Why Do Girls in Rural China Have Lower School Enrollment?

World Development Vol. 34, No. 9, pp. 1639–1653, 2006� 2006 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

doi:10.1016/j.worlddev.2005.12.009www.elsevier.com/locate/worlddev

Why Do Girls in Rural China

Have Lower School Enrollment?

LINA SONG and SIMON APPLETONUniversity of Nottingham, UK

and

JOHN KNIGHT *

University of Oxford, UK

Summary. — Boys are more likely than girls to attend school in rural China. There is evidence thatgender equity is a ‘‘luxury good’’; the demand for female schooling is more income elastic than thatfor male schooling. Maternal education generally has a stronger effect on primary school enroll-ment and on educational expenditure than paternal education does. However, maternal educationhas a weaker effect on girls’ enrollment in secondary school than paternal education does. Thereappears to be no monetary return to schooling for women, but a modest benefit for men. House-holds also appear to face a higher opportunity cost when enrolling young women than when enroll-ing young men.
� 2006 Elsevier Ltd. All rights reserved.
Key words — China, educational enrollment, intra-household allocation, gender discrimination,labor market

* The authors are grateful to Li Shi, Keith Griffin, Carl

Riskin, Xiaoyuan Dong, and three anonymous referees

for comments and discussions. We also thank the Ford

Foundation for funding the data collection, the ESRC

for supporting the research under grant R 0002386, and

both the British Council (Beijing Office) and the CCK

Foundation (RG019-U-01) for financial assistance.Final revision accepted: December 12, 2005.

1. INTRODUCTION

Investment in female education is frequentlyseen as a key policy for social and economicdevelopment. The gender differences in literacyand education that are pervasive in many devel-oping regions are widely regarded as undesir-able on several grounds. In terms of equity,they may be viewed as both inherently unjustand instrumental in causing further genderinequalities in income, work, and status. Short-falls in female education can also be seen asunproductive and a constraint on economicgrowth, given the large contribution of femalelabor to developing economies. What is more,there may be important social costs of low fe-male schooling to the extent that there are par-ticular externalities from female education interms of reduced population growth, betterchild health, and household investments inchildren more generally. Given the potentialsignificance of gender gaps in education fordevelopment, it is desirable to understand

163

how such inequalities arise in order to informappropriate policy responses.

In this paper, we focus on the gender gap inschooling in rural China. Rural China is animportant case in part because of the numberof the world’s poor who are to be found there.Moreover, it is also an interesting setting tostudy gender relations. Officially, the Chinesegovernment is committed to the principle ofequality between the sexes, and indeed the shareof women in employment is high compared tomany other developing countries in Asia. Gen-der inequalities in education, the focus of this

9

1640 WORLD DEVELOPMENT

paper, are also moderate in comparison to someother Asian countries. However, in practice,women and girls are often seen as having a sub-ordinate position in Chinese society, perhapsparticularly in peasant households. For exam-ple, Sen (1990) included China along with sev-eral other Asian countries as places where pro-son bias is so strong that it manifests itself inthe phenomenon of ‘‘missing women’’.

We use household survey data from ruralChina in 1995 to investigate gender inequalitiesin education. We estimate the extent to whichsocio-economic determinants of schooling—for example, household income—impact differ-entially on girls, and boys. It is often arguedthat gender inequality and poverty interact.For instance, Behrman and Knowles (1999) intheir study of Vietnam found that the incomeelasticity of demand for girls’ schooling washigher than that for boys’. Appleton (1995)found that gender differences in access to sec-ondary school in Cote d’Ivoire arose mainlyin the poorer quarter of the population. Suchpatterns might be expected if households prior-itize the education of boys (because of purepro-son bias or perceived higher returns) andonly invest in female schooling if they have suf-ficient income. If such behavior is observed,then there may be implications for policy—forexample, targeting initiatives to promote femaleeducation toward poorer regions or poorerhouseholds within given localities.

A particular focus of our analysis is on the ef-fects of parental education on the gender gap inthe enrollment of children. It has often been ar-gued that maternal education has particularlystrong effects on child schooling. We are mainlyconcerned with whether it has a stronger effecton the schooling of girls than of boys. Such apattern might be consistent with an interpreta-tion of the effect of parental education in termsof bargaining power. It has sometimes beenclaimed that where women have a subordinateposition within the household—perhaps mani-festing itself as having low levels of educa-tion—this may be self-perpetuating, leading tolower investments in girls than in boys. Forexample, Folbre (1984) provides this as onepossible interpretation of the finding of Rosen-zweig and Schultz (1982) that excess femalemortality is more prevalent in areas of Indiawhere female employment is lower. Similarly,Thomas (1994) argues, based on findings fromthe United States, Ghana, and Brazil, thatmothers allocate more resources to their daugh-ters and fathers channel resources to their sons.

We explore this hypothesis by looking at howpro-son differentials in education vary withmaternal and paternal education.

A generalized male or pro-son bias among rur-al households in China might explain why girlsreceive less schooling than boys do. Householdsmay simply give a higher weight to the welfare ofsons than to that of daughters. Alternatively, theinequality in schooling may reflect differences inthe perceived productivity of investments in boysand girls. This argument underlay Rosenzweigand Schultz’s (1982) own interpretation of theirfindings from India. Where girls were less likelyto find employment, poor Indian householdsmay have invested less in their health andnutrition, leading ultimately to excess femalemortality. A similar argument may apply moregenerally to explain low female education indeveloping countries. If women receive a lowerreturn to their education than men, this may ex-plain why girls receive less schooling even whenhouseholds place equal weight on the welfareof boys and girls. While it is commonly foundacross the world that the return to education interms of higher wages is similar for men and wo-men (Psacharopoulos, 1994), this may not applyto rural China. Wage employment in rural Chinais far from universal and more commonly en-gaged in by men than by women. If women aremore confined to activities where education isnot so productive (e.g., farming), then house-holds will perceive lower monetary benefits fromeducating girls than boys. It is harder to arguethat the direct costs of schooling may also behigher for girls, as schools in rural China com-prise mixed sex. However, if girls work more,or more effectively, for the household, then theirschooling may be regarded as having a higheropportunity cost.

