The Effect of Tuition Reforms on SchoolEnrollment in Rural China∗

Hau ChyiWISE, Xiamen University

Bo Zhou †

WISE, Xiamen University

First version, June 5, 2009, this version, May 18, 2010

Abstract

In this study we estimate the effects on school enrollment of three sequential reformsundertaken between 2000 and 2006 on tuition of primary and junior high schools forpoor, rural families in China. Using difference-in-difference approaches and samplechildren from the China Health and Nutrition Survey 2000, 2004 and 2006 waves,we find that tuition control has had little effect on primary and junior high schoolenrollment. Furthermore, a policy that includes tuition waiver, free textbooks andliving expense subsidies for children who live in rural, poor families starting from 2003had a positive and statistically significant effect on school enrollment, especially forthat of rural girls. Finally, the provision of tuition waivers for all rural children since2006 had a statistically significant gender differential effect on school enrollment ingirls’ favor.

JEL Classification: I20, I22, I28Keywords: school enrollment, tuition, primary school, junior high school.

∗The title of earlier versions is “The effect of education policies on school enrollment in China”.†Email: bozhou98@gmail.com. Wang Yanan Institute for Studies in Economics, Xiamen Univerisity,

China, 361005. We would like to thank Jeffery Nugent, Cheng Hsiao and Moshe Buchinsky for excellentadvice. Also Haochung Li and the dicussants of WEAI in July, 2009; the Singapore Economic Review Con-ference in August, 2009; WISE 2009 Young Scholar Forum; 2009 International Symposium on ContemporaryLabor Economics; the All China Economics International Conference 2009; and the Young Economist Soci-ety of China in April, 2010, all of whom provided valuable comments. This paper was funded by the ChinaScholarship Council. We thank the China Health and Nutrition Survey, funded by NIH (R01-HD30880,DK056350, and R01-HD38700), and the Carolina Population Center and the Chinese CDC for providingthese data. Any errors are ours.

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1 Introduction

This paper investigates the effects of three tuition reform initiatives that were aimed atlessening the financial burden of providing education for primary school- and junior highschool-aged children of poor Chinese families. These three reforms were (i) tuition control,(ii) tuition waiver, free textbooks and living stipends for children from poor families, andfinally, (iii) tuition waiver for the rest of rural families. We studied whether these policyinitiatives improved the school enrollment rates of the target population using data fromthe China Health and Nutrition Survey (CHNS, hereafter) 2000, 2004 and 2006 waves. Ina developing country such as China, financial stress is a significant deterrent to obtainingeduction even at the primary- or junior-high school levels (Brown, and Park, 2002; Zhao,and Glewwe, 2010).1 For example, in 2003 the tuition of a primary school student peryear was between 2.1% to 9.1% of the per capita household income in rural areas. Inthe same year, tuition of a junior high school student accounted for anywhere from 3.5to 14.8% of the per capita household income.2 As a result, inner, and generally poorer,provinces had low enrollment rates for junior high school-aged children even though thecentral government regulated that all children should receive at least nine years of schooling.For example, although the national enrollment rate in 2000 in mainland China for the firstyear of primary school-aged children was 99% in 2000, only 95% of graduates of primaryschools entered junior high schools (China Population Statistics Yearbook 2001). For thesame year, the promotion rate of primary school graduates to junior high school in Guizhouwas even lower at 78.72%. Furthermore, the junior high school dropout rate was 9.9% in2000 (China Education Yearbook 2002).

Providing affordable education at the primary and junior high school levels for all citizensis an important goal of the tuition policies of China. Between 2001 and 2006, three reformswere implemented sequentially by the Ministry of Education and provincial governmentsaiming at reducing the education cost for poor, rural families. In 2001, the Ministry ofEducation regulated that the tuition of primary school students in rural areas could be nomore than 160 CHY (about US$23.5) per student per year and that of junior high schoolstudents in rural areas was set at 260 CHY (about US$38.2). Also, schools were not allowedto charge any fees other than the regulated tuitions, which were allowed to float by as muchas 20% of the regulated levels. This policy (tuition control, hereafter) first started in poorcounties across the country and was expanded to the whole nation by the spring of 2005.

In 2003, Liaoning Province first started to provide an education subsidy package thatincluded a tuition waiver, free textbooks and a living stipend for its poor students.3 By the

1Mainland China’s compulsory attendance law passed in 1986 stipulates that all children are required toreceive at least nine years of schooling. The school system generally splits between a six year primary school(6 to 12 years old) and a three year junior high school (13 to 15 years old).

2Authors’ calculation made by dividing the maximum and minimum levels of tuition for rural studentsby provincial per capita household income.

3Government first determined the number of students who were eligible to receive the package and thenassigned various quotas to primary and junior high schools. To a large degree, the teachers in the primaryand junior-high schools, who had much more information on the economic status of their students, decidedwhich students were poor and should receive the package.

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spring of 2006, this policy initiative (two-waiver-one-subsidy, hereafter) had been expandedto all poor students in mainland China. Finally, in the spring of 2006, the Chinese centralgovernment provided a tuition waiver for all primary and junior high school students in ruralareas of western provinces (tuition waiver, hereafter). Two eastern provinces, Jiangsu andLiaoning, and two cities in central China also did so by themselves before the start of fallsemester of 2006.

