averse and less trustful than males.

To test this hypothesis, we analyze the effects of competitive experience caused by the college entrance examinations in China ongender differences in terms of economic preference and academic performance. The college entrance examination in China is gen-erally regarded as among the most competitive exams in the world. The passing rate is relatively low compared with those of mostWestern societies. Students compete for slots in China’s nearly 2000 colleges comprising three different tiers, namely, key uni-versities, regular universities, and technical colleges. The differences among these colleges are based mostly on institutional rankingand duration of programs. Moreover, the national consensus is that studying in a superior university significantly enhances theopportunity to obtain an excellent job in China’s fiercely competitive job market (Li et al., 2012). Given that the Chinese educationalsystem is typically exam-oriented, college entrance is almost entirely determined by scores in the College Entrance Examination(CEE), commonly known as Gaokao in Mandarin. Competitiveness and possibly the Chinese culture have led most high schools todedicate the entire senior year to preparing students for CEE. Students commonly spend several hours studying after returning homefrom 10 h in school, as well as foregoing breaks even on weekends. College entrance is definitely the most stressful experiencetypically shared by high school students in China.

We utilize the differences in college admission rates across provinces in China as an empirical setup to assess the extent throughwhich competitive experience induced by college entrance affects females’ risk and trust preferences and academic performance,compared with their male counterparts. Essentially, we exploit difference-in-difference (DID) strategy, combining gender differencewithin province and cross province difference in terms of college admission rates. Using the Chinese College Students Survey (CCSS),which surveyed 8176 undergraduate students from graduating classes across the different provinces in China, we analyze the effectson risk preference toward gain-oriented and loss-oriented gambles, as well as the trust preference measured by a general trustquestion. We find that, compared to males, the competitive experience makes females become more risk averse for both gain-orientedand loss-oriented gambles, and less trustful towards people in the society. In addition, we find that competitive experience makesfemales perform better in those more competitive examines, relative to males. We further conduct several robustness checks to justifythe validity of our empirical strategy. Our results are consistent with Nolen-Hoeksema and Girgus (1994) on the effect of stressful lifeexperience on behavior. Namely, competitive and stressful experience in school leads females to become more risk averse and lesstrustful than males. We further discuss the possibility that competitive experience makes females handle competitive environmentsbetter than their male counterparts, thereby leading to better performance of the former.

Our study contributes to recent studies on competition and gender gap. In previous experimental studies, Gneezy et al. (2003)show that men were extensively more likely to perform better than women in a competitive environment.1Niederle andVesterlund (2007) observe that compared to women, men are more likely to opt for a competitive environment after controlling forperformance, overconfidence, and risk attitude. Flory et al. (2015) conduct a natural field experiment on job entry decisions andconclude that women disproportionately shy away from competitive work settings. Buser et al. (2014) show that gender differencesin competitiveness could partially account for gender differences in educational choices. Instead of examining preference towardcompetition and performance under competition, the current study investigates the effects of competitive experience on genderdifferences in economic behavior and outcomes.

Our study also contributes to the increasing literature regarding the role of culture and socialization on economic preference andoutcomes (see i.e., Guiso et al., 2006; Nunn, 2009 for review). The long-term effects of early childhood environment on later lifeoutcomes have been discussed extensively in human capital literature (Heckman, 2000; Currie, 2001). Moreover, macroeconomicexperiences, such as great depression, have been shown to affect preference for risk (Malmendier et al., 2011), preference forcorporate financial policies among managers (Malmendier et al., 2011), and preference for redistribution (Giuliano andSpilimbergo, 2014). Natural disaster experiences have also been shown to affect both risk preference (Cameron and Shah, 2015) andtime preferences (Callen, 2015). Moreover, the willing to trust can be affected by firm-level competition (Francois et al., 2010) andcan be traced back to the transatlantic and Indian Ocean slave trades (Nunn and Wantchekon, 2011). In the current study, we showthat the competitive experience of adolescents subsequently affects gender differences in economic preference and academic per-formance.

Apart from the aforementioned studies on the role of socialization, recent studies have also explored the roles of biological factorsin gender differences. In particular, biological differences between men and women, including features related with reproduction,

1 In the real-world settings, Lavy (2013) and Paserman (2010) evaluate gender differences in the performance of high school teachers and professional tennisplayers, respectively, and find minimal evidence to support the view that women turn in inferior performance in highly competitive settings.

Y. Lu et al. Journal of Comparative Economics 46 (2018) 1388–1410

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such as hormonal systems, affect the physical, psychological, and behavioral characteristics between genders. For example, themenstrual cycle has been observed to cause absence from work among women, thereby leading to gender wage gap (Ichino andMoretti, 2009), gender difference in bidding (Chen et al., 2013; Pearson and Schipper, 2013), and competitiveness (Buser, 2012). Theavailability of oral contraceptive pills increases human capital investment and labor force participation for women (Goldin andKatz, 2002). Testosterone, which drives aggression particularly among males, has also been linked to economic preferences (for areview, see Apicella et al., 2014). These studies generally suggest that despite the possible role of biological factors, socialization cantrump these influences in shaping gender differences.

Our study extends the literature focusing on China. Zhang (2018) finds that exogenously imposed marriage reforms in Chinaincrease female competitive inclination but do not eliminate the gender gap. Relatedly, Cameron et al. (2013) show that China’s One-Child Policy (OCP) has produced significantly less trusting, less trustworthy, more risk-averse, less competitive, more pessimistic, andless conscientious individuals. Through an experiment conduced in China, Cassar et al. (2016) find that gender gap in competi-tiveness disappears once incentives are switched from monetary to child-benefiting. Booth et al. (2018) find that exposed to dramaticchanges in socio-economic institutions in China after 1949, Beijing women are more competitive compared with their male coun-terparts as well as Taipei women. Cai et al. (2018) show that females tend to underperform during the college entrance exam inChina, compared to male counterparts. Our paper extends this line of research by linking experiencing college entrance examination,which is an important experience for many people in China, with gender gap in risk and trust preferences and academic performance.

The rest of this paper is divided into the following sections. Section 2 provides background knowledge on college admission inChina. Section 3 describes the data used in this study. Section 4 details the empirical strategy we used. Section 5 presents theempirical results. Section 6 concludes this paper.

2. Background: college admission in China

In order to get into colleges in China, high school graduates need to take a national standardized examination, called the CEE (orGaokao).2 The total score students get in the CEE is the most important factor determining college admission.3 Attending collegesespecially elite colleges can bring high returns in China (Zhang et al., 2005; Li et al., 2012), therefore students begin their pre-parations for the CEE as early as junior high school or primary school in order to get high scores in the CEE. Because students treatperforming well in the CEE as the only goal and they try all their best to obtain high scores, the CEE scores are considered as goodmeasures of student ability and intelligence in China.4

The allocation of college admission quota among provinces is centralized. The Ministry of Education decides how to allocate theadmission quota. However, the criteria or the mechanism used by the Ministry of Education is never publicly available. We run aregression to link provincial admission rates with provincial variables (results are shown in Appendix Table A2) and find that thenumber of high school graduates is significantly correlated with admission rates. Besides, some sporadic information also indicatesthat the number of high school graduates is an important determinant of admission rates.5

In the Chinese college admission, colleges are categorized into different tiers; those in the high tier have the first priority to admitstudents. Students rank their college preferences (4–6 in each tier) and majors in an application form. Students in different provincescan submit their application form in different timing, prior to the exam, after the exam but prior to knowing the scores, or afterknowing the scores. The prevailing admission procedure in most provinces is similar to the Boston mechanism. In the first round,students are only considered by colleges which are listed as their first choice. Colleges only accept students with a total CEE scoreabove the threshold score. Applicants with a score below the threshold are rejected by their first choice and placed in a pool for thecollege next on their list of options. A college only considers admitting students who do not list it as the first choice if there are stillavailable slots after the first round of admission. Given the limited number of colleges, students usually have few chances to beadmitted by their second-choice colleges if the first-choice colleges do not admit them. The selection process is terminated after astudent is admitted, which indicates that a student can obtain only one admission offer. As a final step of the college admission, astudent decides whether to attend the college admitting him/her or not. Because each student can obtain only one admission offer,the student will not go to any college that year if he/she turns down an offer.6

Although the number of higher education institutions, as well as new students enrolled, has substantially increased since 1978, asignificant number of young people still cannot enroll in higher education institutions. In addition, although the admission rate isincreasing every year (from 4.8% in 1977, to 37% in 1995, to approximately 75% in 2012),7 such percentage remains fairly lowcompared to those in Western countries and cannot meet the demands of students. More importantly, enrolling in leading universitieswould generally increase the opportunity of success in both the labor and marriage markets. By contrast, less than 10% of candidatescan be enrolled in tier-1 universities, and less than 0.2% of Gaokao takers will be accepted in China’s top five universities based on areport by the Economist. The importance and competitiveness of college entrance examination exert enormous pressure on high

2 See Davey et al. (2007) for a detailed description of the CEE system in China.3 Applicants to some special programs, such as art departments (audition), military and police schools (political screening and physical exam) and several sports

programs (tryout), are screened using additional criteria.4 Li et al. (2012) use the CEE score as a proxy for students’ ability.5 E.g., see the news report: http://www.moe.gov.cn/jyb_xwfb/s271/201705/t20170510_304227.html , http://news.xinhuanet.com/yuqing/2016-05/15/c_

128983170.htm.6 See Chen and Kesten (2017) for detailed analysis for Chinese college admissions.7 Statistical Yearbook, 2013.


