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The Han-Minority Achievement Gap, Language, and Returns to Schools in Rural ChinaAuthor(s): Yunfan Yang /Huan Wang /Linxiu Zhang /Sean Sylvia /Renfu Luo /Yaojiang Shi/Wei Wang /Scott RozelleSource: Economic Development and Cultural Change, Vol. 63, No. 2 (January 2015), pp. 319-359Published by: The University of Chicago PressStable URL: http://www.jstor.org/stable/10.1086/679070 .

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The Han-Minority Achievement Gap, Language, and
Returns to Schools in Rural China
yunfan yangInstitute for Geographical Sciences and Natural Resources Research, ChineseAcademy of Sciences; and University of the Chinese Academy of Sciences

huan wangNorthwest University of Xi’an

linxiu zhangInstitute for Geographical Sciences and Natural Resources Research, ChineseAcademy of Sciences

sean sylviaRenmin University of China

renfu luoInstitute for Geographical Sciences and Natural Resources Research, ChineseAcademy of Sciences

yaojiang shiCenter for Experimental Economics for Education, Shaanxi Normal University

wei wangCenter for Chinese Agricultural Policy, Chinese Academy of Sciences

scott rozelleStanford University

I. Introduction

Over the past 3 decades, China’s rural population has experienced rapid in-come growth and a dramatic reduction in poverty ðRavallion and Chen 2007;

The authors acknowledge financial support from the National Institutes of Health ðR01HL106023-03Þ, the Chinese Academy of Sciences ðKZZD-EW-06-02Þ, the Institute of Geographic Sciencesand Natural Resources Research, Chinese Academy of Sciences ð2012ZD008Þ, and the StanfordCenter for International Development. Contact the corresponding author, Renfu Luo, at [email protected].

Electronically published November 18, 2014© 2015 by The University of Chicago. All rights reserved. 0013-0079/2015/6302-0006$10.00



Chen and Ravallion 2010Þ. Although the well-being of the population as a

320 E C O N O M I C D E V E L O P M E N T A N D C U L T U R A L C H A N G E

whole has risen sharply, the average economic standing of country’s nearly114 million ethnic minorities has improved relatively less than that of theHan majority ðGustafsson and Li 2003; Gustafsson and Sai 2009a, 2009bÞ.Between 1988 and 1995 the average per capita income of Han living in ruralareas increased by more than 52%, while incomes of the rural minority pop-ulation only grew by nearly 22% ðGustafsson and Li 2003Þ. Over this period,the Han-minority income gap nearly doubled from 19.2% to 35.9% ðGus-tafsson and Li 2003Þ. In 2002, rural minorities remained more than one anda half times as likely as the rural Han majority to be in poverty and twice aslikely to have experienced poverty in the past 2 years ðGustafsson and Sai 2009a;Hannum and Wang 2012Þ.Lagging educational attainment among minorities has undoubtedly played

a significant role in the persistence of the Han-minority income gap ðHannumand Wang 2012Þ. Education is an increasingly important determinant of wagesand access to off-farm employment ðZhang et al. 2005; De Brauw and Rozelle2008Þ. At the same time, educational attainment among minorities lags ðHan-num 2002; Hannum et al. 2008; Hannum and Wang 2012Þ. Analyzing mid-census survey data from 2005, Hannum and Wang ð2012Þ find that—among16–21-year-olds—minorities were nearly one-third as likely as Han to haveattained 9 years of compulsory schooling. Minorities are also significantly lesslikely to enroll at the tertiary level. In a 2008 census of entering freshman atfour tier 1 universities in western China, only 4% were non-Han, while mi-norities comprise approximately 11% of the population cohort ðWang et al.2013Þ. The same survey shows female minority students to be at a particulardisadvantage in college admissions: in this entering class, female minority stu-dents were only 25% of their population share.If the Han-minority differences in educational attainment persist, the rela-

tive well-being of minority populations is likely to continue to fall as China’seconomy increasingly demands a higher-quality workforce. Tightening demo-graphics and a nearly complete transition into off-farm labor in China ðmorethan 80% of 16–30-year-olds are now employed off farmÞ are driving up wagesfor unskilled labor at close to 10% per year ðPark, Cai, and Du 2010; Zhanget al. 2013Þ. As unskilled wages rise and low-paying basic manufacturing jobsare replaced with jobs involving more sophisticated tasks, China’s economy willincreasingly demand a high-quality, educated workforce ðZhang et al. 2013Þ.Educationally disadvantaged minorities will find it more difficult to partici-pate in this new labor market and benefit from the higher wages that will comewith it.



In the context of rural China, poor academic performance in school may

Yang et al. 321

play a significant role in reducing educational attainment or years of school-ing ðYi et al. 2012Þ. In competitive educational systems—such as China’s—lowerexpectations of poorly performing students to thrive in the system may dis-courage continued enrollment ðChuang 1997; Clarke, Haney, and Madaus2000; Reardon and Galindo 2002; Rumberger and Lim 2008Þ. Heavy em-phasis on testing may further lead teachers to direct more attention to higher-performing children and even lead schools to push at-risk students out in aneffort to raise overall test scores ðVickers 1994; Vélez and Saenz 2001; Fortinet al. 2006Þ. These influences are compounded by rising unskilled wages,which drive up the opportunity costs of schooling ðAngrist and Lavy 2009;Fiszbein, Schady, and Ferreira 2009Þ. Indeed, the available evidence highlightsthe correlation between poor performance and dropout among poor stu-dents in western China ðYi et al. 2012Þ. Thus, if minority students performworse than their Han peers, they are likely to attain fewer years of schoolingas they forgo school and opt to enter the labor force in unskilled jobs.Despite the implications of an achievement gap between Han and minority

students, no study that we know of has compared their achievement ðeithergrades or test scoresÞ. Likewise, we find almost no research on the how thedeterminants of achievement may vary between the two groups. Existing em-pirical work on the disparity between Han and minority educational outcomeshas focused on attainment. Hannum ð2002Þ, for example, using a 1992 nationalsurvey of children in China, finds large differences in enrollment between Hanand minority children of primary school age, with enrollment rates lowestamong minorities in western China. She concludes that much of this differ-ence is attributable to geographic composition and family background. Re-search like this, however, is focused on attainment and, presumably due to theabsence of data, has not examined achievement.The overall goal of this article is to document and analyze the achievement

gap between Han and minority students in rural China. To meet this goal wehave two specific objectives. First, we estimate the overall achievement gapðhenceforth, the “Han-minority achievement gap”Þ. We also measure two othersubgaps: the gap between Han and minority students that speak Mandarin asa first language and the gap between Han and minority student that speakMandarin as a second language. Second, we assess what factors contribute mostto these achievement gaps. To do this, we first decompose the achievement gapinto two parts: one part representing the portion of the gap due Han-minoritydifferences in endowments of student, household, peer, teacher, and schoolcharacteristics and a second part due to differences in returns to these char-



acteristics. We then asses what effect specific schools have on the Han-minority


achievement gap and what types of schools narrow or widen this gap. That is,we analyze how returns to attending specific schools ðschool fixed effectsÞ dif-fer between Han and minority students and what school characteristics aremost strongly associated with these Han-minority differences in returns to spe-cific schools.To achieve these objectives, we draw on a large-scale survey of schools

sampled from across rural Shaanxi, Gansu, and Qinghai provinces coveringnearly 21,000 students, approximately 13% of whom are minorities. We mea-sure achievement of Han and minority students using curriculum-based stan-dardized exams in math and Chinese given as part of the survey. To assessfactors that contribute to the Han-minority achievement gap, we use detailedinformation on students, households, teachers, and schools and apply decom-position methods pioneered by Oaxaca ð1973Þ and Blinder ð1973Þ. Oaxaca-Blinder type decomposition, originally used to analyze wage differences be-tween groups, has now been applied in a wide variety of contexts. In education,previous research has used this approach to analyze differences in academicachievement across countries ðe.g., McEwan and Marshall 2004; Ammer-mueller 2007Þ, across time ðBarrera-Osorio et al. 2011Þ, and between indig-enous and nonindigenous students ðMcEwan 2004; McEwan and Trowbridge2007; Sakellariou 2008Þ.Our analysis yields three primary findings. First, we find that minority

students in our sample score significantly below Han students on standardizedexams in math and Chinese. The Han-minority achievement gap is nearly0.3 standard deviations ðSDÞ in math and more than 0.2 SD in Chinese.Among minorities in our sample whose primary language is not standardMandarin ðSalar and Tibetan—henceforth, “Non-Mandarin minorities”Þ, theachievement gap is even more striking: these students score 0.62 SD lowerthan Han in math and 0.65 SD lower than Han in Chinese.Second, our decomposition analysis suggests that the Han-minority

achievement gap for Mandarin-speaking minority students ðHui and Tu—henceforth, “Mandarin minorities”Þ is almost fully explained by differences instudent, peer, teacher, and school characteristics. Of these, the largest con-tributor is student and family background. Differences in school quality playa relatively small role. Endowments, however, explain very little of the achieve-ment gap between Han students and non-Mandarin minorities.Third, we find that—in “mixed” schools with both Han and minority

students—the effects of individual schools play a role in widening the Han-minority achievement gap. In these mixed schools, returns to Han students ofðobserved and unobservedÞ specific school attributes are higher than those for



similar minority students attending the same school. Teachers appear to play

Yang et al. 323

a central role in affecting the relative returns of Han and minority students.The rest of this article is organized as follows. Section II reviews the back-

ground of minorities in China. Sections III and IV describe the survey and datathat we use for the analysis. Sections V and VI discuss the empirical approachand results. The final section concludes and discusses the policy implicationsof our findings.

II. Background: The Education of Minorities in ChinaIn addition to the Han majority, there are 55 officially recognized minoritynationalities in China. According to the 2010 census, minorities comprised8.5% of the total national population, approximately 114 million peopleðCherng, Hannum, and Lu 2012Þ. Geographically, minorities in China areconcentrated in relatively poor regions of western China: 71.6% of the mi-nority population lives in western provinces, and 91.6% of ethnic autono-mous counties are located in western China. Approximately 40% of theseautonomous counties are nationally designated poverty counties ðHannumand Wang 2012Þ.Beyond geographically targeted antipoverty funds ðfrom which minorities

disproportionately benefit due to concentration in poor areas; Park, Wang,and Wu 2002Þ, a number of policies and programs have aimed to expandaccess to education among minority groups. For example, the 1980 Lawon Regional and Ethnic Autonomy recommended subsidization of educationin minority areas beyond standard educational funding ðCherng et al. 2012Þ.More recently, as part of the Tenth 5-Year Plan ð2001–5Þ, the central govern-ment invested approximately ¥34.2 billion for boarding schools and ethnicuniversities in western China andminority areas ðCherng et al. 2012Þ. A numberof affirmative action policies have also been implemented in higher education,such as university admissions spots reserved for minority students and accep-tance of minority students with lower entrance exam scores ðHannum andWang 2012Þ.Although certain policies have been designed to improve educational at-

tainment among minorities, the structure of education for minority groups islargely similar to the rest of the country ðCherng et al. 2012Þ. Curriculum andassessment are generally the same for minority and Han students ðChernget al. 2012Þ. One exception is the language of instruction. While official pol-icy regarding language of instruction emphasizes the use of Mandarin, schoolswith more than 50% minority students who speak a local language are per-mitted to use the local language ðCherng et al. 2012Þ. In practice, however,there are significant challenges to bilingual instruction. For example, some



minority groups with their own language do not have a written language. In


addition, there seldom are financial resources available to develop a local lan-guage curriculum. Schools also are often integrated with students attend-ing class with Han students or students belonging to other minority groupsðHannum and Wang 2012Þ. In our survey of schools in northwest Chinaðdescribed in the next sectionÞ, only 5% have no Han students. No schoolsprovide instruction or teaching material in minority languages.

III. Survey DesignThe data used in this study come from a survey of 300 schools in Shaanxi,Gansu, and Qinghai provinces in western China during the 2011/2012 aca-demic year. Schools were sampled as follows. We first obtained a list of allschools in the following regions: Haidong ðin QinghaiÞ, Longnan, Dingxi,Tianshui ðin GansuÞ, and Ankang ðin ShaanxiÞ. A map of these regions isprovided in figure 1. In total, 26 counties were included in the samplingframe. Within each township located in these five regions, one school wasselected from among all schools with 150–300 students as reported by thelocal education ministry. The survey is thus roughly representative of pri-mary schools in these regions of northwestern China.