These reforms are important. Using data from the China Population Statistics Yearbook2002, we estimate that in 2001 about 216 million children in mainland China were poten-tially affected by these policies.4 Although we do not know the total cost of these policies,the official news paper of the Ministry of Education, China Education Daily estimated anexpenditure of 36.1 billion CHY (about 5.3 billion U.S. dollars) in Western China in 2006(February 28, 2007). Yet the effects of the three reforms are not clear. This study fillsthis gap by exploring the effects these policies had on the enrollment rates of the targetedschool-aged children.5

We used a difference-in-difference method to estimate the effects of these new initiatives.The order of the reforms was usually first tuition control, followed by two-waiver-one-subsidy,and finally tuition waiver.6 These reforms generally started in rural and poorer countieswithin a province and then extended to other counties and cities.7 Hence, we can use thevariation in the timing of the three reforms on tuition in different counties within variousChinese provinces to identify the policy effects on enrollment rates.

It is well-known that Chinese tradition valued male children more highly than femalechildren, especially in rural China. It can be expected that, when facing financial difficultieswhich many rural families arguably do, parents will sacrifice their daughters’ education sothat their sons can go to school. As a result, we may expect the effects of tuition policies,if any, will be more in girls’ favor. We hence also examined whether these tuition reforminitiatives benefited girls more than boys.

Several considerations regarding these policy reforms are worth noting here. First, aswe used individual-level data to identify the policy effects, one potential concern is that anindividual who resided in a region that was part of the control group may have moved tothe treatment group after the reforms in order to take advantage of the benefits. If this wasthe case, we may have over-estimated the policy effects by using a difference-in-differenceestimator. However, we believe this did not happen. As the quality of the school systems in

4This estimate is calculated according to the number of children whose ages were between the interval of5 and above and below 14 in 2000. The same estimate decreased to 162 million in 2006. The data in 2000are from the population census of 2000 while the data in 2006 are from the 2006 National Sample Survey onPopulation Changes (China Population and Employment Statistics Yearbook 2007).

5There were other changes in the financing of primary and junior high schools during this period. Forexample, school expenditures were included in the county budget rather than the town budget from 2000to 2004. Since this was implemented uniformly across the country, we can control for these effects byincorporating year and location dummies in our regression model.

6The only exception is Liaoning, where two-waiver-one-subsidy was implemented before tuition control.7Ravallion and Chen (2007) showed that China’s progress against poverty was uneven over time across

provinces between 1980 and 2001.

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regions that did not have reforms were generally better (which was a main factor in whethera region was chosen to have these reforms), it was unlikely that parents would have moved topoorer areas. Furthermore, school transfer was, and still is, difficult for primary and juniorhigh school-aged children as it is controlled by a student’s registered residency (“Hukou”).

Second, as the financial compensation from local government to schools was based on thenumber of students of a school from the previous year, school officials may had incentive toover report enrollment. Since we have used the self-reported enrollment status of students,this is not an issue in our study. Finally, these reforms were generally unanticipated byboth school officials and parents.8 As a result, a difference-in-difference method is arguablyappropriate to estimate the effects.

We used data from the CHNS 2000, 2004 and 2006 waves. Our estimates suggest thattuition control did not significantly change the enrollment rates of school-aged children oneway or another. On the other hand, two-waiver-one-subsidy had a positive and statisticallysignificant effect on the school enrollments of primary and junior high school-aged children.This effect is especially large for school-aged girls. Finally, tuition waiver significantly in-creased girls’ enrollment rate.

Three supplementary analyses on identification assumptions increased our confidencethat the difference-in-difference method was valid. First, all effects were statistically in-significant when we estimated the same models for senior high school-aged children. Second,we conducted a pre-reform test by using the data from the 1997 and 2000 waves and didnot find statistically significant effects on school enrollment. Third, we used one subsampleof poorer families for two-waiver-one-subsidy and another subsample of richer families fortuition waiver and obtained similar results.

The remainder of this study is organized as follows. In Section 2 we describe the data weused. In Section 3 we discuss the empirical strategy and present the empirical results, testsof identification assumptions, and alternative specifications. The last section concludes.

8In general, policy changes were only publicly discussed or announced several months before they wereimplemented. For example, on September 9 2005 Suzhou was the first to announce the implementation ofthe tuition waiver in the fall semester of 2006. This news shocked people and led to heated discussion (Page11, People’s Daily on September 12, 2005 and Shanghai Securities News on February 23, 2006). In truth, thetuition waiver was implemented in many provinces by the fall semester of 2006. Thus, the tuition waiver wasunanticipated for the observations of the 2000 and 2004 waves. We cannot rule out the possibility that someparents living in the counties and cities that were not implementing the tuition waiver may have anticipatedthe coming tuition waiver when the tuition waiver was enacted in some other counties or cities. Then thismay have affected their school enrollment decision, i.e., the school enrollment decisions of the members ofthe control group of the tuition waiver in 2006. If so, the estimated effects will have been biased to zero. Asfor tuition control and two-waiver-one-subsidy, we cannot find any discussion on them in 2000. Thus, thetwo reforms were unanticipated for the observations of the 2000 wave. Similarly, those observations havingnot received the two reforms may have anticipated the coming reforms, tuition control and two-waiver-one-subsidy. If the parents from the 2004 wave did anticipate the two reforms, our estimated effects have beenattenuated.