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http://www.moe.gov.cn/jyb_xwfb/s271/201705/t20170510_304227.html

http://news.xinhuanet.com/yuqing/2016-05/15/c_128983170.htm

http://news.xinhuanet.com/yuqing/2016-05/15/c_128983170.htm

school students, as well as on their parents and teachers. For example, the China Daily reported that several girls took contraceptivesor received injections to prevent the onset of their menstrual cycle during the week of the exam, while a few students studied in ahospital while hooked up to oxygen tanks in hopes of improving their concentration. Therefore, the highly competitive nature ofcollege entrance examinations in China provides an ideal setup to study the effects of competitive experience on gender difference.

3. Data

The data that we use are derived from the second round of the Chinese College Students Survey (CCSS), which was conducted bythe China Data Center of Tsinghua University in May and June 2011. This survey was conducted from 2010 to 2014. The survey usedthe method of stratified random sampling (with locations (Beijing, Shanghai, Tianjin, Northeastern China, Eastern China, CentralChina, and Western China) and different tiers of colleges as stratifying variables) to sample colleges.8 In the first round of the survey,19 out of 2305 colleges in China were randomly selected as a pretest; the number was expanded to 50 colleges in 2011 and 2012, and65 colleges in 2013 and 2014. In each college, approximately 300 students from the population of graduating classes were randomlysampled.

The questionnaire was designed collaboratively by experts in economics, sociology, and education. In the questionnaire, basicinformation, such as individual characteristics and family backgrounds, is collected. The questionnaire also collects information onCEE scores, college activities, and student placement after graduation. The survey administrators were trained in Beijing for severaldays through intensive meetings before the survey was conducted. The survey administrators in each collage asked the sampledstudents to complete the questionnaires. They collected the questionnaire forms and placed the forms inside coded envelopes toguarantee anonymity. The survey team closely monitored the whole survey process to make sure that the survey was conductedcarefully.

In this study, we focus on data collected in 2011 because it is the only year when the questions on students’ preferences wereincluded in the questionnaire. The sampled colleges are located in 24 provinces. A total of 8176 undergraduate students fromgraduating classes from all 31 provinces in China were selected. Table 1 shows several pre-college characteristics of these students. Atotal of 45% are female. Their average CEE score is 0.226.9 On average, 25% of their parents have college degrees, 13% are cadres,and 28% work in the public sector. A total of 43% entered magnet classes in high school. Generally, these students performed well inhigh school. A total of 16% won awards in several city-level or above competitions, and 75% had GPAs in the top 20% level in highschool.

Our main independent variable is competitiveness experience, which is measured by the provincial college admission rate in 2007when the college graduates in 2011 took the CEE. College admission rate is calculated as the ratio of college admission to the numberof students taking the CEE. We obtain the data from three sources. Firstly, we obtain college admission rates of 19 provinces fromChina Education Examinations Yearbook (2008).10 Then, we obtain college admission rates of 9 provinces from the China GaokaoInformation website.11 For the remaining three provinces (Hubei, Heilongjiang, and Ningxia), we search the news report to obtaincollege admission rates.12 Table A1 lists the detailed information. Table 1 shows that the average value of the competition mea-surement, that is, one minus college admission rate, is 0.374; its variation across different provinces is large and the standarddeviation is 0.121.

We investigate two sets of outcome variables in this study. The first set is on economic preferences, including preference towardrisk and trust. We measure risk preferences over gains and losses (Kahneman and Tversky, 1979). Risk preference over gain-orientedgamble is measured by the following question: Would you rather (a) gain 1000 Yuan with certainty, (b) gain 2000 Yuan with 50%,and nothing with 50%? Option 1 (a), option 2 (b), and option 3 (a) and (b) are indifferent. Risk preference over gain-oriented gambleindexed with “Risk (gain)” is equal to 1 if the answer is option 1; 2 if the answer is option 3; and 3 if the answer is option 2, whichindicate risk aversion, risk neutrality, and risk affinity, respectively. Risk preference over loss-oriented gamble is measured by thefollowing question: Would you rather (a) lose 1000 Yuan with certainty, (b) lose 2000 Yuan with 50%, and no loss with 50%? Option1 (a), option 2 (b), option 3 (a) and (b) are indifferent. Correspondingly, the variable “Risk (loss)” is equal to 1 if the answer is option1; 2 if the answer is option 3; and 3 if the answer is option 2. Similar hypothetical questions are used in Kahneman andTversky (1979), as they argue “the method of hypothetical choices emerges as the simplest procedure by which a large number oftheoretical questions can be investigated”. Recently, Dohmen et al. (2011) compare various measurements of risk preference andtheir economic consequences, and observe that hypothetical lottery questions can predict actual risk taking behavior such as financialinvestment. Table 1 shows that the average value for “Risk (gain)” is 1.895 and the average value for “Risk (loss)” is 2.227. Theseresults suggest that the students are risk averse over gain-oriented gamble and risk seeking over loss-oriented gamble, therebydemonstrating consistency with experimental observations using real incentives.

8 In the sampling process, three metropolises (i.e., Beijing, Shanghai, and Tianjin) were separated from the rest of China because colleges, particularly the topuniversities, are concentrated on these cities.9 The CEE scores are normalized using mean and standard deviation in the province where the student took the examination.10 The yearbook only includes the information for 19 provinces: Shanghai, Liaoning, Beijing, Chongqing, Guangdong, Shandong, Jiangxi, Hunan, Guangxi, Hebei,

Henan, Anhui, Shanxi, Gansu, Tianjin, Jiangsu, Sichuan, Shaanxi, and Yunan.11 http://gaokao.chsi.com.cn/gkxx/zhiding/200709/20070905/1113417.html. These provinces include Hainan, Zhejiang, Jilin, Tibet, Qinghai, Fujian,

Neimenggu, Xinjiang, and Guizhou.12 We get college admission rates for Hubei, Heilongjiang and Ningxia from http://gaokao.eol.cn/dongtai_5640/20070618/t20070618_238318.shtml , http://zqb.

cyol.com/content/2007-05/15/content_1759511.htm , and http://edu.qq.com/a/20070620/000158.htm , respectively.


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http://gaokao.chsi.com.cn/gkxx/zhiding/200709/20070905/1113417.html

http://gaokao.eol.cn/dongtai_5640/20070618/t20070618_238318.shtml

http://zqb.cyol.com/content/2007-05/15/content_1759511.htm


http://edu.qq.com/a/20070620/000158.htm

To measure trust preference, students are asked “Do you agree with the following statement: ‘Generally, you can trust people insociety.’ Option 1, Strongly Disagree; option 2, Disagree; option 3, Agree; and option 4, Strongly Agree.” This question is known asthe general trust question and has been used extensively to measure the willingness to trust, particularly in the General Social Surveyand World Values Survey, which has been shown to be predictive for actual economic outcomes such as financial market partici-pation (Guiso et al., 2008; 2013). Moreover, Francois et al. (2010) show that firm-level competition is shown to have a positiveimpact on trust at the individual level using the World Values Survey. Table 1 shows that the average value of trust is 2.154,suggesting that students on average tend to disagree that they can trust people in society.

The second set of variables is on the academic performance of students in college, including their GPA; an indicator of whether thestudent had failed in any course; an indicator of whether the student had won any awards in the provincial level or above; and theirscores in the College English Level 4 Test (CET4), a national examination on English proficiency for college students. Table 1 showsthe summary statistics of these variables. The average GPA in college is approximately 3 out of 4. A total of 41% of the students hadfailed in at least one course. Meanwhile, 14% had won awards in the provincial level or above. Their average CET4 score is 467 (outof 710).13

Table 1Summary statistics.

