THE REGIONAL
INVESTING IN COMPULSORY EDUCATION:
IMPACT OF EDUCATION EXPENDITURES ON PRIMARY SCHOOL
STUDENTS IN MAINLAND CHINA
Caitlin Keliher
May 2, 2014
The George Washington University
Department of Economics
2115 G St, NW
Washington, DC 20052
ABSTRACT
This project analyzes the relationship between government expenditures on education and primary
school student levels in mainland China from 1997-2010. After dividing China’s provinces into
four regions to control for the endogeneity of educational attainment, I compiled separate balanced
panel datasets using province-level data. My findings indicate that expenditures on education is
not a consistent determinant of primary school student levels in every region. In the coastal region,
there is the strongest positive relationship between these two variables. In the western region,
where I find a negative relationship between education expenditures and primary school students,
my results suggest that crucial omitted variables are negatively biasing my estimates.
Additionally, my results indicate that the implementation date of the Compulsory Education Law
is not a consistent determinant of primary school levels.
Acknowledgements: Thank you to Professor Tara Sinclair for advising me throughout this project.
Thank you to Professor Bruce Dickson and Professor James Foster for their feedback on my research.
I.INTRODUCTION
China’s post-1979 reform period led to rapid economic growth and modernizations, yet
social and economic inequalities remain crucial domestic issues. Uneven distribution of economic
activity and widespread differences in living standards, income, and resources all contribute to
regional inequality. Throughout China, extreme disparities in both access to education and
education quality is one of the most pervasive signs of inequality (Gong, 2011; Song, 2012).
According to Stanford University’s Rural Education Action Program, roughly 80% of students
from China’s developed urban areas are accepted into universities. However, only 3% of poor
rural students in China who start first grade will enroll in a university (REAP, 2013). Historically,
China’s investment in education as a percentage of GDP has been low relative to other developing
countries and countries in East Asia (Heckman, 2012). Recently, annual increases in governmentappropriated public education funding (Chart 1 and Chart 2, p. 33) have modestly improved access
to education and may signal China’s renewed focus on education development. However, regional
differences remain in both education investment and attainment: “a general picture of rising
national per-student spending…may hide severe inequities among subunits” (Gong, 2011).
After initially estimating the national relationship between government expenditures on
education and primary school students from 1997-2010, I then focus on regional comparisons. By
separating China’s thirty-one provinces into four regions—northeast, coastal, central, and
western—and compiling separate balanced panel datasets, I aim to assess geographic patterns
regarding the effective impact of public education funding on primary school attainment.
In addition, I build on previous research to quantify the impact of China’s 1986
Compulsory Education Law, which mandated all children attend nine years of school. Research
estimates that the law increased overall educational attainment by an average of 0.8 years (Fang et
al, 2012)—however, this impact varied greatly by gender and location. I include a control variable
1
to account for differences in the actual date provinces implemented this law to assess its impact on
primary school students in different regions.
As the most populous country with the largest public education system in the world (Liu,
2010), China should be a leader in education development. In March 2014 at the UNESCO
Conference in Paris, Chinese President Xi Jinping declared, “China must actively develop
education through universal education,” (Jiangsu Ministry of Education, 2014). However, recent
studies conclude that access to primary school is not universal, primarily due to insufficient
funding and minimal enforcement of compulsory education. In 2003, seventeen years after China
adopted the Compulsory Education Law, only twelve of the thirty-one mainland provinces had
universalized primary education (Lin, 2013). Financial difficulties in rural provinces and widening
disparities in per-student spending across counties and provinces have inhibited universal access
to primary school. Long-term education inequality would be detrimental to China’s economic and
social outlook: “the result of long-term neglect due to insufficient funding of education has left
nearly one-fifth of the Chinese population illiterate” (Rong, 2001).
While recent literature on education development in China focuses on returns to education
in the labor market, there is minimal empirical research assessing the impact of public education
expenditures at the primary school level. Fang et al’s 2012 paper presents a comprehensive,
quantitative analysis of recent trends in education investment in China and is one of the first
studies to quantify the impact of the Compulsory Education Law. Although the paper’s scope is
limited as it only incorporates data from nine provinces, the results suggest dramatic regional
variation. The authors speculate that coastal provinces with significantly higher returns on
education may signify more effective provincial governance and conclude inland provinces did not
have statistically significant returns to education.
2
In line with Fang et al’s key findings, my results estimate that there is only a consistently
significant, positive relationship between education expenditures and primary school students in
the coastal region. In the central and northeast regions, my findings imply that increased
expenditures since 1997 have minimally impacted primary school attainment. In the western
region, the coefficient on education expenditures is consistently negative and statistically
significant. In my discussion of the western region’s results, I highlight several important omitted
variables that may be negatively biasing my estimates. Finally, when I account for each province’s
effective implementation date of the Compulsory Education Law, I find inconsistent trends
between regions. In line with the previous research of Fang et al (2012) and Gong (2011), I find
that the implementation of the Compulsory Education Law had the most consistent positive impact
in China’s rural western region.
My results imply that there is no straightforward strategy for achieving universal primary
school education in China. This paper highlights the complexity of analyzing primary school
education in China and connects my findings to existing literature to consider the implications of
education inequality on China’s development. Expanding access to all levels of education would
foster and help sustain the country’s economic growth, improve the quality of its labor force, and
increase socioeconomic mobility (Heckman, 2012). Achieving universal primary education is the
first step on the path to regional economic equality.
The remainder of this paper is organized as follows: Section II details my data; Section III
outlines my linear-log panel model; Section IV highlights my main results; Section V considers
robustness checks including additional variables and alternative model specifications; Section VI
discusses the implications and conclusions of my findings; Section VII lists my sources; and
Section VIII contains figures, charts, and tables.
3
II.DATA
China Statistical Yearbooks and China Data Online provide a variety of indicators
measuring many aspects of China’s economy and society. However, it is critical to transform this
data to effectively disseminate the trends in a useful, informative, and contextualized way. To
compile my dataset, I used the China National and Provincial Statistical Yearbooks from 199820111 (see Figures 2 and 4, p. 29 and 31). I focus on this time period because reliable and
consistent data (in English) for each province for several of my variables became available in the
late 1990s. Additionally, 1997 is far removed from China’s extensive 1980s public education
reforms. The Statistical Yearbooks currently provide province-level education indices until 2010.
To accurately estimate the impact of education investment on primary school students and
control for the endogeneity of educational attainment throughout the country, I divided mainland
China’s thirty-one provinces into four economic regions. In line with previous research on
regional trends in China (Demurger, 2002; Jian, 1996; Oizumi, 2010; Whalley, 2004), these four
geographic divisions are based on the provinces’ real GDP, foreign direct investment levels,
population trends, and historical relationships. Figures 1 and 3 (p. 29-30) detail the provinces that
compose each region. As demonstrated below, each region’s trends vary significantly and
demonstrate both China’s heterogeneous demographic composition and pervasive economic
inequality.
Chapter 20 of the National Statistical Yearbooks, “Education, Science, and Technology,”
provides information on the number of primary school students in every province annually. I then
used data from Chapter 9, “Population,” to transform this variable into primary school students per
100,000 people. Chart 3 (p. 34) highlights the averages for each region from 1997-2010; primary
1
Note: The China Statistical Yearbooks provide data for the previous year; ie. The 1998 Yearbook has data for 1997.
4
school students per 100,000 people has declined steadily since 1997 in every region, due to
China’s changing population composition driven by the 1979 adoption of the One-Child Policy.
Since 1997, western provinces, which have typically had the most exemptions from the One-Child
Policy due to large minority populations and agriculture-based economies, have higher average
levels of primary school students. The coastal and northeast regions have seen relatively steeper
declines in primary school students. Figure 4 (p. 31) lists the descriptive statistics for each region
for my main variables. From 1997-2010, western provinces had the highest average (10701.80
primary school students per 100,000 people), while the coastal and northeast regions had the
lowest averages (7976.71 and 7088.96, respectively).