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2 Data

This paper uses data from rural children in the CHNS 2000, 2004, and 2006 waves. Thesewaves covered 54 counties or cities in nine provinces and autonomous region, includingGuangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, and Shandong.Respondents of the CHNS are usually interviewed in a period between August to December.The policy dummies of whether a child had experienced a particular reform could hence begenerated by comparing the survey date and the date of the policy enactment in each countyor city.

These policy reforms targeted children from needy families. Given the sharp variation ineducation quality between rural and urban regions, we focused only on school-aged childrenfrom rural areas, who were arguably more likely to be poor and to enjoy the benefits of thesepolicies. We included all children in the CHNS who were aged between six and sixteen, andwhose household registrations belonged to rural areas.

In general, the fall semester of primary and junior high schools in mainland China startson September 1st. The survey dates of all but less than 5 percent of the sample children wereafter August 31. In other words, parents had already made the school enrollment decisionfor the corresponding semester when they were interviewed.

Table 1 shows the time line of these three reforms. We separated the sample period usedin this research into two periods. The first period is the time after CHNS 2000 wave andbefore the CHNS 2004 wave, and the second period is the time after the CHNS 2004 wave andbefore the CHNS 2006 wave. We then located the period when a specific tuition reform wasimplemented in a specific CHNS county or city.9 Given that the first reform, tuition control,did not start until 2001, no counties from the CHNS 2000 wave experienced the reforms.Between the CHNS 2000 wave, which finished in December of 2000, and the beginning ofthe 2004 wave (which approximately started on August 31, 2004), 29 counties or cities hadimplemented the tuition control policy and 10 regions had enacted the two-waive-one-subsidypolicy (see the upper panel of Table 1).

In the second period, which is the time after the CHNS 2004 wave and before the CHNS2006 wave, an additional 21 counties or cities had implemented the tuition control policywhile 40 counties or cities had implemented the two-waiver-one-subsidy policy. In otherwords, all of the 50 counties or cities where our observations were drawn from had imple-mented the tuition control and the two-waiver-one-subsidy policies before the beginning of

9CHNS does not identify the exact counties or cities in the publicly available data. We have compared thereported total area and population of the counties or cities in CHNS Community Data and the data of areaand population from various yearbooks in China to identify the exact locations. Our sources included theChina City Statistical Yearbook 2005, the China County-Level Economy Yearbook 2005, the China CountyStatistical Yearbook 2005, the China County Statistical Yearbook 2007, the China Statistical Yearbook forRegional Economy 2005, the Guangxi Yearbook 2005, the Heilongjiang Almanac 2003, and the JiangsuAlmanac 2005. Since we are not allowed to give out the names of the exact counties or cities that weresurveyed, they are not reported in this research. Furthermore, we cannot determine whether particularreforms were enacted in 4 of the CHNS locations, and have removed their observations from our calculations.

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the CHNS 2006 wave. In the meantime, the tuition waiver had not been implemented inany of the CHNS locations until the second period, after the end of the CHNS 2004 wave,when 24 locations had enacted the reform. The lower panel of Table 1 indicates how manylocations had more than one reform taking place simultaneously. In the first period, 7 CHNSlocations had enacted both the tuition control and the two-waiver-one-subsidy policies. Tu-ition control was implemented with either the two-waiver-one-subsidy or the tuition waiverin 12 and 3 locations in the second period, respectively. Furthermore, the two-waiver-one-subsidy and the tuition waiver policies were implemented simultaneously in 12 places in thesecond period, while another 6 locations implemented all three reforms.

In Table 3, we describe the summary statistics of variables of interests by year. We seethat the school enrollment rates of all children, especially girls and junior high school-agedchildren, increased substantially from 2000 to 2004 and then again to 2006. But we shouldalso pay attention to the changes of several other key variables in the corresponding period,the ratio of junior high school-aged children among all children, age, gender, and mother’scompleted years of education.10 The variable, junior high school-aged child, equals to 1 if thechild belonged to the age interval [12, 16) and 0 if the child’s age was in [6, 12). gender equalsto 1 if female, and 0 if male. Between 2000 and 2004, the proportion of junior high school-aged children among primary and junior high school-aged children decreased from 0.55 to0.50 while between 2000 and 2006 from 0.55 to 0.38. The average ages of the children fromthe CHNS 2000, 2004 and 2006 waves were 11.48, 11.14 and 10.42 years, respectively. Fromthe CHNS 2000 to 2004 and 2006 waves, mother’s completed years of education increasedfrom 6.36 to 6.83 and 7.19. Per capita household income and the rate of households owninga refrigerator also rose substantially from the CHNS 2000 to the 2004 and 2006 waves. Theper capita household income was adjusted by the provincial CPI and was measured in 2006prices. refrig equals to 1 if the household owned a refrigerator, and 0 otherwise. It was usedas a proxy for the assets of the household. There was a slight decrease in the proportionof villages that had a primary school.11 This fact may had a negative effect on the schoolenrollment of children.

3 Empirical Strategy and Estimation Results

The tuition control, the two-waiver-one-subsidy, and the tuition waiver policies regulated andwaived the tuitions of primary and junior high school students, even provided free textbooksand living stipends to these students from poor, rural families. We first give our treatmenteffects of the three reforms using the unconditional difference-in-difference method in Section3.1. In Section 3.2, we introduce a set of covariates and then estimate the treatment effectsof the three tuition reforms. In Section 3.3, we test identification assumptions and discuss

10Whether a child is school-aged is determined by aging six and above on the enrollment date, August 31of the corresponding year.