The structure of this paper is as follows. InSection 2, we explain the methods and dataused to address our central research questions.Section 3 models the socio-economic determi-nants of school enrollment showing the effectsof individual, parental, household, and spatialcharacteristics. It estimates the models sepa-rately by gender, focusing on the gender gapin senior secondary school enrollment. The in-tra-household allocation of educational spend-ing is examined in Section 4, showing howhousehold spending on girls’ and boys’ school-ing varies with the education of their mothersand fathers. We then turn to the apparent re-turns and opportunity cost of schooling by ana-lyzing the determinants of household income inSection 5. Section 6 summarizes and concludes.

WHY DO GIRLS IN RURAL CHINA HAVE LOWER SCHOOL ENROLLMENT? 1641

2. RESEARCH QUESTIONS, METHODS,AND DATA

The research questions we pursue in this pa-per are the following:

(1) Do the socio-economic variables at ourdisposal have differential effects in determin-ing the school enrollment of boys and girls?(2) What are the effects of income, parents’education, and household composition onhousehold educational spending on boysand on girls?(3) Are gender differences in schooling con-sistent with differences in the returns to andopportunity costs of schooling?

(a) Gender differences in school enrollment

To answer the first question, we employ bin-ary logistic models to explain school enroll-ment. We model whether an individual i isenrolled (Ei = 1)

PrðEi ¼ 1Þ ¼ expða0X iÞ=½1þ expða0X iÞ�; ð1Þ

where Xi is a vector of explanatory variablesand a a vector of associated coefficients. Weestimate (1) separately for boys and girls.

The explanatory variables include the log ofhousehold income per capita. If schooling werepurely an investment in human capital andcapital markets were perfect, then householdincome might not be a relevant determinantof enrollment. However, credit constraints oninvestments in human capital are a feature ofmany settings less poor than rural China.Moreover, it is likely that education in ruralChina is partly a consumption good—later inthe paper we provide evidence that the returnsfor education appear to have been rather mod-est. There is a potential endogeneity problemwith household income as a determinant ofschool enrollment to the extent that individualsin school might otherwise earn income forthe household. Consequently, we use predictedrather than actual household income percapita. 1 We are particularly interested inwhether income has a stronger effect on girls’than boys’ enrollment. This might be expectedif households regard girls’ schooling as beingmore of a consumption good than an invest-ment good. It might also be predicted if bothschooling decisions are regarded as invest-ments, but boys’ schooling is seen as having ahigher return and is thus prioritized by credit-constrained poor households.

Additional variables are included to capturethe household’s demographic composition, spe-cifically the log of household size and variablesfor the proportion of members in various age–sex categories (women, boys, and girls). Sincewe use income per capita rather than applyingany equivalence scales, these demographic vari-ables may be important in adjusting for eco-nomies of consumption or differences in needs.They may also have more complex substitutioneffects—for example, if the presence of otherchildren ‘‘crowds’’ the schooling of youngerones. One limitation with using householddemographics is that family size might be re-garded as endogenous with respect to school-ing, perhaps reflecting a quantity–qualitytrade-off (Becker & Lewis, 1973). Like mostother studies in the literature, we do not havegood instruments to handle this problem. How-ever, we report whether our other results arerobust to the omission of these demographicvariables. As a further set of controls, we in-clude sets of dummy variables for the child’syear of age and for the province.

Among the explanatory variables, we focuson the number of years of education of themother and of the father. Since we already con-trol for income per capita, the estimated coeffi-cients on these parental education variablesshould not reflect income effects, but may cap-ture a number of other factors. Educated par-ents may be more able to help their childrenin out-of-school learning, leading to better per-formance in school and so making enrollmentmore beneficial. In addition, educated parentsmay simply have more positive attitudes toschooling (a ‘‘taste’’ for it). Maternal and pater-nal education may have different effects if oneparent tends to be more involved in or influen-tial over their child’s education. Moreover, theamount of education each parent has may alsoraise the ‘‘bargaining power’’ of that parent inmaking household decisions, such as those onschooling. (More educated partners may con-tribute more in terms of income earned outsidethe household or have better ‘‘fall-back posi-tions’’ in the event of marital dissolution.)However, it is always possible that parentaleducation acts in part as a proxy for otherunobserved factors (Behrman & Rosenzweig,2002). These factors may be personal charac-teristics (‘‘ability’’) or persistently better localopportunities for schooling, etc. Standardhousehold surveys, such as that used in this pa-per, are not well equipped to disentangle thesevarious possible effects. Consequently, care


must be taken not to give too restrictive aninterpretation to the coefficients on the parentaleducation variables.

The conventional wisdom is that mother’seducation has a stronger impact than father’seducation on child schooling (see, e.g., Schultz,1993). This might be consistent with many ofthe interpretations of education effects enumer-ated above if mothers spend more time onchildren than do fathers. In the context of bar-gaining models, it has sometimes been claimedthat women assign more priority to expendi-tures on household or child goods, rather thanto goods or services which they personally con-sume. 2

Less attention has been given to the questionof whether mother’s and father’s educationmay differentially affect the schooling of girlsand boys. One pattern sometimes alluded toin the literature is of sex-specific intergenera-tional effects—mother’s education affectingdaughters and father’s education affecting sons(Thomas, 1994). Following Folbre (1984), thismay be because mothers are more gender-equitable and are able to influence householddecisions more when they are educated. Analternative explanation for sex-specific inter-generational patterns in schooling may stemfrom socialization within the households.Daughters may spend more time with mothersand sons more time with fathers. Such sex-spe-cific socialization has been used to explain thefinding in Pakistan that daughters’ perfor-mance in tests of reasoning is correlated withtheir mothers’, and sons’ reasoning ability iscorrelated with their fathers’ (Alderman, Behr-man, Ross, & Sabot, 1996). By analogy, edu-cated mothers may stimulate daughters inparticular to perform better at school—eitherbecause such mothers provide a role model orbecause they directly assist in learning. Eitherway, if girls with educated mothers performbetter in school, this is likely to increase thehousehold’s demand for their schooling.