Figure 1. Survey regions



Due to the survey’s geographical coverage, our sample includes both com-

Yang et al. 325

pletely Han ð37%Þ and completely minority schools ð5%Þ. A significant num-ber of schools ð58%Þ are mixed Han and minority schools. We focus most ofthe study on the full sample but, in some parts of the analysis, restrict thesample to mixed schools only.1 In the analysis, we define “mixed” schools asschools with at least two minority and two Han students. By restricting theanalysis to mixed schools, we are better able to pick up differences betweenHan and minority students not confounded by differences in location; how-ever, limiting the sample both limits variation in the data ðleading to less pre-cise parameter estimatesÞ and reduces representativeness.Within each school, we collected information on all fourth and fifth grad-

ers ðmore than 21,000 students in totalÞ. A survey questionnaire admin-istered to students collected detailed information on students and theirfamilies. Table 1 lists all additional variables that we use in our analysis andprovides descriptions of each. All of these variables were asked or measuredat the beginning of the school year.As our measure of academic achievement, we use student scores on stan-

dardized exams in math and Chinese administered by the survey team at theend of the school year. Within each classroom, half of the students wererandomly assigned to take a math exam, and the rest took a Chinese exam. Toensure coherence with the national curriculum, the tests were developed withassistance from local department/bureaus of education. Questions used in themath exam were drawn from the question bank of the Trends in InternationalMathematics and Science Study, an international assessment of mathematicsand science knowledge of primary and lower-secondary school students.Questions used in the Chinese exam were taken from national fourth or fifthgrade textbooks. To minimize cheating, two versions of each exam ðwithreordered questionsÞ were randomly assigned to students. The exam also wasproctored closely by the enumerators. For analysis, scores for both subjecttests are normalized by the distribution of scores in each grade. Exams weregiven in Mandarin, just like year-end tests usually given in the schools in oursample.

IV. Characteristics of Students, Peers, Teachers, and SchoolsA. Minority StatusWe solicited minority status directly from students as part of the survey. Outof the full sample, 12.5% of students identified themselves as belonging to a

1 Similar strategies of dealing with differences in location in decomposition analysis has been used in
previous studies ðsee, e.g., van de Walle and Gunewardena 2001Þ.


TABLE 1VARIABLE DESCRIPTIONS

Variable Description

Student and household characteristics:Standardized math exam score Normalized score on standardizedmath exam. Exam

designed using grade-appropriate questions fromthe Trends in International Mathematics and ScienceStudy with assistance from the Chinese Ministry ofEducation.

Standardized Chinese exam score Normalized score on standardized Chinese exam.Exam designed using questions from nationalcurriculumwith assistance from theChineseMinistryof Education.

Female ð0/1Þ Student is female.Boarding student ð0/1Þ Student boards at school.Age ðyearsÞ Student age in yearsHousehold size Total number of individuals living the in the stu-

dent’s householdTravel time to school ðminutesÞ Travel time from student’s home to school in minutesMother has lower secondary degreeor above ð0/1Þ

Student’s mother has completed middle school edu-cation or above.

Father has lower secondary degreeor above ð0/1Þ

Student’s father has completed middle school educa-tion or above.

Father at home ð0/1Þ Father currently living at home ðhas not migratedfor workÞ

Mother at home ð0/1Þ Mother currently living at home ðhas not migratedfor workÞ

Household asset index ð0/1Þ Index of household durable assets. Constructedusing first principal component of motorbike,tractor, car, van, refrigerator, air conditioning,computer, laundry machine, and dummy variablesfor type of housing ðcave house, packed earth,brick, apartment building, otherÞ

Class peer characteristic:Proportion of peers’ mothers with lowersecondary degree or above

Class level mean of “mother has lower secondarydegree or above” excluding student i

Proportion of class peers of same ethnicity Proportion of students in class of same ethnicbackground as student i

Peer average household asset index Class level mean of “household asset index”excluding student i

Teacher characteristic:Female teacher ð0/1Þ Teacher is femaleHan teacher ð0/1Þ Teacher is Han majorityTeacher has higher education degree ð0/1Þ Teacher has completed college or aboveTeacher attended normal college ð0/1Þ Teacher attended normal schoolTeacher has received provincial ornational teaching award ð0/1Þ

Teacher has received a provincial or national levelteaching award

Gongban teacher ð0/1Þ Teacher is a regular teacher, not on a short-term contract.Teacher experience ðyearsÞ Teacher years of teaching experience

School characteristic:School size ðstudentsÞ Number of students in schoolStudent-teacher ratio Student-teacher ratioDistance to farthest village served byschool ðminutesÞ

Travel time to the farthest village in school’scatchment area



minority group. Table 2 shows the distribution of each of the five main ethnic

TABLE 1 (Continued )

Variable Description

School has provided teacher training in

past year ð0/1ÞSchool has provided training to teachers in pastyear

School infrastructure index Index of school infrastructure constructed using firstprincipal component of number of classrooms,library, garden, school wall, cafeteria, playground,number of computers for student use

Yang et al. 327

groups in the sample by province. Of all minority students, 56.5% are Hui,12.3% are Salar, 13.5% are Tibetan, and 17.0% are Tu.2

For comparison, table 2 also gives the ethnic composition of 10-year-oldsfor the counties in our sample from the 2010 national census. The composi-tion found in the school survey largely mirrors census data. Our survey covers aslightly larger proportion of minorities overall ð0.8% moreÞ and a slightlylarger proportion of Tu and Salar and smaller proportion of Hui and Tibetans.Note that some difference is to be expected given time trends and that theschool survey covers a wider age range.

B. The Achievement GapAccording to our data, there is a significant achievement gap between Han andminority students ðfig. 2Þ. Leftmost bars show the mean standardized examscores in math ðdark grayÞ and Chinese ðlight grayÞ for Han students; the nextpair of bars shows mean scores for all minority students; and the remainingbars show mean scores by minority group. The gap between all minoritystudents ðall ethnic groups pooled togetherÞ and Han students is substantial:0.29 SD in math and 0.25 SD in Chinese.The data also show a striking amount of heterogeneity in exam scores

among individual minority groups. For example, students from the Tu mi-nority perform comparably to Han students. In contrast, the scores of Salarstudents are nearly 0.75 SD below those of the Han students. Importantly,figure 2 suggests that language may be a factor contributing to China’s Han-minority achievement gap. The students from the two minority groups thattypically speak non-Mandarin languages ðSalar and TibetanÞ perform muchworse than Han students. At the same time, the achievement gap betweenstudents from the two minority groups that generally speak Mandarin as theirprimary language ðTu and HuiÞ and Han students is much narrower. Givensubstantial differences between the achievement of Mandarin-speaking and

2 And 0.75% belong to other minority groups. We exclude the other category from the analysis giventheir small number.



non-Mandarin-speaking minority students, we analyze these two minority

TABLE 2SAMPLE COUNTY ETHNIC COMPOSITION IN SCHOOL SURVEY AND 2010 CENSUS (%)

Han Hui Tibetan Tu Salar

Census data:Gansu 95.74 3.95 .28 .00 .00Shaanxi 99.16 .80 .00 .00 .00Qinghai 40.62 23.44 13.00 7.72 15.00Total 88.33 7.96 1.79 1.43 .45

School survey data:Gansu 92.50 5.65 .65 .24 1.09Shaanxi 99.25 .63 .06 .00 .00Qinghai 46.38 21.47 9.97 16.46 5.72Total 87.53 7.04 1.69 2.12 1.54

Sources. 2010 census data ðChina Statistics Press, 2012Þ and authors’ survey.


groups separately in addition to analyzing the pooled sample of all minoritystudents.

C. Endowments of Background CharacteristicsThe statistics in table 3 highlight some significant differences between Hanand minority students in terms of student and household characteristics. First,minority students from both categories are significantly older than Han stu-

Figure 2. Standardized exam results by ethnic group. Uses all observations in the data set. “Other mi-nority” group excluded from graph due to small sample size. Error bars give 95% confidence intervalsconstructed using 500 bootstrap replications accounting for clustering at the school level.



dents by around 0.2–0.3 years ðtable 3Þ. In our sample, Han students were

Yang et al. 329

more likely to have repeated a grade compared to all minority groups; thus,this age difference likely reflects longer delays in primary school enrollment onthe part of minorities.3 Available evidence from other countries suggests thatdelayed enrollment may have a positive influence on academic achievementðGlewwe, Jacoby, and King 2001; McEwan and Shapiro 2008Þ; however,delayed enrollment may be due to malnutrition in early childhood ðGlewweand Jacoby 1995; Glewwe et al. 2001Þ. Second, minority students live insignificantly larger households, likely a reflection of differential treatment un-der family planning policies. Given evidence that there is a strong quality-quantity trade-off in rural China, having more siblings may disadvantageminority students ðLi, Zhang, and Zhu 2008Þ. The third significant differencebetween Han and minority students in our sample is that parents of minoritychildren are significantly less educated themselves. Numerous studies from avariety of contexts have shown evidence that parental education—particularlythe mother’s—has a causal influence on the academic achievement of chil-dren. Interestingly the one area in which minority students appear to be un-ambiguously better off is in terms of household asset ownership ðalthough thiscould reflect cheaper prices in regions where minorities are likely to liveÞ.Table 3 also shows differences in class peer characteristics. Minorities at-

tend classes with peers whose mothers are significantly less educated but whosefamilies possess more durable household assets compared to Han students.They also attend classes with a significantly smaller proportion of peers of thesame ethnicity as their own. To examine the distribution of Han and minor-ity students across schools in more detail, figure 3 plots kernel density esti-mates of this variable. These plots clearly show that Han students are muchmore concentrated in ethnically homogenous schools than are minority stu-dents. Nearly 33% of minority students are in the ethnic minority of theirclass, while this figure is only 1% for Han students.A priori it is unclear what affect peer ethnic composition may have on

student achievement for minority and Han students. Minorities ðand theirminority peersÞ are of generally lower socioeconomic status; however, theremay be advantages to attending school with peers of the same ethnicity.Beyond theories related to social identity ðAkerlof and Kranton 2002Þ, non-Mandarin minority students may benefit from classes in which teachers aremore likely to teach ðentirely or partlyÞ in the local language.4

3
In our sample, 39.5% of Han students repeated a grade. This is significantly more likely than forHui ð7 percentage points, p 5 .04Þ and Tu ð11 percentage points, p 5 .01Þ students.4 In our sample, no teachers report doing so.


