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The Journal of Development Studies

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The Impact of Educational Fee Reduction Reformon School Enrolment in Rural China

Xinzheng Shi

To cite this article: Xinzheng Shi (2016) The Impact of Educational Fee Reduction Reform onSchool Enrolment in Rural China, The Journal of Development Studies, 52:12, 1791-1809, DOI:10.1080/00220388.2016.1156094

To link to this article: http://dx.doi.org/10.1080/00220388.2016.1156094

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The Impact of Educational Fee Reduction Reformon School Enrolment in Rural China

XINZHENG SHISchool of Economics and Management, Tsinghua University, Beijing, China

(Final version received December 2015; final version accepted December 2015)

ABSTRACT We investigate the impact of educational fee reduction reform on children’s school enrolment in ruralChina. Using data from Gansu Survey of Children and Families, we find that the reform did not have significantimpacts on school enrolment of 9–12 year old children, while the reform had significant impacts on schoolenrolment of 13–16 year old children. We also find that for children enrolled in 2004, the higher are the feereductions, the higher is the probability for them to stay in school in 2007. Heterogeneous effects in terms ofdifferent characteristics are also found in this paper.

1. Introduction

Investments in education pay off in the form of higher individual earnings and differences in averageeducation levels can explain a significant fraction of the variation in incomes across countries(Hanushek & Woessmann, 2008). Not surprisingly, increasing the educational attainment of thepopulation is often viewed as a central task of government, both to further national developmentand to reduce economic disparities by extending educational access to vulnerable groups. In devel-oping countries, due to the scarcity of budgetary funds, public schools frequently collect tuition andother mandatory fees even for primary education. For the poor, who have limited cash income oraccess to credit, even modest fees can act as a barrier to enrolling children in school. For example, inChina, the financial burden has been shown to be an important deterrent to obtaining education even inthe compulsory education stage; that is, primary and middle school education (Brown & Park, 2002;Zhao & Glewwe, 2010). According to the calculation by Chyi and Zhou (2014), the tuition of aprimary school student per year was between 2.1–9.1 per cent of household income in rural areas inChina. As a result, for example, although the primary school enrolment rate in rural counties in Gansuwhere we collected the data used in this paper was 97 per cent in 2000, middle school enrolment wasonly 84 per cent in 2004.2

In recent years, providing conditional cash transfers (CCTs) to parents who enrol their children inschool has become popular in many developing countries. The first and the most famous one isPROGRESA in Mexico. Then the CCT programmes have been expanded to other countries, such asBangladesh, Cambodia, Indonesia, Pakistan, Turkey, and Yemen in Asia, and Argentina, Brazil, Chile,Colombia, Ecuador, Honduras, Jamaica, and Nicaragua in Latin America and the Caribbean.Beginning in 2005, China implemented sequential reforms to eliminate educational fees for compul-sory schooling, which includes six years of primary school and three years of middle school.3 CCTprogrammes can be viewed as reductions of educational fees not just to zero, but to negative values;

Correspondence Address: Xinzheng Shi, School of Economics and Management, Tsinghua University, Beijing, 100084, China.Email: shixzh@sem.tsinghua.edu.cn.

The Journal of Development Studies, 2016Vol. 52, No. 12, 1791–1809, http://dx.doi.org/10.1080/00220388.2016.1156094

© 2016 Informa UK Limited, trading as Taylor & Francis Group

similarly, educational fee reductions can be thought of as conditional cash transfers equal to theamount of the educational fee reduction. Thus, in many respects, the economics of CCTs andeducational fee reductions are identical.

This study is one of the first to rigorously estimate the impact of China’s educational fee reductionreforms on student enrolment. By 2009, the educational fee had been eliminated for all 156 millionprimary and middle school students in China, making the programme the largest in scope of alleducational fee reduction (or CCT) programmes in the world. By comparison, the largest CCTprogrammes are in Bolivia and Mexico, where 11 million and 5 million households have been reached(Fiszbein & Schady, 2009). Studying the China case is of interest not only because of the programme’sscale, but also to understand how the impact of CCTs varies across different countries and regions ofthe world. Thus far, there are few studies that examine the impact of such programmes in East Asiawhere families traditionally place great emphasis on education. Unlike many other countries thatimplemented CCT programmes or abolished educational fees, China had already achieved relativelyhigh levels of school enrolment rates prior to the abolition of fees, raising questions about whetherfamilies still not enrolling their children in school would be responsive to such incentives. The Chinesereform also may facilitate more accurate estimates of the responsiveness of enrolment to the amount ofsubsidies or transfers. Prior to 2005, due to substantial fiscal decentralisation the amount of feescharged in different villages varied considerably. Removal of such fees thus generated differentmagnitudes of fee reductions across villages. In contrast, most CCT programmes provide a fixedsubsidy amount for all students of the same grade and gender.4

The data used in this paper come from the Gansu Survey of Children and Families (GSCF), alongitudinal study of rural children in Gansu Province in northwest China, one of China’s poorestprovinces. The survey included three waves of data collection in 2000, 2004, and 2007, following acohort of children aged 9–12 in 2000. In 2007 when the original sample was aged 16–19, a new cohortof children aged 9–15 was added. The first two waves of the survey were conducted before the feereduction reform and the third wave was conducted after the reform. The survey also includedquestionnaires for the school principals of the primary and middle schools nearest to each of the100 sample villages.

We follow two different difference-in-difference (DID) strategies to identify the enrolment impactsof the reforms. First, we examine how within-village changes in the enrolment of children of the sameage range are related to the amount of fee reductions across villages (cohort comparison method).Second, we exploit the panel data to test how differences in fee reductions affected enrolment status in2007 conditional on being enrolled in 2004 (panel method).

Previewing the main findings, using the cohort comparison method, we do not find a significantimpact of the programme on the enrolment of 9–12 year old children, but we do find a significantimpact for 13–16 year olds. For the latter group, a 1 per cent reduction in the educational feecorresponds to a 0.03 percentage point increase in the probability of enrolment, and the impact isgreater for girls and for those with less credit access. For both age groups, the reform’s impact isgreater for those in higher grade levels. Using the panel data, we find that for children aged 13–16 in2004, a 1 per cent reduction in school fees leads to a 0.05 percentage point increase in the probabilityof still being in school in 2007, with the effect again being larger for girls.

Results found in our study are consistent with those found by other studies in Latin America, theCaribbean, and Asia. For example, Schultz (2004), Behrman, Sengupta, and Todd (2005), andDubois, de Janvry, and Sadoulet (2011) find that PROGRESA in Mexico had significantly positiveeffects on school enrolment.5 CCT programmes in other counties in Latin America and theCaribbean are also found to have similar effects.6 In Asia, CCT programmes have also beenfound to have significantly positive effects on children’s school enrolment. For example,Chaudhury and Okamura (2012) study the impacts of a pilot CCT programme in the Philippinesand they find that the effects are significantly positive for children eligible for the grant. Khandker,Pitt, and Fuwa (2003) find that the female stipend programme increased girls’ secondary educationsubstantially in Bangladesh. Other studies focusing on CCT programmes in Asian countries havesimilar findings.7

1792 X. Shi

However, existing research suggests that CCTs in Africa might be premature (see Davis, Gaarder,Handa, and Yablonski (2012) for the review). It could be because of the fact that CCT success liesheavily on the supply side and whether service delivery agencies will be able to meet any additionaldemand induced by CCT. Very poor African countries may lack capacity to enrol additional children inschools. There could also be quality issues, especially in areas where teacher absenteeism is high. Insuch situations, the main bottleneck to school enrolment is not just the willingness of the families toinvest in children’s education, but also the constraints of the infrastructure itself. Additionally, impactsof CCT programmes on students’ academic performance are not as strong as their effects on schoolenrolment. For example, Shi (2013) finds that although parents in rural China spent more money savedfrom the school fee reduction programme on children’s education, test scores of their children are notimproved. A report by the World Bank (World Bank, 2009) also points out that in Ecuador and Mexicothere is not a significant effect of the CCT programme on test scores.