(b) Gender differences in household spendingon schooling

Some similar issues arise when consideringour second question, the differential effect ofsocio-economic variables on household educa-tional expenditures on boys and girls. Schoolenrollment and household spending on school-ing are clearly related; arguably both are formsof household investment in human capital. Bylaw, primary school is supposed to be free but

in practice at least five types of fee have beenidentified as being levied—for tuition, text-books, uniforms, financial contributions, andother school-based fees (Bentaouet & Burnett,2000). These charges are likely to vary by re-gion—since compulsory education is identifiedas a local government responsibility. One mightexpect to see lower fees in poorer areas, sinceparents can afford to pay less. However, poorareas are also less able to find resources foreducation from other means (e.g., local taxes)and so may rely more on user charges. Our dataprovide information only on total householdeducational spending, not spending per child.Hence, we must take an inferential approachusing household expenditure functions. Weestimate an augmented Woking-Lesser expen-diture function for the share, W, of total house-hold spending on child schooling (see Deaton,1987, for examples of such functions) 3

W i ¼ aþ b1 lnðY i=NiÞ þ b2 ln N i þ c0Zi

þXm

k¼1

/kN ki=Ni þ ei; ð2Þ

where Y is the household income (predicted), Nthe household size, Nk the number of house-hold members in demographic group k (mgroups), Z the vector of control variables, ethe error term, and a, b, c, and / are para-meters.

As with school enrollment, household in-come may be endogenous and a predicted valueis used with household productive assets andsome geographic variables are employed asinstruments. The model controls for householdsize and demographic composition. The vari-ables for household demographic composition,Nk/N, provide an indirect test of whetherhouseholds allocate fewer funds for the educa-tion of daughters than for that of sons. Forexample, if a higher proportion of boys in ahousehold is associated with more educationalspending than an equivalent higher proportionof girls, then this suggests that householdsspend more on boys’ education than on girls’.As we cannot model spending on individualchildren directly, we adopt a finer disaggre-gation by age when specifying the householddemographic variables. This allows for differentpatterns to be estimated for primary-age andsecondary-age children.

We include the years of education of thefather and of the mother, as well as a set of pro-vincial dummies in the vector of control vari-ables Z. To see whether parental education


has differential effects on household spendingon the schooling of boys and girls, we interactthe parental education variables with thosefor household demographics. If maternal edu-cation has a particularly strong effect on spend-ing on daughters, one would expect a positiveinteraction between maternal education andvariables for girls as a proportion of householdmembers. The approach allows us to investi-gate whether boy–girl discrimination in house-hold spending on schooling varies withmaternal and paternal education.

(c) Gender differences in the returns toeducation

To explore our third research question, weestimate a trans-log production function (seeBerndt & Christensen, 1973) with the log ofhousehold income (ln Y) as the dependent vari-able. Here, income is defined as gross productfrom household enterprises, agricultural, andnonagricultural, plus cash earnings (wagesand remittances). Home consumption of pro-duced items is valued and included as part ofgross product. As explanatory variables, weinclude three factors of production—farmingland, labor, and capital—together with inter-mediate inputs (such as fertilizers and pesticidesin the case of farming). These variables (shownas ln X) are entered in a logarithmic form, eachwith second order terms (squared and interac-tion terms). Capital and intermediate inputsallocated for farming are separated from thoseallocated for nonfarming. 4

Labor is measured using the reported data ontotal days worked. To see the productivity ofdifferent kinds of labor, we include variablesfor the share of days worked by different demo-graphic groups (distinguished by age and sex).Similarly, the education of the workers is mea-sured in terms of the average years of schoolingof workers in each demographic age group.Both the age-sex composition and the educa-tion of the labor input may affect productivity.Hence these variables, together with dummiescapturing various characteristics of the locality,are introduced as shift factors in the vector Z.Thus the function takes the form

ln Y i ¼ a0 þX6

j¼1

aj ln X ji þX6

j¼1

X6

k¼1

cjk ln X ji

� ln X ki þXn

s¼1

bsZsi þ vi; ð3Þ

where vi is a random error term.

A Hausman test indicates that the (log) daysworked is endogenous, so we use two-stageleast squares with the (log) total number ofworking age adults as the identifying instru-ment. Ideally we would have allowed for theendogeneity of other inputs such as capital,but unfortunately no suitable instruments arepresent in the dataset. As a result, the estimatedeffects of education on income are net of anyallocative choices made by households. Theyrepresent only the direct (productive) effectsof education in raising productivity, given thehousehold’s input of factors of production.Allocative effects of education—altering thelevel or mix of factors of production—are notcaptured. To explore such effects, we also re-port the results of the model omitting those in-puts that may alter with the level of education(specifically, purchased inputs, and capital).

(d) Data and samples

We use a rural household sample survey inChina relating to the year 1995. The surveywas conducted by the National Bureau of Sta-tistics (NBS) for the Institute of Economics,Chinese Academy of Social Sciences, based ona sub-sample of the NBS’s annual householdexpenditure survey. The two main strengths ofthese data are its wide geographic coverageand its detailed accounting for income, makingit possible to estimate income according tointernational definitions. The survey contains8,000 households in 19 of the 30 provinces.The exclusion of some provinces was madefor cost reasons, but care was taken to makethe sample still broadly representative ofChina. In particular, the excluded provinceswere evenly distributed among all provinces interms of income and consequently the esti-mated average income of the sample was virtu-ally identical to NBS’s estimate for the countryas a whole (for details see Riskin, Renwei, &Shi, 2001). Some basic descriptive statisticsfor the sample are presented in Table 6.

When modelling school enrollment, we re-strict ourselves to the sub-sample of childrenof the household head living in householdswhere the head has a resident spouse. Thisrestriction was primarily to identify the parentaleducation of the child, since the survey did notask about parental education nor identify par-entage where the parent was not the householdhead. However, there may be a case for treating‘‘two-headed’’ households (i.e., with a head andspouse) differently from those—typically female


headed—where the head has no spouse. Forsimilar reasons, when we model educationalexpenditures, we confine the sample to house-holds where there is a household head, a spouseof the head and children of the head. Our anal-ysis of the effects of education, by contrast, usesthe full sample.

Since the samples used in the logits for schoolenrollments sometimes include children fromthe same household, it is possible that theunobservables determining school enrollmentare correlated among children of the samehousehold. We adjusted the standard errors toallow for this by using the CLUSTER optionin the statistical computer package STATA.