To capture government investment in education, I compile province-level data on total
nominal expenditures on education (in 10,000 Yuan). The Statistical Yearbooks provide individual
province CPI data using the previous year as the base year. I transform theses indices into a new
CPI for every province using 1997 as the base year and then generate my independent variable of
interest: real expenditures on education per primary school student. By using each province’s
individual CPI, the new variable effectively controls for both inflation from 1997-2010 and
discrepancies in price levels between provinces. Chart 4 (p. 34) depicts the regional trends in
education expenditures per student from 1997-2010. This variable has increased for each region
over this time period, but at varying rates. As shown in Figure 4 (p. 31), the coastal region average
expenditures per student is almost four times greater than the central and western averages and
more than double the northeast average. To capture the exponential growth of education spending
per student from 1997-2010, I use the natural log of this variable in my model (see Section III).
Although dividing mainland China’s thirty-one provinces into four regions accounts for
much of the endogeneity between regions, there are still several factors that vary over time and
between provinces in each region. I include each province’s GDP to address a portion of this
5
variability. Using the process described above, I transform China Data Online’s values for each
province’s nominal GDP from 1997-2010 into real GDP values using 1997 as the base year. As
shown in Chart 5 (p. 35), average real GDP has increased exponentially in each region since 1997,
with the coastal, central, and northeast regions consistently demonstrating higher GDP levels than
the western region. These trends depict China’s domestic economic disparities and also
demonstrate the lack of convergence: since the mid-1990s, while China’s regions have all
increased their economic output, the differences between regions are also growing.
My dependent variable, primary school students per 100,000 people, is inherently affected
by population shifts, so I include a control variable for each province’s child dependency ratio,
measured as the percentage of the population 0-14 years old. Chart 6 (p.35) depicts the average
child dependency ratio in each region from 2001-2010. Prior to 2001, data for child dependency
ratios were inconsistent and unreliable. As I describe in the next section, I use a five-year time lag
on education expenditures. This lag narrows the time period for education expenditures to 19972005 and other variables to 2002-2010, allowing me to include the most reliable child dependency
ratio data. As illustrated in Chart 6, the child dependency ratio has decreased annually in every
region. The western region consistently has the highest dependency ratio and the northeast region
has the lowest. Controlling for child dependency ratio is essential to address China’s changing
demographics (including differences in One-Child Policy regulations and enforcement) and will
isolate changes in primary school students that are independent of population changes.
Continuing Fang et al’s research on the impact of China’s 1986 Compulsory Education
Law, I include a control variable to account for years since implementation in each province. I
used Fang et al’s data on nine of China’s provinces from the 2009 China Health and Nutrition
Survey and then compiled translations of documents from the remaining twenty-two provinces’
Ministries of Education to ascertain at which date each province implemented the law. As shown
6
in Figure 5 (p. 32), every province adopted the law between 1986 and 1995. To streamline my
data, I rounded each date of implementation to the start of the next academic year. Including the
three controls discussed above—child dependency ratio, real GDP, and years since
implementation of the Compulsory Education Law—will help us to evaluate and further isolate
the impact of per-student expenditures on primary school student levels.
III.MODEL
I use five separate panel datasets and conduct the below regression models for each. The
first dataset is a compilation of every region and includes data from all of mainland China’s thirtyone provinces to ascertain the national trend. In the other four datasets, I use the regional divisions
discussed in the previous section with province-level data for each region.
I start with a basic linear-log balanced panel model. As detailed in the previous section,
primary school students per 100,000 people is my dependent variable (PrimStudentsit, for a given
province, i, in a given year, t). To address simultaneous causality concerns and more accurately
estimate the relationship, I apply a five-year time lag on my independent variable of interest, real
education expenditures on education per primary school student (LnPerStudentit-5). Previous
research estimates that it takes an average of five years for a province to begin to see an impact
from its investments in education (Lin, 2013). Additionally, in some provinces, governments may
have increased total expenditures on education in the last two decades because they expect to see
improvements within this time frame. As discussed in the previous section, education expenditures
per student has increased exponentially, so I use the natural log of this variable in my model. In
each panel model, β0 denotes the intercept term and uit is the error term. The β1 coefficient on my
regressor of interest can be interpreted as follows: 0.01β1 is the change in primary school students
per 100,000 people associated with a 1% change in expenditures per student. For the nation-wide
7
panel and each of the four regional panels, I first conduct the basic linear-log panel model
including only my two main variables:
(1) PrimStudentsit = β0 + β1LnPerStudentit-5 + uit
I conduct the initial model specification described above for each of the regions separately. I then
add three control variables that are observed annually in each province in every region: natural log
of the real GDP (LnGDPit), child dependency ratio (ChildDepit), and years since implementation
of the Compulsory Education Law (YearsCELit):
(2) PrimStudentsit = β0 + β1LnPerStudentit-5 + β2ChildDepit +β3LnGDPit + β4YearsCELit + uit
Finally, to control for difference between provinces and variation over time in each region,
I include entity and time fixed effects in my model. My initial findings indicate the control
variable for years since implementation of the Compulsory Education Law is a perfect linear
combination of both time and entity fixed effects and, therefore, it will not be included in models
with both of these specifications:
(3) PrimStudentsit = β0 + β1LnPerStudentit-5 + β2ChildDepit +β3LnGDPit + αi + λt + uit
In (3) above, αi denotes entity fixed effects and λt represents time fixed effects. I use a t-test with
the following hypotheses to determine if β1 is statistically significant from zero:
H0: β1=0
H1: β1≠0
I conduct the model specifications described above first for the national panel dataset and
then for each of the four regions separately.
IV. RESULTS
i. National Results
After conducting the panel models outlined above, I present the nation-wide and regional
results in Tables 1-6 (p. 36-40). First, my national panel results (Table 1, p. 36), highlight that
8
there initially appears to be an inverse relationship between education expenditures and primary
school students. However, once I include controls and fixed effects, the coefficient estimate on
education expenditures per student changes to be slightly more positive. The primary takeaway
from the national regression results is that the significant impact of entity fixed effects (Models 3
and 5) suggests there is high variability between provinces—this is in line with previous empirical
studies on education attainment in China (Connelly, 2003; Fang et al, 2012; Gong, 2011). In
particular, Gong (2011) emphasizes that regional differences in education expenditures are due in
part to China’s 1980s education reforms, which decentralized the allocation of education funding,
but “caused large disparities in per-student spending among areas and regions because not all local
governments have the capacity to mobilize sufficient resource for education.” These and other
findings insinuate that regional differences in education spending should be further analyzed.
Additionally, in the national results, the coefficient on my control variable for child
dependency ratio is positive and statistically significant at the 1% level across every model. This
indicates that demographic composition of school-age children is a major determinant of primary
school students and is, therefore, a crucial control variable in my models. While the national
results provide these two insights, they nonetheless are unclear estimates as to the isolated impact
of education expenditures on primary school student levels. Therefore, the four regional models,
which inherently control for some of the regional variation in educational attainment, may provide
more useful and accurate insight.
ii. Coastal Region Results
Table 2 (p. 36) presents the main results across the four regions and Tables 3-6 show
additional models for each region.2 The primary takeaway from Table 2 is that there is the
2
Note: Table 2 presents my main model for each region. This model is also Model 5 in each of the individual regional
tables (Tables 3-6) and Equation 3 from the previous section (III. Model, p. 8).
9
strongest positive correlation between education expenditures per student and primary school
students per 100,000 people in China’s coastal region once I include controls and fixed effects. 3
Additionally, in the central and northeast regions, the coefficient on education expenditures per
student is positive, though statistically insignificant, and there is a strong negative correlation
between my two main variables in the western region. To further understand the implications and
magnitude of these estimates, I will analyze and discuss each region’s models in depth.
In the coastal region’s results (Table 3, p. 37), Model 1 highlights the basic panel model:
regressing primary school students per 100,000 people on the natural log of real expenditures per
student. The coefficient on primary school students per 100,000 people is negative and statistically
significant at the 1% level. As shown in Tables 4-6, these results are consistent across Model 1 for
every region and in line with my nation-wide findings in Table 1. With no control variables or
fixed effects, there is consistently a negative and statistically significant relationship between these
two variables in every region. However, this result is expected, as shown in Chart 3 and Chart 4
(p.34) and discussed in Section II, average real expenditures per primary school student has
increased exponentially while average primary school students per 100,000 people has steadily
declined from 1997-2010. It is difficult to analyze these initial regression estimates, however,
because they do not address the complete picture or control for other variables that are correlated
with expenditures on education and determinants of primary school students.