11From 2000 to 2006, many primary schools merged. Since this happened in all regions, its potential effecton enrollment rates are absorbed by year dummies. We further included as explanatory variables whetherthere was a primary school in a village, and whether there was a junior high school in a village.

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alternative specifications.

3.1 Results using unconditional difference-in-difference

Tables 4 to 6 show the unconditional difference-in-difference results of the three reforms.Tuition control, two-waiver-one-subsidy and tuition waiver are associated with a 2.68, 6.85and 1.75 percentage points increase in school enrollment, respectively. But only the effect oftwo-waiver-one-subsidy was statistically significant at the 5 percent significance level. Theother two effects were not statistically significant, even at the 10 percent significance level.

The results in these tables were obtained through the unconditional difference-in-differenceapproach. However, many sample children have observationally different characteristics, andare from very different CHNS regions that may have policy implications on school enroll-ments. We have thus controlled for more covariates in the next section.

3.2 Results using conditional difference-in-difference

Since the three reforms overlapped in some CHNS regions, we had to disentangle the effectof one reform from others. As tuition waiver was not implemented until 2006, we couldestimate the effects of tuition control and two-waiver-one-subsidy using only data from theCHNS 2000 and 2004 waves. The empirical model is as follows:

schoolit = α0 + α1tctrli + α2twosi + α3year + α4tctrli ∗ year+α5twosi ∗ year + α6Zit + vit (3.1)

Our dependent variable is the dummy variable schoolit which equals to 1 if individuali was in school at that time in year t, and 0 otherwise. The dummy variables tctrli andtwosi represent tuition control and two-waiver-one-subsidy, respectively. The variable tctrliequals to 1 if individual i lived in one of counties or cities where tuition control had beenimplemented by the beginning of the CHNS 2004 wave, and 0 otherwise. twosi is definedsimilarly. year equals to 1 if the data was from the CHNS 2004 wave, and 0 otherwise.Control variable Zit includes a child’s gender, the mother’s completed years of education,whether the child was junior high school-aged, whether there was a primary school in thechild’s village, whether there was a junior high school in the child’s village, per capitahousehold income, and whether the household owned a refrigerator. vit is an error term.

In these specifications, α1 and α2 capture the time invariant differences in school enroll-ment between control groups and treatment groups. α3 captures the difference in generaltime trend. α4 and α5 capture the school enrollment effects of tuition control and two-waive-one-subsidy.

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Table 2 shows that there were 663, 809 and 1,150 observed children from the CHNS2006, 2004 and 2000 waves, respectively.12 There were 371 and 253 observations in thecontrol groups while there were 779 and 556 observations in the treatment groups of thetuition control policy from the CHNS 2000 and 2004 waves, respectively. As for two-waiver-one-subsidy, the numbers of observations in the control groups in 2000 and 2004 were 959and 672, respectively, while those in the treatment groups were 191 and 137, respectively.

The first column of Table 7 estimates a specification incorporating only the difference-in-difference estimator of tuition control. As we did not control for the two-waiver-one-subsidypolicy, the difference-in-difference estimator picked up its effect and over-estimated the effectof tuition control. However, although positive in sign, the difference-in-difference estimate(represented by tctrl ∗ year) is insignificant. In column 2, we further separated the effect oftwo-waiver-one-subsidy. However, we still could not find a statistically significant difference-in-difference coefficient. As a result, we concluded that the tuition control policy did notsignificantly alter parents’ decisions on their children’s school enrollment. This insignificanteffect of tuition control was not surprising, as many violations of this policy had been reportedin the media. As none of the difference-in-difference estimates of the effect of tuition controlwere significant, we did not include it in the later estimations of the remaining policies.13

The last two columns of Table 7 list the empirical estimates of two versions of Equation(3.1). The first one (in column 3) estimates the average difference-in-difference effect withoutseparating the possible differential treatments for different genders. As shown in column 3,two-waiver-one-subsidy had led to a statistically significant 5.47 percentage points increase(at a 10 percent significance level) in school enrollment for primary and junior high school-aged children.

In column 4, we allowed for the existence of differential treatment effects for differentgenders, which are represented by the interaction term between the difference-in-differenceestimator and the girl dummy (year ∗ twos ∗ gender). As can be seen, the two-waiver-one-subsidy policy significantly increased girls’ school enrollment rates by 7.65 percentage points.Combined with the average difference-in-difference derived in column 3, it could be inferredthat most of the increase in enrollment from implementing the two-waiver-one-subsidy policywere from increases in girls’ enrollment.

The signs of other covariates are as expected. The school enrollment rate of all childrendeclined as they age. As a result, junior high school-aged children were also less likely to bein school at the time of the survey than primary school-aged children. A mother’s completedyears of education had a positive but statistically insignificant effect on her children’s schoolenrollment. The likelihood of boys enrolling in school was statistically significant (at the 1percent level) higher than that of the girls. When there were schools in a village, especiallyif there was a junior high school, the school enrollment rate of the children in the village did

12The age structure changed substantially from 2000 to 2006 in mainland China, thus the number ofprimary and junior high school-aged children decreased substantially during this period. Please refer tofootnote 4.

13Actually, adding tuition control into the estimations only slightly affected the significance levels of somecoefficients.

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increase. Finally, children from rich households were more likely to be in school than thosefrom poor households.