3. SOCIO-ECONOMIC DETERMINANTSOF SCHOOL PARTICIPATION

Our first exercise is to estimate binary logitequations to model school enrollment. Table 1shows the age-specific enrollment rates for boysand girls aged 7–18. There is considerablevariation in enrollment decisions in ruralhouseholds in China, partly reflecting regionaldifferences in educational systems. Childrenaged six or below typically do not attend for-mal schooling, although often they may attendpre-school, sometimes entering as young asthree. Beyond this, the Law on Nine-YearCompulsory Education passed in 1986 envis-aged all children receiving nine years of educa-tion, commonly six years of primary and three

Table 1. Age-specific school enrollment rates by sex

Age Boys Girls

7 78.5 77.18 88.7 85.59 92.7 92.710 94.0 97.1**

11 96.2 96.312 95.7 91.9**

13 93.6 92.114 92.7 88.2**

15 82.6 74.3**

16 64.7 58.8*

17 47.0 42.918 30.1 28.4

Sample size 4,568 4,166

Asterisks denote significance of gender difference inenrollments.* Significant at 10%.** Significant at 5%.

of lower secondary schooling. However, whenit came to planning to implement this goal,the Law divided the country into three areas.Cities and economically advanced areas, mainlyin coastal regions (totally around 25% of Chi-na’s population), were intended to reach nineyears of universal education by 1990. Areas ofmiddle development (around 50% of the popu-lation) were set a deadline of 1995 (the year ofour data) to achieve nine years of universaleducation. No deadline for universal educationwas set for the most economically backwardareas (around 25% of the population). Theimplementation of the policy is decentralized,with county governments being responsiblebut township governments actually carrying itout. The result is considerable spatial variationin the quantity and quality of ‘‘compulsory’’schooling (Wong, 2002). In terms of post-compulsory schooling, pupils performing wellin examinations could continue on to seniorsecondary school (typically lasting for threeyears).

Looking at the reported behavior in Table 1,we decided to model enrollment separately forthe age groups 7–14 years and 15–18 years.Enrollment rates are very high in the lowerage group, averaging 92% for boys and 90%for girls. Most pupils in this age will be attend-ing primary or lower secondary schools, whereattendance is supposed to be universal andentry is not rationed by examination perfor-mance. Enrollments start to decline sharplyin the upper age group, which corresponds towhen students might enter upper secondaryschool. The sex difference is larger in this olderage group: 57% of boys are enrolled in schoolcompared to 51% of girls. It seems appropriateto model the determinants of school enrollmentseparately for the two age groups. The school-ing of the older group will be unaffected byany compulsion. Moreover, the opportunitycosts of schooling are likely to be higher for thisgroup and competition for entrance to uppersecondary school may be tough.

Table 2 presents our model of school enroll-ment. We focus our discussion on three notableresults. First, we consider income effects. The(predicted) log of household income per capitais positive and significant only in the case ofgirls aged 15–18 years. 5 Income is positivebut insignificant for younger children. This lackof a significant income effect for younger chil-dren may reflect low fees for primary schoolsand low opportunities cost of enrolling youngchildren (who may not be economically very

Table 2. Logistic models for school enrollment

Variable Girls 7–14 Boys 7–14 Girls 15–18 Boys 15–18

Marginaleffect

T-ratio Marginaleffect



T-ratio

Father’s years of education 0.002 0.68 0.002 0.74 0.028 4.01*** 0.020 2.94***Mother’s years of education 0.008 2.51** 0.006 2.77*** 0.012 1.8* 0.026 3.82***Log of householdincome per capita (predicted)

0.034 0.82 0.024 0.73 0.170 1.73* �0.044 �0.47

Log household size 0.004 0.08 �0.037 �1.16 �0.097 �1.15 �0.103 �1.14% Boys in household 0.115 1.16 0.163 1.97** 0.331 1.98** 0.507 2.68***% Girls in household 0.067 0.68 0.201 2.5** 0.139 0.76 0.523 2.79***% Women in household 0.139 0.99 0.178 1.75* 0.292 1.51 0.742 4.12***No. of observations 2,450 2834 1393 1427Mean of dependent variable 90.2% 92.2% 51.3% 57.2%Pseudo-R squared 0.0945 0.0888 0.1534 0.1893

Notes:

(1) The models also include dummy variables for the age of the child and for the province. For brevity, they are notpresented in this table.(2) *** Denotes statistical significance at 1% level, ** at 5%, and * at 10% level.(3) The sample for these models has been restricted to children of household heads, where the head has a residentspouse.(4) The marginal effects are evaluated at the mean of the dependent variables. T-ratios are those on the associatedcoefficients, with the standard errors adjusted to allow for clustering within households.


productive). Official government data for 1999imply that primary school fees per student aver-aged 3.0% of rural per capita incomes (Heck-man, 2005). Costs rise as students move upthe school system—for general secondaryschools, the corresponding figure was 8.4%.For boys aged 15–18, these fees may not be suf-ficiently large to affect demand: income was stillinsignificant and indeed the sign was negative.By contrast, these secondary school fees mayhelp explain the income elasticity of the oldergirls’ enrollment. Fees and income may mattermore for the enrollment of girls than boyseither for preference or for productivity rea-sons. In terms of household preferences, theremay simply be pro-son bias—households mayprioritize the schooling of boys and enrol girlsonly if they can afford it after making the sacri-fices necessary to send their sons to school.Alternatively, it may reflect a perception thatthere are higher returns to investing in the edu-cation of sons—boys’ education may be seen asmore of an investment good, to be funded al-most regardless of income, whereas girls’ edu-cation may be seen as more of a consumptiongood, almost a ‘‘luxury good.’’ Given that ac-cess to education after lower secondary schoolis rationed by exam performance, the explana-tion may not be purely to do with household

demand. Using data from Cote d’Ivoire, Apple-ton (1995) found girls from poorer householdsto be less likely than boys from similar back-grounds to pass the primary leaving exam thatrationed access to secondary school. Ulti-mately, however, this differential seemed to belinked to household decisions—female primaryschool students from poorer households werespending fewer hours in school and the numberof hours in school was found to be one of theproximate determinants of subsequent examperformance. Our data—a fairly standardhousehold survey—do not allow us to investi-gate the causes of this interaction between in-come and gender inequality in schooling inrural China. However, with its wide geographiccoverage and large sample, it is powerful evi-dence that there is such an interaction that isworthy of further research.