Once I include control variables and entity fixed effects in the coastal region (Models 3
and 5), the coefficient on education expenditures is positive and statistically significant at the 1%
level. These results suggest that once I control for other differences between coastal provinces,
there is a strong positive correlation between education expenditures and primary school students.
3
I also note that while the R2 and adjusted R2 values are very high across every region, it is difficult to interpret the
model’s explanatory power as fixed effects are included and I am comparing different datasets.
10
Specifically, Model 5 includes both entity and time fixed effects and represents one of the most
accurate estimates of the correlation between my two variables. Based on the estimates of this
model, a 1% increase in expenditures per student is associated with an increase in 18 primary
school students per 100,000 people when I include fixed effects and hold constant the child
dependency ratio and real GDP. As outlined in Figure 4 (p. 31) the mean expenditures per student
in the coastal region was 15,929.33 Yuan from 1997-2010; therefore, a 1% increase would be an
additional 160 Yuan per student, or approximately 19.32 USD.4 Although magnitude of the
associated increase (18 primary school students) is not economically significant in the context of
the region,5 when compared to the other regions, the coastal region is the only one in which I find
a consistently positive and statistically significant relationship between education expenditures
and primary school students. This is especially interesting because, as shown in Charts 4 and 5 (p.
34-35), the coastal region consistently has the highest average expenditures per student and the
highest real GDP. Additionally, these two variables are increasing annually at faster rates than in
the other regions.
My results imply that China’s wealthiest region, with the highest levels of education
expenditures per student, also has the strongest positive correlation between education
expenditures and primary school students when I control for other factors. A number of reasons
may contribute to this critical finding: most notably, education quality—in terms of teachers,
resources, and implementation of compulsory education—in China’s coastal region far exceeds
standards in the other three regions (Wu, 2008). Furthermore, studies indicate that the return to an
additional year of primary school education (measured in estimated expected lifetime earnings) is
4
In 1997, the average exchange rate between Chinese Yuan and United States Dollars was 8.28 Yuan/USD (U.S.
Treasury, “Treasury Reporting Rates of Exchange.”). Please see Section VII, p. 27. Alternatively, if evaluated in 2014
exchange rates, the increase is roughly 26.02 USD per student. (http://www.fms.treas.gov/intn.html#rates).
5
From 1997-2010, the coastal region had an average of 7976.71 primary school students per 100,000 people (or
7.977%). As Model 5 estimates an increase in 18.19 students per 100,000 people, the new level would be: 7994.9
primary school students per 100,000 (or 7.995%). The magnitude of this increase is not economically impactful.
11
higher in China’s coastal region (Fang et al, 2012; Song, 2012). Whereas school-age children in
the western region, for example, face a higher opportunity cost of staying in school versus earning
a wage and a lower expected return to an additional year of education, children in China’s coastal
region have greater access to education, achieve more years of schooling, and benefit from the
highest government investment in their education (Fang et al, 2012; Song, 2012). My findings in
the coastal region are in line with recent literature suggesting that regional disparities in education
spending and attainment are growing. As demonstrated below, when I analyze my regression
estimates for the other three regions, I find weaker relationship between my two key variables in
China’s central and northeast regions, and a strong negative relationship in the western region.
iii. Central Region Results
In comparison to the coastal region’s results, the central region (Table 4, p. 38) presents a
more complex outlook on the relationship between education expenditures and primary school
students. With entity fixed effects in Model 3, the coefficient on education expenditures per
student is positive and statistically significant at the 5% level. This finding is in line with my
results in the coastal region, indicating that differences between provinces for my variables are
significant and, therefore, entity fixed effects has a substantial impact on my estimates. However,
the magnitude of my coefficient on expenditures per student decreases when I add time fixed
effects. In Model 5, when both time and entity fixed effects are included, my results indicate that
there is a slight positive, but not statistically significant, relationship between expenditures per
student and primary school students per 100,000 people. This model estimates that a 1% increase
in expenditures per student is associated with an increase of 9.29 primary school students per
100,000 people (holding constant real GDP and child dependency ratio). From 1997-2010, the
mean expenditures per student was 4055.83 Yuan, thus a 1% increase would be roughly 40.56
12
Yuan or 4.90 USD6 per student. The associated increase in primary school students is not
economically meaningful in the context of central China’s average primary school student levels.
In the central regions, other factors outside of education expenditures may be stronger
determinants of primary school student levels. This implication is consistent with the findings of
Fang et al (2012) and Connelly (2003) in the central provinces. In Model 5, the positive and
statistically significant coefficient on real GDP estimates that a 1% increase in a central province’s
GDP is associated with and increase of 37.20 primary school students per 100,000—holding
constant other variables. This represents an economically significant impact and suggests that, in
the central provinces, economic development may be one of the major determinants of primary
school students.
Most importantly, my findings imply a multidimensional relationship between my
variables in the central region. There is not evidence of a consistently strong correlation between
expenditures per student and primary school students, especially once I include control variables
and fixed effects. However, the slight positive correlation suggests the impact of education
expenditures is beneficial, but not as strong or effective as in the coastal region.
iv. Western Region Results
The results for the western region (Table 5, p. 39) present the most complex estimate of the
relationship between education expenditures and primary school students. In all five models, the
coefficient on expenditures per student is negative and statistically significant at the 1% level;
moreover, the coefficient increases in absolute value when I add control variables in Model 2 and
fixed effects in Models 3-5. My findings imply that as western provinces have increased education
expenditures since 1997, there is a consistent corresponding decrease in primary school students
6
In 1997, the average exchange rate between Chinese Yuan and United States Dollars was 8.28 Yuan/USD (U.S.
Treasury, “Treasury Reporting Rates of Exchange.”). Please see Section VII, p. 27.
13
per 100,000 people even when I include fixed effects and account for child dependency ratio, real
GDP, and years since implementation of the Compulsory Education Law. However, Models 2-4
estimate that years since implementation of the Law has a positive impact on primary school
students. When compared to the three other regions, the western region is the only one in which I
observe this consistent positive relationship. This indicates that, in the western region especially,
education development programs may have improved as a result of this law in particular, and
therefore in subsequent years since its implementation, primary school student levels have an
associated increase (holding constant expenditures per student, child dependency ratio, and real
GDP). Additionally, similar to the national and other regional results, the coefficient on child
dependency ratio is positive and statistically significant across every model—indicating that shifts
in western China’s demographics may be the major determinant of primary school students.
For my independent variable of interest, there are two key takeaways from the western
region’s results. First, after including controls and fixed effects, there is consistently a negative
and statistically significant relationship between education expenditures per student and primary
school students per 100,000 people.7 Although, the magnitude of this effect is marginal: Model 5
estimates that a 1% increase in education expenditures per student is associated with a 26.45
decrease in primary school students per 100,000 people (holding constant child dependency ratio
and real GDP). From 1997-2010, the mean expenditures per student was 4310.35 Yuan (Figure 4,
p. 31). A 1% increase in expenditures per student—43.10 Yuan or roughly 5.21 USD8 per
student—is quite large in magnitude, especially considering that the western region provinces
have the highest level of primary school students. The average western province had roughly 3.18
million primary school students each year from 1997-2010, thus a 1% increase in expenditures per
7
When compared to my nation-wide results (Table 1, p. 36), this strong negative coefficient suggests that the western
provinces dominated (or perhaps overshadowed) the national estimates.
8
In 1997, the average exchange rate between Chinese Yuan and United States Dollars was 8.28 Yuan/USD (U.S.
Treasury, “Treasury Reporting Rates of Exchange.”). Please see Section VII, p. 27.