Our findings, together with finding in other developing countries, have some implications formaking future policies. Firstly, financial incentives are still effective to keep children in school, butmore efforts should be exerted to identify the more vulnerable groups on which the CCT programmeshave larger effects. Secondly, besides the demand side intervention, governments should also investmore to improve school capacity to meet the higher demand for education induced by the CCTprogrammes. Thirdly, keeping children in school itself might not be enough to improve their humancapital accumulation, more investments are also needed to improve education quality especially inpoor areas.

The rest of the paper is organised as follows. Section 2 reviews China’s earlier educational feereduction reform and Section 3 introduces the data and shows some descriptive evidence. Section 4presents the empirical strategy. Section 5 shows the results. Section 6 shows a robustness check, andSection 7 concludes.

2. Educational Fee Reduction Reform in Rural China

We evaluate the impact on student enrolment of China’s ‘Two Exemptions One Subsidy (TEOS)’policy initiated in 2005, which targets children during the first nine years of formal schooling,which have been compulsory by law since 1986 but never strictly enforced. In China, this includesprimary school (typically grades 1–6) and lower secondary, or middle school (grades 7–9).According to government documents, TEOS was implemented at the beginning of 2005 in allnationally designated poor counties, which account for about one fourth of China’s total ruralpopulation (World Bank, 2009). The policy was expanded to include all of rural China in 2006, atwhich time it was also called the New Security System for Rural Compulsory Education (nongcunyiwu jiaoyu baozhan xin jizhe) (Guangming Daily, 2007).

The policy’s two exemptions refer to exemptions from paying miscellaneous school fees (xuezafei) and textbooks (keben fei), which previously were paid by students.8 The one subsidy refers toa subsidy to partially cover the living costs of students living in dormitories. The three compo-nents differ in their coverage and financing. The exemption from paying school fees targeted allrural children. Funding for this exemption is shared proportionally by the central and localgovernments.9 The exemption from paying for textbooks targeted children of poor householdsonly, and was fully financed by the central government. The subsidy for living expenses alsotargeted the poor, but was financed by local governments who were asked to put their programmesin place by the end of 2007. Authors’ interviews in some of the study sites found that if a studentwas fully exempted from all three types of expenses (school fees, textbook fees, and dormitoryliving costs), the only remaining fee charged to students was a very modest fee for purchasingnotebooks.

Fee reduction and school enrolment 1793

3. Data and Descriptive Evidence

The data are from the Gansu Survey of Children and Families (GSCF), a longitudinal study of ruralchildren in Gansu province in western China. In 2007, Gansu had a population of about 26 million(China Statistical Year Book, 2008), the second lowest rural income per capita of all provinces inChina, and a GDP per capita of 10,346 RMB or about $1500 dollars.10 Like much of northern China,water is scarce, and the loose soil that covers much of the province forms hills that are easily eroded.The sample was drawn to be representative of rural Gansu, excluding minority counties, and includes100 villages located in 42 townships and 20 counties. The GSCF follows a cohort of rural childrenaged 9–12 in the year 2000 when the first wave of the survey was conducted. The children and theirfamilies were re-interviewed in 2004 (wave 2) when the children were 13–16 years old, and again in2007 (wave 3) when the children were 16–19 years old. The third wave also surveyed a new cohort ofchildren aged 9–15 and their families. The GSCF has a linked survey design which includes ques-tionnaires for target children, households, mothers, fathers, homeroom teachers, school principals, andvillage leaders.11 The survey collected detailed information on the enrolment status and enrolmenthistory of all target children and their siblings, as well as on household income, expenditures, andwealth. In the third wave, a special module was designed as part of the household questionnaire aboutfee exemptions and living subsidies received by each enrolled child. Questions about the implementa-tion of TEOS were also included in questionnaires completed by school principals and countyeducation bureau officials.

First, we focus on comparisons of two groups of children: children aged 9–12 in 2000 and in 2007,and children aged 13–16 in 2004 and in 2007. Children aged 9–12 generally attend primary school andchildren aged 13–16 generally attend middle school. Table 1 presents summary statistics for thesechildren. Columns 1 and 2 are for children aged 9–12 in the years 2000 and 2007, respectively, andcolumn 3 calculates the differences in mean values. Columns 4–6 present the same statistics for thesample of children aged 13–16 in 2004 and 2007. The enrolment rate of 9–12 year olds increases from97 per cent in 2000 to 99.5 per cent in 2007, while the enrolment rate of 13–16 year olds increasesfrom 87 per cent in 2004 to 90 per cent in 2007. Both differences are statistically significantly differentfrom zero at conventional significance levels. However, the higher enrolment rates in 2007 mightreflect a general trend toward higher enrolment rates or reflect the impacts of the reform; distinguish-ing between these interpretations requires more detailed analysis.

The mean characteristics of the samples of 9–12 year-old children in 2000 and 2007 exhibit somedifferences which are statistically significantly different from zero (Columns 1 and 2 of Table 1).These include mean age (10.543 in 2000 and 10.704 in 2007), average schooling years of mothers(4.192 in 2000 and 5.049 in 2007), and household real income per capita (1048 Yuan in 2000 and1408 Yuan in 2007). Other characteristics are relatively similar and not statistically significantly

Table 1. Individual characteristics of the sample.

Year 2000 2007 2007–2000 2004 2007 2007–2004

Age 10.543 10.704 0.161*** 14.549 14.716 0.167***(1.038) (1.112) (0.050) (1.041) (1.133) (0.046)

Girl 0.459 0.426 −0.033 0.463 0.455 −0.009(0.498) (0.495) (0.023) (0.499) (0.498) (0.021)

Enrolment rate 0.966 0.995 0.029*** 0.871 0.903 0.033**(0.181) (0.069) (0.007) (0.336) (0.296) (0.014)

Mother’s schooling year 4.192 5.049 0.857*** 4.270 4.674 0.404***(3.513) (3.491) (0.164) (3.432) (3.340) (0.144)

Father’s schooling year 6.935 7.156 0.221 7.041 7.097 0.056(3.537) (3.005) (0.149) (3.524) (3.269) (0.144)

Household income per capita 1048.193 1407.712 359.519*** 1442.157 1699.660 257.503(1414.22) (2221.908) (95.271) (2392.084) (6753.911) (242.271)

OBS 1622 629 1608 827

1794 X. Shi

different; these include the share that are girls (46% in 2000 and 43% in 2007) and the average yearsof schooling of fathers (6.94 in 2000 and 7.16 in 2007). The differences between the sample of13–16 year-old children in 2004 and 2007 are similar (Columns 4–6). Again, some mean character-istics are clearly different, including age (14.55 in 2004 and 14.72 in 2007) and schooling years ofmothers (4.27 in 2004 and 4.67 in 2007), but other mean differences are not statistically different fromzero, such as the share of girls (46.3% in 2004 and 45.5% in 2007), schooling years of fathers (7.04 in2004 and 7.10 in 2007), and real household income per capita (1442 Yuan in 2004 and 1700 Yuan in2007).