Second, parental education has a variety ofsignificant and positive effects on child school-ing in the four models listed in Table 2. Foryoung children, maternal education signifi-cantly increases the probability of enrollmentwhereas paternal education is wholly insignifi-cant, with t-ratios less than one. However, suchis the lack of precision around the estimatesthat Wald tests do not reject the equality ofthe coefficients on maternal and paternal


education (the p-values are 0.31 for the sampleof young boys and 0.21 for the sample of younggirls). 6 The marginal effects of even maternaleducation on the enrollment of the young ap-pear modest—an extra year of maternal educa-tion raises the enrollment of girls by 0.8 of apercentage point. This is partly because weevaluate at the mean proportion where childrenare overwhelmingly (90% +) likely to be en-rolled anyway. When it comes to the enroll-ment of older children, where the samples aremore evenly split between those in school andout, parental education effects appear to havelarger impacts. An extra year of paternal educa-tion increases the probability of enrollment by2–3 percentage points (1–3 points for maternaleducation). All the parental education variablesare significant in the models for the enrollmentof older children, although the effect of mater-nal education is only significant at the 10% levelfor older girls.

In no case is the effect of parental educationon girls’ enrollment significantly different fromthat on boys’ enrollment. We also find no evi-dence of sex-specific intergenerational effectscomparable to those reported by Thomas(1994). That is to say, school enrollment doesnot seem to depend particularly on the educa-tion of the parent of the same sex as the child.If anything, there is some sign of the reverse—the coefficient on maternal education is largerin the sample of older boys than in the sampleolder girls, while the reverse is true for paternaleducation.

The third set of variables of interest is forhousehold demographics. 7 The log of house-hold size typically has a negative coefficient,but is never statistically significant at conven-tional levels. If the size of the household doesnot seem to matter, its demographic composi-tion does. Specifically, our results imply thathigher proportions of men in the householdare associated with lower enrollment probabili-ties for boys. Variables for the proportions ofhousehold members in different age-sex groupsare often significant in the models for boys’schooling. The default demographic group ismen and the variables for the proportions ofmembers in other demographic groups alwayshave positive coefficients. This implies that hav-ing a greater proportion of men in the house-hold reduces the probability of children beingenrolled in school. This effect appears fairlystrong for boys in both age groups—theirschooling prospects benefit significantly fromhigher proportions of boys, girls, and women

in the household. There are suggestions ofsimilar effects for girls—the relevant coefficientshave the same sign as for boys—but if theyexist, such effects are quantitatively smallerand usually not statistically significantly differ-ent from zero at conventional levels. The onlysignificant effect of demographics on girls isthat having a higher proportion of boys in thehousehold raises their probability of being inschool when aged 15–18.

It is not clear what explains the estimated ef-fects of household demographics on schoolenrollment. As previously noted, demographicsmay affect household behavior in a variety ofways, some of which do not seem so relevantto our particular results. 8 We offer two alterna-tive conjectures for our findings. One is thatthey reflect gender differences in preferencesand consequent effects of household composi-tion on intra-household allocation. Parallelresearch (Song, 2001) on the same dataset hasshown that an increased proportion of men inthe household is associated with a larger shareof spending on alcohol and cigarettes (which in-deed accounts for about the same average shareof household spending as education). Suchspending on ‘‘men’s goods’’ may crowd outspending on education. Alternatively, thedemographic variables may reflect unobserveddifferences in productivities rather than prefer-ences. The presence of more men in the house-hold may be an indication that householdproduction—whether farming or nonfarm busi-ness—is particularly important for the house-hold. In such a situation, there may be ahigher opportunity cost to boys going to schooland a lower perceived benefit (if education isseen as mainly useful for work outside thehousehold). Further research is required to testwhether either conjecture can explain why thepresence of greater proportions of men in thehousehold appears to reduce the chances ofboys being sent to school in rural China. 9

4. GENDER DIFFERENCES INHOUSEHOLD SPENDING ON

EDUCATION

Are the results from the enrollment functionsmirrored in the household expenditure func-tions? Households in our sample (couple-headed households with children) spend nearly5% of their income on schooling. Table 3 re-ports the expenditure function for householdspending on schooling, without interactions be-

Table 3. Two-stage least squares regression for budgetshare of educational spending

Variable Coefficient T-ratio

Constant �0.0030 �0.13Father’s years

of education0.0005 1.45

Mother’s yearsof education

0.0008 2.35**

Log of household size 0.0105 2.41**% Male aged 0–6 0.0112 0.89% Male aged 7–12 0.0883 8.23***% Male aged 13–15 0.1459 11.85***% Male aged 16–18 0.1528 11.52***% Male aged

19–55 (default)0

% Male aged 56–65 �0.0316 �1.69*% Male aged 66 0.0220 1.09% Female aged 0– 6 0.0201 1.52% Female aged 7–12 0.0971 8.86***% Female aged 13–15 0.1671 12.77***% Female aged 16–18 0.1442 10.45***% Female aged 19–55 0.0318 2.35**% Female aged 56–65 0.0124 0.57% Female aged 66 0.0210 1.19Log of household income

per capita (predicted)�0.0018 �0.83

Household located in anofficially designatedpoor county

�0.0061 �2.79***

Number of observations 5,943Mean of dependent

variable0.04785

Adjusted R squared 0.1830

Notes:(1) Province dummy variables are included in the modelbut omitted from the table for brevity.(2) Omitted dummy variable is ‘‘% male aged 19–55’’.(3) *** Denotes statistical significance at 1% level, ** at5%, and * at 10% level.(4) The sample for this exercise has been restricted tohouseholds headed by a couple with children.


tween parental education and household demo-graphics. Parental education raises the share ofspending on schooling, but the effect is statisti-cally significant only in the case of mothers’education, not fathers’. Mothers’ education ap-pears to have around 50% more effect on schoolspending than fathers’ education, although thedifference in the coefficients is not statisticallysignificant.