14
student (43.10 Yuan) represents a total increase of 137 million Yuan (or 16.5 million USD8). This
significant increase in education expenditures is associated with a mere 26.45 decrease in primary
school students per 100,000 people.9
However, I do not interpret these regression estimates as causal; primarily, I think the
results suggest that omitted factors are leading to the decrease in primary school students in the
western region. Previous research and case studies indicate that there are two major explanations
for decreasing levels of primary school students in western China. First, outside of the decreasing
child dependency ratio (which is controlled for in my models), there are other demographic shifts
that may impact primary school student levels. As western China has the most exemptions to the
One-Child Policy due to large minority populations and agriculture-based economies, families
with the two or more children may choose to keep one child out of school (Wu, 2008). In
particular, as discussed in my coastal region results section, when a student is deciding to stay in
school or join the labor force, both Song (2012) and Fang et al (2012) found that there was a lower
return to an additional year of education in rural China—this may make students more likely to
withdraw from school compared to other regions. Song (2012), in particular, points to rural
China’s relatively weak school quality and hindered access to schools as contributing factors to
this disparity and highlights that the average return to primary education is significantly higher in
urban areas. Additionally, due to China’s test-based education system, which effects both
students’ future options and teachers’ salaries, if students’ test scores are relatively low, teachers
or school administrators may encourage them to withdraw. Fang et al (2012) term this
phenomenon “self-selective education” and attribute it to the variety of reasons discussed above
which may impact western China more than any other region.
9
The estimated associated decrease of 26.45 in primary school students per 100,000 people, using the average western
levels (10701.80 per 100,000, or 10.7%), results in a decrease to 10675.35 per 100,000 people (or 10.68%). When
evaluated in the context of the western region’s high average primary student levels, this decrease is not economically
significant.
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As the selective education factor is negatively correlated with primary school student
levels and, instead of decreasing as education expenditures have increased since 1997 (as one
would expect), has become more prevalent (Fang et al, 2012; Lin, 2013), I speculate that this
omitted variable negatively biases my empirical estimates. As current data does not allow us to
control for this phenomenon and it is one of the most undeniable causes behind western China’s
decrease in primary school students, I believe my coefficient estimates on expenditures per student
in the western region represent underestimates.
Second, although the western region’s average expenditures on education have increased
exponentially from 1997-2010 (Chart 4, p. 34), there are concerns as to the distribution of these
funds due to poor infrastructure, geographical challenges, and high levels of corruption (Rong,
2001; Lin, 2013). As a result, education expenditures are not allocated properly throughout a
province, but rather are concentrated in more urban areas and funneled to top-performing schools
(Connelly, 2003). Previous research suggests that improving infrastructure to overcome
geographic barriers and increase access to education are “fundamental to increasing western
growth” (Bao, 2002), highlighting the impactful relationship between geography and education
inequality in western China. Connelly (2003) cites differences in “terrain” as one of the key
determinants of regional differences in educational enrollment. Additionally, Lin (2013) argues
that corruption and lack of oversight is one of the primary causes behind regional education
inequality, suggesting “officials often diverted education resources for other uses, sometimes
spending…as freely as personal gains.” Similarly, Feldstein (2012) argues that the two most
crucial issues inhibiting China’s development are corruption and inequality.
Local corruption, poor infrastructure, and geographic impediments in western China act as
barriers to the distribution of education funds throughout a province and, therefore, correspond to
decreasing primary school levels. Research implies that the prevalence of these factors has not
16
decreased, but rather increased over the past two decades—leading to a positive correlation
between education expenditures and barriers to funding distribution. Therefore, these crucial
omitted variables in my model may be negatively biasing my estimates and leading to
underestimates of the coefficients on real expenditures per student.
To conclude for the western region, although these provinces have increased expenditures
per student annually since 1997, I attribute the associated decrease in primary school students to
other complex, highly influential factors that are omitted in my models.
v. Northeast Region Results
Finally, Table 6 (p. 40) highlights my results for China’s northeast region. Across Models
2-5, once I include controls and fixed effects, there is a positive relationship between expenditures
per student and primary school students per 100,000 people, but only in Model 4 is the coefficient
statistically significant. In my main model, Model 5, my results estimate that a 1% increase in
expenditures per student (roughly 77.6 Yuan, or 9.37 USD,10 per student using averages for the
northeast region over this time period) is associated with a 5.24 increase in primary school
students per 100,000 people—a minor and economically insignificant impact.11 As the coefficients
on my two control variables are also statistically insignificant, the results for Model 5 may imply
that in China’s northeast provinces, factors other than education expenditures, child dependency
ratio, or GDP have a greater impact on primary school students. There is one critical unique
finding in the northeast region: in every model in which it is included, there is a significant
negative correlation between implementation of the Compulsory Education Law and primary
school student levels. As shown in Figure 5 (p. 32), China’s northeast provinces all implemented
the Compulsory Education Law within the first two years of its adoption by the national
10
In 1997, the average exchange rate between Chinese Yuan and United States Dollars was 8.28 Yuan/USD (U.S.
Treasury, “Treasury Reporting Rates of Exchange.”). Please see Section VII, p. 27.
11
An increase of 5.24 in primary school students per 100,000 people has minimal impact: the average primary student
level was 7088.96 per 100,000 (or 7.09%), thus an increase of 5.24 students yields 7094.2 students per 100,000.
17
government. As discussed in the following section, early adoption of the Law is not indicative of a
province’s commitment to enforcing it and as depicted in Chart 3 (p. 34), the northeast region has
the steepest decline in primary school student levels. Therefore, I do not interpret this correlation
as causal. The minimal effective impact of education expenditures per student in China’s northeast
is in line with the findings of Gong (2011) and Rong (2001), who cite northeast China’s increased
emigration, strict enforcement of the One-Child Policy, and rapidly declining population—these
factors are all leading to rapidly decreasing primary school student levels.
vi. . Summary of Main Results and Implications
To summarize my findings on my primary regressor of interest, I find the most compelling
and consistent correlations in the coastal and western regions. In the coastal region, there is the
strongest positive correlation between expenditures per student and primary school students per
100,000 people. In the western region, across every model, the coefficient on education
expenditures per student is negative and statistically significant at the 1% level. I believe two
crucial omitted variables—selective education and impediments to the distribution of education
funding—impact my variables in the western region and are negatively biasing my results. In the
central and northeast regions, I find a slightly positive relationship between my two variables.
Overall, my findings contribute to recent literature suggesting that regional disparities in
education spending and attainment are increasing in China. Once I include controls and fixed
effects, education expenditures have the most beneficial impact in the coastal region—the region
that is also spending the most per student on education and has the highest average real GDP.
Recent literature highlights the exacerbation of regional disparities, not only in terms of education
spending and attainment, but also for key economic indicators. Using province-level Gini
coefficients to assess regional inequality, Gong (2011) found consistently large and overall
increased disparities among provinces and among regions in his study between 1993 and 2008.
18
My findings suggest that the effective impact of per-student education expenditures also varies
greatly by region and may be contributing to larger trends in China’s growing regional inequality.
V. ROBUSTNESS CHECKS
i. Alternative Models
In the process of compiling my regression results reported above, I conducted additional
panel models for each region to determine if my results yielded the most accurate estimates (given
currently data availability). Ideally I would have data for primary school enrollment in each
province. However, China Data Online does not consistently provide province-level primary
school enrollment data. In addition to compiling data on primary school students per 100,000
people, I also considered using primary school graduates per 100,000 people as my dependent
variable. Table 7 (p. 41) highlights regression results for each region using primary school
graduates per 100,000 people. I replicated my model from Table 2 (p. 36), which includes entity
and time fixed effects and controls for child dependency ratio and real GDP. With this new
dependent variable, it is difficult to interpret and assess the magnitude of the coefficient on real
expenditures per student. Additionally, we must consider that factors that impact the number of
graduating students may differ from the factors that determine enrollment. Nonetheless, consistent
with my results in Table 2, the coefficient on the western region’s expenditures per student is
negative and statistically significant. One noticeable deviation from my original model is that the
coefficient on education expenditures per student for the central region is now statistically
significant and negative. However, the results indicate that a 1% increase in education
expenditures per student in the central region is associated with an approximate increase of 7
primary school graduates per 100,000 people—this corresponding impact is economically
insignificant. Another interesting note is that the coefficient on child dependency ratio is not
19
statistically significant for any region, implying that changes in population composition may not
be a critical determinant of primary school graduates per 100,000 people.