TABLE

3SU

MMARYST

ATIST

ICS Minority

Stud

ents

Full

Sample

ð1Þ

Han

Stud

ents

ð2Þ

All ð3Þ

Man

darin

Spea

king

ð4Þ

Non

-Man

darin

Spea

king

ð5Þ

Differen

ceð4Þ2

ð2Þ

½P-Va

lue�

Differen

ceð5Þ2

ð2Þ

½P-Va

lue�

Stud

entan

dho

useh

oldch

aracteristic

s:Stan

dardized

mathex

amscore

.00

.04

2.25

2.14

2.58

2.17

2.62

ð1.00Þ

ð1.00Þ

ð.98Þ

ð.97Þ

ð.93Þ

½.00�

½.00�

Stan

dardized

Chine

seex

amscore

.00

.03

2.22

2.07

2.62

2.10

2.65

ð1.00Þ

ð.99Þ

ð1.06Þ

ð1.00Þ

ð1.11Þ

½.11�

½.00�

Femaleð0/1Þ

.49

.49

.48

.48

.50

2.01

.01

ð.50Þ

ð.50Þ

ð.50Þ

ð.50Þ

ð.50Þ

½.45�

½.62�

Boa

rdingstud

entð0/1Þ

.07

.07

.10

.08

.16

.02

.09

ð.26Þ

ð.25Þ

ð.30Þ

ð.28Þ

ð.36Þ

½.58�

½.19�

Ageðye

arsÞ

10.83

10.80

11.03

11.01

11.08

.21

.28

ð1.13Þ

ð1.14Þ

ð1.04Þ

ð1.08Þ

ð.90Þ

½.00�

½.00�

Hou

seho

ldsize

5.32

5.27

5.64

5.63

5.68

.35

.40

ð1.55Þ

ð1.52Þ

ð1.71Þ

ð1.64Þ

ð1.92Þ

½.00�

½.01�

Travel

timeto

scho

olðm

inutesÞ

25.72

25.91

24.34

23.85

25.63

22.07

2.29

ð26.26

Þð26.55

Þð24.03

Þð22.90

Þð26.71

Þ½.2

9�½.9

4�Mothe

rha

slower

seco

ndarydeg

reeor

abov

eð0/1Þ

.27

.28

.19

.21

.14

2.08

2.15

ð.44Þ

ð.45Þ

ð.39Þ

ð.40Þ

ð.34Þ

½.00�

½.00�

Father

haslower

seco

ndarydeg

reeor

abov

eð0/1Þ

.46

.48

.36

.37

.32

2.11

2.16

ð.50Þ

ð.50Þ

ð.48Þ

ð.48Þ

ð.47Þ

½.00�

½.00�

Father

atho

með0/1Þ

.58

.58

.55

.55

.52

2.03

2.06

ð.49Þ

ð.49Þ

ð.50Þ

ð.50Þ

ð.50Þ

½.27�

½.18�

Mothe

rat

Hom

eð0/1Þ

.68

.69

.66

.67

.63

2.02

2.05

ð.46Þ

ð.46Þ

ð.47Þ

ð.47Þ

ð.48Þ

½.48�

½.14�

Hou

seho

ldassetindex

ð0/1Þ

.00

2.05

.36

.30

.53

.35

.58

ð1.44Þ

ð1.43Þ

ð1.48Þ

ð1.45Þ

ð1.53Þ

½.00�

½.00�

330



Minority

Stud

ents

Class

pee

rch

aracteristic

:Prop

ortio

nof

peers’mothe

rswith

lower

seco

ndary

degree

orab

ove

.27

.28

.20

.22

.14

2.06

2.14

ð.17Þ

ð.17Þ

ð.16Þ

ð.17Þ

ð.10Þ

½.02�

½.00�

Prop

ortio

nof

classpee

rsof

sameethn

icity

.93

.96

.68

.70

.64

2.26

2.32

ð.18Þ

ð.10Þ

ð.36Þ

ð.36Þ

ð.36Þ

½.00�

½.00�

Peer

averag

eho

useh

oldassetindex

.00

2.05

.35

.28

.56

.33

.61

ð.63Þ

ð.62Þ

ð.61Þ

ð.54Þ

ð.74Þ

½.00�

½.00�

Teache

rch

aracteristic

:Fe

maleteache

rð0/1Þ

.40

.39

.49

.48

.51

.09

.11

ð.49Þ

ð.49Þ

ð.50Þ

ð.50Þ

ð.50Þ

½.10�

½.18�

Han

teache

rð0/1Þ

.90

.96

.51

.51

.48

2.44

2.48

ð.30Þ

ð.20Þ

ð.50Þ

ð.50Þ

ð.50Þ

½.00�

½.00�

Teache

rha

shighe

red

ucationdeg

reeð0/1Þ

.81

.80

.89

.89

.89

.09

.09

ð.39Þ

ð.40Þ

ð.32Þ

ð.31Þ

ð.31Þ

½.00�

½.05�

Teache

rattend

edno

rmal

colle

geð0/1Þ

.79

.78

.88

.88

.89

.10

.11

ð.40Þ

ð.41Þ

ð.32Þ

ð.32Þ

ð.31Þ

½.01�

½.01�

Teache

rha

srece

ived

provinc

ialo

rna

tiona

ltea

ching

awardð0/1Þ

.08

.08

.10

.11

.10

.03

.02

ð.27Þ

ð.26Þ

ð.30Þ

ð.30Þ

ð.30Þ

½.38�

½.72�

Gong

ban

teache

rð0/1Þ

.87

.88

.81

.82

.78

2.06

2.09

ð.34Þ

ð.33Þ

ð.39Þ

ð.39Þ

ð.41Þ

½.32�

½.09�

Teache

rex

perienc

eðyea

rsÞ

13.39

13.53

12.45

12.16

13.11

21.38

2.42

ð10.96

Þð11.11

Þð9.86Þ

ð10.16

Þð8.79Þ

½.29�

½.74�

Scho

olch

aracteristic

:Sc

hool

size

ðstud

entsÞ

221.05

221.47

218.08

224.03

201.71

2.59

219

.74

ð59.25

Þð58.65

Þð63.25

Þð65.51

Þð53.59

Þ½.8

5�½.1

0�Stud

ent-teache

rratio

17.41

17.29

18.28

18.50

17.63

1.21

.34

ð5.11Þ

ð5.17Þ

ð4.55Þ

ð4.57Þ

ð4.36Þ

½.14�

½.72�

Distanc

eto

farthe

stvillageserved

byscho

olðm

inutesÞ

66.83

67.70

60.77

57.56

69.62

210

.14

1.92

ð50.63

Þð50.75

Þð49.39

Þð39.84

Þð68.78

Þ½.1

7�½.9

2�

331



TABLE

3(C

ontin

ued)

Minority

Stud

ents

Full

Sample

ð1Þ

Han

Stud

ents

ð2Þ

All ð3Þ

Man

darin

Spea

king

ð4Þ

Non

-Man

darin

Spea

king

ð5Þ

Differen

ceð4Þ2

ð2Þ

½P-Va

lue�

Differen

ceð5Þ2

ð2Þ

½P-Va

lue�

Scho

olha

sprovided

teache

rtraining

inpastye

arð0/1Þ

.92

.95

.77

.77

.77

2.17

2.18

ð.26Þ

ð.23Þ

ð.42Þ

ð.42Þ

ð.42Þ

½.04�

½.12�

Scho

olinfrastruc

ture

index

.00

.01

2.07

.13

2.61

.12

2.62

ð1.21Þ

ð1.18Þ

ð1.38Þ

ð1.16Þ

ð1.77Þ

½.55�

½.22�

Sample

size:

Totaln

umber

ofstud

ents

19,129

16,741

2,38

81,75

361

7Num

ber

ofstud

ents—mathsample

9,46

88,28

61,18

287

130

1Num

ber

ofstud

ents—Chine

sesample

9,66

18,45

51,20

688

231

6Num

ber

ofscho

ols

300

285

191

167

64

Note.Variablesareas

described

intable

1.Stan

darderrors

ðinparen

thesesÞa

ccou

ntforclusterin

gat

thescho

olleve

l.



In terms of teacher and school quality ðtable 3Þ, minorities appear to be,

Figure 3. Distribution of class peer ethnic composition by ethnic group. Kernel density estimated usinga bandwidth of 0.07.

Yang et al. 333

if anything, better off than their Han counterparts. For example, teachers ofminority students are significantly more likely to have a higher education degreeand to have attended a specialized teaching college. Schools attended by Hanand minority students are similar in terms of size, student-teacher ratio, re-moteness, and infrastructure ðalthough minority schools are slightly less likelyto have provided teacher training in the past yearÞ. This may be a result of sig-nificant government educational investment focused on minority areas.Characteristics of students in mixed Han and minority schools ðwith at

least two Han and two minority studentsÞ are given in the appendix ðta-ble A1Þ. We construct mixed school samples for both types of minority stu-dents. Mixed Mandarin minority schools have at least two Mandarin minoritystudents, and mixed non-Mandarin minority schools have at least two non-Mandarin minority students. For the most part, mean differences in char-acteristics between Han and minority students attending the same schools areless significant than the full sample, as would be expected.

V. Returns to Minority StatusWe take a first look at the relationship between the achievement gap andobserved characteristics directly by estimating how the Han-minority achieve-



ment gap changes as we adjust for characteristics collected as part of our sur-


vey. That is, we estimate variants of the following regression

Yis 5 a1 b1Mandarin Minority

1 b2Non Mandarin Minority 1 X 0v1 εis;

ð1Þ

where Yis is the normalized test score of student i in schools;MandarinMinorityis a dummy variable equal to 1 if the student is Hui or Tu; Non_MandarinMinority is a dummy variable equal to 1 if the student is Tibetan or Salar; X 0 is avector of student and household, peer, teacher, and school characteristics; and εisis an error term possibly correlated at the school level. The coefficients ofinterest are b1 and b2. How these two coefficients change as we add char-acteristics to the X 0 vector from the error term provides a first look at the abilityof these characteristics to account for differences in achievement between Hanstudents and Mandarin and non-Mandarin minority students.The results of this analysis for standardized math scores are shown in

table 4.5 The raw mean differences are20.17 SD for Mandarin minority stu-dents and 20.62 SD for non-Mandarin minority students ðcol. 1Þ. Controll-ing for student and household characteristics reduces the size of these es-timates to 20.12 and 20.51 SD, respectively ðcol. 2Þ. Sequentially addingpeer, teacher, and school characteristics ðcols. 3–5Þ shows that once studentand peer characteristics are controlled for, the Mandarin minority coefficientdecreases in size and becomes insignificant. The coefficient on non-Mandarinminorities remains large ð20.2 SDÞ and significant even after controlling forschool fixed effects ðcol. 6Þ. In other words, Mandarin minority students scorean average of 0.2 SD less than Han students with similar individual, peer,and teacher characteristics in the same schools. Adding school fixed effectsði.e., controlling for all observed and unobserved school-level characteris-ticsÞ does reduce the estimated gap for this group by more than half, whichsuggests that—despite detailed controls—unobserved school-level heteroge-neity is an important factor.

VI. Decomposing the Han-Minority Achievement GapTo decompose the Han-minority achievement gap, we first estimate educa-tional productions functions, or achievement regressions, that quantify returnsto individual, family, teacher, and school-level characteristics for each of ourstudent classifications ðHan, Mandarin minority, and non-Mandarin minor-ityÞ. We then use the traditional Oaxaca-Blinder decomposition method

5 Results for Chinese scores are similar. These results are in table A2.



ðBlinder 1973; Oaxaca 1973Þ to decompose the achievement gap—between

TABLE 4MATH ACHIEVEMENT REGRESSIONS (POOLED, FULL SAMPLE)

ð1Þ ð2Þ ð3Þ ð4Þ ð5Þ ð6ÞMandarin-speaking minority 2.17*** 2.12** 2.09 2.08 2.06 2.03

ð.058Þ ð.056Þ ð.063Þ ð.065Þ ð.063Þ ð.066ÞNon-Mandarin-speaking minority 2.62*** 2.51*** 2.47*** 2.46*** 2.46*** 2.19*

ð.097Þ ð.096Þ ð.101Þ ð.093Þ ð.090Þ ð.111ÞStudent and household characteristics Yes Yes Yes Yes YesClass peer characteristics Yes Yes Yes YesTeacher characteristics Yes Yes YesSchool characteristics YesSchool fixed effects YesConstant .04 1.92*** 1.27** 1.18* 1.57** 1.13*

ð.027Þ ð.623Þ ð.631Þ ð.647Þ ð.643Þ ð.628ÞAdjusted R 2 .013 .091 .100 .101 .114 .209

Note. Each column represents a separate regression. Standard errors ðin parenthesesÞ account for clus-tering at the school level. Student and household characteristics, class peer characteristics, teacher char-acteristics, and school characteristics include those in table 1. Estimation sample includes a randomlychosen half of all sample students ðthose who were given a standardized exam in mathÞ. N 5 9,468.* p < .1.** p < .05.*** p < .01.