Variables Mean S.D.Female 0.446 0.497Minority 0.074 0.262Competition 0.374 0.121CEE scores 0.226 0.623Parent has a college degree 0.251 0.434Parent is a cadre 0.133 0.340Parent works in public sector 0.283 0.450Enrolled in magnet classes in middle school 0.367 0.482Win in competition in middle school 0.215 0.411GPA in top 20% in middle school 0.861 0.346Enrolled in magnet classes in high school 0.430 0.495Win in competition in high school 0.162 0.369GPA in top 20% in high school 0.748 0.434GPA in college 3.038 0.489Have Failed in Courses 0.405 0.491Award in Provincial Level or above 0.142 0.349CET4 Scores 467.027 56.277Trust 2.154 0.646Loss 2.227 0.791Gain 1.895 0.871OBS 8176

Note: Competition is measured by one minus college enrollment rate in each province. The values of “Trust”come from answers to the question “Do you agree with the following statement: ‘Generally, you can trustpeople in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with50%. 1. (a); 2. (b); 3. (a) and (b) are indifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and3 if the answer is 2. The values of “Gain” come from the question “You would rather (a) Gain 1000 yuan withcertainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2.

Table 2Balancing test using 2005 population mini-census.

(1) (2) (3) (4) (5) (6) (7)Minority Schooling years Married Migrant Having non-agricultural hukou Healthy Ln(income+1)

Female 0.004 −0.408*** 0.018* −0.002 −0.002 0.008*** −1.268***(0.010) (0.096) (0.010) (0.005) (0.009) (0.002) (0.257)

Female*Competition −0.019 0.096 0.024 0.002 0.004 −0.001 0.868(0.032) (0.241) (0.024) (0.012) (0.024) (0.006) (0.774)

Province fixed effects Yes Yes Yes Yes Yes Yes YesConstant 0.013*** 10.553*** 0.724*** 0.374*** 0.682*** 0.985*** 4.610***

(0.003) (0.033) (0.003) (0.001) (0.003) (0.001) (0.076)Observations 1,828,689 1,828,689 1,828,689 1,828,689 1,828,689 1,828,689 1,828,689R-squared 0.245 0.059 0.008 0.102 0.080 0.003 0.035

Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%.Competition is measured by one minus college enrollment rate in each province.

13 A score of 425 is necessary to pass this test.


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Table3

Balanc

ingtest.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Parent

Stud

ents’p

erform

ance

inmiddlescho

olStud

ents’p

erform

ance

inhigh

scho

olMinority

College

Cad

reWorkin

public

sector

Mag

netclass

Win

inco

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GPA

intop

20%

Mag

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Win

inco

mpe

tition

GPA

inTo

p20

%Ln

(#of

Stud

ents

having

CEE

Scores

inthesamede

cile)

Female

−0.01

30.02

50.00

3−0.00

10.03

4−0.04

9*0.01

90.01

2−

0.05

30.05

3*0.03

2(0.019

)(0.055

)(0.033

)(0.045

)(0.034

)(0.025

)(0.032

)(0.030

)(0.035

)(0.027

)(0.223

)Fe

male*Com

petition

0.08

60.09

40.07

80.16

9−

0.04

40.07

1−

0.01

70.04

60.09

8−0.08

4−

0.25

5(0.067

)(0.136

)(0.087

)(0.120

)(0.090

)(0.064

)(0.088

)(0.068

)(0.089

)(0.064

)(0.531

)CEE

Scores

−0.00

60.02

3**

0.01

30.01

9*0.07

0***

0.01

60.06

9***

0.03

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7***

0.39

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(0.009

)(0.010

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)(0.011

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)(0.010

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)Con

stan

t0.06

7***

0.20

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0.11

7***

0.27

2***

0.41

4***

0.15

6***

0.74

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0.34

8***

0.20

5***

0.86

0***

2.08

9***

(0.004

)(0.007

)(0.005

)(0.006

)(0.005

)(0.005

)(0.005

)(0.006

)(0.006

)(0.004

)(0.051

)Observa

tion

s66

4962

1266

7966

7966

4065

4566

5066

3665

5366

2931

1R-squ

ared

0.15

20.04

70.02

80.04

70.05

70.03

30.03

10.04

40.03

70.03

20.20

5

Rob

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4. Estimation strategy

To identify the effects of competitive experience (generated by the college entrance) on individuals’ preferences and performance,we compare individuals across provinces with different college admission rates. Two empirical challenges are present; that is, sampleselection issue and heterogeneity across provinces.

As an illustration, start with a case that there is a random assignment of college admission rates across provinces. The rando-mization ensures that the college admission rate is not correlated with any other provincial characteristics. However, the differentadmission rates across provinces could generate a sample selection issue; that is, our sample consists of students admitted to collegesand different admission rates select different quantiles of students even from the same population. For example, provinces with loweradmission rates, compared with those with higher admission rates, could have admitted exceptional students. Consequently, thedifferences in individuals’ preferences and performance across provinces may not be caused by competitive experience but simplyreflect the selection caused by the different admission rates. A unique feature of our research setting is that the selection of collegeadmission is primarily based on the CEE score. This one-dimensional selection rule enables us to avoid the sample selection issue bycomparing individuals with the same CEE scores across provinces. This strategy resembles the approach used by Lee (2009) in thesetting of randomized experiment. As Lee (2009) does not know the exact selection rule in his research setting, he examines the worstand the best selection scenarios to pin down the bounds on treatment effects.

In the reality, however, the distribution of provincial college admission rates in 2007, our regressor of interest to determine thedifferent competitive pressures experienced by different individuals, is unlikely to be random. To overcome this identification issue,we focus on the competitive experience effect on gender difference in preferences and performance.14 This DID style strategy enablesus to include province-fixed effects in the regressions, which effectively controls for all differences across provinces in the cross-sectional data such as preferences over gender, economic development stage, geographic features, etc.

Our regression specification is then

= + + + +y βCompetition Female αFemale CEE λ· ɛ ,ip p ip ip ip p ip (1)

where yip are the preferences and performance measures of individual i taking the CEE in province p; Femaleip equals 1 if individual i isa female and 0 otherwise; CEEip is individual i’s CEE score; λp is a set of province fixed effects, capturing all differences acrossprovinces such as preferences over gender, economic development stage, geographic features, etc.; and εip is the error term. Wecalculate the standard errors by clustering over the province level to deal with any potential heteroskadasticity problem.

Competitionp is our key regressor, which measures the college admission rate in province p. Specifically, it is measured as 1-collegeadmission rate; hence, the higher value is Competitionp, the more competitive is the college admission. In the baseline analysis, we usethe rate in 2007, the year when our concerned individuals took the CEE.15

4.1. Threats to the identification and balancing tests

The identifying assumption underlying our estimation framework (1) is that there is no gender differential response to otherprovincial characteristics, conditional on province fixed effects and CEE scores. In other words, conditional on province fixed effectsand CEE scores, gender differentials are balanced across provinces with different college admission rates before taking CEE. Hence, expost different gender differentials in preferences and performance across provinces can then be attributed to different degrees ofcompetitive pressures experienced.

To check whether this balancing has been achieved in our estimation framework, it is first important to understand what de-termined the different college admission rates across provinces, and then we can condition out these confounding factors. To this end,we regress Competitionp on various provincial characteristics in Appendix Table A2, including population, GDP per capita, industryoutput composition, average labor income, government income, trade, educational attainments, ratio of government expenditure oneducation, numbers of colleges and college students, and numbers of high schools and high school students. Except for the number ofhigh school graduates, all these potential determinants are highly insignificant.16 In robustness checks, we include the interactionbetween Femaleip and the number of high school graduates as an additional control to insolate the competitive experience effects.

To lend further support to our identifying assumption, we use the micro-level data from the 2005 Chinese population census tocheck whether there is no gender differential response to other provincial characteristics across provinces with different collegeadmission rates two years before the CEE used in our setting. Specifically, we examine ethnicity, years of schooling, marriage status,percentage of migrants, household registration status, health condition, and income. Estimation results are reported in Table 2.

14 Azmat et al. (2016) investigate the gender difference in response to increased incentives using the similar strategy, comparing gender difference of test scores intests with different stakes. Cai et al. (2018) study how female and male examination performance are differentially affected by examinations with different competitivepressure by comparing difference in test scores in mock examination (lower pressure) and college entrance examination (higher pressure) between male and femalestudents.15 To capture the possibility that students may face the competitive pressure of the CEE during the whole senior high school period, we also experiment with the

average college admission rate from 2006 to 2007. It is better to use the average college admission rate from 2005 to 2007 since students spend three years in highschools in China; however, provincial college admission rates in 2005 are not available.16 One might be interested to see the insignificant coefficient of GDP per capita which is not consistent with the observation that it is easier to get into colleges in

cities with higher GDP per capita like Beijing or Shanghai. One potential reason is that these cities might also have more high school graduates, which leads to highercollege admission rates in these cities.