As for the effect of the tuition waiver policy, we used a similar difference-in-differencestrategy as follows:

schoolit = β0 + β1twaivi + β2year + β3twaivi ∗ year + β4Zit + uit (3.2)

The dummy variable twaivi equals to 1 if individual i lived in one of the counties or citiesimplementing tuition waiver, and 0 otherwise. year equals to 1 if the data was from theCHNS 2006 wave, and 0 otherwise. Zit included the same variables as in Equation (3.1). uitis an error term. The coefficient β3 captures the school enrollment effect of tuition waiver.

The data we used came from only the CHNS 2000 and 2006 waves. The reasons forexcluding the CHNS 2004 wave in estimating the effect of the tuition waiver policy are asfollow: first, tuition control and two-waiver-one-subsidy were implemented after the end ofthe CHNS 2000 wave, and by the eve of the CHNS 2006 wave, all CHNS regions had alreadyimplemented the two policies. As a result, the year dummy was sufficient to capture theeffect of both policies when only CHNS 2000 and 2006 data are used.

Second, incorporating the CHNS 2004 wave data created a multicollinearity issue be-tween the policy variables and year dummies. For example, when we estimated a linearregression using the difference-in-difference estimate of two-waiver-one-subsidy as the de-pendent variable, and year dummy, twos, twaiv, as well as the interaction term betweenyear dummy and twaiv as independent variables, R2 was at 0.8561. This suggested a highlycollinear relationship between the difference-in-difference estimate and the other explanatoryvariables.14

Table 2 shows that the numbers of observations in the control groups for the tuitionwaiver policy were 637 and 328 from the CHNS 2000 and 2006 waves, respectively. We hadalmost the same as those in its treatment groups.

Table 8 estimates the effect of tuition waiver using Equation (3.2) and data from theCHNS 2000 and 2006 waves. Again, column 1 estimates an average difference-in-differenceeffect and column 2 identifies whether the tuition waiver policy benefited girls more. As canbe seen in column 1, this policy did not have a statistically significant effect on the generalrural poor population. However, as column 2 shows, it had a statistically significant effecton the enrollment of girls at the 10 percent significance level. As mentioned in Section 1,the two-waiver-one-subsidy policy focused on poor children, while the tuition waiver policy

14Specifically, we used data from the CHNS 2004 and 2006 waves to estimate the following model:twosi ∗ year = η0 + η1year + η2twosi + η3twaivi + η4twaivi ∗ year + εitWe also estimated an alternative specification using data from all three waves and found similar results.

See a detailed discussion in Appendix.

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covered the rest of the rural children. As many of the remaining children covered by thetuition waiver policy were members of families that did not have financial difficulties insupporting their education, it was not surprising that the tuition waiver policy did not haveas strong an effect as that of the two-waiver-one-subsidy policy.

3.3 Tests of the identification assumptions

There are couple concerns with the difference-in-difference methodology.

First, it is important that the difference-in-difference estimators capture only the effectsof the policy initiatives we intended to estimate. In other words, no effects from other timevarying factors can be captured by our difference-in-difference estimators. Some policy ini-tiatives happened concurrently with the three reforms which indeed raise such concerns. Forexample, in January 2000, the Chinese government nominated a group of leadership regionsunder the Great Western Development Project. In subsequent years, many developmentprojects were implemented and general household income levels in these regions may havebeen raised because of these policy initiatives.

To the extent that other policy initiatives, other than the tuition reforms, raised the levelsof general household income, we should expect that the effects of these initiatives increasedthe enrollment rates of all levels of all schools, not having been restricted to only primary andjunior high schools. As a result, one way to investigate whether our difference-in-differenceestimators captured the effects of different policy initiatives, other than time-varying effectsfrom other factors, is to use the observations of senior high school-aged children to estimatethe Equations (3.1) and (3.2). As shown in columns 1 and 2 of Table 9, we cannot findstatistically significant effects of the two-waiver-one-subsidy or tuition waiver policies on theenrollment of senior high school-aged children. Hence, we may rule out the possibility oftime varying effects from other factors rather than the tuition reforms.

Second, one implicit assumption of the difference-in-difference method is that time trendsin the school enrollment rates of treatment groups are the same as those of control groups.We can test this assumption by comparing whether there is a difference between treatmentand control groups using pre-reform data. As the policy reforms only took place after theCHNS 2000 wave, the trends of enrollment rates between treatment and control groups inthe CHNS 1997 and 2000 waves should be similar. The insignificant difference-in-differenceestimates in columns 3 and 4 of Table 9 indicate that the pre-reform trends between the twogroups are indeed the same. This provides a further support for the validity of our empiricaldifference-in-difference strategy.

3.4 Results based on two subsamples

Since the two-waiver-one-subsidy policy focused on poor children while the tuition waiverfocused on the rest of the children, we separated the sample into two subsamples based

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on whether the per capita household income of a child’s family was higher than four timesthe absolute poverty line in rural mainland China. Children whose per capita householdincome was lower than four times the absolute poverty line were used to estimate the effectof two-waiver-one-subsidy while those higher than four times the absolute poverty line wereused to estimate the effect of tuition waiver.15 In Table 10, we show the treatment effectsof two-waiver-one-subsidy and tuition waiver based on the two subsamples. As shown incolumn 2, the effect of two-waiver-one-subsidy on girls was statistically significant at 13.43percentage points. The effect on boys was statistically indifferent from zero. As shown incolumn 3, there was a statistically significant 10 percentage point increase (at the 1 percentsignificance level) in school enrollment for children whose per capita household income washigher than four times the poverty line, resulting from the tuition waiver. We found thatthe gender differential effect of tuition waiver was positive but not statistically significant aswe show this in column 4.