Next, I estimate an alternative nonlinear model specification with the natural log of
primary school students per 100,000 people as my dependent variable and highlight the results in
Table 8 (p. 41). When compared to my original model in Table 2 (p. 36), the coefficient estimates
on education expenditures per student are consistent in terms of sign, significance, and magnitude,
and there does not appear to be a meaningful difference between these two specifications.
For my third robustness check, I calculate real education expenditures per student and real
GDP using China’s National Consumer Price Index for every year from 1997-2010 (using 1997 as
the base year). As outlined in Section II, in all previous models, I used individual province’s CPI
for every year to transform education expenditures per student and GDP to real values. When
compared to my main results in Table 2, these results (Table 9, p. 42) present consistent estimates
of the sign and statistical significance of the coefficient on expenditures per student across every
region. The main difference occurs in the absolute value of the coefficient estimates. Specifically,
for the coastal and western regions, the coefficient estimate is larger in absolute value when I use
my original model with provincial CPI estimates. As my original models using province-specific
CPI calculations, I believe they present more accurate estimates as they inherently control for
price level differences between provinces.
Finally, in Table 10 (p. 42), I use percentage of GDP spent on education as my primary
independent variable of interest. My results indicate that the coefficient estimates on this new
independent variable are negative and statistically insignificant for the central, western, and
northeast region. In the coastal region, the coefficient is negative and statistically significant at the
10% level. As the results are statistically insignificant for three of the four regions, I chose not to
include percentage of GDP spent on education in my final model. I believe my original model,
20
which includes controls for real GDP, offers the best estimate of the impact of education spending
on primary school students. Percentage of GDP spent on education does not represent an accurate
depiction of a province’s education spending levels as both real GDP and real expenditures on
education have risen exponentially (with similar slopes) since 1997 (see Charts 4-5, p. 34-35).
ii. Discussion of Research Limitations
Researching China’s development is unquestionably limited by available data and source
reliability. While China Data Online offers a plethora of indicators, the specific measurements
make data dissemination and analysis difficult. For example, the number of primary school
students in each province is not the ideal measurement of educational attainment. To contextualize
the data, I transformed it to primary school students per 100,000 people, but even this variable
does not adequately measure educational attainment; additionally, this variable is innately effected
by population shifts. I believe alternative variables, such as enrollment rates, attendance
percentages, matriculation rates, and graduation rates, would more accurately capture primary
school attainment and achievement. However, data is not currently available for these
measurements. In addition to education spending per student and primary school attainment,
education quality must also be addressed to effectively assess China’s education development and
achievement. Although the Chinese Communist Party has prioritized promoting universal access
to education, it has not established basic education quality standards. Therefore, future research
must also look to consider differences in education quality—perhaps including measurements such
as test scores, teachers’ years of experience, and classroom resources.
Moreover, there are many other variables that impact a child’s ability to enroll in or
regularly attend primary school, including parents’ income, gender, distance from a student’s
home to school (or if the school offers boarding options, as many do), number of siblings, class
size, and the opportunity cost of staying in school versus earning a wage. According to the
21
findings of Connelly (2003), “place of residence and sex and the interactions between them are the
most important categories for understanding school enrollment in China,” indicating that it is
extremely difficult to assess the relationship between government education expenditures and
primary school students without addressing these critical components.
To better analyze the impact of China’s Compulsory Education Law, it would be ideal to
include not only the implementation date in each province, but also some measurement of
enforcement or commitment to achieving universal primary education. As Fang et al (2012) note,
“the compulsory schooling law has been enforced unevenly in different parts of the country.”
Furthermore, some provinces may have implemented the law as a formality without any intention
of enforcing it or adopting policies to improve access to education. My results using years since
implementation of the Compulsory Education Law were inconsistent across my models—this may
be an indication of different provinces’ varying commitments to enforcing the law. According to
Gong (2011), “it is crucial for the central government to keep making more and stronger
[education] equalizing efforts and more importantly, to monitor their implementations”—
signifying that simply adopting education reforms is not sufficient to improve widening disparities
in education attainment.
One critical aspect of my method was separating China’s provinces into four regions.
Educational attainment throughout China is highly endogenous and minimizing biases due to
regional differences was crucial to producing accurate estimates. However, as previous research
indicates and as demonstrated by the drastic changes in my models with entity fixed effects, there
is high variation between provinces within the same region. In fact, several scholars argue that
intraprovincial inequality may be greater than interprovincial or regional differences (Gong, 2011;
Lin, 2013). To fully account for regional variability, it may be necessary to divide my four regions
22
into smaller cohorts or evaluate each province individually using county-level data12. However,
current data availability limits the education indicators available for sub-province data analysis.
Regarding my model specification, I chose to include a five-year time lag to address
simultaneous causality concerns and more accurately estimate the relationship between my two
main variables. However, in some instances, it may take longer than five years for a province to
see substantial, related improvements in its’ education system. Provincial government officials
serve five-year terms before rotating locations. Thus, results-oriented officials may be more
inclined to allocate funds on relatively short-term projects so they receive credit for the success.
This phenomenon is widespread in western and central China (Lin, 2012) and may negatively bias
my estimates if provincial governments are less inclined to spend money on education because the
impact is delayed and somewhat uncertain. Furthermore, while I believe using real education
expenditures per student is the best independent variable given the data available, a different
measurement to address the distribution of education funding would provide more accurate
estimates. For example, as I discussed in Section IV, the inconsistent allocation and distribution of
funds is unaccounted for in my models, specifically in the western region (where there are more
impediments to funding distribution, such as poor infrastructure and widespread local corruption),
this phenomenon may be negatively biasing my estimates.
There are also concerns as to the internal and external validity of this project. China’s
demographic composition is incredibly complicated and has seen drastic changes over the past two
decades. Additionally, the Chinese Communist Party overhauled the public education system in
the 1980s with a series of widespread reforms—some of which impact certain areas more than
12
Note: In additional models (not included in this paper), I used county-level data (the smallest micro-level measure
currently available on China Data Online) for Yunnan and Jiangsu Provinces to estimate the impact of real GDP on primary
school students from 2003-2010. My results indicated that in coastal China’s Jiangsu Province, there was a positive
relationship between real GDP and primary school students; however, in western Yunnan Province, there was a negative
correlation between real GDP and primary school students. In future research, I would like to expand on these findings.
However, China Data Online currently does not provide a county-level breakdown of education expenditures.
23
others (such as the 1986 Compulsory Education Law). It is difficult to relate my findings from
1997-2010 to other time periods in China given these conditional circumstances. Additionally, it
may be insightful to compare China’s education spending levels to other developing countries and
countries in East Asia. However, assessing the effective impact of education funding on primary
school students within China is extremely complex and highly endogenous and it would not be
appropriate to relate these findings to other countries.
iii. Areas for Future Research
Two final issues that I would like to address in future research on this topic are selective
education and domestic migration. As discussed in Section IV, for a variety of reasons, “selective
education according to ability,” impacts educational attainment in China—especially in the
western region—more than in other countries (Fang et al 2012). Due to factors such as poor school
quality, far distance to schools, high opportunity cost of staying in school versus joining the labor
force, low returns to an additional year of school in some regions, and the rigid test-based
education system, students may choose to withdraw from primary school. Universal primary
school education is not a realistic goal until these motives for withdrawing are addressed or
reduced. Additionally, the Hukou System, China’s strict permanent residence registration
requirements, inhibits rural to urban migration within a province, thus prohibiting rural students
from attending better school systems in China’s urban regions (Whalley, 2002). Although China’s
urbanization has increased rapidly over the past two decades, underprivileged families have
limited means of seeking better education systems through migration. Scholars point to the strict
Hukou System as one source contributing to China’s domestic socioeconomic inequality over the
past two decades and suggest that it may be leading to a rural poverty trap (Lin 2013; Whalley
2002). Furthermore, Lin (2013) argues that the politics of migration impact a province’s decision
to fund public education. The few students who succeed in the rural provinces often go on to
24
attend high school or university in a wealthier coastal province and have little incentive to return
to their home province or region. As Lin highlights, why would rural provincial governments
invest in improving education if they will not benefit from the investment in the future?