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Through all the specifications, we consistently do not find any statistically and economically significant estimates, implying thatgender differentials are balanced across provinces with different college admission rates two years before taking the CEE.17

Furthermore, we conduct analyses following the idea proposed by Lee and Lemieux (2010).18 First, we check whether the genderdifference in pre-determined socioeconomic characteristics and density in college entrance exam scores is similar across provinces.Second, we examine whether gender difference in individual performance in their middle school periods when there is no CEE issimilar across provinces. No significant gender difference in pre-determined characteristics and performance just a few years beforethe CEE may indicate no significant gender different responses to other provincial characteristics, justifying our empirical strategy.

Table 3 presents the regression results. In column 1, we consider whether an individual is a minority, and the coefficient ofCompetitionp · Femaleip is statistically insignificant and small in magnitude, suggesting that the gender difference in ethnicity arebalanced across provinces. In columns 2–4, we examine the balancing of parental characteristics—specifically, whether they havecollege degrees, whether they are government cadres, and whether they work in public sector. Consistently, we find that our re-gressor of interest is statistically insignificant and has small magnitudes, suggesting that there is no selection by students’ parents suchas moving into better provinces.

In columns 5–7, we examine whether there were any selections due to the admission into middle schools. Specifically, we look atduring the middle school period, whether individuals were enrolled in magnet classes, whether they won any competitions at theprovince level or above, and whether their GPAs were at the top 20% level. We continue to find no statistically and economicallysignificant gender differences across provinces in all these outcomes. These results indicate that there may not be significant selectiondue to the high school admission. We then check the balancing among individuals’ performance during their senior high school tofurther examine the selection due to the high school admission and whether there were any changes caused by the competitiveexperience during the high school entrance exam. Towards this end, we look at during the high school period, whether individualswere enrolled in magnet classes, whether they won any competitions at the province level or above, and whether their GPAs were atthe top 20% level. As shown in columns 8–10, there is no statistically and economically significant gender difference across provincesin all these outcomes. These results suggest that genders were quite balanced during the high school period. Lastly, we check whetherthere is any discontinuity in the density function of college entrance exam scores across provinces. McCrary (2008) argues that amixture of discontinuities in individual characteristics resulted from selection would further imply a discontinuity in the densitydistribution. Following this argument, we check and find that there is no gender difference in the number of individuals obtainingsame CEE scores across provinces in column 11.

In summary, these analyses largely demonstrate that there is no significant selection and genders were balanced across provincesbefore the CEE, which implies that our research design is generally valid.

5. Empirical findings

5.1. Main results

5.1.1. PreferencesWe start with presenting the results on economic preferences in Table 4. We use the ordered Logit model to capture the categorical

nature of our outcome variables. However, the results from the linear probability model are qualitatively similar and available uponrequest.

In columns 1 and 2, we consider the effects on risk preference—specifically, two measures solicited from questions on losing andgaining money, respectively. In a world without any competitive experiences, females are similar as males in their risk seekingtowards losing and gaining money. The coefficients of Competitionp · Femaleip are both negative and significant. These results suggestthat the competitive experience enlarges the gender gap in risk preference. Specifically, relative to males, females become more riskaverse towards both gain-oriented and loss-oriented gambles after they experience competition in the college entrance. In column 3,we investigate the effects of competitive experience on preference towards trust. The coefficient of Female is positive and statisticallysignificant, implying that in a world with 100% college admission (hence free of competitive experience), females are more willing totrust than males. Regarding our central interest, we find the coefficient of Competitionp · Femaleip to be negative and statisticallysignificant. This suggests that the competitive experience makes the gender gap in trust to become considerably narrow. In particular,at the admission rate of 48%, females and males become equally trustful.19

5.1.2. PerformanceIn Table 5, we examine a number of performance indicators by individuals during their four-year college period. Specifically, we

look at the GPA (in log), whether an individual had failed any courses, whether an individual had won any awards at the provincelevel or above, and the CET4 score.

17 One caveat we need to bear in mind is that the balanced gender difference in demographic and household characteristics across provinces could still not rule outthat gender difference in risk attitudes and trust might differ across provinces. Our check can be considered as checking necessary conditions.18 Lee and Lemieux (2010) argue that in the regression discontinuity setting, testing whether the pre-determined variables are balanced at the threshold is a way to

check the validity of local randomness.19 As trust preference is often linked to risk preference, the observed effect on trust preference could be due to the effect on risk preference. To examine this

possibility, we include risk preference in the analysis, and we find that the magnitude of coefficient of Competitionp · Femaleip remains the same (results available uponrequest).


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We consistently find that in a world without any competitive experiences, females perform better than males in the colle-ges—specifically, they have 9.2% higher GPAs and 29.1% less likely to fail courses. However, we do not find that competitiveexperience reduces the gender gap in GPAs, likelihood of failing courses and CET4 scores, but the gender gap in winning provincial ornational awards is enlarged by the competitive experience. Given that the former two outcomes pertain to competition within acollege and CET4 is an English exam, it is much more competitive to win provincial or national awards. These results suggest that thecompetitive experience improves females’ performance in more competitive environment relative to males.

5.1.3. SummaryWe find that competitive experience causes females to become more risk averse, less trustful, and have better performance in

more competitive environments, compared with male counterparts. In the next subsection, we first present several robustness checksto substantiate our findings. Thereafter, we provide an explanation along the psychology literature in Section 5.3.

5.2. Robustness checks

We conduct a battery of robustness checks on our aforementioned results in this subsection. All regression results are contained inthe Appendix.

Inclusion of pre-determined characteristics. As the first check, we follow the suggestion by Lee and Lemieux (2010) by including pre-determined characteristics: if our research design is valid, including these controls should have little effect on our estimates. Re-gression results are reported in Tables A3 and A4 for economic preference and academic performance, respectively. Indeed, we findour estimates barely change in both statistical significance and magnitudes, further implying the validity of our research design.

Inclusion of provincial variables. To condition out the effects from other provincial characteristics which determines college ad-mission rates (i.e., the number of high school graduates, as shown in Table A2), we further control for the interaction of the number of

Table 4Gender difference in the impacts of experiencing competition on preference: ordered Logit.

(1) (2) (3)Loss Gain Trust

Female 0.201 −0.136 0.630***(0.131) (0.136) (0.123)

Female*Competition −0.676* −0.615* −1.222***(0.346) (0.318) (0.315)

CEE Scores 0.031 0.019 0.066(0.032) (0.032) (0.059)

Observations 5748 5768 5866

Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; ***significant at 1%; Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question“Do you agree with the following statement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3.Agree; 4. Very Agree.” The values of “Loss” come from the question “You would rather (a) Lose 1000 yuan with certainty; (b)Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) are indifferent.” “Loss” is equal to 1 if the answer is 1, 2 ifthe answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “You would rather (a) Gain 1000 yuan withcertainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” is equal to 1 if theanswer is 1, 2 if the answer is 3, and 3 if the answer is 2. Competition is measured by one minus college enrollment rate in eachprovince.

Table 5Gender difference in the impacts of experiencing competition on performance in college: OLS.

(1) (2) (3) (4)Ln(GPA in College) Have Failed in Courses Award in Provincial Level or above CET4 Scores

Female 0.092*** −0.291*** −0.031 14.109(0.024) (0.064) (0.031) (8.323)

Female*Competition 0.016 −0.006 0.153** 23.732(0.057) (0.146) (0.069) (18.880)

CEE Scores 0.007 −0.048*** −0.000 15.799***(0.005) (0.012) (0.006) (3.546)

Constant 1.049*** 0.557*** 0.121*** 453.380***(0.002) (0.009) (0.004) (1.241)

Observations 4832 5885 4944 4796R-squared 0.117 0.107 0.015 0.160

Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. Ln(GPA in College) is natural logarithm of GPA students reported. “Have Failed in Courses” is adummy variable, with 1 indicating that students have failed any course in college and 0 vice verse. “Award in Provincial Level or above” is a dummyvariable, with 1 indicating that students have won any awards in provincial level or above and 0 vice verse. “CET4 Scores” is scores students got inCollege English Test (Level 4). Competition is measured by one minus college enrollment rate in each province.