In Tables 2–3, we report evidence on the timing of the implementation of the TEOS reform based ondata from the county and household questionnaires. Among the 20 counties surveyed, 15 per centreported implementing school fee exemptions in primary schools in the fall of 2004, another 50 percent reported starting in spring 2005, and the rest reported starting in spring 2006 (Table 2). Thetextbook fee exemptions began a little bit earlier, with 30 per cent of counties starting in fall 2004, andanother 45 per cent in spring 2005. Just as for the school fees exemption, by spring 2006 all countieshad implemented the textbook fee exemption. The results are nearly identical for middle schools(Table 2). Provision of living cost subsidies was much slower, especially in primary schools. Only 10per cent of counties reported providing living subsidies in spring 2005, 65 per cent of countiesreported having started the programme by spring 2006 when the two exemption policies were fullyimplemented, and 30 per cent of counties still had not begun providing subsidies by the time of thesurvey in summer 2007. Implementation was much faster in middle schools, which typically havemore students living in dormitories. 60 per cent of counties started providing living subsidies in spring2005, and by spring 2006, 95 per cent of counties had begun.

Evidence from the household data is presented in Table 3. Because coverage by the reforms is basedon recall, we restrict attention to students who were in the same school in 2004 and 2007 (primarystudents in grades 3–6 and middle school students in grade 9). Since the textbook fees exemption andliving subsidies are targeted to children from poor households, it is possible that the coverage rates forstudents in the sample could be significantly lower than for schools or counties. In fact, this is true forthe living subsidy. No students report receiving living subsidies until spring 2005 and by spring 2007only 0.92 per cent of primary students and 8.96 per cent of middle school students report receivingliving subsidies (Table 3). In contrast, a very high proportion of students receive the textbook feeexemption by the spring of 2007; specifically, 86 per cent of primary students and 78 per cent ofmiddle school students (Table 3). The large difference in the coverage of textbook exemptions andliving subsidies is likely due to the fact that as a poor province Gansu receives a large amount ofcentral subsidies for textbook exemptions, but lacks local resources to finance living subsidies whichare not centrally financed. Finally, a small percentage of primary school students (4%) and middleschool students (9%) report not receiving the school fees exemption in 2007. This could be due tomisreporting or enrolment in private schools.

Table 2. Percentage of counties claiming to implement TEOS in each semester (%).

Fall, 2004 Spring, 2005 Fall, 2005 Spring, 2006 Fall, 2006 Spring, 2007

Primary schoolSchool fees exemption 15 65 65 100 100 100Textbook exemption 30 75 75 100 100 100Living subsidy 0 10 15 65 70 70Middle schoolSchool fees exemption 15 65 65 100 100 100Textbook exemption 30 80 80 100 100 100Living subsidy 0 60 65 95 95 95Num. of counties 20 20 20 20 20 20

Fee reduction and school enrolment 1795

Prior to the TEOS reform, schools charged a single comprehensive educational fee (CEF), whichincluded school fees, textbook charges, and notebook fees. The value of the school fees exemptionthus varied by school level, depending on the value of the single fee established prior to TEOS. Wecalculate the exempted school fees (SF) in 2007 for each primary and middle school student as theCEF minus 2007 textbook and notebook fees (SF = CEF – notebook fees – textbook fees). For eachstudent, we assign fees for the closest primary and middle school to where they live. The CEF isrecorded in the school questionnaire and varies by school. We assume common values for the textbookand notebook fees: 70 and 10 Yuan for primary school students and 140 and 15 Yuan for middleschool students. The textbook fees are based on the subsidy amounts for 2007 announced in a circularissued by the Gansu Educational Bureau. Since counties within the province buy the same textbooks,it is reasonable to assume that the value of the textbooks provided and the value of the subsidy areuniform throughout the province. The costs of notebooks (10 and 15 yuan) are based on fieldinterviews in several study sites in the province by the authors.

After making these calculations, we estimate the average value of exempted school fees to be 54.26Yuan for primary school students, with a standard deviation of 54.66, and 71.75 Yuan for middleschool students, with a standard deviation of 56.13. Figure 1 shows the kernel densities of exemptedschool fees for the 100 villages. We can see that there is considerable variation in the amount ofexempted school fees, and that the mean value of exempted school fees is significantly higher formiddle school students than for primary school students.

The announced criterion for allocating the free textbooks is the students’ household income percapita.12 Overall, 79.58 per cent of primary school students and 72.69 per cent of middle schoolstudents reported receiving textbook fee exemptions in the past year. The provincial education bureaurecommended a living subsidy for poor students of one Yuan per day for 239 days per year, or 239Yuan per year and 120 Yuan per semester. However, according to the 2007 survey data, only 0.92 and8.96 per cent of primary and middle school students received the subsidy. Therefore, for each childenrolled in primary or middle school, total educational fee reductions from the reform are equal to theexempted school fees plus 70 or 140 if she is exempted from the textbook fee; plus 239 if she gets theliving subsidy.

Table 4 shows the magnitude of fee reductions relative to other categories of household expendi-tures for the sample used in this paper. We can see from this table that fee reductions per capita areabout 41 Yuan, accounting for 3 per cent of total household expenditures. For households withexpenditures per capita in the lowest 15 per cent, fee reductions per capita are about 35 Yuan and

Table 3. Percentage of children claiming to have TEOS in each semester (%).

Fall,2004

Spring,2005

Fall,2005

Spring,2006

Fall,2006

Spring.2007

Total studentnumber

Primary SchoolSchool feesexemption

7.32 44.24 52.10 61.94 92.07 95.73 1311

Textbook exemption 7.70 40.12 48.97 60.34 80.70 85.74 1311Living subsidy 0.00 0.31 0.31 0.69 0.84 0.92 1311

Middle schoolSchool feesexemption

8.53 40.30 49.47 56.29 88.06 91.04 469

Textbook exemption 8.10 41.36 49.89 56.50 74.63 78.04 469Living subsidy 0.21 3.20 3.20 4.90 7.25 8.96 469

Notes: (1) The total student sample is constructed according to the following: (a) children must be in school, (b)children reporting policy time later than ‘liu ji’ time were dropped; (c) children having drop-out experience weredropped; (d) primary school student sample includes students in grade three to grade six in 2007; middle schoolstudent sample includes student in grade three. (2) Those reporting policies earlier than the year when the policiesexisted or later than the time when the surveyed was conducted were dropped.

1796 X. Shi

account for 8.1 per cent of total household expenditures. In contrast, for households with expendituresper capita in the highest 15 per cent, fee reductions per capita are 41 Yuan, accounting for only 1.4 percent of total household expenditure. One possible reason for richer households to have more feereductions is that richer households may have lived in richer villages where educational fees werehigher before the reform.