Additionally, we assumed that the reforms varied across only counties and cities whilethey actually varied across individuals. That is to say, if some of the children receivedtwo-waiver-one-subsidy or tuition waiver in a county or city, all children were assumed tohave received two-waiver-one-subsidy or tuition waiver in this county or city. Obviously, thismeasurement error leads to a downtowards bias but it does not undermine our conclusion onthe effects of two-waiver-one-subsidy and tuition waiver. This is one of the reasons that theestimated effects based on the two subsamples are larger than those based on all observationsfrom the CHNS 2000 and 2004 or 2006 waves.

4 Conclusion

In this study, we identified the effects of tuition control, two-waiver-one-subsidy and tuitionwaiver policies by difference-in-difference strategies that compared the children who lived incounties or cities that had undertaken a reform to the children who lived in the countiesor cities that had yet to implement the reform. We found that two-waiver-one-subsidy hada positive and statistically significant effect on the school enrollment of all children whiletuition waiver only a positive and statistically significant effect on the children whose percapita household income was higher than four times the absolute poverty line. Both two-waiver-one-subsidy and tuition waiver had a gender differential effect on school enrollmentand it was in the girls’ favor. But tuition control had only just a statistically insignificanteffect on the school enrollment of the children. Since the Chinese have a long tradition ofvaluing boys more than girls, two-waiver-one-subsidy and tuition waiver very welcome asthey improved the school enrollment of primary and junior high school-aged girls.

15The absolute poverty lines in rural China are 625 CHY, 637 CHY, and 683 CHY in the correspondingyear prices in 1999, 2003, and 2005, respectively (Department of Rural Surveys, National Bureau of Statistics,2007b).

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16. Office of Magazine Jiangsu Almanac, The, 2005, Jiangsu Almanac 2005, The Office ofMagazine Jiangsu Almanac, Nanjing, Jiangsu.

17. Schultz, T. Paul, 2004, School subsidies for the poor: evaluating the Mexican Progresapoverty program, Journal of Development Economics, 74(1): 199-250.

18. Zhao, Meng, and Paul Glewwe, 2010, What determines basic school attainment indeveloping countries? Evidence from rural China, Economics of Education Review,29(3): 451-460.

13

Table 1: The number of counties/cities with reforms in the two periodsPeriod 2000.12 2004.9.01 total

-2004.8.31 -2006.8.31tuition control 29 21 50two-waiver-one-subsidy 10 40 50

tuition waiver - 24 24both tuition control and two-waiver-one-subsidy 7 12both tuition control and tuition waiver - 3both two-waiver-one-subsidy and tuition waiver - 12all three reforms - 6

Table 2: Number of observations for each reformYear tuition control two-waiver-one-subsidy tuition waiver total

0 1 0 1 0 12000 371 779 959 191 637 513 11502004 253 556 672 137 408 401 8092006 230 433 544 119 328 335 663

14

Table 3: Summary statistics

Wave 2000 2004 2006school 0.8965 0.9444 0.9412

(0.3047) (0.2293) (0.2355)

primary school enrollment 0.9480 1 0.9565(0.2223) (0) (0.2042)

junior high school enrollment 0.8542 0.8892 0.9152(0.3523) (0.3143) (0.2785)

school enrollment of girls 0.8771 0.9383 0.9452(0.3286) (0.2410) (0.2282)

school enrollment of boys 0.9130 0.9505 0.9377(0.2820) (0.2172) (0.2421)

junior high school-aged children 0.5487 0.5019 0.3756(0.4978) (0.5003) (0.4846)

age 11.48 11.14 10.42(2.516) (2.851) (2.851)

gender 0.4600 0.5006 0.4676(0.4986) (0.5003) (0.4993)

mother’s education 6.3636 6.8272 7.1883(3.465) (2.894) (3.005)

primary school in a village 0.8330 0.7417 0.7315(0.3731) (0.4380) (0.4435)

junior high school in a village 0.2400 0.2979 0.3092(0.4273) (0.4576) (0.4625)

per capita household income 3.590 4.246 5.382(4.099) (3.852) (8.090)

refrig 0.2139 0.2596 0.3695(0.4102) (0.4387) (0.4830)

N 1150 809 663

1. Standard deviations are shown in the parentheses.

Table 4: The school enrollment effect of tuition controlWave 2000 2004 Differencetuition control 0.8896 0.9460 0.0564***

(0.3136) (0.2261) (0.0148)

No tuition control 0.9111 0.9407 0.0297(0.2851) (0.2366) (0.0210)

DiD 0.0268(0.0257)

1. Standard deviations are shown in the parentheses.

2. ***: represents significance at 1% confidence level.

15

Table 5: The school enrollment effect of two-waiver-one-subsidyWave 2000 2004 Differencetwo-waiver-one-subsidy 0.8586 0.9635 0.1049***

(0.3493) (0.1882) (0.0300)

No two-waiver-one-subsidy 0.9041 0.9405 0.0364***(0.2947) (0.2368) (0.0132)

DiD 0.0685**(0.0327)

1. Standard deviations are shown in the parentheses.

2. ***, **: represent significance at 1% and 5% confidence levels, respectively.