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high school graduates and the female dummy. To capture the impacts of provincial population size, we scale the number of highschool graduates by population.20 In addition, Shang-Jin and Zhang (2011) show that sex ratio improves marriage competition,which could affect parents’ behavior and therefore their children’s behaviors. To address this concern, we also control for theinteraction of sex ratio and the female dummy.21 Estimation results are reported in Tables A5 and A6. Our estimates remain robust tothese additional control variables, suggesting that they are not biased due to the gender differential impacts from other provincialcharacteristics.

Inclusion of the interaction of college entrance examination scores with female dummy. One might be concerned that the CEE scores inmore competitive provinces are likely to be higher. If women with higher CEE scores are more risk averse, less trusting and performbetter academically than men, then our estimates could be biased. To address this concern, we conduce a robustness check byincluding an interaction of the female dummy and CEE scores in the regression. The results are shown in Tables A7 and A8. Thecoefficient of the female dummy and competition remains robust, and the coefficient of the interaction of the female dummy and CEEscores is mostly not significant. It suggests that our findings are not driven by different effects of CEE scores on women relative tomen.

Inclusion of college dummies. Our sample comes from various colleges, which may have different curricula and exam standards. Toaddress this potential incompatibility issue, we further add a set of college dummies in our analysis. However, a compromise is that asmany provinces allow students to select colleges after the CEE, competitive experience may lead females and males to select collegesdifferently; hence, comparing individuals in the same colleges may overlook this competitive experience effect. Regression results arereported in Tables A9 and A10. Similar patterns on gender differences in preferences and performance caused by the competitiveexperience are also uncovered.

Placebo test. We conduct a placebo test to verify further whether our research design is valid. Specifically, instead of using the realdistribution of college admission rates across provinces to measure the competitive experiences across provinces, we randomly assignthese admission rates across provinces, and construct a false variable, Competitionp

false. We then replace Competitionp withCompetitionp

false in regression (1). Given this random data generating process, we expect Competition female·pfalse

ip to cast zero effects onour outcomes; otherwise, it indicates some mis-specification of our regression. We conduct this exercise 500 times to avoid rareevents and increase the power of the test. The mean, standard deviation, and p-value are reported in Table A11. We find that all themean coefficients of Competition female·p

falseip are statistically insignificant, lending further support to our research design.

Competitive experience in the high school. To incorporate the possibility that students may face competitive pressures since theywere enrolled in high schools, we use the average competition between 2006 and 2007 as the measure of the competitive experi-ence.22 Regression results are reported in Tables A12 and A13. We continue to find the similar patterns of competitive experienceeffects on gender differences in preference and performance.

Competition measurement. As discussed in data section, we obtain college admission rates for Hubei, Ningxia and Heilongjiang bysearching news reports. One might concern about the accuracy of the college admission rates of these three provinces. To checkwhether it has any impacts on our estimates, we drop these three provinces and re-estimate the impacts. Results are shown inTables A14 and A15. We can still see the similar impacts of competitive experience on gender differences in preferences and per-formance.

5.3. Interpretation

We find that competitive and stressful experiences cause females to become less willing to trust and more risk averse, comparedwith males. To explore potential interpretations, we discuss the literature on gender difference in stress response. In biology andpsychology literature, the dominant model of response to stress, originally proposed by Walter Cannon in the 1920s, is characterizedby a fight-or-flight response, that is, either confronting a stressor (fight) or fleeing from it (flight). While both men and women showthe fight-or-flight pattern of arousal, such as, elevated heart rate and blood pressure, Taylor et al. (2000) observe that females aremore likely than males to seek support from others in times of stress. They further propose an alternative model of stress response forwomen, dubbed Tend-and-befriend, in which tending involves nurturing activities designed to protect the self and offspring thatpromote safety and reduce distress and befriending is the creation and maintenance of social networks that may aid in this process.

Men and women differ not only in how they respond to the epoch of stress, but also in how chronic stress experience impacts themin longer term. In particular, stressful life events, many of which are novel and challenging, have been shown to account for numerousmedical and psychiatric conditions such as anxiety, depression, and alcohol dependence. Nolen-Hoeksema and Girgus (1994) hy-pothesize that, after experiencing chronic stress, females are more likely to report internalizing symptoms, while males are morelikely to report externalizing symptoms. This hypothesis has been proposed to account for the extensively observed gender differencein psychiatric disorders. That is, across different countries and different ethnic groups, the rates of depression are 2 to 3 times higheramong women than men, while alcohol dependence is approximately twice as common in males compared to females.Maciejewski et al. (2001) find that, among males and females who did not experience a stressful life event, no gender difference isobserved among those suffering from major depression. By contrast, females who had been exposed to a stressful life event have athreefold increase in risk for major depression relative to males exposed to a stressful life event. Using data from the 2001–2002

20 We would like to thank one referee for pointing out this point.21 Provincial sex ratio is measured in 1997.22 It is better to use the average competition between 2005 and 2007 but the competition in 2005 is not available.


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National Epidemiologic Survey on Alcohol and Related Conditions, Dawson et al. (2005) find that the association between thenumber of stressful life events and alcohol consumption is more pronounced among males than females, and that particular stressfullife events differentially impacted the level of alcohol consumption among males. These studies support the hypothesis of Nolen-Hoeksema and Girgus (1994) on gender difference in response to stressful experiences.

We hypothesize that the pressure of college entrance is the most stressful life event that high school students commonly en-counter. On the basis of the hypothesis proposed by Nolen-Hoeksema and Girgus (1994), if stress experience leads females to displaymore withdrawal and internalizing behaviors, it is reasonable to hypothesize that females experiencing more stressful and compe-titive environment would become less willing to take risk and less willing to trust others. Conversely, if stress experience makes malesto exhibit more externalizing behaviors, it is reasonable to hypothesize that males experiencing more stressful and competitiveenvironment would become more willing to take risk, and more willing to trust. Thus, our results are consistent with psychologicalliterature on gender difference in stress response. Moreover, our study goes beyond simple correlation, and identifies the casual roleof stress experience on gender difference in economic preference.

We also find that competitive experience causes females to perform better in more competitive environments, compared withmale counterparts. One possible explanation is that the gender differential effect on academic preference is partially due to the genderdifferential effect on economic preference. This argument follows Buser et al. (2014), in which they find that competitivenessmeasured in the laboratory setting is positively correlated with the selection of academic tracks, and the gender difference incompetitiveness accounts for a substantial portion of the gender difference in track choice. An alternative explanation is that thecompetitive experience provides a learning opportunity to handle the subsequent competitive situations and males and females learndifferently. For example, Wozniak et al. (2014) show that feedback about relative performance removes the average gender dif-ference in compensation choices, which suggests that learning may affect males and females differently. Although we could notexplore these two possible explanations completely because of data limitations, we attempt to assess the first possibility. In particular,we include risk and trust preferences (and their interactions with the female dummy) in the analyses of academic performancepresented in Table 5, and examine whether the magnitude of coefficients of Competitionp · Femaleip for academic performance remainthe same. We find that the inclusion of preferences does not alter the magnitude for the effect on performance (Appendix A16). Thissuggests that the differential effect on academic performance is unlikely to be caused by the differential effect on risk and trustpreferences.

5.4. Heterogeneous effects

In this section, we investigate whether and how the effects of competitive experience on gender difference in economic preferenceand academic performance may differ across various groups. We estimate a specification similar with Eq. (1) but controlling forparental characteristics, such as indictors for minority, having a college degree, being a government cadre and working in publicsector, and pre-college variables measuring students’ performance in high schools and middle schools. Due to the space limit, Table 6only presents the estimated coefficients of the interaction of competition and the female dummy.

First, we compare the heterogeneous effect between students attending magnet high schools and regular high schools, as thesetwo types of schools may have different competitive schooling environments. Columns 1 and 2 are for students attending magnet andregular high schools, respectively. Experiencing the same level of competition, female students attending magnet high schools be-come more risk averse and less trustful relative to their male counterparts than female students attending regular high schools.However, the effect on gender difference in terms of college performance is relatively the same for students attending magnet andregular high schools.

Table 6Effects of competitive experience by different groups.