Yang et al. 335

Han students and both types of minority students. We decompose the gapinto two components. First, there is a component that can be explained bydifferences in student, peer, teacher, and school characteristics. In the rest ofthe analysis, we refer to this component as that due to “differences in char-acteristics.” The second component is due to between-group differences in re-turns to characteristics.The achievement regressions that we use in the decomposition are based on

the following linearized specification of the educational production function:

Yis 5 a1 b1Iis 1 b2Pis 1 b3Tis 1 b4Sis 1 εis; ð2Þ

where, as above, Yis is the observed test score of student i in schools, Iis is avector of individual student and household variables, Pis is a vector of peergroup variables, Tis is a vector of teacher characteristics, Sis is a vector of schoolvariables, and εis is an error term. The error term is allowed to be correlatedat the school level to account for clustering effects. In some specifications, wesubstitute Sis for school fixed effects ðgsÞ to control for unobserved hetero-geneity at the school level.The Han-minority achievement gap ðdifference in test scoresÞ can be ex-

pressed as

Y *H 2 Y *

M

� �5 X *

H 2 X *M

� �bH 1 X *

M bH 2 bMð Þ; ð3Þ



where Y *H and Y *

M are the predicted mean standardized test scores of Han and* *


minority students, XH and XM are the mean characteristics of Han and minor-ity students ðIis, Pis, Tis, and SisÞ; and bH and bM are the returns to characteristicsfor Han and minority students estimated using equation ð2Þ above. Note that,because individual school fixed effects cannot be estimated for minority ðHanÞstudents in schools where no minority ðHanÞ students attend, we restrict thesample to only mixed schools with at least two Han and two minority studentsin analysis that includes school fixed effects.The overall difference in exam scores can, therefore, be decomposed into

two components. One is the portion attributable to differences in the quan-tity of characteristics, evaluated using Han returns: bH X *

H 2 X *Mð Þ. The other

portion, X *M bH 2 bMð Þ, is that attributable to differences in returns to the

characteristics of Han and minority students.

A. Returns to Characteristics by Ethnic GroupTable 5 reports the results of separate math achievement regressions for Hanstudents, Mandarin minority students, and non-Mandarin minority stu-dents.6 The odd-numbered columns in the table include all characteristics intable 3; even-numbered columns substitute school characteristics for schoolfixed effects. The coefficients from these regressions ðwhich are the measuredachievement returns to the characteristicsÞ are used in the Oaxaca-Blinder de-compositions below.A few insights emerge from comparing the estimated returns to inputs

across groups. First, the pattern of returns for Han and Mandarin minoritystudents are similar ðcomparing the coefficients in table 5 cols. 1 and 2 for theHan students with the coefficients in cols. 3 and 4 for the Mandarin minor-ity studentsÞ. While some coefficient estimates for Mandarin minorities arenot significant, point estimates largely coincide. One exception is the coeffi-cient on age: after controlling for school-level fixed effects, it appears thatMandarin-speaking minority students benefit from delayed school enrollment.But, some estimated returns for non-Mandarin minority students differ

from the other two groups. For example, non-Mandarin students appear tobe strongly and negatively affected by a larger proportion of classmates ofthe same ethnicity ðcols. 5 and 6Þ. The differences in estimated returns toclass peer ethnic composition are highlighted in figure 4. While both Han

6 Results for Chinese scores are in table A3. Because power is reduced by separating the two minority
groups ðMandarin and non-Mandarin minoritiesÞ, we also conducted all analyses pooling studentswho were given the Chinese exam and students who were given the math exam to estimate returnswith more precision. Qualitative results of the analyses do not change substantially when using thepooled sample.


students and Mandarin minority students benefit slightly from being in classes

Yang et al. 337

with more students of their same ethnicity, there is a negative correlation amongnon-Mandarin minority students even after controlling for fixed school-levelfactors. In other words, having more class peers of a students’ same ethnicityhas a large, negative relationship with achievement of non-Mandarin minoritystudents. Given the large degree of underperformance of students from the non-Mandarin minority group, this correlation may be in part due to the effect ofhaving lower-achieving peers. Non-Mandarin-speaking students also appearto be strongly influenced by the quality of teaching. Both the coefficient onhaving a teacher who has received a teaching award ðcols. 3 and 6Þ and thecoefficient on the school having provided teacher training are large and sig-nificant for this group but not in others.Table 6 repeats these regressions for the sample of mixed schools.7 Com-

pared to the full sample, estimated returns are much more similar for Hanand minority students ðof both typesÞ attending the same schools. This sug-gests that the large differences in returns observed in the full sample are largelydue to differences between Han students in Han-only schools and minoritiesin minority-only schools.

B. Returns to Schools by Ethnic GroupWhile estimated returns to observed school characteristics are similar for Hanand minority students, there may still be differences in estimated school fixedeffects for Han and minority students. That is, returns to specific schoolsðaccounting for observed and unobserved characteristicsÞ may differ betweenHan and minority students. To examine this in more detail, we estimate theschool fixed effect version of equation ð2Þ for Han and minority studentsseparately, using the sample of mixed schools with at least two Han studentsand two minority students:8

Y His 5 aH 1 X H 0

bH 1 gHs 1 εHis ; ð4aÞ

Y Mis 5 aM 1 XM 0

bM 1 gMs 1 εMis ; ð4bÞ

where X includes the same student, peer, and teacher characteristics as above,and gs is a vector of school dummy variables. We interpret the estimatedschool fixed effects for Han students ðgH

s Þ and minority students ðgMs Þ as the

return of attending a specific school for Han and minority students, respectively,

7 Results for Chinese scores are in table A4.8 For this part of the analysis, we pool both types of minority students.



TABLE

5MATH

ACHIEVEMENTREGRESS

IONSBYETH

NICITY

Han

Stud

ents

Man

darin-Spea

king

Minority

Non

-Man

darin-Spea

king

Minority

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

Stud

entan

dho

useh

oldch

aracteristic

s:Fe

maleð0/1Þ

2.22***

2.22***

2.25***

2.24***

2.10

2.13

ð.021

Þð.0

20Þ

ð.061

Þð.0

72Þ

ð.076

Þð.0

85Þ

Boa

rdingstud

entð0/1Þ

2.17***

2.15***

2.34*

2.28

2.24

.05

ð.042

Þð.0

46Þ

ð.173

Þð.1

98Þ

ð.148

Þð.1

01Þ

Ageðye

arsÞ

2.16

2.13

.49

1.09

*.77

2.72

ð.113

Þð.1

07Þ

ð.533

Þð.5

78Þ

ð.855

Þð1.099

ÞAge2

.00

2.00

2.02

2.05**

2.03

.03

ð.005

Þð.0

05Þ

ð.023

Þð.0

25Þ

ð.038

Þð.0

47Þ

Hou

seho

ldsize

2.01**

2.01

2.02

2.03

.02

.01

ð.007

Þð.0

06Þ

ð.020

Þð.0

23Þ

ð.021

Þð.0

25Þ

Travel

timeto

scho

olðm

inutesÞ

.00***

.00***

.00

.00

.00

.00

ð.000

Þð.0

01Þ

ð.001

Þð.0

02Þ

ð.002

Þð.0

02Þ

Mothe

rha

slower

seco

ndarydeg

reeor

abov

eð0/1Þ

.03

.03

.04

.08

2.35**

2.23

ð.025

Þð.0

25Þ

ð.084

Þð.0

93Þ

ð.141

Þð.1

39Þ

Father

haslower

seco

ndarydeg

reeor

abov

eð0/1Þ

.25***

.20***

.19***

.21***

.13

.14

ð.024

Þð.0

24Þ

ð.071

Þð.0

80Þ

ð.145

Þð.1

66Þ

Father

atho

með0/1Þ

2.00

.01

2.13*

2.11

2.11

2.07

ð.022

Þð.0

22Þ

ð.069

Þð.0

83Þ

ð.140

Þð.1

69Þ

Mothe

rat

homeð0/1Þ

2.00

2.03

.12

.07

2.13

2.08

ð.026

Þð.0

24Þ

ð.076

Þð.0

94Þ

ð.104

Þð.1

15Þ

Hou

seho

ldassetindex

ð0/1Þ

.02**

.02**

.02

.02

2.00

.01

ð.008

Þð.0

08Þ

ð.024

Þð.0

29Þ

ð.030

Þð.0

36Þ

Class

pee

rch

aracteristic

:Prop

ortio

nof

pee

rs’mothe

rswith

lower

seco

ndarydeg

reeor

abov

e.17

2.43**

.23

.60

2.46

1.81

***

ð.154

Þð.2

01Þ

ð.246

Þð.5

37Þ

ð.867

Þð.5

33Þ

Prop

ortio

nof

classpee

rsof

sameethn

icity

.20

.09

.10

.44

2.66**

23.28

***

ð.171

Þð.5

20Þ

ð.109

Þð.5

36Þ

ð.272

Þð1.207

ÞPe

eraverag

eho

useh

oldassetindex

.08**

2.06

.16*

2.05

.13

2.23

ð.039

Þð.0

62Þ

ð.084

Þð.1

53Þ

ð.155

Þð.1

44Þ

338



Han

Stud

ents

Man

darin-Spea

king

Minority

Non

-Man

dar-

in-

Spea

king

Minority

Teache

rch

aracteristic

:Fe

maleteache

rð0/1Þ

.01

2.01

2.05

2.13

2.28*

.08

ð.047

Þð.0

47Þ

ð.083

Þð.1

39Þ

ð.164

Þð.1

33Þ

Han

teache

rð0/1Þ

.04

.02

2.05

2.13

2.26

2.84***

ð.094

Þð.1

46Þ

ð.077

Þð.1

11Þ

ð.168

Þð.2

16Þ

Teache

rha

shighe

red

ucationdeg

reeð0/1Þ

2.01

.03

.19

2.38*

.12

.20

ð.059

Þð.0

76Þ

ð.132

Þð.2

15Þ

ð.202

Þð.1

20Þ

Teache

rattend

edno

rmal

colle

geð0/1Þ

2.03

2.02

.03

2.32

.13

2.28

ð.049

Þð.0

50Þ

ð.165

Þð.1

99Þ

ð.196

Þð.1

74Þ

Teache

rha

srece

ived

provinc

ialo

rna

tiona

ltea

chingaw

ardð0/1Þ

2.05

.03

2.04

2.24

1.03

***

1.14

***

ð.057

Þð.0

65Þ

ð.119

Þð.1

76Þ

ð.243

Þð.2

13Þ

Gong

ban

teache

rð0/1Þ

2.01

.07

.25

.38

2.11

.05

ð.059

Þð.0

53Þ

ð.169

Þð.3

43Þ

ð.138

Þð.0

85Þ

Teache

rex

perienc

eðye

arsÞ

2.00

2.00

2.00

2.02***

.01

.03***

ð.002

Þð.0

03Þ

ð.005

Þð.0

08Þ

ð.010

Þð.0

08Þ

Scho

olch

aracteristic

:Sc

hool

size

ðstud

entsÞ

2.00

.00*

.00

ð.000

Þð.0

01Þ

ð.002

ÞStud

ent-teache

rratio

2.02***

2.00

2.01

ð.004

Þð.0

10Þ

ð.012

ÞDistanc

eto

farthe

stvillageserved

byscho

olðm

inutesÞ

.00

.00*

2.00

ð.000

Þð.0

01Þ

ð.002

ÞSc

hool

hasprovided

teache

rtraining

inpastye

arð0/1Þ

.02

2.01

.61***

ð.083

Þð.1

10Þ

ð.193

ÞSc

hool

infrastruc

ture

index

.01

.12***

.08**

ð.024

Þð.0

31Þ

ð.040

ÞSc

hool

fixed

effects

No

Yes

No

Yes

No

Yes

Con

stan

t1.81

***

1.59

23.27

25.53

25.31

5.78

ð.676

Þð1.044

Þð3.072

Þð3.356

Þð4.817

Þð6.541

ÞObservations

8,28

687

130

1Adjusted

R2

.109

.205

.107

.153

.078

.172

Note.Ea

chco

lumnrepresentsaseparateregression.

Stan

darderrors

ðinparen

thesesÞa

ccou

ntforc

lusteringat

thescho

olleve

l.Estim

ationsample

includ

esarand

omlych

osen

halfof

allsam

ple

stud

ents

ðthosewho

weregiven

astan

dardized

exam

inmathÞ.

*p<.1.

**p<.05.

***p<.01.

339



Figure4.