Household income per capita does not ap-pear to affect the share of spending on school-ing, although a dummy variable for living in a

county officially designated as poor does havea significant negative effect. Such poor countiesare likely to have fewer and less well resourcedschools, as well as lower fees for post-primaryeducation.

The share of spending rises with householdsize and also responds to the age-sex composi-tion of the household. As with the enrollmentlogits, the default age-sex group is adult men,although here we adopt a finer disaggregationby age. Naturally, a larger proportion ofschool-age children raises spending on school-ing, with the effect being larger for the post-pri-mary-age children (13–18 years) than for thoseof primary age (7–12 years). What is surprisingis that in the 7–12 and 13–15 years age ranges,the presence of girls does not have a smaller ef-fect on spending than the presence of boys.This is unexpected given that fewer girls areenrolled at school even in these age ranges. Itsuggests, at the very least, that lower femaleenrollment is not compounded by lower spend-ing per female student. Only in the older agerange, 16–18 years, do we see the presence ofgirls being associated with less school spendingthan the presence of boys. Even here, the differ-ence in the size of the relevant coefficients ismodest compared to the difference in enroll-ment ratios. Boys aged 16–18 years have a 6%larger effect on the share of household spendingon schooling than girls in the same age range.However, they are 9% more likely to be en-rolled in school.

Of the other demographic effects, perhapsthe one most worthy of comment is the effectof greater proportions of women rather thanmen. As in the logits for school enrollment,the presence of women rather than men appearsto increase the demand for human capital. Thecoefficient on the proportion of women aged19–55 years is significant and positive.

Is there any interaction between parentaleducation and boy–girl discrimination? Weexplore this in Table 4 by augmenting themodel in Table 3 with interaction terms betweenparental education and household demographicvariables. Preliminary work revealed no signifi-cant interactions for primary age children (7–12years) and hence such terms are not retained inTable 4. Interestingly, the interactions betweenparental education and the proportions of olderchildren do suggest sex-specific inter-genera-tional effects. The presence of girls aged 13–15years and 16–18 years interacts positively andsignificantly with maternal education, notpaternal education. The presence of boys aged

Table 4. Two-stage least squares regression for budgetshare of educational spending augmented by interactions

between parental education and demographics

Coefficient T-ratio

Constant 0.0051 0.22Father’s years of education �0.0002 �0.4Mother’s years of education 0.0003 0.56Father’s education

· girl aged 13–15�0.0039 �0.95

Mother’s education· girl aged 13–15

0.0067 1.89**

Mother’s education· boy aged 13–15

�0.0050 �1.55

Father’s education· boy aged 13–15

0.0085 2.34***

Father’s education· girl aged 16–18

0.0048 1.19

Mother’s education· girl aged 16–18

0.0077 2.04***

Mother’s education· boy aged 16–18

0.0065 1.92**

Father’s education· boy aged 16–18

0.0070 1.98**

Log of household size 0.0089 2.02***% Male aged 0–6 0.0137 1.08% Male aged 7–12 0.0912 8.49***% Male aged 13–15 0.1110 4.2***% Male aged 16–18 0.0786 3.02***% Male aged 56–65 �0.0295 �1.58% Male aged 66 0.0233 1.16% Female aged 0–6 0.0219 1.66*% Female aged 7–12 0.1006 9.17***% Female aged 13–15 0.1630 5.37***% Female aged 16–18 0.0801 2.84***% Male aged 19–55

(default)0

% Female aged 19–55 0.0332 2.46***% Female aged 56–65 0.0114 0.52% Female aged 66 0.0232 1.31Log of household

income per capita(predicted)

�0.0017 �0.79

Household locatedin an officiallydesignated poor county

�0.0061 �2.83***

Number of observations 5,943Mean of dependent variable 0.04785Adjusted R squared 0.1850

Notes:(1) Province dummy variables are included in the modelbut omitted from the table for brevity.(2) Omitted dummy variable is ‘‘% male aged 19–55’’.(3) *** Denotes statistical significance at 1% level, ** at5%, and * at 10% level.(4) The sample for this exercise has been restricted tohouseholds with both parents and with children.


13–15 years interacts significantly and positivelywith paternal education, not maternal educa-tion. Among boys aged 16–18 years, there areinteractions with both kinds of parental educa-tion. The sex-specific intergenerational effectshave no echo in the enrollment results and arethus presumably translated into differences inspending per pupil.

5. GENDER DIFFERENCES IN THECOSTS AND BENEFITS OF SCHOOLING

Is the explanation for the gender difference inschool enrollment to be found in the costs andbenefits of additional schooling? We explorethis question by means of a household incomefunction (where income comprises gross prod-uct from household enterprises, wage incomeand remittances from migrants). We estimatethe productivity of the labor of young menand women in order to make inferences aboutthe opportunity cost of their schooling. Thefunction also provides an estimate of the over-all monetary benefits of educating men andwomen.

Table 5 shows the translog income functions(although for brevity the second order termsand provincial dummies are not reported). Wepresent two variants—we begin by discussingthe first specification which includes capitaland purchased inputs, before turning to con-sider the second which omits them. FollowingJacoby (1992), we scale the logged inputs bysubtracting the log of their sample means. Thismakes it possible to interpret the coefficients onthe logged inputs as being the income elastici-ties when evaluating the sample means. 10 Thuswe can see that output is most elastic with re-spect to total labor input, followed by land,then purchased inputs and finally capital. How-ever, our focus is on the disaggregated effects oflabor and education.