(1) (2) (3) (4) (5) (6)Magnet high school Regular high school Natural science track Other tracks Only child Having siblings

Loss −0.832** −0.576 −0.189 −2.582*** −0.215 −0.687(0.421) (0.755) (0.384) (0.619) (0.640) (0.470)

Gain −0.666 −0.148 −0.839** −0.725 −0.803* −0.251(0.410) (0.513) (0.357) (0.520) (0.471) (0.567)

Trust −1.589*** −0.728 −1.134*** −2.242** −0.954** −1.297**(0.569) (0.671) (0.278) (1.038) (0.381) (0.535)

Ln(GPA in College) 0.050 −0.040 0.046 −0.118* 0.087 −0.055(0.074) (0.060) (0.092) (0.064) (0.074) (0.086)

Have Failed in Courses 0.079 −0.218 −0.181 0.223 0.032 −0.019(0.170) (0.143) (0.169) (0.143) (0.195) (0.121)

Award in Provincial Level or above 0.139* 0.113 0.145* 0.112 0.143 0.192**(0.076) (0.189) (0.085) (0.147) (0.094) (0.087)

CET4 Scores 8.598 37.167 17.038 20.335 29.020 13.593(23.146) (26.432) (21.846) (31.890) (29.451) (28.697)

Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%.The same regressions as those in Tables A3 andA4 are estimated using different groups of samples. The coefficients shown in the table are those onthe interaction of the female dummy and the competition measurement.


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Second, high schools in China offer the natural science track, liberal arts and social science track, and other more minor tracks.The natural science track is more popular among males while the arts and social science track is more popular among females.Columns 3 and 4 show the results for students taking the natural science track and other tracks, respectively. We can see that with thesame amount of increase in competition, female students taking other tracks are less trustful, more risk averse in losing money, andless risk averse in gaining money than female students taking the natural science track relative to their male counterparts. The effectof experiencing competition on gender difference of college performance is approximately the same for students taking differenttracks.

Third, we examine the differences between those who are single child and those who have siblings, as it has been recently shownthat the one child policy has produced significantly less trusting, less trustworthy, more risk-averse, less competitive, more pessi-mistic, and less conscientious individuals (Cameron et al., 2013). As shown in columns 5 and 6, experiencing the same level ofcompetition, female students having siblings become less trustful and risk averse in gaining money, but perform better in college thanthose who are single child, relative to their male counterparts.

6. Conclusion

This study investigates the extent to which competitive experience affects gender differences in economic preference and aca-demic performance. To address this issue, we use the empirical setup of differences in college admission rates across provincesbecause the pressure of college admission is probably the most competitive experience commonly shared by high school students inChina. Our data reveal that the competitive experience induced by college admission makes females, compared to males, becomemore risk averse toward both gains and losses, less trustful, and perform better in highly competitive environments. This observedbehavioral pattern is consistent with gender difference in stress response as proposed by Nolen-Hoeksema and Girgus (1994) inpsychology literature. After experiencing stress, females are more likely to display withdrawal and internalizing behavior, such as lesswilling to take risks and to trust other people. By contrast, males are more likely to display approach and externalizing behavior, suchas highly willing to take risks and to trust other people.

Our study contributes to the policy implications regarding the effect of college expansion, which is a worldwide phenomenon.From a special report on university education published in the Economist on March 28, 2015, the global tertiary admission rateincreased from 14% to 32%, and the number of countries with a ratio of more than half increased from five to 54 in the two decadesprior to 2012. Our results suggest that college expansion leading to less competitive environment would have implications on genderdifferences in economic preference, academic performance, and perhaps labor market outcomes. It would be of interest to furtherexamine the effect of college expansion on other economic behavior and preferences, including willingness to compete (Niederle andVesterlund, 2007; Flory et al., 2015; Buser et al., 2014).

Our study also contributes to understanding the relationship between stress and gender. Based on The Stress in America survey(2012) conducted by American Psychological Association, males and females do differ in the experienced stress and employ differentstress management techniques. For example, females are likely to report experiencing extreme stress, and are more likely to engage insocial and sedentary activities such as reading and shopping to manage stress. A number of recent studies investigate the role of stresshormones in competition in the Buser et al. (2017), Buckert et al. (2015), Halko and Sääksvuori (2015), Zhong et al. (2018), whilethey find that gender difference in stress response could not account for gender difference in willingness to compete. Despite itsimportance, the effects of stressful experience have not yet been much explored in economics. For example, Evans and Kim (2007)find that the number of years spent living in poverty during childhood is associated with elevated overnight cortisol and a moredisregulated cardiovascular response, while concurrent poverty, i.e., during adolescence, does not affect these physiological stressoutcomes. More recently, Chemin et al. (2013) observe that low levels of rain in the preceding year increased the level of cortisol, thestress hormone, among farmers in Kenya. Goh et al. (2015) find that stressors in the workplace contribute substantially to healthcarecost and mortality in the United States. Given the well-established gender difference in stress response in psychology and biology(Nolen-Hoeksema and Girgus, 1994; Taylor et al., 2000), investigating the effects of different sources of stress arising from earlychildhood poverty, parenting style, and workplace on gender gap in economic preferences and outcomes would be of significantinterest.

Finally, we would like to acknowledge some limitations of our study in relation to the measurements of college admission rates,risk and trust preferences, and academic performance. First, while our results are valuable for those highly competitive environmentssuch as eastern Asian countries/regions including South Korea, Japan, Hong Kong, and Taiwan where similar examinations exist,caution needs to be taken when one concerns the generalization to other less completive settings. In this regard, it would be ofinterest to study related questions in societies with less competitive examination system. Second, risk and trust preferences aremeasured using hypothetical choice and academic performance is based on self-report of the students, which may bias the results. Itwould be highly valuable for the future studies to measure risk and trust preferences with actual incentive and measure academicperformance using administrative data.

Acknowledgements

We would like to thank Hongbin Li (the editor) and three anonymous referees for helpful comments. Xinzheng Shi acknowledgesthe financial support from the China Natural Science Foundation (project ID: 71673155).


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Appendix

Table A1Provincial admission rates used in the paper.

Hainan 85.10%Shanghai 83.75%Jiangsu 78.61%Tianjin 77.86%Heilongjiang 77.68%Liaoning 76.90%Beijing 73.59%Zhejiang 72.50%Shandong 72.41%Chongqing 72.08%Guangdong 69.41%Jilin 68.70%Tibet 65.10%Qinghai 64.30%Fujian 59.60%Ningxia 58.58%Neimenggu 57.90%Hubei 57.40%Jiangxi 57.31%Guangxi 57.22%Xinjiang 56.60%Hebei 56.19%Sichuan 55.18%Hunan 53.40%Henan 52.34%Anhui 51.51%Yunnan 49.48%Shaanxi 49.18%Shanxi 47.95%Guizhou 43.00%Gansu 40.98%

Source: We get the information from China Education Examinations Yearbook (2008) for 19provinces, including Shanghai, Liaoning, Beijing, Chongqing, Guangdong, Shandong, Jiangxi,Hunan, Guangxi, Hebei, Henan, Anhui, Shanxi, Gansu, Tianjin, Jiangsu, Sichuan, Shaanxi, andYunnan. We get the information from China Gaokao Information (http://gaokao.chsi.com.cn/gkxx/zhiding/200709/20070905/1113417.html) for 9 provinces, including Hainan, Zhejiang,Jilin, Tibet, Qinghai, Fujian, Neimenggu, Xinjiang, and Guizhou. For the remaining threeprovinces (Hubei, Heilongjiang, and Ningxia), we search the news reports and we get thecollege admission rates in 2007 from three news reports: http://gaokao.eol.cn/dongtai_5640/20070618/t20070618_238318.shtml , http://zqb.cyol.com/content/2007-05/15/content_1759511.htm , and http://edu.qq.com/a/20070620/000158.htm.

Table A2Determinants of college admission rates in provinces.

College admission ratesPopulation (billion) −4.311

(3.329)GDP per capita (100 thousand) −0.280

(0.608)Percentage of GDP in the first industry 0.518

(1.163)Percentage of GDP in the second industry 0.856

(0.822)Average labor income (100 thousand) −0.026

(0.813)Government income (billion) −0.001

(0.001)Export (10 thousand) 0.000

(0.000)Import (10 thousand) 0.000

(0.000)

(continued on next page)


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Table A2 (continued)

Ratio of individuals having primary education in 6+ years old population 0.598(0.754)

Ratio of individuals having middle school education in 6+ years old population 0.079(0.506)

Ratio of individuals having high school education in 6+ years old population −1.359(1.321)

Ratio of individuals having college or above education in 6+ years old population 1.856(1.068)

Ratio of education expenditure in government total expenditure −0.683(0.795)

Number of colleges −0.001(0.003)

Number of college students (10 thousand) −0.044(0.065)

Number of college teachers (10 thousand) −0.013(0.467)

Number of high schools 0.000(0.000)

Number of high school students (10 thousand) −0.034(0.038)

Number of high school graduates (10 thousand) 0.256**(0.109)

Constant −0.027(0.957)

Observations 31R-squared 0.889

Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

Table A3Gender difference in the impacts of experiencing competition on preference: ordered Logit.