Pred

ictedreturnsto

classpee

rethn

icco

mpositio

nbyethn

icgroup

.A,M

ath,

noscho

olfi

xedeffects;

B,m

athwith

scho

olfi

xedeffects;

C,Chine

se,no

scho

olfi

xed

effects;D,C

hine

sewith

scho

olfi

xedeffects.



relative to a reference school ðthe school whose dummy variable is omitted9

Yang et al. 341

from the regressionsÞ.We estimate that, on average, school fixed effects estimated for Han stu-

dents are 0.3 SD higher in math and 0.39 SD higher in Chinese comparedto those for minority students. Both of these differences are significant at 1%.To compare the effects of a specific school on Han and minority students di-rectly, figure 5 plots the school coefficients for Han ðgH

s Þ against those es-timated for minority students ðgM

s Þ. Figure 5A does this for math scores,and 5B for Chinese scores. In these figures, the majority of schools ð63% ofschools for math and 72% for ChineseÞ lie below the 45° line ðwhere gH

s andgMs are equalÞ. Individual schools tend to generate larger returns for Han com-

pared to similar minority students attending the same school. In other words,the benefits that Han students receive from ðobserved and unobservedÞ attri-butes of individual schools tend to be larger than the benefits received by mi-nority students.What types of schools have larger differences in their effect on Han and

minority students? We examine school-level differences in Han and minor-ity effects by estimating the following regression:

gHs 2 gM

s

� �5 a1 bXs 1 εs; ð5Þ

where Xs is a vector of school-level characteristics and εs is an error term.Here, the Xs vector includes the same teacher characteristics ðaggregated to theschool levelÞ and school characteristics as above, as well as the proportion ofstudents belonging to a Mandarin minority group and the proportion be-longing to a non-Mandarin minority group. We use White-Huber standarderrors to account for heteroskedasticity.The results of this analysis are in table 7. In the full models for math and

Chinese, observed covariates explain more than 25% of the variation of thedifference between the return of school characteristics to Han students andto minority students. Focusing on the results for math, it appears that teach-ers play the most significant role in reducing the Han-minority difference inreturns. Coefficients on variables related to teachers’ education and experi-ence are negative and highly significant. Assuming that these variables ðhaving ahigher education degree, attending a normal college, and teaching experienceÞreflect teaching quality, these results suggest that pedagogical practice in theclassroom highly influences how much Han and minority students benefit fromspecific schools.

9 This analysis is similar to that used in Meng ð2004Þ to examine the effect of firm-level wage policies
on gender wage gaps.


TABLE

6MATH

ACHIEVEMENTREGRESS

IONSBYETH

NICITY

(MIXED

SCHOOLS

ONLY

)

Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Han

Stud

ents

Man

darin-Spea

king

Minority

Stud

ents

Han

Stud

ents

Non

-Man

darin-

Spea

king

Minority

Stud

ents

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

ð7Þ

ð8Þ

Stud

entan

dho

useh

oldch

aracteristic

s:Fe

maleð0/1Þ

2.20***

2.17***

2.40***

2.31***

2.13

2.12

2.25

2.22

ð.054

Þð.0

58Þ

ð.094

Þð.1

03Þ

ð.088

Þð.0

89Þ

ð.162

Þð.1

76Þ

Boa

rdingstud

entð0/1Þ

2.30***

2.26**

2.70***

2.56***

.13

.10

.17

.14

ð.084

Þð.1

12Þ

ð.186

Þð.1

79Þ

ð.163

Þð.2

09Þ

ð.117

Þð.0

90Þ

Ageðyea

rsÞ

.18

2.12

.24

.31

1.10

**1.34

***

.92

.43

ð.262

Þð .2

51Þ

ð.638

Þð.6

75Þ

ð.421

Þð.4

46Þ

ð1.086

Þð1.271

ÞAge2

2.01

2.00

2.01

2.02

2.06***

2.07***

2.03

2.02

ð.011

Þð.0

11Þ

ð.027

Þð.0

28Þ

ð.017

Þð.0

19Þ

ð.048

Þð.0

56Þ

Hou

seho

ldsize

2.02

2.02

.03

.00

2.00

2.01

.04

.05

ð.018

Þð.0

18Þ

ð.032

Þð.0

36Þ

ð.045

Þð.0

47Þ

ð.040

Þð.0

39Þ

Travel

timeto

scho

olðm

inutesÞ

.00

2.00

.00*

.00**

2.00

2.00

.00

.00

ð.002

Þð.0

02Þ

ð.001

Þð.0

01Þ

ð.003

Þð.0

03Þ

ð.002

Þð.0

02Þ

Mothe

rha

slower

seco

ndary

deg

reeor

abov

eð0/1Þ

.06

.05

.04

.04

2.05

2.08

2.38

2.18

ð.063

Þð.0

65Þ

ð.114

Þð.1

19Þ

ð.157

Þð.1

67Þ

ð.228

Þð.2

28Þ

Father

haslower

seco

ndarydeg

ree

orab

oveð0/1Þ

.32***

.30***

.32***

.39***

.26**

.27**

.22

.21

ð.064

Þð.0

65Þ

ð.114

Þð.1

29Þ

ð.109

Þð.1

15Þ

ð.219

Þð.2

28Þ

Father

atho

með0/1Þ

.06

.08

2.16

2.11

2.09

2.10

2.16

2.10

ð.060

Þð.0

66Þ

ð.106

Þð.1

18Þ

ð.115

Þð.1

15Þ

ð.235

Þð.2

16Þ

Mothe

rat

homeð0/1Þ

.05

.06

.20*

.13

2.12

2.11

2.17

2.09

ð.068

Þð.0

70Þ

ð.108

Þð.1

42Þ

ð.109

Þð.1

15Þ

ð.245

Þð.2

87Þ

Hou

seho

ldassetindex

ð0/1Þ

.03

.03

2.01

2.03

.03

.03

.03

.02

ð.021

Þð.0

21Þ

ð.034

Þð.0

36Þ

ð.052

Þð.0

55Þ

ð.056

Þð.0

65Þ

Class

pee

rch

aracteristic

:Prop

ortio

nof

pee

rs’mothe

rswith

lower

seco

ndarydeg

reeor

abov

e.07

2.10

.08

.65

.05

.98

.02

2.59

ð.304

Þð.5

66Þ

ð.370

Þð.8

76Þ

ð.843

Þð1.154

Þð2.162

Þð3.253

ÞProp

ortio

nof

classpee

rsof

sameethn

icity

2.20

.13

.38

.61

.06

.92

.38

.24

ð.263

Þð.5

92Þ

ð.241

Þð.4

54Þ

ð.328

Þð1.026

Þð.8

44Þ

ð1.802

ÞPe

eraverag

eho

useh

oldassetindex

2.06

2.36**

.30**

2.10

2.22

2.06

2.40

2.53

ð.100

Þð.1

62Þ

ð.135

Þð.3

40Þ

ð.181

Þð.2

03Þ

ð.325

Þð.3

95Þ

342



Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Teache

rch

aracteristic

:Fe

maleteache

rð0/1Þ

2.18**

2.22**

2.25**

2.27

.12

2.29

.03

.03

ð.078

Þð.1

07Þ

ð.110

Þð.2

24Þ

ð.139

Þð.3

03Þ

ð.267

Þð.4

69Þ

Han

teache

rð0/1Þ

.06

.09

2.03

2.16

2.41**

2.20

2.75***

2.50

ð.158

Þð.1

82Þ

ð.149

Þð.1

86Þ

ð.148

Þð.1

75Þ

ð.249

Þð.3

04Þ

Teache

rha

shighe

red

ucationdeg

reeð0/1Þ

2.04

2.09

2.27

21.06

***

.23

.47**

2.02

2.29

ð.105

Þð.1

25Þ

ð.210

Þð.3

08Þ

ð.157

Þð.2

17Þ

ð.273

Þð.3

41Þ

Teache

rattend

edno

rmal

colle

geð0/1Þ

2.00

.05

.01

2.37

2.29

.03

2.54

21.34

***

ð.083

Þð.1

24Þ

ð.244

Þð.2

62Þ

ð.187

Þð.2

89Þ

ð.342

Þð.2

96Þ

Teache

rha

srece

ived

provinc

ial

orna

tiona

ltea

chingaw

ardð0/1Þ

2.03

.01

2.19

2.20

2.65

2.85***

.40

.47

ð.097

Þð.1

42Þ

ð.126

Þð.1

73Þ

ð.404

Þð.2

31Þ

ð.398

Þð.4

66Þ

Gong

ban

teache

rð0/1Þ

.23***

.18

.77***

.74

.60**

.58

.41

1.70

ð.072

Þð.1

35Þ

ð.239

Þð.4

75Þ

ð.253

Þð.5

26Þ

ð.731

Þð1.357

ÞTe

ache

rex

perienc

eðyea

rsÞ

2.01*

2.01

2.01

2.03***

2.01

.01

2.01

2.04

ð.004

Þð.0

06Þ

ð.006

Þð.0

08Þ

ð.008

Þð.0

16Þ

ð.020

Þð.0

37Þ

Scho

olch

aracteristic

:Sc

hool

size

ðstud

entsÞ

2.00

.00

2.00**

2.00

ð.001

Þð.0

01Þ

ð.001

Þð.0

03Þ

Stud

ent-teache

rratio

2.03***

2.03*

.00

.06*

ð.011

Þð.0

18Þ

ð.019

Þð.0

27Þ

Distanc

eto

farthe

stvillageserved

byscho

olðm

inutesÞ

.00

.00**

.00

2.00

ð.001

Þð.0

02Þ

ð.001

Þð.0

02Þ

Scho

olha

sprovided

teache

rtraining

inpastye

arð0/1Þ

2.23

2.28

21.07

***

2.16

ð.174

Þð.2

55Þ

ð.278

Þð.2

39Þ

Scho

olinfrastruc

ture

index

.09**

.03

2.16**

2.09

ð.041

Þð.0

47Þ

ð.062

Þð.1

28Þ

Scho

olfix

edeffects

No

Yes

No

Yes

No

Yes

No

Yes

Con

stan

t.50

1.26

21.47

2.80

24.05

28.13

***

26.09

22.85

ð1.566

Þð1.518

Þð3.782

Þð4.038

Þð2.515

Þð2.738

Þð6.261

Þð7.364

ÞObservations

1,33

840

139

113

9Adjusted

R2

.149

.187

.163

.234

.092

.094

.082

.193

Note.Ea

chco


Stan

darderrorsðin

paren

thesesÞa

ccou

ntforclusteringat

thescho

olleve

l.Estim

ationsampleinclud

esarand

omlych

osen

half

ofallsam

ple

stud

ents

ðthosewho

weregiven

astan

dardized

exam

inmathÞ

inmixed

scho

ols.

*p<.1.

**p<.05.

***p<.01.

343



C. Decomposition Results

Figure 5. School fixed effects by ethnicity. A, Math; B, Chinese. Estimated using all mixed schools withmore than two minority students and two Han students.


The results of the Oaxaca-Blinder decomposition for math are presented intable 8.10 The first three columns show results for the full sample, the nextthree for the mixed school sample without including school fixed effects, and

10 Results for Chinese are shown in table A5.



TABLE 7CORRELATES OF DIFFERENCES IN RETURNS TO SCHOOL CHARACTERISTICS (SCHOOL FE)

BETWEEN HAN AND MINORITY STUDENTS

Math Chinese

ð1Þ ð2Þ ð3Þ ð4Þ ð5Þ ð6ÞProportion Mandarin minority students 2.11 .12 .12 2.26 .22 .22

ð.328Þ ð.304Þ ð.417Þ ð.230Þ ð.262Þ ð.375ÞProportion non-Mandarin minority students .55* .54 .69 1.11*** 1.36*** .22

ð.328Þ ð.433Þ ð.558Þ ð.310Þ ð.340Þ ð.571ÞProportion of female teachers 2.06 2.08 .16 .19

ð.175Þ ð.205Þ ð.202Þ ð.200ÞProportion of Han teachers 2.31 2.29 .43** .37

ð.314Þ ð.419Þ ð.206Þ ð.292ÞProportion of teachers with higher

education degree 2.81*** 2.92*** 2.19 2.23ð.294Þ ð.308Þ ð.313Þ ð.303Þ

Proportion of teachers who attendednormal college or university 21.48*** 21.45*** 2.36 2.35

ð.242Þ ð.254Þ ð.250Þ ð.230ÞProportion of teachers who have received

provincial or national teaching awards .25 .28 2.51* 2.63*ð.251Þ ð.312Þ ð.303Þ ð.347Þ

Proportion of Gongban teachers .34 .23 2.58 2.33ð.390Þ ð.389Þ ð.505Þ ð.471Þ

Average teacher experience ðyearsÞ 2.03** 2.03*** 2.01 2.01ð.012Þ ð.011Þ ð.013Þ ð.011Þ

School size ðstudentsÞ .00 2.00ð.002Þ ð.001Þ

Student-teacher ratio 2.02 .02ð.016Þ ð.013Þ

Distance to farthest village servedby school ðminutesÞ 2.00 .00***

ð.002Þ ð.001ÞSchool has provided teacher training

in past year ð0/1Þ 2.10 2.44ð.250Þ ð.278Þ

School infrastructure index 2.07 2.01ð.091Þ ð.079Þ

Constant .46*** 2.59*** 3.17*** .52*** 1.19** 1.24*ð.123Þ ð.598Þ ð.859Þ ð.102Þ ð.560Þ ð.713Þ

Observations 75 75 75 73 73 73Adjusted R 2 .080 .297 .260 .147 .193 .254

Note. Dependent variable is the difference between the estimated school fixed effect ðFEÞ for Han studentsand the estimated school fixed effect for minority students. School fixed effects used to construct the de-pendent variablewere estimated using ordinary least squares regressions of student standardized exam scoreson all student, peer, and teacher characteristics in table1 and school dummy variables using the sample ofmixed schools only. All covariates are at the school level.* p < .1.** p < .05.*** p < .01.