4. Empirical Strategy

We seek to estimate the causal effect of the educational fee reduction reform on children’s schoolenrolment. Taking advantage of the fact that the GSCF collected data both before the reform (2000and 2004) and after the reform (2007), we can compare children’s enrolment in 2000 and 2004 withchildren’s enrolment in 2007. However, as noted earlier, comparisons of students before and afterthe reforms cannot distinguish treatment effects from time effects. Fortunately, schools in differentvillages charged different fees before the reform; which created significant variation in the value offees that were eliminated by the reforms. This enables us to pursue a DID approach in which we testwhether the magnitude of changes in children’s enrolment before and after the reform are greater forvillages with larger fee reductions compared to those with smaller fee reductions.

The regression function estimated is as follows:

Enrollivt ¼ β0 þ β1FRvt þ γXivt þ θðYear2007 � VvÞ þ Yt þ μv þ εivt: (1)

In Equation (1), Enrollivt is an indicator variable for whether child i living in village v in year t.It is equal to 1 if child t is enrolled in schools and 0 if this child is not enrolled in schools inyear t. FRvt measures educational fee reductions children living in village v can get in year t ifthey are enrolled in schools. This variable is the village level average values of fee reductionsreceived by currently enrolled primary school students and middle school students respectively.13

It is therefore equal to zero in 2000 and 2004 since there was no educational fee reduction beforethe reform.

The main identification concern is that differences across villages in the educational fees chargedbefore the reform may be correlated with other village attributes or reforms that independentlyinfluence changes in children’s enrolment. To reduce the potential bias from this concern, we followDuflo (2001) and control for village specific changes correlated with village characteristics by

Primary school

Middle school

0.0

05.0

1.0

15.0

2.0

25D

ensi

ty

0 50 100 150Exempted school fee (yuan per year)

Figure 1. Kernel density of exempted school fees.

Fee reduction and school enrolment 1797

including in the regressions interactions of a vector of village level variables in 2000 or 2004 with adummy variable indicating year 2007.

Although we control for village level variables to alleviate endogenous problems, one might stillbe concerned that there exist unobservable variables affecting both the educational fee reductionsand children’s enrolment in each village. Taking an advantage of the fact that cohorts enrolled inmiddle school or above before the reform would not be affected after the reform because of theirage, we can first compare the change of enrolment of cohorts affected by the reform with cohortsliving in the same village but not affected by the reform. This comparison uses exogenousvariation coming from different ages of these cohorts. We then compare the different enrolmentchange of different cohorts across villages in order to parcel out pre-existing cohort differences.GSCF is a panel study following the same children from 2004 to 2007; we therefore focus onenrolled children (including children enrolled in primary schools and children enrolled in higherlevel schools) in 2004 and investigate how the reform affected the change of their enrolment byusing children enrolled in primary schools in 2004 as a treatment group while using childrenenrolled in higher level schools in 2004 as a control group. The following regression function isestimated:

ΔEnrollivc ¼ α0 þ FRvc þ X 2004ivc þ V 2004

v þ μv þ εivc: (2)

ΔEnrollivc in Equation (2) is change of enrolment from 2004 to 2007 of children i belonging to cohortc and living in village v. FRvc is log value of fee reductions received by enrolled children. It is equal tozero for children enrolled in middle schools or higher level schools in 2004 since they would not beaffected by the reform in 2007. X 2004

ivc includes age, gender, years of schooling completed, householddemographic variables, household endowments and log value of household income per capita. Thesevariables are defined the same as those in Equation (1), but measured in 2004 values. V 2004

v is a vectorof village characteristics defined the same as in Equation (1) but measured in 2004 value. μvis villagefixed effect and εivc is an error term with mean equal to zero.

One caveat we need to bear in mind is that, in order to get unbiased estimates from Equation (2), thepre-assumption is that the pre-existing cohort difference should be the same across villages aftercontrolling for village level characteristics. Otherwise, the estimates will still be biased.

Table 4. Fee reduction and household expenditures in 2007.

AllLowest 15 per cent

expenditure per capitaHighest 15 per centexpenditure per capita

Variables MeanPercentage

(%) MeanPercentage

(%) MeanPercentage

(%)

Total expenditure per capita 1331.350 100 437.716 100 2948.013 100In which:Food expenditure per capita 401.719 30.174 123.966 28.321 766.022 25.984Non-food expenditure per capita 565.110 42.446 209.182 47.789 1258.868 42.702Health expenditure per capita 187.226 14.063 47.027 10.744 507.421 17.212Educational expenditure percapita

178.705 13.423 57.541 13.146 415.702 14.101

Fee reductions per capita 40.766 3.062 35.399 8.087 40.640 1.379OBS 1435 1435 170 170 170 170

Note: (1) Percentages in this table are percentage of total expenditure per capita. (2) All money is deflated to year2000 value.

1798 X. Shi

5. Empirical Results

5.1 Average Effects of the Reform on Children’s School Enrolment

Table 5 shows the main results. For the brevity of presenting results, the coefficients of individual,household and village characteristics, year dummies, and village dummies are not shown.14 Columns 1and 2 show the results from estimating Equation (1). Results in column 1 come from the regressionusing the sample of 9–12 year old children while results in column 2 come from the regression usingthe sample of 13–16 year old children. Column 3 shows the results from estimating Equation (2).

The educational fee reduction reform did not have impacts on primary school enrolment, but didhave significantly positive impacts on middle school enrolment. From column 1 in Table 5, we can seethat coefficient on fee reduction is equal to 0.011, but not statistically significantly different from zero.It shows that the reform did not have any impacts on primary school enrolment. One possible reasonmight be that parents thought it necessary for their children to finish primary education, thereforesending children to primary schools or not was not sensitive to educational fees. Column 2 shows theimpact of the reform on the enrolment of 13–16 year old children. We can see that the coefficient onfee reductions is equal to 0.026, and it is statistically significantly different from zero at the 5 per centlevel. It means that with a 1 per cent increase of the educational fee reductions, the school enrolmentrate for 13–16 children increases by 0.03 percentage points. In rural areas in China, although nine-year

Table 5. Impacts of the reform on children’s school enrolment.

Dependent variable: Enrolled in schools = 1Enrolment status in 2007 – enrolment

status in 2004

9–12 year oldchildren

13–16 year oldchildren

2004 enrolled children

Log(educational feereductions)

0.011(0.013)

0.026(0.010)**

0.041(0.007)***

Village variables in year 2000or 2004*Dummy for year 2007

YES YES

Household characteristics YES YES YESIndividual characteristics YES YES YESYear fixed effect YES YESVillage fixed effect YES YES YESObservations 2251 2435 2739R-squared 0.12 0.21 0.22

Notes: Robust standard errors in parentheses are cluster over villages; * significant at 10 per cent; ** significant at5 per cent; *** significant at 1 per cent. (1) Village variables include illiterate ratio, ratio of primary schoolgraduates in labour force, primary school enrolment rate, middle school enrolment rate, indicator for havingpreschool classes, distance of primary school from the village seat, log value of money given to schools byvillage, indicator for having primary school run by the village, indicator for having middle school run by thevillage, indicator for having railway through the village, indicator for having bus stop in the village, log value ofaverage agricultural income per capita, log value of average industrial income per capita, ratio of householdsengaged in non-agricultural work, ratio of households running household enterprise, log value of wage for mendoing non-agricultural work, log value of wage for women doing non-agricultural work, log value of wage to hirelabour for agricultural production, indicator for having enterprises owned by county, indicator for havingenterprises owned by township, and indicator for having private enterprises. (2) Household characteristics includehousehold endowments, including mother’s years of schooling, father’s years of schooling, and land area percapita; household demographic structure, including ratio of male aged 0–5, ratio of male aged 6–12, ratio of maleaged 13–16, ratio of male aged 17–19, ratio of male aged 20–29, ratio of male aged 30–39, ratio of male aged40–49, ratio of male aged 50–54, ratio of male aged above 54, ratio of female aged 0–5, ratio of female aged 6–12,ratio of female aged 13–16, ratio of female aged 17–19, ratio of female aged 20–29, ratio of female aged 30–39,ratio of female aged 40–49, ratio of female aged 50–54 and total number of family members; and log value ofhousehold income per capita. (3) Individual characteristics include years of schooling completed, age and a femaledummy. (4) Individual characteristics and household characteristics included in column 3 are all in 2004 values.