Table 6: The school enrollment effect of tuition waiverWave 2000 2006 Differencetuition waiver 0.9025 0.9552 0.0527***

(0.2969) (0.2071) (0.0173)

No tuition waiver 0.8917 0.9268 0.0351*(0.3110) (0.2608) (0.0190)

DiD 0.0175(0.0257)

1. Standard deviations are shown in the parentheses.

2. ***, *: represent significance at 1% and 10% confidence levels,

respectively.

16

Table 7: Treatment effects of tuition control and two-waiver-one-subsidyVariables I II III IV

year 0.0194 0.016 0.0296∗∗ 0.0297∗∗

(0.0205) (0.0205) (0.0131) (0.0131)

tctrl -0.0262 -0.0223(0.0184) (0.019)

tctrl∗year 0.0285 0.0214(0.025) (0.0258)

twos -0.0285 -0.0326 -0.0325(0.0284) (0.0274) (0.0274)

twos∗year 0.0495 0.0547∗ 0.0071(0.0332) (0.032) (0.0426)

year∗twos∗gender 0.0765∗∗

(0.0381)

junior high school aged -0.0188 -0.0176 -0.0171 -0.0166(0.0185) (0.0185) (0.0185) (0.0185)

age -.0179∗∗∗ -0.0178∗∗∗ -0.0179∗∗∗ -0.018∗∗∗

(0.0046) (0.0046) (0.0046) (0.0046)

mother’s education 0.0012 0.0014 0.0012 0.0011(0.0021) (0.0021) (0.0021) (0.0021)

gender -0.0241∗ -0.0246∗∗ -0.0248∗∗ -0.0299∗∗

(0.0123) (0.0124) (0.0124) (0.013)

primary school 0.027∗ 0.0273∗ 0.0258 0.0256in a village (0.0158) (0.016) (0.016) (0.016)

junior high school 0.0267∗∗ 0.0268∗∗ 0.0241∗ 0.0251∗

in a village (0.0133) (0.0134) (0.0132) (0.0131)

per capita household income 0.0015 0.0016 0.0016 0.0016(0.0011) (0.0011) (0.0011) (0.0011)

refrig 0.0539∗∗∗ 0.0528∗∗∗ 0.0534∗∗∗ 0.0546∗∗∗

(0.0125) (0.0127) (0.0126) (0.0128)

Const. 1.0871∗∗∗ 1.0875∗∗∗ 1.0767∗∗∗ 1.0794∗∗∗

(0.0507) (0.051) (0.0498) (0.0499)

R2 0.0638 0.0649 0.0641 0.0653N 1959 1959 1959 1959

1. Standard deviations are shown in the parentheses.

2. ***, **, *: represent significance at 1%, 5% and 10% confidence levels, respectively.

17

Table 8: Treatment effects of tuition waiverVariables I II

year 0.0139 0.0137(0.0186) (0.0186)

twaiv 0.0152 0.0152(0.0178) (0.0178)

year∗twaiv 0.0139 -0.0096(0.0255) (0.0303)

year∗twaiv∗gender 0.0481∗

(0.0279)

junior high school aged 0.0611∗∗∗ 0.0611∗∗∗

(0.0212) (0.0208)

age -0.0024 -0.0027(0.0055) (0.0055)

mother’s education 0.0025 0.0025(0.0022) (0.0022)

gender -0.0195 -0.0284∗

(0.0133) (0.0154)

primary school 0.0196 0.0191in a village (0.0173) (0.0173)

junior high school 0.0101 0.0110in a village (0.0144) (0.0144)

per capita household income 0.0004 0.0004(0.0007) (0.0007)

refrig 0.0529∗∗∗ 0.0536∗∗∗

(0.0129) (0.0129)

Const. 0.9140∗∗∗ 0.9204∗∗∗

(0.0621) (0.0625)

R2 0.0352 0.0363N 1813 1813

1. Standard deviations are shown in the parentheses.

2. ***, **, *: represent significance at 1%, 5% and 10% confidence

levels, respectively.

18

Table 9: Tests of identification assumptionsVariables I II III IV

year 0.0522 -0.0006 -0.0092 -0.0211(0.0434) (0.0456) (0.0136) (0.0161)

twos -0.0202 0.0547∗∗

(0.0563) (0.0223)

year∗twos -0.0779 -0.0204(0.1156) (0.0465)

year∗gender∗twos 0.0769 -0.1176(0.1478) (0.0796)

twaiv 0.0289 0.0358∗∗

(0.0709) (0.0181)

year∗twaiv 0.0316 -0.0178(0.2275) (0.0286)

year∗gender∗twaiv 0.0848 0.0202(0.2545) (0.0314)

age -0.1406∗∗∗ -0.1378∗∗∗ -0.0116∗∗ -0.0958∗∗∗

(0.0222) (0.027) (0.0051) (0.0128)

mother’s education 0.0186∗∗∗ 0.0176∗∗∗ 0.0042∗∗ 0.0048∗∗∗

(0.005) (0.0053) (0.0017) (0.0017)

gender 0.017 0.0305 -0.0273∗∗ -0.0367∗∗∗

(0.0368) (0.0377) (0.0127) (0.0141)

junior high school aged -0.0448∗∗ 0.0086(0.0198) (0.0166)

primary school in a village 0.0785∗ 0.0662 -0.0066 0.00005(0.0419) (0.0449) (0.0166) (0.0142)

junior high school in a village 0.0897∗∗ 0.0638 0.0052 0.0029∗∗

(0.0444) (0.0454) (0.0141) (0.0013)

per capita household income 0.0021 -0.0014 0.0037∗∗∗ 0.0096∗∗

(0.005) (0.0047) (0.0013) (0.0043)

refrig 0.2913∗∗∗ 0.2872∗∗∗ 0.0767∗∗∗ 0.069∗∗∗

(0.0461) (0.048) (0.011) (0.012)