Female 0.194 −0.123 0.629***(0.129) (0.134) (0.124)

Female*Competition −0.646* −0.638** −1.198***(0.346) (0.312) (0.313)

CEE Scores 0.019 0.020 0.068(0.031) (0.033) (0.060)

Minority −0.054 0.050 0.042(0.084) (0.133) (0.166)

Parent has a college degree −0.075 0.010 −0.002(0.072) (0.084) (0.087)

Parent is a cadre −0.013 −0.067 −0.077(0.101) (0.088) (0.094)

Parent works in public sector −0.047 −0.058 −0.103(0.065) (0.056) (0.077)

Enrolled in magnet classes in middle school 0.012 −0.008 −0.129**(0.047) (0.052) (0.053)

GPA in top 20% in middle school 0.161** −0.061 −0.052(0.078) (0.086) (0.081)

Win in competition in middle school 0.131* 0.124* 0.088(0.077) (0.074) (0.081)

Enrolled in magnet classes in high school 0.018 −0.028 0.097*(0.060) (0.066) (0.053)

GPA in top 20% in high school 0.039 0.055 0.048(0.063) (0.079) (0.072)

Win in competition in high school −0.073 −0.020 −0.206*(0.071) (0.073) (0.119)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agree with the followingstatement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) areindifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “Youwould rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2.Competition is measured by one minus college enrollment rate in eachprovince.


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Table A4Gender difference in the impacts of experiencing competition on performance: OLS.


Female 0.090*** −0.289*** −0.027 14.849*(0.026) (0.066) (0.030) (8.078)


CEE Scores 0.003 −0.038*** −0.003 13.045***(0.005) (0.012) (0.006) (3.176)

Minority −0.009 0.018 0.013 −9.089**(0.009) (0.021) (0.024) (3.459)

Parent has a college degree 0.008 −0.009 0.019 10.094***(0.009) (0.020) (0.017) (1.905)

Parent is a cadre −0.005 0.004 −0.005 2.329(0.008) (0.020) (0.018) (3.316)

Parent works in public sector −0.012** 0.046** −0.007 5.208***(0.006) (0.022) (0.017) (1.772)

Enrolled in magnet classes in middle school −0.001 −0.008 0.005 2.221(0.006) (0.012) (0.009) (1.740)

GPA in top 20% in middle school 0.021** −0.066*** 0.022 14.836***(0.009) (0.021) (0.013) (3.538)

Win in competition in middle school −0.012 0.007 0.012 11.739***(0.008) (0.017) (0.015) (1.949)

Enrolled in magnet classes in high school 0.006 −0.049*** −0.007 7.800***(0.005) (0.012) (0.010) (1.495)

GPA in top 20% in high school 0.024*** −0.032* −0.009 5.589***(0.006) (0.016) (0.013) (1.437)

Win in competition in high school 0.044*** −0.068*** 0.149*** 9.163***(0.009) (0.015) (0.021) (2.773)

Constant 1.010*** 0.656*** 0.080*** 425.096***(0.009) (0.021) (0.017) (3.464)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. Ln(GPA in College) is natural logarithm of GPA students reported. “Have Failed in Courses” is adummy variable, with 1 indicating that students have failed any course in college and 0 vice verse. “Award in Provincial Level or above” is a dummyvariable, with 1 indicating that students have won any awards in provincial level or above and 0 vice verse. “CET4 Scores” is scores students got inCollege English Test (Level 4).Competition is measured by one minus college enrollment rate in each province.



Female −1.891 1.573 0.566(1.661) (2.496) (2.182)

Female*Competition −0.896** −0.420* −1.461***(0.425) (0.223) (0.431)

Female*(High school graduates/Population) 0.000 −0.001 0.009(0.003) (0.005) (0.006)

Female*Sex Ratio 0.021 −0.017 −0.004(0.016) (0.025) (0.022)

CEE Scores 0.019 0.020 0.069(0.031) (0.033) (0.059)

Minority −0.057 0.052 0.052(0.085) (0.131) (0.168)


Parent is a cadre −0.013 −0.066 −0.078(0.101) (0.088) (0.094)







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Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agree with the followingstatement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.“ The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) areindifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “Youwould rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. Competition is measured by one minus college enrollment rate in eachprovince.



Female −0.196 0.754 0.409 89.317(0.224) (0.668) (0.410) (160.008)

Female*Competition −0.033 0.190 0.189** 26.158(0.061) (0.152) (0.079) (24.506)

Female*(High school graduates/Population) 0.001** −0.003* 0.000 0.059(0.000) (0.001) (0.001) (0.207)

Female*Sex Ratio 0.003 −0.009 −0.005 −0.786(0.002) (0.006) (0.004) (1.549)

CEE Scores 0.003 −0.038*** −0.003 13.118***(0.005) (0.012) (0.006) (3.169)

Minority −0.009 0.016 0.014 −8.905**(0.009) (0.021) (0.024) (3.454)


Parent is a cadre −0.005 0.004 −0.005 2.349(0.008) (0.020) (0.018) (3.318)








Constant 1.010*** 0.656*** 0.080*** 425.045***(0.009) (0.021) (0.016) (3.468)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. Ln(GPA in College) is natural logarithm of GPA students reported. “Have Failed in Courses” is adummy variable, with 1 indicating that students have failed any course in college and 0 vice verse. “award in provincial level or above” is a dummyvariable, with 1 indicating that students have won any awards in provincial level or above and 0 vice verse. “CET4 Scores” is scores students got inCollege English Test (Level 4).Competition is measured by one minus college enrollment rate in each province.


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Female 0.186 −0.103 0.612***(0.128) (0.132) (0.123)

Female*Competition −0.668* −0.591* −1.227***(0.350) (0.316) (0.307)

Female*CEE Scores 0.071 −0.161 0.116(0.078) (0.112) (0.074)

CEE Scores −0.009 0.078 0.019(0.040) (0.048) (0.078)

Minority −0.054 0.051 0.040(0.084) (0.131) (0.165)


Parent is a cadre −0.013 −0.065 −0.080(0.101) (0.087) (0.095)









Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agree with the followingstatement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) areindifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “Youwould rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. Competition is measured by one minus college enrollment rate in eachprovince.



Female 0.088*** −0.288*** −0.028 13.194(0.026) (0.066) (0.030) (8.280)


Female*CEE Scores 0.012 −0.010 0.013 11.660*(0.010) (0.020) (0.017) (6.117)

CEE Scores −0.001 −0.034** −0.008 7.891**(0.006) (0.015) (0.007) (3.546)

Minority −0.010 0.018 0.013 −9.109**(0.009) (0.021) (0.024) (3.478)


Parent is a cadre −0.005 0.004 −0.006 2.164(0.009) (0.020) (0.018) (3.355)




Win in competition in middle school −0.012 0.007 0.012 11.609***



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(0.008) (0.017) (0.015) (1.971)Enrolled in magnet classes in high school 0.006 −0.048*** −0.007 7.697***

(0.005) (0.012) (0.010) (1.472)GPA in top 20% in high school 0.024*** −0.032* −0.009 5.522***

(0.006) (0.016) (0.012) (1.405)Win in competition in high school 0.045*** −0.068*** 0.149*** 9.509***

(0.009) (0.015) (0.021) (2.785)Constant 1.011*** 0.655*** 0.082*** 426.723***

(0.010) (0.021) (0.016) (3.581)Observations 4832 5885 4944 4796R-squared 0.131 0.118 0.043 0.213


Table A9Gender difference in the impacts of experiencing competition on preference, adding college dummies: ordered Logit.


Female 0.278** −0.121 0.584***(0.136) (0.135) (0.121)

Female*Competition −0.822** −0.718** −1.096***(0.352) (0.308) (0.303)

CEE Scores 0.002 0.028 0.068(0.031) (0.033) (0.059)

Minority −0.104 0.020 0.071(0.086) (0.135) (0.170)


Parent is a cadre −0.016 −0.066 −0.053(0.098) (0.091) (0.093)



GPA in top 20% in middle school 0.147* −0.037 −0.069(0.079) (0.091) (0.081)

Win in competition in middle school 0.120 0.140* 0.058(0.076) (0.073) (0.078)

Enrolled in magnet classes in high school 0.001 −0.021 0.109**(0.066) (0.067) (0.051)


Win in competition in high school −0.057 0.004 −0.202*(0.070) (0.079) (0.118)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies and college dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agreewith the following statement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of“Loss” come from the question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3.(a) and (b) are indifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from thequestion “You would rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) areindifferent.” “Gain” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. Competition is measured by one minus collegeenrollment rate in each province.