Fee reduction and school enrolment 1799

compulsory education is required by the government, some children might choose not to continue theireducation after they finish primary school because of high educational fees. Indeed, the schoolenrolment rate for 13–16 year old children was only 85.6 per cent in 2004; therefore, removal ofeducational fees induced more children to be enrolled in middle schools.

The educational fee reduction reform also increased the probability for students enrolled in 2004 tostill stay in school in 2007. Column 3 shows results from estimating Equation (2). The sample used toestimate Equation (2) includes all enrolled children in 2004. The outcome variable in column 3 is thechange of enrolment status from 2004 to 2007 (0 means the child was enrolled in school in both 2004and 2007; −1 means the child was enrolled in school in 2004 while not being enrolled in school in2007). In other words, we investigate the impact of the reform on the probability of staying in schoolin 2007 for those children enrolled in 2004. From the table, we can see that the coefficient on the feereduction is 0.041, statistically significant at the 1 per cent level. It shows that with a 1 per centincrease in the educational fee reductions, the probability for children enrolled in 2004 to stay inschool in 2007 is 0.041 percentage points higher.

5.2 Heterogeneous Effects of the Reform on Children’s School Enrolment

Table 6 and Table 7 show heterogeneous effects of the reform on the 9–12 year old children andthe 13–16 year old children respectively. The formats of these two tables are the same. Theregression function we estimate follows Equation (1) but adding interaction terms of fee reduc-tions with fee reductions itself (column 1), log household income per capita (column 2), loghousehold accessible credit (column 3), bad harvest dummy (column 4), female dummy (column5), years of schooling completed (column 6), and pre-reform village level school enrolment rate(column 7).

The results in Table 6 show that the reform had larger effects on 9–12 year old children whocompleted more years of schooling. In Table 6, we firstly investigate whether the reform had differenteffects with respect to the level of fee reductions (column 1), the coefficient on the squared log feereduction is −0.005 and not significant. Then, we see from column 2 that the coefficient on theinteraction of fee reductions with log value of household income per capita is 0.001 and insignificant.It means that the effects of the reform were not different for children from poor and rich households.However, if households can borrow from banks or their relatives, then household income might not bea good measurement of resources available to households. Therefore, we investigate the heterogeneouseffects of the reform in terms of household accessible credit, which is measured by the log value of themoney household heads thought they would be able to borrow. The coefficient on the interaction termis −0.001 and insignificant (column 3). Column 4 investigates whether the reform had different effectson households experiencing temporary income shocks. We use a dummy indicating whether house-holds had a bad harvest last year to measure income shock. The coefficient on the interaction term is0.002 and insignificant. One possible interpretation might lie in that households could make con-sumption decisions based on their permanent income, so temporary income shocks would not affecttheir decision about whether to send their children to school. Then we check whether the reform haddifferent effects on boys and girls. The results are shown in column 5 in Table 6. The coefficient on theinteraction term is 0.005 and insignificant. The result shows that the effects of the reform are notdifferent for boys and girls. Column 6 investigates whether the reform had different effects on childrencompleting different years of schooling. The coefficient on the interaction term is 0.006, statisticallysignificant at the 5 per cent level. It means that although on average the reform did not have asignificant impact on children’s primary school enrolment, it had a bigger effect on children who hadcompleted more years of schooling. One explanation would be that, when parents felt it was difficultto pay for all children’s educational fees, they were more likely to keep children finishing lesseducation in school if they would like their children to be educated equally. Therefore, when parentsdid not need to pay educational fees after the reform, children returning to school were more likely tobe those who had finished more years of schooling. The last column (column 9) shows whether thereform had different effects for villages having different pre-reform primary school enrolment rates.

1800 X. Shi

Table 6. Heterogeneous effects of the reform on 9–12 year old children.

Dependent variable: Enrolment(1 = enrolled in schools; 0 = notenrolled in schools) (1) (2) (3) (4) (5) (6) (7)

Log(educational fee reductions) 0.057 0.010 0.012 0.011 0.009 −0.019 −0.081(0.139) (0.013) (0.013) (0.013) (0.013) (0.015) (0.435)

Log(educational fee reductions)*Log(educational feereductions)

−0.005(0.015)

Log(educational fee reductions)*Log(household income percapita)

0.001(0.001)

Log(educational fee reductions)*Log(household creditconstraint)

−0.001(0.004)

Log(educational fee reductions)*bad harvest dummy

0.002(0.003)

Log(educational fee reductions)*female dummy

0.005(0.003)

Log(educational fee reductions)*years of schooling completed

0.006(0.002)**

Log(educational fee reductions)*village primary schoolenrolment rate in 2000

0.093(0.440)

Village variables in year 2000*Dummy for year 2007

YES YES YES YES YES YES YES

Household characteristics YES YES YES YES YES YES YESIndividual characteristics YES YES YES YES YES YES YESYear fixed effect YES YES YES YES YES YES YESVillage fixed effect YES YES YES YES YES YES YESObservations 2251 2251 2251 2251 2251 2251 2251R-squared 0.12 0.12 0.12 0.12 0.12 0.13 0.12

Notes: Robust standard errors in parentheses are clustered over villages; * significant at 10 per cent; ** significantat 5 per cent; *** significant at 1 per cent. (1) Village variables include illiterate ratio, ratio of primary schoolgraduates in labour force, primary school enrolment rate, middle school enrolment rate, indicator for havingpreschool classes, distance of primary school from the village seat, log value of money given to schools byvillage, indicator for having primary school run by the village, indicator for having middle school run by thevillage, indicator for having railway through the village, indicator for having bus stop in the village, log value ofaverage agricultural income per capita, log value of average industrial income per capita, ratio of householdsengaged in non-agricultural work, ratio of households running household enterprise, log value of wage for mendoing non-agricultural work, log value of wage for women doing non-agricultural work, log value of wage to hirelabour for agricultural production, indicator for having enterprises owned by county, indicator for havingenterprises owned by township, indicator for having private enterprises. (2) Household characteristics includehousehold endowments, including mother’s years of schooling, father’s years of schooling, and land area percapita; household demographic structure, including ratio of male aged 0–5, ratio of male aged 6–12, ratio of maleaged 13–16, ratio of male aged 17–19, ratio of male aged 20–29, ratio of male aged 30–39, ratio of male aged40–49, ratio of male aged 50–54, ratio of male aged above 54, ratio of female aged 0–5, ratio of female aged 6–12,ratio of female aged 13–16, ratio of female aged 17–19, ratio of female aged 20–29, ratio of female aged 30–39,ratio of female aged 40–49, ratio of female aged 50–54 and total number of family members; and log value ofhousehold income per capita. (3) Individual characteristics include years of schooling completed, age and a femaledummy. (4) In column (4), interaction of log educational fee reductions and log household income per capita,interaction of log educational fee reductions and log household credit constraint, interaction of log householdincome per capita and log household credit constraint, and log household credit constraint are also included in theregression.