Const. 2.501∗∗∗ 2.4743∗∗∗ 1.028∗∗∗ .8935∗∗∗

(0.3899) (0.4726) (0.0533) (0.024)

R2 0.1913 0.19 0.06 0.06N 628 578 2025 2025

1. Standard deviations are shown in the parentheses.

2. ***, **, *: represent significance at 1%, 5% and 10% confidence levels, respectively.

19

Table 10: Treatment effects of two-waiver-one-subsidy and tuition waiver-different groupsVariables I II III IV

year 0.0456∗∗ 0.0458∗∗ -0.0356 -0.0355(0.0208) (0.0208) (0.0257) (0.0257)

twos 0.0107 0.0109(0.0341) (0.0341)

year∗twos 0.0168 -0.0809(0.0436) (0.0755)

year∗twos∗gender 0.1343∗

(0.0717)

twaiv -0.0398 -0.0396(0.0246) (0.0247)

year∗twaiv 0.1019∗∗∗ 0.0784∗∗

(0.0332) (0.0398)

year∗twaiv∗gender 0.0489(0.0302)

junior high school aged -0.0210 -0.0227 -0.0560∗∗ -0.0544∗

(.0278) (0.0279) (0.0279) (0.0278)

age -0.0171∗∗ -0.0169∗∗ -0.0034 -0.0037(0.0070) (0.0070) (0.0075) (0.0074)

mother’s education 0.0016 0.0015 0.0005 0.0004(0.0028) (0.0028) (0.0032) (0.0033)

gender -0.0371∗ -0.0449 0.0067 -0.0033(0.0190) (0.0196) (0.0177) (0.0216)

primary school 0.0219 0.0212 -0.0080 -0.0078in a village (0.0269) (0.0268) (0.0203) (0.0203)

junior high school 0.0247 0.0257 0.0176 0.0185in a village (0.0194) (0.0193) (0.0187) (0.0188)

per capita household income 0.0163 0.0166 0.0004 0.0004(0.0114) (0.0114) (0.0007) (0.0007)

refrig 0.0686∗∗∗ 0.0680∗∗∗ 0.0357∗∗ 0.0372∗∗

(0.0187) (0.0188) (0.0181) (0.0182)

Const. 1.0505∗∗∗ 1.0522∗∗∗ 0.9825∗∗∗ 0.9901∗∗∗

(0.0833) (0.0833) (0.0770) (0.0770)

R2 0.0608 0.0636 0.0395 0.0408N 987 987 909 909

1. Standard deviations are shown in the parentheses.

2. ***, **, *: represent significance at 1%, 5% and 10% confidence levels, respectively.

20

A Appendix

A.1 An alternative specification

We can also estimate the effects of two-waiver-one-subsidy and tuition waiver simultaneouslyas shown in Equation (5.1).16

schoolit = γ0 + γ1year04 + γ2year06 + γ3twosi + γ4twaivi

+γ5twosi ∗ year04 + γ6twaivi ∗ year06 + γ7Zit + ωit (A.1)

where year04 and year06 equal to 1 if the data was from the CHNS 2004 wave and 2006wave, respectively, and 0 otherwise. twosi and twaivi are the same as in Equations (3.1)and (3.2). Zit includes the same variables as in Equation (3.1). ωit is an error term. Themeanings of γ5 and γ6 in Equation (A.1) are the same as those of α5 and β3.

We estimate Equation (A.1) and show our results in Table 11. The observations ofprimary and junior high school-aged children from all three waves were used here. As shownin Table 11, both two-waiver-one-subsidy and tuition waiver were in girls’ favor and thegender differential effects were statistically significant at the 10 percent significance level.As a result, the least we can say is that both two-waiver-one-subsidy and tuition waiver didimprove the school enrollment of girls.

16Tables 4 and 7 show that tuition control had a statistically insignificant effect on school enrollment.Thus, we did not consider tuition control in this alternative specification.

21

Table 11: Alternative specificationsVariables I

year04 0.0347∗∗∗

(0.0128)

year06 0.0142(0.0178)

twaiv 0.0230∗

(0.0124)

twos -0.0258(0.0190)

year04∗twos -0.0038(0.0384)

year04∗twos∗gender 0.0689∗

(0.0377)

year06*twaiv -0.0149(0.0268)

year06∗twaiv∗gender 0.0498∗

(0.0261)

age -0.0090∗∗

(0.0040)

mother’s education 0.0024(0.0018)

gender -0.0248∗∗

(0.0119)

junior high school age -0.0399∗∗∗

(0.0156)

primary school in a village 0.0201(0.0133)

junior high school in a village 0.0154(0.0109)

per capita household income 0.0007(0.0007)

refrig 0.0472∗∗∗

(0.0106)

Const. 0.9773∗∗∗

(0.0436)

R2 0.0496N 2630

1. Standard deviations are shown in the parentheses.

2. ***, **, *: represent significance at 1%, 5% and 10%

confidence levels, respectively.

22