TABLE

8OAXACA

DECOMPOSITIONS

(MATH

)

Mixed

Scho

olsOnly

FullSa

mple

NoSc

hool

FESc

hool

FE

All

Minority

ð1Þ

Man

darin

Minority

ð2Þ

Non

-Man

darin

Minority

ð3Þ

All

Minority

ð4Þ

Man

darin

Minority

ð5Þ

Non

-Man

darin

Minority

ð6Þ

All

Minority

ð7Þ

Man

darin

Minority

ð8Þ

Non

-Man

darin

Minority

ð9Þ

Totalg

ap.29***

.17***

.62***

.10

.01

.24*

.10

.01

.24

ð.058

Þð.0

54Þ

ð.084

Þð.0

66Þ

ð.068

Þð.1

29Þ

ð.073

Þð.0

67Þ

ð.150

ÞDifferen

tch

aracteristic

s.15***

.15**

.18**

2.02

2.00

2.09

.10

.15

2.10

ð.059

Þð.0

61Þ

ð.070

Þð.0

78Þ

ð.105

Þð.0

94Þ

ð.146

Þð.1

64Þ

ð.216

ÞStud

entan

dho

useh

old

.06***

.05***

.10***

.05**

.03

.06

.04*

.03

.06

ð.015

Þð.0

17Þ

ð.019

Þð.0

23Þ

ð.027

Þð.0

68Þ

ð.024

Þð.0

29Þ

ð.073

ÞPe

er.04

.03

.04

2.03

2.06

.05

2.03

.14

.35

ð.050

Þð.0

46Þ

ð.064

Þð.0

80Þ

ð.081

Þð.1

26Þ

ð.241

Þð.2

05Þ

ð.340

ÞTe

ache

r.02

.02

.03

2.00

.05

.12

.01

.07

.23

ð.046

Þð.0

47Þ

ð.046

Þð.0

45Þ

ð.064

Þð.1

51Þ

ð.051

Þð.0

64Þ

ð.206

ÞSc

hool

.03

.04

.02

2.04

2.02

2.32*

ð.023

Þð.0

26Þ

ð.034

Þð.0

31Þ

ð.047

Þð.1

89Þ

Scho

olfix

edeffects

.08

2.09

2.75*

ð.272

Þð.2

90Þ

ð.412

ÞDifferen

treturnsto

characteristic

s.14**

.03

.44***

.12

.01

.33***

2.00

2.14

.34

ð.065

Þð.0

63Þ

ð.099

Þð.0

77Þ

ð.097

Þð.1

15Þ

ð.153

Þð.1

51Þ

ð.259

ÞObservations

9,46

89,15

78,58

72,58

21,73

953

02,58

21,73

953

0

Note.Firstthree

columns

useallsch

oolsin

sample;lastsixco

lumns

usesample

ofmixed

scho

olson

ly.E

stim

ationsample

includ

esarand

omlych

osen

halfof

allsam

ple

stud

ents

ðthosewho

weregiven

astan

dardized

exam

inmathÞ.S

tand

arderrors

ðinparen

thesesÞa

ccou

ntforclusterin

gat

thescho

olleve

l.FE

5fixe

deffects.

*p<.1.

**p<.05.

***p<.01.



the last three for the mixed sample with school fixed effects included. Within

Yang et al. 347

each set of columns we give results for the comparison between Han andð1Þ all minority students, ð2Þ Mandarin minority students, and ð3Þ non-Mandarin minority students. The first row shows the estimated total gap. Thesecond row gives the total portion of the gap estimated to be due to differ-ences in Han and minority characteristics. Estimated subtotals for each cate-gory of included characteristic ðstudent and household characteristics, peercharacteristics, teacher characteristics, and school characteristics or fixed effectsÞadd up to row 2. The penultimate row gives the portion of the gap due todifferences in returns to characteristics.The first key result of the decomposition analysis is that, for both groups,

differences in student and household endowments are the largest explainedcontributor to the Han-minority achievement gap ðfirst rowÞ. For Mandarinminorities, differences in these variables account for 29.4% of the gap inmath. Likewise, for non-Mandarin minority students these variables explain16.1% of the math gap—more than any of the other explained components.The second key finding is that a much larger portion of the gap can be

explained for Mandarin minority students compared to non-Mandarin mi-nority students. We estimate that differences in endowments explain 88% ofthe math gap for Mandarin minority students while only explaining 29% of thegap for non-Mandarin minority students ðcols. 2 and 3, second rowÞ.A third key finding is that the gap between Han and Mandarin minority

students disappears when we restrict the sample to schools with both Han andminority students but remains large for non-Mandarin minority students ðcols.4–9Þ. The achievement gap between Han and Mandarin minority students isthus nearly entirely due to the high performance of Han students in schoolswithout minority students. The math gap for non-Mandarin minority studentsðcols. 6 and 9Þ is reduced by 0.38 SD ð61%Þ yet remains large with non-Mandarin minority students scoring 0.24 SD below their Han counterparts.This gap is fully due to differences in returns to characteristics. Results changelittle when we substitute school characteristics for school fixed effects.Following this set of findings, the decomposition analysis implies that dif-

ferences in characteristics are unable to explain 0.44 SD ð71%Þ of the gap inmath11 between Han students and non-Mandarin minority students in thefull sample and none of the gap after restricting the sample to mixed schoolsonly. This unexplained gap has several possible interpretations. First, it mayindicate that some inputs that are important determinants of learning for

11 Note that 0.64 SD ð98.5%Þ of the gap in Chinese scores is unexplained by differences in char-
acteristics.


these students are omitted. However, it is likely that the influence of these is


limited given the large portion of the gap explained between Han and Man-darin minority students.Another explanation is that, even when given similar educational resources

ðor inputsÞ, non-Mandarin minority students benefit less from these inputs.This could be due to these students facing a different schooling environment,even when in the same class as Han students. For example, lower teacher ex-pectations could lead them to focus instruction on Han who they believe maybenefit more from their instruction ðcf. McEwan and Trowbridge 2007Þ. Thiscould also be due to students having difficulty comprehending instructionin Mandarin. Even though instruction in local ethnic languages is permittedin China, this is often difficult in practice ðCherng et al. 2012Þ. For example,instruction in Salar and Tibetan languages is not feasible when these studentsattend school with students of other ethnicities, a common occurrence in oursample.

VII. Summary and ConclusionsThe goal of this article was to document and explain the gap in educationalachievement between Han and minority students in primary schools inwestern China. In our survey of 300 schools in Shaanxi, Gansu, and Qinghaiprovinces ðinvolving nearly 21,000 fourth- and fifth-grade studentsÞ, we findlarge differences in achievement on standardized exams between Han andminority students. On average, minority students perform 0.25 SD lower inmath and 0.22 SD lower in Chinese. Most strikingly, minority students whodo not generally speak Mandarin as their primary language score 0.62 SDlower than Han in math and 0.65 SD lower than Han in Chinese.Using decomposition methods pioneered by Oaxaca ð1973Þ and Blinder

ð1973Þ, we find that most of the achievement gap between Han and minoritystudents with no alternative ethnic language can be explained by differencesin endowments of student, family, and school characteristics. Of these, dif-ferences in students and family characteristics appear to contribute the mostto differences in achievement. Little of the gap between Han students andnon-Mandarin minority students ðSalar and Tibetan in our sampleÞ, however,can be explained by endowment differences. Comparing these students onlyto Han students in the same schools significantly reduces the size of theachievement gap, yet a difference of more than 0.2 SD persists. None of thisremaining gap is explained by differences in endowments. Although severalexplanations are possible, we believe that a likely explanation is that the abilityof students to learn may be hindered by difficulty comprehending instruction



in Mandarin ðgiven that no schools in our sample provided instruction or

Yang et al. 349

texts in minority languagesÞ. While we cannot say with certainty why thesestudents may benefit less from a given amount of schooling inputs, ouranalysis suggests that teachers play a significant role.While we believe that the findings of this article are important, admittedly,

the study has a number of limitations. First, although our sample contains suf-ficient numbers of minority students to conduct analyses, studies involving alarger sample of minority students ðparticularly non-Mandarin minority stu-dentsÞ would provide further insight into the achievement gap. Second, our sur-vey did not collect information on the Mandarin ability of individual studentsðalthough we tested students on the Chinese curriculum, this may be distinctfrom pure language abilityÞ. Future studies should employ such information toassess to what degree language is contributing to the underperformance of stu-dents belonging to groups that do not speakMandarin as their primary language.Despite these limitations, however, our results call for the attention of

policy makers to approaches to address the underperformance of minoritystudents in China’s rural areas. Given the large and increasing importance ofeducational attainment to economic well-being, addressing the large achieve-ment gap between Han and minority students may help to mitigate economicdisparities in the future. On the basis of our results, promising approaches toaddress the achievement gap would include those focused on improving thereturns to minority students of given schooling inputs ðe.g., through peda-gogical practiceÞ. Further, if future studies show language to contribute sig-nificantly to the gap, interventions such as remedial tutoring in Mandarin mayalso yield large benefits.