There is suggestive evidence that the oppor-tunity cost of school attendance is greater forgirls than for boys. Looking at the variablesfor the proportion of days worked by eachdemographic group, Table 5 implies that thenumber of days worked by girls aged 15–18has a 6% lower return than the number of daysworked by men (a difference that is not statisti-cally significant). 11 By contrast, the number ofdays worked by boys aged 15–18 is 23% lessproductive (significant at the 10% level). Thisevidence is only indicative, however, as thecoefficients for boys and girls are not signifi-

Table 5. Translog production function for household earned income

Variable (a) Including capitaland purchased inputs

(b) Excluding capitaland purchased inputs

Coefficient T-ratio Coefficient T-ratio

Factors of production (scaled and logged)

Land 0.1681 6.2*** 0.12632 4.8***Purchased farming inputs 0.0129 1.1Purchased nonfarm inputs 0.1434 13.5***Farming capital 0.0814 7.6***Nonfarm capital 0.0413 4.4***Labor (total days worked per annum) (instrumented) 0.3161 4.0*** 0.59924 7.7***

Average education (years per worker)

Males aged 15–18 not in school �0.0050 �0.8 �0.00848 �1.3Females aged 15–18 not in school �0.0103 �1.6* �0.0147 �2.3**Males aged 19–55 0.0092 3.1*** 0.00956 3.2***Females aged 19–55 �0.0001 �0.3 �0.00035 �0.1Males aged 56–65 �0.0104 �1.6 �0.01403 �2.1**Males aged 56–65 0.0077 0.6 0.00907 0.7

Ratio: days worked to that of total household

Males aged 15–18 not in school �0.2628 �1.7* �0.03389 �0.2Females aged 15–18 not in school �0.0640 �0.4 �0.26321 �1.6Males aged 19–55 (default) 0 0Females aged 19–55 �0.0692 �1.9** �0.0821 �2.2**Males aged 56–65 0.0631 0.8 �0.55904 �4.6***Females aged 56–65 �0.5621 �4.7 0.05974 0.8Males aged 66 �0.2946 �2.6*** �0.16853 �0.7Females aged 66 �0.1128 �0.5 �0.32759 �2.9***

Geographic variables

Located in mountainous area 0.1648 �6.8*** �0.17298 �7.1***Located in hilly area �0.1472 �7.6*** �0.15857 �8.1***Government-defined poor county �0.2304 �10.9*** �0.23628 �11.0***Adjusted R-sq 0.3452 0.327Mean of dependent variable 8.8158No. of observations 7,557

Notes:(1) The dependent variable is the log of household income. Here, household income is defined as gross product fromhousehold farming and nonfarm enterprises plus wage earnings and migration remittances, all net of tax.(2) Omitted dummy variables in this model are ratio of days worked by males aged 19–55, location in plain area,nonpoor county. Province dummies and intercept are included but not reported here. Second-order terms for factorsof production are not reported.(3) *** Denotes statistical significance at 1% level, ** at 5% level and * at 10% level.


cantly different from each other at conventionallevels. Nevertheless, the findings do suggestthat households might be less willing to allowyoung women than young men to continuewith their education because the opportunitycost to the household is higher.

Furthermore, educating men appears tobring greater benefits in terms of household in-come. The variable for the average education of

women aged 19–55 has a coefficient that iseffectively zero and wholly insignificant. Bycontrast, the coefficient on the average educa-tion of men aged 19–55 is positive and signifi-cant at the 1% level (the two coefficients, formen and women, also differ significantly fromeach other at the 1% level). Admittedly, eventhe returns to male education appears low—an extra year of education raises income by


only 0.9%, compared to the 10% premiumto education often found in wage functionsin OECD countries (Psacharopoulos, 1994).However, our 0.9% estimate is less of an outlierwhen compared with the estimates from agri-cultural production functions in developingcountries. Studies often find small and insignif-icant effects, with the average effect estimatedfrom a meta-analysis of 56 studies being thatfour years of farmer education raises productiv-ity by 6% (i.e., a 1.5% return per year; Phillips,1994). Nevertheless, the evidence suggests thatboy’s preferential access to upper secondaryschooling may partly be as the result of higherperceived economic returns.

Given that we cannot control for the poten-tial endogeneity of some nonlabor factors ofproduction, our estimates of the effects of edu-cation in the first model in Table 5 are net ofany allocative choices made by the household.If education allows households to invest morecapital and use more purchased inputs, thenour estimates of the returns to education mightbe regarded as something of a lower bound.The second model in Table 5 omits factorsof production that are potentially endogenous.The resulting estimate of the effects of educa-tion may be something of an upper bound. Itis not clear whether they are preferable tothose of the first model, as any correlationsbetween education and potentially endogenousfactors of input do not necessarily reflect cau-sality running from education. As it happens,the estimate effects of education are relativelyrobust to the omission of capital and pur-chased inputs from the production function.The return to the education of men aged 19–55 years remains significant and just short of1% per year, while the return to the educationof women aged 19–55 remains very close tozero.

6. CONCLUSIONS

Enrollment in basic education in rural Chinais higher than in the rural areas of most eco-nomically comparable countries. This reflectsin part the Chinese government’s policy ofattempting to make the first nine years of edu-cation compulsory. Only beyond the age of 14does the enrollment rate in our sample beginto fall from high levels. It is beyond this age,also, that a gender gap in enrollment becomesapparent. Similarly, there is no boy–girl dis-

crimination in educational expenditures up tothe age of 14, but boys are favored beyond thatage.

As in some countries, we find that gender dif-ferences in school enrollment are particularlypronounced among poorer households. House-hold income generally has little effect on schoolenrollment in rural China, except for girls in the15–18 years group (upper secondary school agerange). Older boys from poorer households stillappear able to attend school.

Maternal education is associated with higherschool enrollment and higher household spend-ing on education than does paternal education,which has positive but statistically insignificanteffects. Furthermore, higher proportions of wo-men as opposed to men in the household areassociated with higher school enrollment andhigher educational spending. Various explana-tions might be given for these results, withinthe framework either of unitary or collectivemodels of the household. For example, the re-sults are consistent with an argument made insome contexts that women may place a greaterweight on the welfare of their children than domen (Hoddinott & Haddad, 1995; Parker &Pederzini, 2000). As women’s education andnumbers rise, they may have more influenceover household decision-making. However, ifwomen have a larger role than fathers in raisingchildren, then their skills and their availabilitymay be more important even in a unitary modelof the household.

One interesting question is whether genderinequalities in education among children andyoung adults are related to the education oftheir parents. A variable for the ratio of mater-nal education to total parental education wasnot statistically significant in preliminary ver-sions of any of the models when the absolutelevels of maternal and paternal education werecontrolled for. Beyond this, the logits forenrollment and the budget models gave differ-ing results on how parental education differ-entially affected boys and girls. Educationalspending seems subject to sex-specific intergen-erational effects, whereas enrollment does not.Maternal education appears to increase spend-ing on the schooling of daughters, whereaspaternal education seems not to matter. Thereverse is true for boys aged 13–15 years. Nocorresponding findings were obtained in themodels for school enrollment.