Given the data available and the undoubtedly complex nature of educational attainment in
China, I believe my results offer an interesting outlook on one aspect of the effective impact of
education funding. Additionally, this project represents one of the only empirical studies on
primary school students in China. Many of the concerns listed above, especially the data
limitations, must be considered in our interpretation of the results, but they should not overshadow
the compelling implications.
VI. CONCLUSION
The above models and robustness checks highlight China’s complex education landscape
and present intriguing estimates for the regional impact of education expenditures on primary
school attainment given the current availability of data. By using separate balanced panel models
for each region, I control for much of education attainment’s inherent endogeneity and make
cross-regional comparisons. My results indicate that increases in government expenditures on
education have the strongest positive impact on primary school students in China’s coastal region.
In the western region, I find consistent estimates that there is a statistically significant and
negative correlation between government expenditures on education and primary school students
per 100,000. These findings indicate that increases in per-student spending have been futile in this
region since 1997 and my estimates may be negatively biased by crucial omitted variables.
In China’s central and northeast regions, my results suggest that expenditures per student
have a slightly positive impact on primary school students, but not as strong as in the coastal
region. The key finding—that education expenditures are the most effective at improving primary
school attainment in China’s coastal region—insinuates that the current gap in educational
25
attainment between China’s urban and rural regions will increase in the future and may contribute
to greater regional economic inequality.
Additionally, my findings highlight that the effective implementation date of the
Compulsory Education Law is an inconsistent determinant of primary school student levels and I
find the most consistent positive impact in the western region. In the future, to more accurately
measure the impact of the Compulsory Education Law, I would like to include a variable to
capture a province’s commitment to enforcing compulsory primary school. While separating
mainland China into four regions controlled for elements of endogeneity and omitted variable
bias, I find that including entity fixed effects leads to significant changes across models in every
region. This noticeable impact signifies that there is high variability between provinces within the
same region and suggests that further research using county-level data to analyze intraprovincial
trends may prove more insightful.
Overall, the intriguing cross-region comparisons highlight that measuring primary school
attainment and isolating its key determinants is incredibly complex. One outcome is strikingly
clear: there is no uniform prescription for improving primary school attainment and reaching
universal enrollment in China. My findings reveal that increasing expenditures per student does
not have a consistently positive impact across every region and implementing the Compulsory
Education Law yielded mixed results in each region. I believe enforcement of compulsory
education and more emphasis on education quality are critical to achieving regional parity. To
effect lasting, positive improvements in its primary school education system and achieve universal
enrollment, China will need to explore untraditional, region-specific policies that adapt to the
country’s changing demographic and economic outlook.
26
VII. REFERENCES
Connelly, Rachel, Zhenzhen Zheng. 2002. “Determinants of School Enrollment and Completion
of 10 to 18 year olds in China.” Economics of Education Review 22 (2003) p. 379-388.
http://iple.cass.cn/upload/2012/10/d20121016155008003.pdf.
Demurger, Sylvie, Jeffrey D. Sachs, Wing Thye Woo, Shuming Bao, Gene Chang, Andrew
Mellinger. “Geography, Economic Policy, and Regional Development in China.” NBER Working
Paper No. 8897. April 2002. http://www.nber.org/papers/w8897.
Fang, Hai, Karen Eggleston, John Rizzo, Scott Rozelle, and Richard Zeckhauser. 2012. “The
Returns to Education in China: Evidence from the 1986 Compulsory Education Law.” NBER
Working Paper No. 18189. June 2012. http://www.nber.org/papers/w18189.
Feldstein, Martin. “China’s Biggest Problems are Political, Not Economic.” The Wall Street
Journal. August 2, 2012. http://www.nber.org/feldstein/wsj08022012.html.
Gong, Xin and Mun C. Tsang. “Interprovincial and Regional Inequity in the Financing of
Compulsory Education in China.” The Impact and Transformation of Education Policy in China
15(15) 2011 p. 43-78.
Heckman, James and Junjian Yi. “Human Capital, Economic Growth, and Inequality in China.”
NBER Working Paper No. 18100. May 2012. http://www.nber.org/papers/w18100.pdf.
Jian, Tianlun, Jeffrey D. Sachs, and Andrew M. Warner. “Trends in Regional Inequality in
China.” NBER Working Paper No. 5412. January 1996. http://www.nber.org/papers/w5412.
Jiangsu Ministry of Education. “President Xi Jinping Delivered Important Speech in the
Headquarters of UNESCO.” March 27, 2014.
http://english.jsjyt.gov.cn/news/keynews/folder613/2014/04/2014-04-022931.html.
Lin, Tingjin. 2013. The Politics of Financing Education in China. Basingstoke, UK: Palgrave
MacMillan.
Liu, Mingxing, “Institutional and Fiscal Arrangements for Primary and Junior Secondary
Education in China.” UNESCO Bangkok Education Policy and Reform Unit, 2010. (Note:
Professor Liu emailed me this paper directly and it is currently unavailable online).
Oizumi, Keiichiro. “A Geographical View of China’s Economic Development—Observations
Focusing on 337 Prefecture-Level Cities.” Pacific Business and Industries Volume 10(35) 2010.
http://www.jri.co.jp/MediaLibrary/file/english/periodical/rim/2010/35.pdf.
Rural Education Action Program (REAP). “Keeping Kids in School.” November 2013.
http://reap.stanford.edu/docs/652/.
27
Rong, Xue Lan and Tianjin Shi. “Inequality in Chinese Education.” Journal of Contemporary
China 10(26) 2001 p. 107-124.
http://unpan1.un.org/intradoc/groups/public/documents/apcity/unpan002178.pdf.
Song, Yang. “Poverty Reduction in China: The Contribution of Popularizing Primary Education.”
China and World Economy 20(1) 2012 p. 105-122. http://se.ruc.edu.cn/upload/jl00797.pdf.
U.S. Department of Treasury. “Treasury Reporting Rates of Exchange.” December 31, 1997.
http://webcache.googleusercontent.com/search?q=cache:T7_FrFMpiJgJ:www.gpo.gov/fdsys/pkg/
GOVPUB-T63_100-df6e98d19bcf6ba2d231a10f6c9d5260/pdf/GOVPUB-T63_100df6e98d19bcf6ba2d231a10f6c9d5260.pdf+&cd=3&hl=en&ct=clnk&gl=us&client=safari
Whalley, John and Shunming Zhang. “Inequality Change in China and (Hukou) Labour Mobility
Restrictions.” NBER Working Paper No. 10683. August 2004.
http://www.nber.org/papers/w10683.
Wu, Fangwei, Deyuan Zhang, and Jinghua Zhang. “Unequal Education, Poverty, and Low
Growth—A Theoretical Framework for Rural Education of China.” Economics of Education
Review 27 (2008) p. 308-318. https://gwu.illiad.oclc.org/illiad/pdf/246632.pdf.