TABLE

A1

SUMMARYST

ATIST

ICSFO

RMIXED

SCHOOLSA

MPLE

Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Han

Stud

ents

ð1Þ

Man

darin

Minority

Stud

ents

ð2Þ

Differen

ceð2Þ2

ð1Þ

½P-value�

Han

Stud

ents

ð3Þ

Non

-Man

darin

Minority

Stud

ents

ð4Þ

Differen

ceð4Þ2

ð3Þ

½P-value�

Stud

entan

dho

useh

oldch

aracteristic

s:Stan

dardized

mathex

amscore

2.08

2.06

.03

2.16

2.43

2.27

ð1.00Þ

ð.97Þ

½.68�

ð.95Þ

ð.94Þ

½.08�

Stan

dardized

Chine

seex

amscore

2.04

2.05

2.01

2.02

2.39

2.37

ð1.02Þ

ð1.01Þ

½.95�

ð.99Þ

ð1.1Þ

½.03�

Femaleð0/1Þ

.48

.45

2.03

.5.5

0ð.5

Þð.5

Þ½.1

3�ð.5

Þð.5

Þ½.9

4�Boa

rdingstud

entð0/1Þ

.08

.09

.01

.08

.26

.18

ð.27Þ

ð.28Þ

½.77�

ð.27Þ

ð.44Þ

½.13�

Ageðyea

rsÞ

10.85

10.92

.07

10.72

11.19

.47

ð1.08Þ

ð1.1Þ

½.31�

ð1.08Þ

ð1.06Þ

½.00�

Hou

seho

ldsize

5.24

5.32

.08

5.19

5.33

.14

ð1.46Þ

ð1.54Þ

½.38�

ð1.39Þ

ð1.71Þ

½.36�

Travel

timeto

scho

olðm

inutesÞ

23.52

22.43

21.08

25.53

31.4

5.87

ð25.7Þ

ð25.53

Þ½.6

7�ð22.99

Þð32.27

Þ½.2

9�Mothe

rha

slower

seco

ndarydeg

reeor

abov

eð0/1Þ

.27

.23

2.04

.2.15

2.05

ð.44Þ

ð.42Þ

½.25�

ð.4Þ

ð.36Þ

½.22�

Father

haslower

seco

ndarydeg

reeor

abov

eð0/1Þ

.46

.42.06

.38

.34

2.04

ð.5Þ

ð.49Þ

½.16�

ð.49Þ

ð.48Þ

½.42�

Father

atho

með0/1Þ

.59

.57

2.03

.52

.59

.07

ð.49Þ

ð.5Þ

½.51�

ð.5Þ

ð.49Þ

½.30�

Mothe

rat

Hom

eð0/1Þ

.69

.69

0.68

.61

2.07

ð.46Þ

ð.46Þ

½.94�

ð.47Þ

ð.48Þ

½.34�

Hou

seho

ldassetindex

ð0/1Þ

.01

.4.39

.13

.18

.06

ð1.37Þ

ð1.43Þ

½.00�

ð1.38Þ

ð1.5Þ

½.80�

Class

pee

rch

aracteristic

:Prop

ortio

nof

pee

rs’mothe

rswith

lower

seco

ndarydeg

reeor

abov

e.26

.26

2.01

.2.16

2.03

ð.17Þ

ð.17Þ

½.82�

ð.11Þ

ð.11Þ

½.25�

Appendix

350



Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Prop

ortio

nof

classpee

rsof

sameethn

icity

.9.52

2.38

.81

.43

2.38

ð.15Þ

ð.33Þ

½.00�

ð.21Þ

ð.25Þ

½.00�

Peer

averag

eho

useh

oldassetindex

.02

.35

.33

.12

.22

.1ð.5

6Þð.5

3Þ½.0

0�ð.6

3Þð.6

7Þ½.5

5�Te

ache

rch

aracteristic

:Fe

maleteache

rð0/1Þ

.36

.49

.12

.4.48

.07

ð.48Þ

ð.5Þ

½.08�

ð.49Þ

ð.5Þ

½.54�

Han

teache

rð0/1Þ

.92

.62

2.3

.86

.61

2.25

ð.27Þ

ð.49Þ

½.00�

ð.35Þ

ð.49Þ

½.05�

Teache

rha

shighe

red

ucationdeg

reeð0/1Þ

.78

.93

.15

.89

.9.01

ð.41Þ

ð.25Þ

½.00�

ð.31Þ

ð.3Þ

½.86�

Teache

rattend

edno

rmal

colle

geð0/1Þ

.79

.91

.12

.77

.83

.05

ð.41Þ

ð.28Þ

½.00�

ð.42Þ

ð.38Þ

½.46�

Teache

rha

srece

ived

provinc

ialo

rna

tiona

ltea

chingaw

ardð0/1Þ

.09

.19

.1.03

.15

.12

ð.29Þ

ð.39Þ

½.05�

ð.18Þ

ð.36Þ

½.26�

Gong

ban

teache

rð0/1Þ

.9.91

.01

.97

.83

2.14

ð.3Þ

ð.28Þ

½.62�

ð.18Þ

ð.38Þ

½.07�

Teache

rex

perienc

eðyea

rsÞ

13.61

13.47

2.15

15.73

15.13

2.61

ð11.28

Þð9.84Þ

½.90�

ð10.08

Þð9.62Þ

½.70�

Scho

olch

aracteristic

:Sc

hool

size

ðstud

entsÞ

225.77

212.43

213

.28

218.74

206.27

212

.47

ð61.88

Þð66.01

Þ½.4

3�ð58.76

Þð57.87

Þ½.4

7�Stud

ent-teache

rratio

18.06

17.15

2.92

17.39

17.36

2.04

ð5.32Þ

ð4.26Þ

½.30�

ð4.39Þ

ð5.66Þ

½.98�

Distanc

eto

farthe

stvillageserved

byscho

olðm

inutesÞ

68.34

60.12

28.21

65.51

106.05

40.55

ð59.43

Þð42.92

Þ½.4

0�ð48.98

Þð82.67

Þ½.1

6�Sc

hool

hasprovided

teache

rtraining

inpastye

arð0/1Þ

.93

.95

.01

.96

.72

2.24

ð.25Þ

ð.23Þ

½.76�

ð.19Þ

ð.45Þ

½.12�

Scho

olinfrastruc

ture

index

.15

.18

.03

.03

.53

.5ð1.2Þ

ð1.17Þ

½.91�

ð1.35Þ

ð1.31Þ

½.21�

Sample

size:

Totaln

umber

ofstud

ents

4,68

293

21,03

629

4Num

ber

ofstud

ents—mathsample

2,33

047

150

814

2Num

ber

ofstud

ents—Chine

sesample

2,35

246

152

815

2Num

ber

ofscho

ols

8686

2323

Note.Mixed

Man

darin

minority

scho

olsarescho

olswith

atleasttwoHan

stud

entsan

dtw

oMan

darin

minority

stud

ents.M

ixed

non-Man

darin

minority

scho

olsarescho

olswith

atleasttwoHan

stud

entsan

dtw

ono

n-Man

darin

minority

stud

ents.V

ariablesareas

described

intable

1.Stan

darderrorsðin

paren

thesesÞa

ccou

ntforc

lusteringat

thescho

olleve

l.

351



TABLE A2CHINESE ACHIEVEMENT REGRESSIONS (POOLED, FULL SAMPLE)

ð1Þ ð2Þ ð3Þ ð4Þ ð5Þ ð6ÞMandarin-speaking minority 2.10 2.05 2.02 2.05 2.01 2.25***

ato

Student and household

This content downloadedAll use su

ð.064Þ2.65***

ioch

Han Stu

ð1Þ

from 171.66bject to JSTO

ð.065Þ2.59***

rdic

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ð2Þ

.209.5 on WeR Terms and

ð.071Þ2.54***

e

Minor

d, 7 Jan 2015 Conditions

ð.071Þ2.57***

er

ity

16:28:44 PM

ð.067Þ2.55***

tc

ð.062Þ2.20*
Non-Mandarin-speaking minority
ð.106Þ
ð.104Þ ð.120Þ ð.121Þ ð.118Þ ð.111Þ Student and household characteristics Yes Yes Yes Yes Yes Class peer characteristics Yes Yes Yes Yes Teacher characteristics Yes Yes Yes School characteristics Yes School fixed effects Yes Constant .03 .22 2.39 2.74 2.74 2.43
ð.026Þ
ð.678Þ ð.682Þ ð.653Þ ð.660Þ ð.656Þ Adjusted R2 .014 .068 .077 .088 .099 .201
Note. Each column represents a separ
e regress n. Standa errors ðin parenthes sÞ accoun for clus- usehold aracterist s, class pe r characte istics, tea her char- tering at the school level. Student and h
acteristics, and school characteristics in
lude thos in table . Estimatio sample cludes a andomly c e 1 n in rchosen half of all sample students ðthose who were given a standardized exam in ChineseÞ. N 5 9,661.* p < .1.*** p < .01.
TABLE A3CHINESE ACHIEVEMENT REGRESSIONS BY ETHNICITY

Mandarin-SpeakingNon-Mandarin-

Speaking

ð3Þ ð4Þ
Minority
ð5Þ ð6Þ

characteristics:
.10*** .08*** .14* .18* .20* .17 Female ð0/1Þ
Boarding student ð0/1Þ
ð.021Þ ð.020Þ ð.077Þ ð.090Þ ð.115Þ ð.146Þ 2.14**ð.056Þ
2.15***ð.055Þ

2.37**ð.172Þ

2.32*ð.187Þ

2.03ð.285Þ

.06ð.461Þ

Age ðyearsÞ
.16 .15 2.43 2.59* 1.28 .83 ð.120Þ ð.124Þ ð.344Þ ð.332Þ ð.780Þ ð.980Þ
Age2
2.01** 2.01** .02 .02 2.06* 2.04 ð.005Þ ð.006Þ ð.015Þ ð.014Þ ð.033Þ ð.042Þ
Household size
2.02** 2.01 2.01 2.01 .06* .08*** ð.008Þ ð.008Þ ð.018Þ ð.020Þ ð.029Þ ð.022Þ
Travel time to school ðminutesÞ
.00 .00 2.00 2.00 2.00 2.00 ð.001Þ ð.001Þ ð.003Þ ð.003Þ ð.003Þ ð.004Þ
Mother has lower secondary
degree or above ð0/1Þ 2.02 2.02 .01 .05 2.04 2.03
ð.025Þ
ð.025Þ ð.105Þ ð.124Þ ð.205Þ ð.222Þ Father has lower secondary degreeor above ð0/1Þ .20*** .17*** .14** .13* 2.15 2.21
ð.023Þ
ð.021Þ ð.068Þ ð.071Þ ð.154Þ ð.171Þ Father at home ð0/1Þ 2.04*
ð.023Þ
.00ð.021Þ
2.15***ð.055Þ

2.03ð.052Þ

2.11ð.154Þ

2.23ð.158Þ

Mother at home ð0/1Þ
.02 2.03 .05 2.06 .13 .17 ð.027Þ ð.025Þ ð.067Þ ð.072Þ ð.167Þ ð.165Þ
Household asset index ð0/1Þ
.02*** .03*** .09*** .06* .10** .10** ð.008Þ ð.008Þ ð.030Þ ð.032Þ ð.047Þ ð.042Þ
52
3

TABLE A3 (Continued )

Non-Mandarin-
Mandarin-Speaking Speaking
Han Students Minority

ð1Þ ð2ÞClass peer characteristic:

tp

This content downloadedAll use su

io r

from 171.66.209.5 on Webject to JSTOR Terms and

ð3Þ ð4Þ

n sh

d, 7 Jan 2015 16:28:44 PM Conditions

Minority

ð5Þ ð6Þ

Proportion of peers’ mothers with
.16 2.19 2.30 .03 .50
n

3.70*
lower secondary degree or above ð.135Þ ð.209Þ ð.377Þ ð.473Þ ð.995Þ ð1.873Þ
Proportion of class peers of sameethnicity
2.07 .34 .73*** 2.06 2.60 3.42
ð.168Þ
ð.557Þ ð.141Þ ð.530Þ ð.376Þ ð2.356Þ Peer average household asset index .08**
ð.035Þ
.09ð.064Þ
.08ð.105Þ

2.04ð.152Þ

2.05ð.159Þ

.07ð.292Þ

Teacher characteristic:
Female teacher ð0/1Þ .14*** .08* .14 .29*** 2.11 2.05
ð.041Þ
ð.044Þ ð.106Þ ð.105Þ ð.226Þ ð.456Þ Han teacher ð0/1Þ .06 2.11 .12 .16 2.07 2.47**
ð.084Þ
ð.095Þ ð.092Þ ð.116Þ ð.117Þ ð.182Þ Teacher has higher education degree ð0/1Þ .08 .01 .27 2.05 2.24 .12
ð.060Þ
ð.086Þ ð.203Þ ð.227Þ ð.269Þ ð.417Þ Teacher attended normalcollege ð0/1Þ .07* .01 .17 .28** 2.15 2.52
ð.045Þ
ð.050Þ ð.140Þ ð.129Þ ð.250Þ ð.459Þ Teacher has received provincial ornational teaching award ð0/1Þ 2.01 .18** 2.02 2.24 2.21 2.11
ð.055Þ
ð.075Þ ð.105Þ ð.147Þ ð.207Þ ð.448Þ Gongban teacher ð0/1Þ .05
ð.061Þ
.01ð.070Þ
.23ð.145Þ

2.01ð.157Þ

.11ð.175Þ

.36**ð.159Þ

Teacher experience ðyearsÞ
.00 2.00 2.00 2.01 .01 .05** ð.002Þ ð.003Þ ð.005Þ ð.009Þ ð.011Þ ð.017Þ
School characteristic:
School size ðstudentsÞ 2.00** .00* .00**
ð.000Þ
ð.001Þ ð.002Þ Student-teacher ratio 2.01** 2.01 .00
ð.005Þ
ð.009Þ ð.017Þ Distance to farthest village served by school ðminutesÞ .00*** .00 2.00
ð.000Þ
ð.001Þ ð.002Þ School has provided teachertraining in past year ð0/1Þ .01 .15 2.14
ð.076Þ
ð.130Þ ð.445Þ School infrastructure index .04*
ð.021Þ
.11**ð.044Þ
.05ð.074Þ