Fee reduction and school enrolment 1801

Table 7. Heterogeneous effects of the reform on 13–16 year old children.

Dependent variable: Enrolment(1 = enrolled in schools; 0 = notenrolled in schools) (1) (2) (3) (4) (5) (6) (7)

Log(educational fee reductions) 0.007 0.026 0.036 0.023 0.021 −0.010 0.115(0.034) (0.010)** (0.011)

***(0.010)** (0.010)** (0.019) (0.044)**

Log(educational fee reductions)*log(educational feereductions)

0.004(0.006)

Log(educational fee reductions)*Log(household income percapita)

−0.000(0.002)

Log(educational fee reductions)*Log(household creditconstraint)

−0.021(0.008)**

Log(educational fee reductions)*bad harvest dummy

0.011(0.007)

Log(educational fee reductions)*female dummy

0.010(0.006)

*Log(educational fee reductions)*years of schooling completed

0.005(0.002)**

Log(educational fee reductions)*village middle schoolenrolment rate in 2004

−0.099(0.048)**

Village variables in year 2004*Dummy for year 2007

YES YES YES YES YES YES YES

Household characteristics YES YES YES YES YES YES YESIndividual characteristics YES YES YES YES YES YES YESYear fixed effect YES YES YES YES YES YES YESVillage fixed effect YES YES YES YES YES YES YESObservations 2435 2435 2435 2435 2435 2435 2435R-squared 0.21 0.21 0.21 0.21 0.21 0.21 0.21

Notes: Robust standard errors in parentheses are clustered over villages; * significant at 10 per cent; ** significantat 5 per cent; *** significant at 1 per cent. (1) Village variables include illiterate ratio, ratio of primary schoolgraduates in labour force, primary school enrolment rate, middle school enrolment rate, indicator for havingpreschool classes, distance of primary school from the village seat, log value of money given to schools byvillage, indicator for having primary school run by the village, indicator for having middle school run by thevillage, indicator for having railway through the village, indicator for having bus stop in the village, log value ofaverage agricultural income per capita, log value of average industrial income per capita, ratio of householdsengaged in non-agricultural work, ratio of households running household enterprise, log value of wage for mendoing non-agricultural work, log value of wage for women doing non-agricultural work, log value of wage to hirelabour for agricultural production, indicator for having enterprises owned by county, indicator for havingenterprises owned by township, indicator for having private enterprises. (2) Household characteristics includehousehold endowments, including mother’s years of schooling, father’s years of schooling, and land area percapita; household demographic structure, including ratio of male aged 0–5, ratio of male aged 6–12, ratio of maleaged 13–16, ratio of male aged 17–19, ratio of male aged 20–29, ratio of male aged 30–39, ratio of male aged40–49, ratio of male aged 50–54, ratio of male aged above 54, ratio of female aged 0–5, ratio of female aged 6–12,ratio of female aged 13–16, ratio of female aged 17–19, ratio of female aged 20–29, ratio of female aged 30–39,ratio of female aged 40–49, ratio of female aged 50–54 and total number of family members; and log value ofhousehold income per capita. (3) Individual characteristics include years of schooling completed, age and a femaledummy. (4) In column (4), interaction of log educational fee reductions and log household income per capita,interaction of log educational fee reductions and log household credit constraint, interaction of log householdincome per capita and log household credit constraint, and log household credit constraint are also included in theregression.

1802 X. Shi

Table 8. Heterogeneous effects of the reform on children enrolled in 2004.

Dependent variable:

△Enrolment (1) (2) (3) (4) (5) (6) (7) (8)

Log(educational feereductions)

0.025 0.040 0.047 0.040 0.032 0.079 0.012 0.052

(0.034) (0.007)***

(0.008)***

(0.007)***

(0.008)***

(0.016)***

(0.087) (0.014)***

Log(educational feereductions)*log(educationalfee reductions)

0.003(0.006)

Log(educational feereductions)*Log(householdincome per capita)

−0.004(0.003)

Log(educational feereductions)*Log(householdcredit constraint)

−0.013(0.008)

Log(educational feereductions)*bad harvestdummy

0.009(0.009)

Log(educational feereductions)*female dummy

0.021(0.008)***

Log(educational feereductions)*years of schoolingcompleted

−0.008(0.003)***

Log(educational feereductions)*village primaryschool enrolmentrate in 2004

0.030(0.090)

Log(educational feereductions)*village middleschool enrolmentrate in 2004

−0.012(0.016)

Householdcharacteristics in2004

YES YES YES YES YES YES YES YES

Years of schoolingcompleted in 2004

YES YES YES YES YES YES YES YES

Village fixed effect YES YES YES YES YES YES YES YESObservations 2739 2739 2739 2739 2739 2739 2739 2739R-squared 0.22 0.22 0.22 0.22 0.22 0.23 0.22 0.22

Notes: Robust standard errors in parentheses are clustered over villages; * significant at 10 per cent; ** significantat 5 per cent; *** significant at 1 per cent. (1) Household characteristics include household endowments, includingmother’s years of schooling, father’s years of schooling, and land area per capita; household demographicstructure, including ratio of male aged 0–5, ratio of male aged 6–12, ratio of male aged 13–16, ratio of maleaged 17–19, ratio of male aged 20–29, ratio of male aged 30–39, ratio of male aged 40–49, ratio of male aged50–54, ratio of male aged above 54, ratio of female aged 0–5, ratio of female aged 6–12, ratio of female aged13–16, ratio of female aged 17–19, ratio of female aged 20–29, ratio of female aged 30–39, ratio of female aged40–49, ratio of female aged 50–54 and total number of family members; and log value of household income percapita.(2) In column (4), interaction of log educational fee reductions and log household income per capita, interaction oflog educational fee reductions and log household credit constraint, interaction of log household income per capitaand log household credit constraint, and log household credit constraint are also included in the regression.

Fee reduction and school enrolment 1803

The coefficient on the interaction term is 0.093 and insignificant, meaning that the effects of thereform were not different with respect to the pre-reform primary school enrolment rate.

Results in Table 7 show that, among 13–16 year old children, the reform had bigger effects on girlsand those completing more years of schooling, while the reform had smaller effects on children fromhouseholds having more accessible credit and villages with higher pre-reform middle school enrolmentrates. As in Table 6, column 1 in Table 7 investigates whether the reform’s effects are different withrespect to levels of fee reductions. The coefficient on the interaction term is 0.004 and insignificant,showing that the reform’s effects were not different in villages receiving different fee reductions. Thesecond column exams the heterogeneous effects of the reform with respect to household income. Thecoefficient on the interaction term is −0.000 and insignificant, meaning that the effects of the reformwere the same for rich and poor families. As discussed above, household accessible credit would be abetter measure for households’ financial resources. Column 3 shows the heterogeneous effects of thereform with respect to household accessible credit. The coefficient on the interaction term is −0.021and significant at the 5 per cent level, meaning that households with more accessible financialresources were less affected by the reform. Column 4 investigates whether the reform had differenteffects on households experiencing different income shocks. The coefficient on the interaction term is0.011 and insignificant. It is consistent with what we find in Table 6, supporting the permanent incomehypothesis of household consumptions. Column 5 shows the different effects of the reform on boysand girls. The coefficient is 0.010, statistically significant at the 10 per cent level. The result found in

Table 9. Test of the pre-existing time trend, using 2000 and 2004 samples.