28
VIII. FIGURES, CHARTS, AND TABLES
FIGURE 1: MAINLAND CHINA'S FOUR ECONOMIC REGIONS
NORTHEAST
Heilongjiang
Inner Mongolia
Jilin
Liaoning
COASTAL
Beijing
Fujian
Guangdong
Hainan
Hebei
Jiangsu
Shandong
Shanghai
Tianjin
Zhejiang
CENTRAL
Anhui
Henan
Hubei
Hunan
Jiangxi
Shanxi
WESTERN
Chongqing
Gansu
Guangxi
Guizhou
Ningxia
Qinghai
Shaanxi
Sichuan
Tibet
Xinjiang
Yunnan
FIGURE 2: VARIABLE DESCRIPTIONS
ABBREVIATION
VARIABLE
DESCRIPTION
PrimStudents
Primary School Students Observed annually in
per 100,000 people
each province
LnPerStudent
Natural Log (Real
Education Expenditures
per Primary School
Student)
ChildDep
Child Dependency Ratio
LnGDP
Natural Log (Real GDP)
YearsCEL
Implementation of the
Compulsory Education
Law
Observed annually in
each province in Yuan
Percentage of the
Population 0-14 years
old, observed annually
in each province
Observed annually in
each province in 100
million Yuan (1997 is
the base year)
Years since each
province implemented
the Law
29
SOURCE
China Statistical
Yearbook, China
Data Online
China Statistical
Yearbook, China
Data Online
China Statistical
Yearbook, China
Data Online
China Statistical
Yearbook, China
Data Online
Provincial
Ministries of
Education
3:
C
’
E
R
(Custom map created by Ellen Christiansen, GW ESIA ’14, used by permission)
30
FIGURE 4: DESCRIPTIVE STATISTICS FOR KEY VARIABLES
VARIABLE
PRIMARY SCHOOL STUDENTS
(PER 100,000 PEOPLE)
Mean
Median
Maximum
Minimum
Standard Deviation
Skewness
Observations
REGION
COASTAL
CENTRAL
WESTERN
NORTHEAST
7976.71
7299.50
14681.01
2870.18
3081.78
0.27
140
9719.00
10250.81
12883.05
6280.82
1705.32
-0.45
84
10701.80
10551.35
14325.65
6930.35
1723.44
0.042
154
7088.96
6658.48
10657.03
4903.75
1596.33
0.57
56
15929.33
8092.86
98718.79
1251.78
19406.43
2.03
140
4055.83
3345.30
11667.05
1037.42
2694.61
0.99
84
4310.35
3288.11
16666.51
587.41
3217.05
1.45
154
7757.10
6837.62
20715.22
1826.07
5038.86
0.75
56
8452.67
6230.49
40377.51
411.16
7736.98
1.77
140
5678.01
4544.99
19021.47
1476.00
3617.58
1.44
84
2527.29
1889.30
13377.63
77.24
2366.18
1.71
154
4939.63
4232.97
15600.15
1153.51
3103.73
1.25
56
23.88
22.61
50.08
8.61
9.23
0.63
100
30.40
30.54
41.60
13.91
6.63
-0.23
60
33.37
32.52
57.78
14.71
7.45
0.20
110
20.88
19.53
35.20
11.42
5.30
0.54
40
REAL EXPENDITURES PER STUDENT
(1997 YUAN)
Mean
Median
Maximum
Minimum
Standard Deviation
Skewness
Observations
REAL GDP
(100 MILLION 1997 YUAN)
Mean
Median
Maximum
Minimum
Standard Deviation
Skewness
Observations
CHILD DEPENDENCY RATIO13
(% OF THE POPULATION 0-14 YEARS OLD)
Mean
Median
Maximum
Minimum
Standard Deviation
Skewness
Observations
13
Note: China Data Online provides consistent province-level data on Child Dependency Ratio beginning in 2001. I
include data for this variable from 2001-2010. Refer to further discussion of this variable in Section II, page 6.
31
FIGURE 5: EFFECTIVE IMPLEMENTATION DATE OF THE
COMPULSORY EDUCATION LAW
COASTAL PROVINCES
WESTERN PROVINCES
Beijing
Fujian
Guangdong
Hainan
Hebei
Jiangsu
Shandong
Shanghai
Tianjin
Zhejiang
Average:
1986
1988
1986
1991
1994
1986
1986
1993
1986
1994
Chongqing
Gansu
Guangxi
Guizhou
Ningxia
Qinghai
Shaanxi
Sichuan
Tibet
Xinjiang
Yunnan
1989
Average:
CENTRAL PROVINCES
1995
1990
1991
1988
1986
1988
1987
1995
1994
1988
1986
1990
NORTHEAST PROVINCES
Anhui
1987 Heilongjiang
1986
Henan
1986 Inner Mongolia
1988
Hubei
1987 Jilin
1987
Hunan
1991 Liaoning
1986
Jiangxi
1986
1987
Shanxi
1986 Average:
Average:
1987
Sources: China National Health and Nutrition Survey and Provincial Ministries of Education
32
33
34
35
TABLE 1: NATIONAL REGRESSION RESULTS
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
MODEL 1
MODEL 2
MODEL 3
MODEL 4
MODEL 5
26669.89*** 5218.80***
4111.03
5131.54***
-1126.07
Constant
(3790.74)
(5335.30)
(939.61)
(1613.91)
(1578.59)
436.69
-449.66***
-133.26
Ln Real Expenditures -2255.59*** -458.89***
per Student (t-5)
(115.13)
(131.45)
(301.94)
(124.30)
(327.44)
248.72***
74.36***
281.27*** 141.05***
Child Dependency
Ratio
(15.20)
(14.09)
(14.17)
(18.00)
-100.90
424.79
-58.73
831.56
Ln Real GDP
(75.32)
(580.28)
(66.28)
(573.61)
80.21***
-273.86***
12.85
Years Since
Implementation
(17.06)
(96.90)
(17.71)
Entity Fixed Effects
No
No
Yes
No
Yes
Time Fixed Effects
No
No
No
Yes
Yes
SUMMARY STATISTICS
SER
1624.05
1063.45
541.01
916.97
514.20
0.58
0.82
0.96
0.87
0.96
R2
2
Adjusted R
0.58
0.82
0.95
0.87
0.96
Sample Size
279
279
279
279
279
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
TABLE 2: KEY REGRESSION RESULTS BY REGION
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
COASTAL
CENTRAL
WESTERN
NORTHEAST
-4325.13
-36939.19
23227.55**
-497.99
Constant
(10821.24)
(19562.24)
(9220.05)
(10722.11)
1819.91***
929.35
-2644.51***
523.93
Ln Real Expenditures
per Student (t-5)
(655.89)
(784.16)
(661.87)
(812.15)
133.25***
221.71***
78.26**
-44.12
Child Dependency
Ratio
(40.33)
(43.99)
(31.73)
(85.31)
-824.65
3720.42*
641.04
350.95
Ln Real GDP
(1317.44)
(1994.43)
(1230.99)
(481.82)
Entity Fixed Effects
Yes
Yes
Yes
Yes
Time Fixed Effects
Yes
Yes
Yes
Yes
SUMMARY STATISTICS
SER
446.31
515.94
507.45
168.64
2
0.98
0.92
0.91
0.97
R
2
Adjusted R
0.97
0.89
0.89
0.95
Sample Size
90
54
99
36
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
36
TABLE 3: COASTAL REGION REGRESSION RESULTS
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
MODEL 1
MODEL 2
MODEL 3
MODEL 4
MODEL 5
25304.49**
-2659.94
5669.51
-2162.01
-4325.13
Constant
(1594.49)
(2119.14)
(7432.72)
(1851.91)
(10821.24)
-2102.73***
49.34
2235.56***
116.01
1819.91***
Ln Real Expenditures
per Student (t-5)
(181.61)
(170.77)
(519.25)
(144.77)
(655.89)
362.26***
83.56***
395.16***
133.25***
Child Dependency
Ratio
(22.76)
(30.41)
(19.40)
(40.33)
115.98
-1455.15
35.02
-824.65
Ln Real GDP
(106.97)
(1131.57)
(89.69)
(1317.44)
33.46
-408.39**
-28.02
Years Since
Implementation
(23.73)
(167.81)
(22.58)
Entity Fixed Effects
No
No
Yes
No
Yes
Time Fixed Effects
No
No
No
Yes
Yes
SUMMARY STATISTICS
SER
1756.88
881.44
450.42
719.07
446.31
0.60
0.90
0.98
0.94
0.98
R2
2
Adjusted R