Further insight into why girls receive lessschooling in rural China was obtained by esti-


mating a model of the determinants of house-hold income. This income function was usedto make inferences about both the likely bene-fits and the costs of schooling. It provided somesuggestive evidence that households might con-sider the opportunity costs of sending youngwomen to school as greater than the corre-sponding costs for young men. In particular,the hours worked by young men appearedto be significantly less productive than thoseof prime-age men, whereas those worked byyoung women were not. Moreover, male educa-tion might be perceived as providing greaterbenefits in terms of household income. In par-ticular, the education of men in the householdwas associated with significantly higher house-hold income, ceteris paribus, whereas there ap-peared to be no return to women’s education.

It is not clear whether these differences incosts and benefits can explain the entire gender

gap in enrollment. Even for men, the returns toeducation in China appear rather low and somay not entirely explain the demand for theireducation. However, if the gender gap doespartly reflect monetary factors, then this hasimplications for the policy. For example, sup-pose the government wishes to equalize theenrollment rates of the sexes, either for equityreasons or because of perceived greater exter-nalities to female education, then the fact thathouseholds’ education appears to respond toeconomic factors implies that subsidizing fe-male education may be an effective means ofaddressing inequality. For an example of sucha policy in practice, China might look to Ban-gladesh, which has raised the enrollment ofgirls (in some cases to exceed that of boys) bypolicies such as making schooling free forgirls and/or providing bursaries specificallyfor girls.

NOTES

1. The identifying instruments were geographic dum-mies (whether the household was in plain, mountainousor hilly areas) and measures of household productiveassets. These instruments were all highly significant inaffecting income and the adjusted R-squared for thereduced form income model was 0.38 (Song, 2001, Table6 refers). They also passed tests for over-identifyinginstruments in both enrollment and subsequent expen-diture models.

2. See, for example, Hoddinott and Haddad (1995).However, it is notable that in their empirical work,Hoddinott and Haddad do not estimate the effect offemale bargaining power on educational expenditures.

3. Although the dependent variable is bounded be-tween 0 and 1, we use a linear model. Since we estimatethe model only over households with children, there islittle bunching of the data around zero. Furthermore,recent work reported by Deaton (1997) suggests thatTobit models—because of their reliance on normalityassumptions—are not necessarily superior to linearmodels when dealing with such censored dependentvariables.

4. Adding one to all values solved the problem oftaking the log of a zero.

5. Interestingly, Hausman tests for the endogeneity ofincome per capita with respect to schooling rejected (atthe 5% significance level) exogeneity only in the case of

girls aged 15–18. This may simply be a corollary ofincome not being important for other sub-samples, sothat it does not matter whether it is instrumented or not.However, the finding is consistent with results presentedlater in Section 5 that girls of that age appear to be moreproductive in generating household income. Nonethe-less, to retain comparability, we use the predicted valueof income rather than the actual income in all sub-samples.

6. We also explored a specification of the parentaleducation variables which attempted more explicitly tocapture the relative bargaining power of mothers andfathers. Specifically, in preliminary estimates, we aug-mented the models in Table 2 with a variable for themother’s years of education as a ratio to the combinedsum of the parents’ years of education. This proxy forrelative bargaining power was never statisticallysignificant at conventional levels if the absolute yearsof mothers’ and fathers’ education were also controlledfor. Consequently, although parental education maycapture effects via bargaining power, the performance ofthis proxy does not lead us to place much emphasis uponsuch an interpretation.

7. We noted that these variables might be regarded asendogenous to child schooling. However, the resultsreported above were very robust to the exclusion of thevariables for household size and demographic variables.The only change of interest following their omission wasthat the positive effect of income on girls schooling


became 12% larger and more significant (at the 5% level,rather than just the 10% level). Consequently, we focusour discussion on the results given in Table 2, which doinclude household size and composition.

8. An explanation in terms of income effects—that theycapture different needs (men making greater demands onresources)—seems unconvincing given that income percapita itself was seldom significant. Similarly, interpret-ing them in terms of time use—that more women andchildren release individual children from householdchores—also seems questionable given that the effectsare most pronounced for boys rather than for girls.

9. A referee wondered whether demographics maycapture the effect of birth order rather than householdcomposition per se. Unfortunately, the survey does notprovide information on children who are not household

members, so we are not able to include birth order as anexplanatory variable. However, when we distinguishadult brothers from other men in the models in ourdemographic categories, the presence of either categoryhas large negative effects on boys’ enrollment. Hence, itis not the presence of adult brothers that drives ourresults.

10. Specifically, in Eqn. (3), we substitute ðln X ji�ln X �j Þ for ln Xji, where X �j is the sample mean of inputXji.

11. The 6% figure refers to the coefficient on the shareof days worked attributable by females aged 15–18 notin school. If all days worked were attributable to thisdemographic group, then the model implies that incomewould be 6% (exp(�0.064) � 1 = 6%) lower than if itwere all done by the default group (prime-age men).

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APPEND

Table 6. Descrip

Variable

Land per capita (mu)Household annual income per capita (RMB yuan)Household sizeHousehold located in mountainous area %Household located in hilly area %Household located in plain area %Household located in officially defined poor county %

Personal characteristics

Father’s education (years)Mother’s education (years)

Demographic features

Number of childrenPercentage of households with no childrenPercentage of households headed by a couplePercentage of single parent households

Notes:(1) The sample is from CASS Rural Household Survey 19(2) The table uses the full sample, with 7998 households.

Wong, C. (2002). Providing education in China, InPresentation at the workshop on decentralization andintergovernmental fiscal reform, The World Bank,May 13–15.

IX A

tive statistics

Means Standard deviation

1.67 1.442,467.18 2,704.88

4.34 1.290.230.300.470.23

6.56 2.774.50 3.03

1.400.230.950.05

95.

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Why Do Girls in Rural China Have Lower School Enrollment?