Dependent variable: Enrolled in schools = 1Enrolment status in 2004 – enrolment

status in 2000

9–12 yearschildren

13–16 years oldchildren

2000 enrolled children

Log(educational fee reductions) −0.034 0.011 −0.103(0.030) (0.048) (0.108)

Village variables in year2000*Dummy for year 2004

YES YES

Household characteristics YES YES YESIndividual characteristics YES YES YESYear fixed effect YES YESVillage fixed effect YES YES YESObservations 2551 2988 2255R-squared 0.09 0.15 0.14

Notes: Robust standard errors in parentheses are cluster over villages; * significant at 10 per cent; ** significant at5 per cent; *** significant at 1 per cent. (1) Village variables include illiterate ratio, ratio of primary schoolgraduates in labour force, primary school enrolment rate, middle school enrolment rate, indicator for havingpreschool classes, distance of primary school from the village seat, log value of money given to schools byvillage, indicator for having primary school run by the village, indicator for having middle school run by thevillage, indicator for having railway through the village, indicator for having bus stop in the village, log value ofaverage agricultural income per capita, log value of average industrial income per capita, ratio of householdsengaged in non-agricultural work, ratio of households running household enterprise, log value of wage for mendoing non-agricultural work, log value of wage for women doing non-agricultural work, log value of wage to hirelabour for agricultural production, indicator for having enterprises owned by county, indicator for havingenterprises owned by township, indicator for having private enterprises. (2) Household characteristics includehousehold endowments, including mother’s years of schooling, father’s years of schooling, and land area percapita; household demographic structure, including ratio of male aged 0–5, ratio of male aged 6–12, ratio of maleaged 13–16, ratio of male aged 17–19, ratio of male aged 20–29, ratio of male aged 30–39, ratio of male aged40–49, ratio of male aged 50–54, ratio of male aged above 54, ratio of female aged 0–5, ratio of female aged 6–12,ratio of female aged 13–16, ratio of female aged 17–19, ratio of female aged 20–29, ratio of female aged 30–39,ratio of female aged 40–49, ratio of female aged 50–54 and total number of family members; and log value ofhousehold income per capita. (3) Individual characteristics include years of schooling completed, age and a femaledummy. (4) Individual characteristics and household characteristics included in column 3 are all in 2000 value.

1804 X. Shi

Table 7 is different from that in Table 6, where the reform is found not to have different impacts on9–12 year old boys and girls. One reason could be that although parents might think it necessary forboth boys and girls to finish primary school, girls were more likely to drop out of middle school whentheir parents felt it difficult to pay for educational fees. Therefore, after the reform removed theeducational fees, more girls were enrolled in middle school. Column 6 shows how differently thereform affected children completing different years of schooling. The coefficient is 0.005 andstatistically significantly different from zero at the 5 per cent level. This finding is consistent withthat found using the sample of younger children (shown in Table 6). The last column shows that theeffects of the reform were weaker in villages with lower pre-reform middle school enrolment rates.The coefficient on the interaction term is −0.099 and significant at the 5 per cent level. The higher wasthe pre-reform middle school enrolment rate, the harder was the reform to increase the middle schoolenrolment of 13–16 year old children.

We also investigate heterogeneous effects of the reform on the probability for children enrolled in2004 to stay in school in 2007. The regression follows Equation (3) but adding an interaction term offee reductions with fee reduction itself (column 1), log household income per capita (column 2), loghousehold accessible credit (column 3), dummy for bad harvest (column 4), female dummy (column5), years of schooling completed in 2004 (column 6), village primary school enrolment rate in 2004(column 7), and village middle school enrolment rate in 2004 (column 8).

Results in Table 8 show that the effects of the reform on the probability of children enrolled in 2004to stay in school in 2007 were larger for girls but smaller for children completing more years ofschooling. Coefficient on the interaction term in column 1 is 0.003 and insignificant, showing that theeffects of the reform were not different with respect to levels of fee reductions. Coefficient on theinteraction term in column 2 is −0.004 and insignificant. It means that the reform did not have differentimpacts on the dropouts of children from households having different incomes. Column 3 shows theheterogeneous effects of the reform in terms of household accessible credit. The coefficient on theinteraction term is −0.013 and statistically insignificant. Comparing with the coefficient in column 2,we can see that although the coefficient in column 3 is also insignificant, the magnitude is bigger,meaning credit constraint might be more important in parents’ decision about whether to keep theirchildren in school. The coefficient on the interaction of fee reductions and the dummy indicating badharvest is 0.009 (shown in column 4) and not statistically significantly different from zero. It isconsistent with the finding shown in Table 6 and Table 7. Column 5 shows heterogeneous effects ofthe reform on boys and girls. We can see that the coefficient on the interaction term is 0.021 andstatistically significantly different from zero at the 1 per cent level, showing that the reform had abigger impact on girls. In column 6, we investigate whether the impact of the reform is different forchildren completing more years of schooling. The coefficient shown in this column is −0.008 andstatistically significantly at the 1 per cent level, meaning that the reform had smaller effects on theprobability to stay in school of those children completing more years of schooling. The impact of thereform on children’s school enrolment might go through two channels: keeping enrolled children atschool and inducing those dropouts to return to school. Compared with results shown in Table 6 andTable 7, which show that the impacts of the reform on school enrolment were bigger for childrenfinishing more years of schooling, the result in Table 8 suggests that the second channel mightoverwhelm the first one. Columns 7 and 8 show that the effects of the reform were not different forvillages having different pre-reform primary school enrolment rates and different middle schoolenrolment rates respectively. The coefficients on the interaction terms are 0.030 (column 8) and−0.012 (column 9).

6. Testing the Validity of DID Strategy

While we estimate Equation (1), an assumption that villages having different levels of educational feereductions should have the same pre-existing time trend for school enrolment if there were noeducational fee reduction reform is needed. We also need to justify that the pre-existing cohort

Fee reduction and school enrolment 1805

difference among villages having different educational fee reductions is the same when we estimateEquation (2). In this section, we test whether these two assumptions hold using 2000 and 2004 datawhich are from the pre-reform period.

Firstly, we estimate Equation (1) using 9–12 year old children in 2000 and 2004 and 13–16 year oldchildren in 2000 and 2004, respectively.15 To mimic the treatment, we assign the same village levelaverage values of educational fee reductions in 2007 for 9–12 year old children and 13–16 year oldchildren to the children with the same age and living in the same village in 2004. This variable is stillequal to zero in 2000. All other variables measured in 2007 values in Equation (1) are now replacedwith the same variables measured in 2004 values. The estimation results using 9–12 year old childrenand 13–16 year old children are reported in columns 1 and 2 in Table 9, respectively. We can see thatthe coefficients on the educational fee reductions in these two columns are not significant, whichshows that the time trend of the outcome variable is not significantly different for villages havingdifferent educational fee reductions.