School fixed effects
No Yes No Yes No Yes Constant 2.31 2.39 1.22 3.60* 27.42 27.78
ð.679Þ
ð.865Þ ð2.015Þ ð1.911Þ ð4.545Þ ð6.560Þ Observations 8,455 8,455 882 882 316 316 Adjusted R 2 .092 .187 .155 .258 .109 .243
e regress
n. Standa d errors ði parenthe esÞ accou t for clus- Note. Each column represents a separatering at the school level. Estimation sam le include s a random ly chosen alf of all sa mple stude nts ðthose who were given a standardized exam in hineseÞ. C* p < .1.** p < .05.*** p < .01.
353


TABLE

A4

CHIN

ESE

ACHIEVEMENTREGRESS

IONSBYETH

NICITY

(MIXED

SCHOOLS

ONLY

)

Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Han

Stud

ents

Man

darin-Spea

king

Minority

Stud

ents

Han

Stud

ents

Non

-Man

darin-

Spea

king

Minority

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

ð7Þ

ð8Þ

Stud

entan

dho

useh

oldch

aracteristic

s:Fe

maleð0/1Þ

.15***

.15***

.14

.19

.10

.09

.25

.09

ð.043

Þð.0

43Þ

ð.113

Þð.1

38Þ

ð.061

Þð.0

65Þ

ð.228

Þð.2

79Þ

Boa

rdingstud

entð0/1Þ

2.50***

2.52***

2.91***

2.76***

.17

.24

.19

.46

ð.140

Þð.1

67Þ

ð.253

Þð.2

55Þ

ð.214

Þð.2

16Þ

ð.503

Þð.4

31Þ

Ageðyea

rsÞ

2.01

.09

2.71*

2.82*

2.89

2.71

1.29

1.57

ð.323

Þð.3

19Þ

ð.406

Þð.4

19Þ

ð.692

Þð.7

55Þ

ð.941

Þð1.216

ÞAge2

2.00

2.01

.03

.03*

.04

.03

2.06

2.08

ð.014

Þð.0

14Þ

ð.017

Þð.0

17Þ

ð.031

Þð.0

34Þ

ð.039

Þð.0

50Þ

Hou

seho

ldsize

.02

.02

2.02

2.04

.04

.04

.15**

.12*

ð.021

Þð.0

20Þ

ð.030

Þð.0

34Þ

ð.027

Þð.0

27Þ

ð.060

Þð.0

56Þ

Travel

timeto

scho

olðm

inutesÞ

.00

2.00

.00

.00

2.00

2.00

2.00

2.00

ð.001

Þð.0

01Þ

ð.003

Þð.0

03Þ

ð.002

Þð.0

02Þ

ð.006

Þð.0

06Þ

Mothe

rha

slower

seco

ndarydeg

reeor

abov

eð0/1Þ

2.02

2.04

.14

.21

.12

.12

2.17

2.37

ð.066

Þð.0

68Þ

ð.133

Þð.1

69Þ

ð.140

Þð.1

49Þ

ð.238

Þð.2

88Þ

Father

haslower

seco

ndarydeg

reeor

abov

eð0/1Þ

.09*

.06

.09

.15

.18**

.19**

2.10

2.06

ð.050

Þð.0

48Þ

ð.108

Þð.1

14Þ

ð.078

Þð.0

74Þ

ð.201

Þð.2

26Þ

Father

atho

með0/1Þ

2.05

2.02

2.12*

2.02

2.08

2.10

.21

.05

ð.053

Þð.0

60Þ

ð.072

Þð.0

88Þ

ð.100

Þð.1

17Þ

ð.242

Þð.2

34Þ

Mothe

rat

homeð0/1Þ

2.03

2.10

2.06

2.20*

.06

.05

2.21

.14

ð.057

Þð.0

60Þ

ð.093

Þð.1

13Þ

ð.069

Þð.0

78Þ

ð.195

Þð.2

08Þ

Hou

seho

ldassetindex

ð0/1Þ

.02

.03

.09**

.07

.03

.03

.04

.02

ð.020

Þð.0

19Þ

ð.046

Þð.0

57Þ

ð.047

Þð.0

49Þ

ð.060

Þð.0

62Þ

Class

pee

rch

aracteristic

:Prop

ortio

nof

pee

rs’mothe

rswith

lower

seco

ndarydeg

reeor

abov

e2.52*

.04

2.83*

.25

21.80

**2.83

21.65

4.82

ð.282

Þð.5

53Þ

ð.419

Þð.7

20Þ

ð.800

Þð2.121

Þð1.910

Þð4.262

ÞProp

ortio

nof

classpee

rsof

sameethn

icity

2.23

2.33

.41*

2.06

2.46

1.29

2.06

3.24

ð.266

Þð.7

98Þ

ð.231

Þð.7

76Þ

ð.350

Þð1.694

Þð1.332

Þð2.992

ÞPe

eraverag

eho

useh

oldassetindex

.06

.28

.24

.01

.47***

2.24

2.59

2.69

ð.103

Þð.2

01Þ

ð.149

Þð.2

43Þ

ð.095

Þð.4

96Þ

ð.516

Þð.4

68Þ

354



Mixed

Man

darin

Minority

Scho

ols

Mixed

Non

-Man

darin

Minority

Scho

ols

Teache

rch

aracteristic

:Fe

maleteache

rð0/1Þ

.11

.16

.07

.44***

2.18**

2.57

.25

21.79

**ð.0

98Þ

ð.134

Þð.1

22Þ

ð.145

Þð.0

67Þ

ð.351

Þð.1

59Þ

ð.772

ÞHan

teache

rð0/1Þ

2.02

2.15

2.02

.27

.03

2.14

2.38

2.99**

ð.089

Þð.1

11Þ

ð.138

Þð.2

05Þ

ð.120

Þð.2

88Þ

ð.269

Þð.3

62Þ

Teache

rha

shighe

red

ucationdeg

reeð0/1Þ

.10

.12

.40*

.16

.45***

1.23

2.34

2.89

**ð.1

62Þ

ð.177

Þð.2

11Þ

ð.419

Þð.1

27Þ

ð.805

Þð.3

56Þ

ð1.305

ÞTe

ache

rattend

edno

rmal

colle

geð0/1Þ

.09

.23

.19

2.02

2.30

21.21

*2.19

26.60

**ð.1

17Þ

ð.149

Þð.2

50Þ

ð.297

Þð.1

75Þ

ð.641

Þð.3

61Þ

ð3.037

ÞTe

ache

rha

srece

ived

provinc

ialo

rna

tiona

ltea

chingaw

ardð0/1Þ

.08

.00

2.02

2.28**

21.32

***

21.43

**.96

23.41

ð.172

Þð.2

02Þ

ð.118

Þð.1

37Þ

ð.357

Þð.6

08Þ

ð1.193

Þð2.069

ÞGong

ban

teache

rð0/1Þ

.08

2.22

.09

.12

.73***

.04

.17

...

ð.180

Þð.2

42Þ

ð.179

Þð.2

62Þ

ð.126

Þð.2

87Þ

ð.505

Þ...

Teache

rex

perienc

eðyea

rsÞ

.00

.01

2.00

2.00

2.02*

.03

.04**

.29**

ð.004

Þð.0

05Þ

ð.008

Þð.0

15Þ

ð.009

Þð.0

35Þ

ð.016

Þð.1

23Þ

Scho

olch

aracteristic

:Sc

hool

size

ðstud

entsÞ

.00

2.00*

.00

2.00

ð.001

Þð.0

01Þ

ð.001

Þð.0

02Þ

Stud

ent-teache

rratio

2.03***

2.02

.00

.02

ð.011

Þð.0

18Þ

ð.008

Þð.0

29Þ

Distanc

eto

farthe

stvillageserved

byscho

olðm

inutesÞ

.00***

2.00

.00**

2.01***

ð.001

Þð.0

01Þ

ð.001

Þð.0

04Þ

Scho

olha

sprovided

teache

rtraining

inpastye

arð0/1Þ

.31*

.10

.26

21.82

***

ð.160

Þð.2

79Þ

ð.218

Þð.5

32Þ

Scho

olinfrastruc

ture

index

.02

.11

.00

2.49***

ð.051

Þð.0

69Þ

ð.031

Þð.1

62Þ

Scho

olfix

edeffects

No

Yes

No

Yes

No

Yes

No

Yes

Con

stan

t.52

.17

4.35

4.73

*4.28

3.16

25.54

29.97

ð1.891

Þð2.001

Þð2.638

Þð2.633

Þð3.948

Þð4.665

Þð5.803

Þð7.624

ÞObservations

1,42

938

734

412

2Adjusted

R2

.102

.170

.215

.288

.076

.075

.233

.280

Note.Ea

chco


Stan

darderrors

ðinparen

thesesÞa

ccou

ntforc

lusteringat

thescho

olleve

l.Estim

ationsample

includ

esarand

omlych

osen

halfof

allsam

ple

stud

ents

ðthosewho

weregiven

astan

dardized

exam

inChine

seÞinmixed

scho

ols.

*p<.1.

**p<.05.

***p<.01.

355



TABLE

A5

OAXACA

DECOMPOSITIONS

(CHIN

ESE

)

Mixed

Scho

olsOnly

FullSa

mple

NoSc

hool

FESc

hool

FE

All

Minority

ð1Þ

Man

darin

Minority

ð2Þ

Non

-Man

darin

Minority

ð3Þ

AllMinority

ð4Þ

Man

darin

Minority

ð5Þ

Non

-Man

darin

Minority

ð6Þ

AllMinority

ð7Þ

Man

darin

Minority

ð8Þ

Non

-Man

darin

Minority

ð9Þ

Totalg

ap.25***

.10

.65***

.10

.02

.27

.10

.02

.27

ð.070

Þð.0

63Þ

ð.102

Þð.0

89Þ

ð.086

Þð.1

81Þ

ð.091

Þð.0

83Þ

ð.192

ÞDifferen

tch

aracteristic

s.02

.03

.01

2.20**

2.21**

2.14

2.18**

2.19**

2.08

ð.054

Þð.0

53Þ

ð.072

Þð.0

91Þ

ð.091

Þð.1

27Þ

ð.091

Þð.0

92Þ

ð.211

ÞStud

entan

dho

useh

old

.05***

.05***

.06**

.02

2.01

.06

.02

2.02

.08

ð.014

Þð.0

15Þ

ð.022

Þð.0

20Þ

ð.023

Þð.0

43Þ

ð.021

Þð.0

24Þ

ð.053

ÞPe

er2.04

2.03

2.05

2.18*

2.12

2.29*

2.07

2.23

.43

ð.049

Þð.0

46Þ

ð.060

Þð.0

99Þ

ð.103

Þð.1

54Þ

ð.350

Þð.3

25Þ

ð.668

ÞTe

ache

r2.01

2.01

2.01

2.02

2.05

.09

2.04

2.07*

.28

ð.036

Þð.0

35Þ

ð.042

Þð.0

31Þ

ð.044

Þð.1

61Þ

ð.034

Þð.0

45Þ

ð.241

ÞSc

hool

.02

.03

.01

2.02

2.04

2.00

ð.022

Þð.0

21Þ

ð.045

Þð.0

41Þ

ð.060

Þð.1

02Þ

Scho

olfix

edeffects

2.09

.13

2.88

ð.286

Þð.3

12Þ

ð.698

ÞDifferen

treturnsto

characteristic

s.23***

.08

.64***

.30***

.23***

.42***

.28***

.21**

.36

ð.077

Þð.0

71Þ

ð.113

Þð.0

91Þ

ð.083

Þð.1

44Þ

ð.087

Þð.0

93Þ

ð.223

ÞObservations

9,66

19,33

78,77

12,57

11,81

653

22,57

11,81

653

2

Note.Firstthree

columns

useallsch

oolsin

sample;lastsixco

lumns

usesample

ofmixed

scho

olson

ly.E

stim

ationsample

includ

esarand

omlych

osen

halfof

allsam

ple

stud

ents

ðthosewho

weregiven

astan

dardized

exam

inChine

seÞ.Stan

darderrors

ðinparen

thesesÞa

ccou

ntforclusterin

gat

thescho

olleve

l.FE

5fixe

deffects.

*p<.1.

**p<.05.

***p<.01.



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