0.60
0.90
0.97
0.93
0.97
Sample Size
90
90
90
90
90
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
37
TABLE 4: CENTRAL REGION REGRESSION RESULTS
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
MODEL 1
MODEL 2
MODEL 3
MODEL 4
22889.60***
13042.71**
-22358.00
-4332.39
Constant
(3011.13)
(6213.37)
(13759.08)
(10637.12)
1736.44**
-1096.05
Ln Real Expenditures -1806.68*** -2298.42***
per Student (t-5)
(389.95)
(564.79)
(750.60)
(859.63)
110.05**
97.07***
168.29***
Child Dependency
Ratio
(46.44)
(33.34)
(57.08)
572.36
3423.102
1142.29**
Ln Real GDP
(388.87)
(2069.63)
(452.22)
295.55***
-782.33**
375.09***
Years Since
Implementation
(57.70)
(333.20)
(84.11)
Entity Fixed Effects
No
No
Yes
No
Time Fixed Effects
No
No
No
Yes
SUMMARY STATISTICS
SER
1332.70
960.17
922.37
922.37
2
R
0.29
0.65
0.73
0.73
2
Adjusted R
0.28
0.63
0.65
0.65
Sample Size
54
54
54
54
MODEL 5
-36939.19
(19562.24)
929.35
(784.16)
221.71***
(43.99)
3720.42*
(1994.43)
Yes
Yes
515.94
0.92
0.89
54
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
38
TABLE 5: WESTERN REGION REGRESSION RESULTS
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
MODEL 1
MODEL 2
MODEL 3
MODEL 4
23098.07*** 19055.85*** 18047.04*** 21300.07***
Constant
(1892.27)
(2824.30)
(6265.94)
(4045.94)
Ln Real Expenditures -1681.44*** -1201.97*** -1836.91*** -1592.65***
per Student (t-5)
(244.69)
(258.06)
(557.48)
(368.05)
85.22***
38.86**
132.12***
Child Dependency
Ratio
(23.46)
(18.15)
(30.01)
-519.87***
583.61
-504.12***
Ln Real GDP
(98.24)
(1171.44)
(108.87)
104.05***
37.12
54.95**
Years Since
Implementation
(22.58)
(158.40)
(25.45)
Entity Fixed Effects
No
No
Yes
No
Time Fixed Effects
No
No
No
Yes
SUMMARY STATISTICS
SER
1233.37
833.99
516.73
792.70
0.33
0.70
0.90
0.75
R2
2
Adjusted R
0.32
0.69
0.88
0.72
Sample Size
99
99
99
99
MODEL 5
23227.55**
(9220.05)
-2644.51***
(661.87)
78.26**
(31.73)
641.04
(1230.99)
Yes
Yes
507.45
0.91
0.89
99
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
39
TABLE 6: NORTHEASTERN REGION REGRESSION RESULTS
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
MODEL 1
MODEL 2
MODEL 3
MODEL 4
MODEL 5
17321.52***
1962.96
5417.33
3700.76
-497.99
Constant
(4016.69)
(3797.40)
(4239.85)
(10722.11)
(877.85)
774.25
866.43
1078.89*
523.93
Ln Real Expenditures -1349.15***
per Student (t-5)
(105.23)
(608.00)
(549.00)
(547.38)
(812.15)
59.61*
-40.82
35.57
-44.12
Child Dependency
Ratio
(32.21)
(35.80)
(32.81)
(85.31)
516.17**
452.31
944.96***
350.95
Ln Real GDP
(328.56)
(481.82)
(196.86)
(143.09)
-407.96*** -503.93*** -799.24***
Years Since
Implementation
(125.04)
(140.63)
(107.55)
Entity Fixed Effects
No
No
Yes
No
Yes
Time Fixed Effects
No
No
No
Yes
Yes
SUMMARY STATISTICS
SER
316.29
271.25
179.50
167.56
168.64
0.83
0.89
0.95
0.97
0.97
R2
2
Adjusted R
0.82
0.87
0.94
0.95
0.95
Sample Size
36
36
36
36
36
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
40
TABLE 7: ROBUSTNESS CHECK #1
DEPENDENT VARIABLE: PRIMARY SCHOOL GRADUATES (PER 100,000 PEOPLE)
REGRESSOR
COASTAL
CENTRAL
WESTERN
NORTHEAST
615.24
6282.54
7783.12***
6163.06
Constant
(4769.03)
(5330.00)
(2433.20)
(4529.61)
-285.54
-735.92***
-325.26*
14.83
Ln Real Expenditures
per Student (t-5)
(289.06)
(213.66)
(174.67)
(343.10))
15.25
3.63
-8.81
-58.62
Child Dependency
Ratio
(17.77)
(11.99)
(8.37)
(36.04)
319.49
108.70
-431.00
-465.10**
Ln Real GDP
(580.61)
(543.41)
(324.86)
(203.55)
Entity Fixed Effects
Yes
Yes
Yes
Yes
Time Fixed Effects
Yes
Yes
Yes
Yes
SUMMARY STATISTICS
SER
196.69
140.57
133.92
71.24
0.88
0.86
0.74
0.94
R2
Adjusted R2
0.85
0.80
0.66
0.90
Sample Size
90
54
99
36
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
TABLE 8: ROBUSTNESS CHECK #2
DEPENDENT VARIABLE: LN PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
COASTAL
CENTRAL
WESTERN
NORTHEAST
11.19***
3.57
11.34***
7.64***
Constant
(2.29)
(1.78)
(0.96)
(1.65)
0.200*
0.0598
-0.291***
0.0452
Ln Real Expenditures
per Student (t-5)
(0.11)
(0.092)
(0.069)
(0.12)
0.0061
0.0275***
0.0062*
-0.0069
Child Dependency
Ratio
(0.0066)
(0.005)
(0.0033)
(0.013)
-0.012
0.095
-0.481**
0.489**
Ln Real GDP
(0.216)
(0.23)
(0.13)
(0.074)
Entity Fixed Effects
Yes
Yes
Yes
Yes
Time Fixed Effects
Yes
Yes
Yes
Yes
SUMMARY STATISTICS
SER
0.0733
0.0603
0.053
0.026
0.98
0.92
0.91
0.97
R2
2
Adjusted R
0.97
0.89
0.88
0.96
Sample Size
90
54
99
36
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
41
TABLE 9: ROBUSTNESS CHECK #3
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
COASTAL
CENTRAL
WESTERN
NORTHEAST
596.85
-71980***
19975.46**
-5365.95
Constant
(11066.94)
(20156.15)
(9493.31)
(10603.58)
1586.17***
315.04
-2571.83***
961.36
Ln Real Expenditures
1
per Student (t-5)
(564.40)
(701.48)
(679.31)
(869.15)
136.60***
212.95***
75.85**
-23.58
Child Dependency
Ratio
(38.80)
(39.90)
(32.04)
(85.70)
-1156.58
8283.28***
1002.18
450.15
Ln Real GDP 1
(1249.54)
(2326.41)
(1161.92)
(431.37)
Entity Fixed Effects
Yes
Yes
Yes
Yes
Time Fixed Effects
Yes
Yes
Yes
Yes
SUMMARY STATISTICS
SER
446.37
464.68
513.88
165.18
R2
0.98
0.94
0.91
0.97
2
Adjusted R
0.97
0.91
0.88
0.95
Sample Size
90
54
99
36
1
note: Real Expenditures per Student and Real GDP are calculated using the National CPI
for each year; compare to using individual province’s CPIs in all other models.
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
TABLE 10: ROBUSTNESS CHECK #4
DEPENDENT VARIABLE: PRIMARY SCHOOL STUDENTS (PER 100,000 PEOPLE)
REGRESSOR
COASTAL
CENTRAL
WESTERN
NORTHEAST
3451.36***
4350.57**
8716.19***
7490.12***
Constant
(790.70)
(1660.00)
(1284.24)
(1163.02)
-290.19*
-516.57
-187.83
-31.99
Percentage of GDP
on Education (t-5)
(153.20)
(330.21)
(142.86)
(124.14)
230.42***
235.45***
74.17**
-71.04
Child Dependency
Ratio
(26.44)
(44.07)
(34.30)
(68.68)
Entity Fixed Effects
Yes
Yes
Yes
Yes
Time Fixed Effects
Yes
Yes
Yes
Yes
SUMMARY STATISTICS
SER
456.51
520.44
550.06
166.76
0.98
0.92
0.89
0.97
R2
Adjusted R2
0.97
0.89
0.86
0.95
Sample Size
90
54
99
36
***, **, * indicates coefficients are statistically significant from zero at the 1%, 5%, and 10% level.
42