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Table A10Gender difference in the impacts of experiencing competition on performance, adding college dummies: OLS.


Female 0.095*** −0.252*** −0.023 19.966***(0.024) (0.065) (0.035) (6.353)

Female*Competition 0.006 −0.057 0.141* 13.367(0.059) (0.146) (0.077) (14.302)

CEE Scores 0.000 −0.035*** 0.004 7.324***(0.004) (0.011) (0.007) (2.446)

Minority −0.015 0.032 0.003 −12.977***(0.009) (0.022) (0.024) (3.917)

Parent has a college degree −0.000 −0.001 0.028 5.639***(0.008) (0.018) (0.018) (1.769)

Parent is a cadre −0.001 0.003 0.002 −0.523(0.009) (0.017) (0.019) (2.862)

Parent works in public sector −0.014** 0.055** −0.010 2.481(0.006) (0.021) (0.016) (1.675)

Enrolled in magnet classes in middle school 0.001 −0.005 0.004 3.500**(0.005) (0.012) (0.009) (1.654)

GPA in top 20% in middle school 0.023** −0.068*** 0.024* 10.582***(0.010) (0.020) (0.014) (3.795)

Win in competition in middle school −0.015* 0.018 0.017 7.049***(0.008) (0.017) (0.015) (2.005)


GPA in top 20% in high school 0.025*** −0.032** −0.006 1.104(0.005) (0.014) (0.013) (1.391)

Win in competition in high school 0.040*** −0.051*** 0.156*** 3.519(0.009) (0.016) (0.021) (2.819)

Constant 0.987*** 0.711*** 0.283*** 432.081***(0.016) (0.050) (0.050) (5.740)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies and college dummies are controlled in all regressions. Ln(GPA in College) is natural logarithm of GPA students reported. “HaveFailed in Courses” is a dummy variable, with 1 indicating that students have failed any course in college and 0 vice verse. “Award in Provincial Levelor above” is a dummy variable, with 1 indicating that students have won any awards in provincial level or above and 0 vice verse. “CET4 Scores” isscores students got in College English Test (Level 4). Competition is measured by one minus college enrollment rate in each province.

Table A11Balancing test, Monte Carlo experiment.

Mean S.D. T P-valueLoss 0.008 0.377 0.020 0.492Gain 0.012 0.359 0.033 0.487Trust −0.024 0.493 −0.049 0.961Ln(GPA in college) 0.003 0.046 0.076 0.470Have Failed in Courses 0.002 0.151 0.011 0.496Award in Provincial Level or above 0.003 0.083 0.032 0.487CET4 Scores 1.061 22.012 0.048 0.481



Female 0.197 −0.126 0.628***(0.128) (0.133) (0.124)

Female*Average competition −0.655* −0.632** −1.195***(0.344) (0.310) (0.313)

CEE Scores 0.019 0.020 0.068(0.031) (0.033) (0.060)

Minority −0.054 0.050 0.042(0.084) (0.133) (0.166)




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Parent is a cadre −0.013 −0.067 −0.077(0.101) (0.088) (0.094)









Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agree with the followingstatement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) areindifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “Youwould rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. Average competition is measured by the average of one minus provincialcollege admission rates in 2006 and 2007.



Female 0.090*** −0.290*** −0.026 14.975*(0.026) (0.066) (0.030) (8.101)

Female*Average competition 0.023 −0.012 0.149** 19.088(0.060) (0.148) (0.066) (18.797)

CEE Scores 0.003 −0.038*** −0.003 13.045***(0.005) (0.012) (0.006) (3.176)

Minority −0.009 0.018 0.013 −9.089**(0.009) (0.021) (0.024) (3.460)


Parent is a cadre −0.005 0.004 −0.005 2.329(0.008) (0.020) (0.018) (3.316)








Constant 1.010*** 0.656*** 0.080*** 425.094***(0.009) (0.021) (0.017) (3.465)


Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. Ln(GPA in College) is natural logarithm of GPA students reported. “Have Failed in Courses” is adummy variable, with 1 indicating that students have failed any course in college and 0 vice verse. “Award in Provincial Level or above” is a dummyvariable, with 1 indicating that students have won any awards in provincial level or above and 0 vice verse. “CET4 Scores” is scores students got inCollege English Test (Level 4). Average competition is measured by the average of one minus provincial college admission rates in 2006 and 2007.


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Table A15Gender difference in the impacts of experiencing competition on performance, dropping three provinces: OLS.


Female 0.098*** −0.318*** −0.022 17.187*(0.025) (0.073) (0.033) (8.841)

Female*Competition 0.004 0.042 0.143* 14.831(0.058) (0.164) (0.072) (20.418)

CEE Scores 0.002 −0.040*** −0.002 12.774***(0.005) (0.013) (0.006) (3.238)

Minority −0.008 0.016 0.010 −8.712**(0.010) (0.023) (0.024) (3.656)


Parent is a cadre −0.005 0.005 −0.003 2.087(0.009) (0.022) (0.019) (3.366)

Parent works in public sector −0.011* 0.045* −0.014 5.189***(0.006) (0.023) (0.018) (1.789)


GPA in top 20% in middle school 0.018* −0.073*** 0.014 14.716***(0.010) (0.022) (0.014) (3.534)





Table A14Gender difference in the impacts of experiencing competition on preference, dropping three provinces: ordered Logit.


Female 0.272** −0.080 0.659***(0.119) (0.128) (0.123)

Female*Competition −0.807** −0.698** −1.285***(0.332) (0.298) (0.303)

CEE Scores 0.030 0.018 0.054(0.031) (0.032) (0.060)

Minority −0.020 0.003 0.093(0.086) (0.136) (0.168)


Parent is a cadre 0.026 −0.066 −0.071(0.109) (0.091) (0.101)



GPA in top 20% in middle school 0.139* −0.033 −0.062(0.083) (0.090) (0.089)

Win in competition in middle school 0.118 0.135* 0.051(0.078) (0.079) (0.083)

Enrolled in magnet classes in high school 0.019 −0.023 0.117**(0.064) (0.070) (0.053)




Robust standard errors in parentheses are calculated by clustering over province; * significant at 10%; ** significant at 5%; *** significant at 1%;Province dummies are controlled in all regressions. The values of “Trust” come from answers to the question “Do you agree with the followingstatement: ‘Generally, you can trust people in the society.’ 1. Very Disagree; 2. Disagree; 3. Agree; 4. Very Agree.” The values of “Loss” come fromthe question “You would rather (a) Lose 1000 yuan with certainty; (b) Lose 2000 with 50% and no loss with 50%. 1. (a); 2. (b); 3. (a) and (b) areindifferent.” “Loss” is equal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. The values of “Gain” come from the question “Youwould rather (a) Gain 1000 yuan with certainty; (b) Gain 2000 with 50% and nothing with 50%. 1. (a); 2 (b); 3. (a) and (b) are indifferent.” “Gain” isequal to 1 if the answer is 1, 2 if the answer is 3, and 3 if the answer is 2. Competition is measured by one minus college enrollment rate in eachprovince.


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References

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Table A16Gender difference in the impacts of experiencing competition on performance in college: OLS.


Female 0.110*** −0.327*** −0.004 10.025(0.029) (0.085) (0.063) (9.640)

Female*Competition 0.011 0.008 0.141* 23.270(0.059) (0.144) (0.075) (19.066)

Loss 0.005 −0.023 −0.003 −1.080(0.005) (0.016) (0.009) (1.508)

Gain 0.003 0.000 0.006 1.769(0.004) (0.013) (0.008) (1.144)

Trust 0.002 0.003 −0.004 −1.825(0.005) (0.010) (0.009) (1.130)

Female*Loss −0.000 0.003 0.010 0.664(0.006) (0.014) (0.011) (1.354)

Female*Gain −0.004 −0.010 0.010 −1.336(0.006) (0.017) (0.014) (1.969)

Female*Trust −0.004 0.018 −0.029 2.069(0.007) (0.018) (0.019) (2.622)

CEE Scores 0.007 −0.047*** −0.001 15.297***(0.005) (0.012) (0.006) (3.561)

Constant 1.027*** 0.601*** 0.123*** 455.684***(0.015) (0.043) (0.030) (5.083)




Win in competition in high school 0.044*** −0.058*** 0.159*** 8.031**(0.009) (0.015) (0.021) (2.927)

Constant 1.007*** 0.665*** 0.086*** 425.086***(0.010) (0.022) (0.018) (3.574)




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