Secondly, we estimate Equation (2) using the 2000 and 2004 panel of children enrolled in 2000. Theoutcome variable is now the difference of enrolment status between 2004 and 2000. Using the samemethod as in the above, we mimic the treatment by assigning the village level average values ofeducational fee reductions students get in the reform to students with the same age and living in thesame villages before the reform. All other variables measured in 2004 values in Equation (2) are nowreplaced with 2000 values. The estimation results are shown in column 3 in Table 9. We can also seethat the coefficient of the educational fee reductions is not significant, which provides evidence thatthe time trend of the outcome variable is not different for different cohorts before the reform.

In summary, the results presented in this section show that the assumption for DID strategy holdsand our results estimated using this strategy are valid.

7. Conclusion

Improving educational attainment of vulnerable populations, especially children, is often the centraltask of government programmes in developing countries. In this paper, we investigate the impact of aneducational fee reduction reform taking place in 2005 on children’s school enrolment in rural China.

Using a sample of 9–12 year old children surveyed in 2000 and 2007, we do not find any significantimpact of the reform on children’s school enrolment; while using a sample of 13–16 year old childrensurveyed in 2004 and 2007, we find that the impact of the reform is statistically significant and a 1 percent increase in the educational fees reduction leads to a 0.03 percentage point increase in schoolenrolment rates. We also find that the reform’s impact on those having completed more years ofschooling was larger for both groups of samples; and for the sample of 13–16 year old children, theimpact is smaller for children from households having looser credit constraints and is bigger for girls.In addition, we find that for children enrolled in 2004, a 1 per cent increase in the fee reductions leadsto a 0.04 percentage point increase in their probability to stay in school in 2007. And this impact issmaller for children having completed more years of schooling and is bigger for girls.

Our results suggest that in China where the average school enrolment rate has already been high, inorder to further improve the educational attainment of their people, governments need to exert moreefforts to target their policies on those more vulnerable groups such as children from poor families orgirls, on whom the impacts of reducing educational fees are found to be larger. On the other hand,besides the direct effect of the reform on students’ school enrolment, the reform has other indirecteffects. For example, as shown in Shi (2012), parents still spend the money saved from the reform onchildren’s education, which could affect children’s other outcomes. This indirect effect should be takeninto account while policy makers consider future policy change.

Despite the effects on school enrolment, the existing research has shown that CCT programmesmight not be the most cost effective, compared with other programmes. For example, Reis (2013)studies the impacts of CCTs on girls’ school enrolment in Pakistan and finds that although CCTs havepositive effects, they are not the most cost effective, compared with school building programmes.

1806 X. Shi

Muralidharan and Prakash (2014) study the impact of a programme in India which provides girls witha bicycle to help them improve access to school. They find that the enrolment in secondary school hasincreased and the gender gap has decreased. In addition, they find that this programme is more costeffective than CCT programmes. Baird, McIntosh, and Ozler (2010) find that the cash transferprogramme in Malawi has substantially increased girls’ school attendance but they do not find adifference between the conditional cash transfer programme and unconditional cash transfer pro-gramme. Future research is needed to understand the underlying reasons and governments are alsoadvised to compare different alternatives while they are making decisions.

Acknowledgement

I thank Albert Park for his contribution in the early stage of this paper, and I thank two referees fortheir useful comments. I acknowledge financial support from the National Natural Science Foundationof China (Project ID: 71103108). Data and STATA codes used for this paper are available uponrequest. All remaining errors are mine.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. We calculate the enrolment rates using data from Gansu Survey of Children and Families, which we introduce in Section 3.2. It includes exemption of school fees (xue za fei in Chinese) and textbook fees and subsidising living expenses.3. Most CCT programmes have a fixed value of subsidies, so that elasticities compare enrolments at only two prices, which

may provide unreliable estimates of elasticities at other price levels.4. Furthermore, Attanasio, Meghir, and Santiago (2005) find that the positive effects of PROGRESA are mostly driven by its

effects on secondary school children. De Janvry, Finan, Sadoulet, and Vakis (2006) find that the conditional transfers inMexico can help protect enrolment from income shocks faced by households. Lalive and Alejandra Cattaneo (2009) findthat PROGRESA had spillover effects on ineligible children’s school enrolment.

5. Ferrando (2012) evaluates CCTs’ impacts on children in Uruguay and finds that the programme only had significant effectson girls. Glewwe and Olinto (2004) evaluate the impact of a demand intervention that provided monetary payment tofamilies if their children were enrolled and the impact of a supply intervention that provided assistance to schools inHonduras. They find positive effects of the demand intervention but no effect of the supply intervention. Oosterbeek, Ponce,and Schady (2008) study the impact on school enrolment of a programme in Ecuador that gives cash transfer to poorfamilies and they find positive effects for the poorest households but no effects for relatively richer households. Schady andAraujo (2008) investigate whether conditionality is important for the cash transfer programme to have effects in Ecuador andthey find that the effects of the CCT programme are positive and larger for households believing that the school enrolmentrequirement is attached to transfers. Barrera-Osorio, Bertrand, Linden, and Perez-Calle (2008) find that CCT programmes inColombia increase attendance, pass rates, enrolment, and graduation rates. Attanasio, Fitzsimons, and Gomez (2005) findthe CCT programme in Colombia is effective at increasing enrolment especially for 14–17 year old children in both urbanand rural areas. Cardoso and Souza (2003) find no effects of CCT programmes in Brazil on child labour but significantlypositive effects on children’s school attendance. Bourguignon, Ferreira, and Leite (2003) find that about 60 per cent of10–15 year old children not enrolled in school re-attended schools in response to the CCT programme in Brazil.

6. Chaudhury and Parajuli (2010) study the impact of gender-targeted CCTs on girls’ school enrolment in Pakistan and find amodest but significantly positive effect. Cameron (2002) evaluates the impact of the Indonesian Social Safety NetScholarships Programme on children’s dropout and find that this programme had reduced dropouts in the lower secondaryschool but not in primary and upper secondary schools. Filmer and Schady (2006) find significant impacts of a scholarshipprogramme in Cambodia on girls’ school enrolment and the effects are the largest for those from the poorest households.

7. Official regulations in China have long banned the charging of tuition for compulsory schooling, but did not rule outcollection of fees for specific purposes.

8. The proportion depends on individual province’s economic and financial status.9. China Statistical Year Book (2008).10. One Dollar = 6.86 Yuan. The exchange rate is from China State Administration of Foreign Exchange (http://www.safe.gov.

cn).11. More detailed information about the GSCF is available at the project website: http://china.pop.upenn.edu/.

Fee reduction and school enrolment 1807

12. But there was also variation in the number of free textbooks available to students in specific grade levels or schools, dueto unexpected variation in class size or poor planning. Also, variation in the number of needy students across grades andschools could lead to differences in the extent to which the poorest students received the exemption. In our fieldresearch, we encountered a number of cases in which students received the textbook fee exemption in some semestersbut not in others.

13. We assign the average primary school fee reductions to children living in the same village and having completed zero–four years of schooling and we assign the average middle school fee reductions to children living in the same village andhaving completed six–eight years of schooling. For children having completed five years of schooling, we assign them theaverage middle school fee reductions if they live in the village where there are only five grades in primary schools, while weassign them the average primary school fee reductions if they live in villages having six grades in primary schools.

14. Available upon request from the authors.15. Children aged 9–12 in 2004 and those aged 13–16 in 2000 are siblings of those sampled children.

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