Conducting Policy-Based Education
Finance Research in China
The China Institute for Educational
Finance Research (CIEFR)
• China’s first academic institution for
educational finance research (Oct. 2005)
• An innovative joint-venture: MOF, MOE, and
Peking University.
• Two major areas of activity:
– Policy consulting
– Policy-related research
Policy Consulting
• Challenges:
–
–
–
–
Small windows of time
Topics change quickly
Little empirical evidence on which to base suggestions
Pressure…
• Opportunities from engaging in policy consulting:
– Immediately access to policymakers
• Understand key concerns
• Learning
• Dissemination of research findings
– Support for conducting research projects
Policy consulting: Process
• Refer to past empirical studies in China
• Utilize existing data
– Our own past surveys
– Other large-scale survey/census data
– government statistics (public)
• Review foreign country practices and policies
• Short-term surveys, interviews, site visits
Policy Consulting: for the MOF/MOE
Examples:
• Reform of Education Finance Statistics System
• Reform of the Rural Compulsory Education Guarantee Funding
Mechanism
• Key Policies of Increasing Funding for China’s Education During
the Eleventh Five-Year Plan Period
• National Plan for Medium and Long-Tem Education Reform and
Development: Issues of Educational Finance
• Financial Support Mechanisms for R&D at Higher Education
Institutions
• Budget Provision for National Universities
Policy-related research
(1) Descriptive
– using randomly sampled, representative data
(2) Policy or Program Impact Evaluation
– Randomized Control Trials (RCTs)
– Quasi-experiments
– Assessment using reliable/valid measures/outcomes
(3) Action-based Research
Some Current Areas of Research
(1) Vocational Education
(2) Academic High School Education
(3) Higher Education
(4) Migrant Education
(5) Early Childhood Education
(6) Disabilities and Education
First…Background:
Changes in the supply/demand of
Human Capital in China
The size of economy in 2008 was more than
16 times that in 1978
It took the US nearly 100 years from 1870 to 1970 … to grow by 10 times!
“Iron Law of Economic Development”
Percent of
Population
in the
Agricultural
Sector
Ethiopia, Rwanda, etc.
This is sometimes
called the Kuznet’s
curve
US and other OECD
nations
Income per Capita
Data from the
World Bank
Development = Industrialization
Modernization = Urbanization
Percent
of Pop’n
in Ag.
Sector
Zero: there are
no high income
countries in
world with
more than 10%
of their
populations that
live in
agriculture
10-20%
Income per Capita
“Miracle Development—with Korean
Characteristics”
Percent
of Pop’n
in Ag.
Sector
Korea—1950s
Korea—1974
Korea—1987
Korea—today
Income per Capita
China at the start of Economic Reforms
Percent
of Pop’n
in Ag.
Sector
In 1980, China was:
China in 1980s
• Poor
• Rural
• Agricultural
Income per Capita
China is moving along the
Transformation Path,
according to the Iron Law
• From left to right … INCOME
• From top to down … URBANIZATION/INDUSTRIALIZATION
Becoming better off … income rising …
Shenzhen in
1980 …
… and 2000
Overall Increase in Off-farm Work
63%
100%
80%
60%
In 2008 more than 90% of households
have at least 1 family member (or son /
daughter) working off the farm
40%
20%
In 1980:
only 4% 0%
worked full
time off the
farm
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year
off-farm
busy season
part time
farm only
2008
Transformation Path
Percent
of Pop’n
in Ag.
Sector
So it is clear that as China is
growing (moving left to right
across the graph), it also is
beginning to move “down” the
transformation path …
this is “development”
Income per Capita
The movement of this labor … in vast
quantities is what helps drive growth in
the early stages of development …
A low unskilled wage in the 1980s/1990s is why such a large share of the things the world
makes are manufactured in China today!
28
27.52
20
23.65
美元/小时
24
24.91
21.76
16
13.56
12
8
4.09
4
2.63
0.50
0.7
0.52
0
中国
China
美国
US
日本
Japan 欧盟15国
EU
韩国
Korea
Hourly Wage, 1990s
澳大利亚
Australia 墨西哥
Mexico 巴西
Brazil 斯里兰卡
Sri Lan.
This was also enabled by China’s education and
health systems during the 1970 and 1980s seemed
to have played an important role
• Infectious diseases were controlled; infant
mortality fell …
• School authorities got everyone into school
(at least elementary school)
 to teach the rudiments of reading and
arithmetic …
 instill discipline to be a good worker!
Annual wage (1978 real yuan)
Wage
rises in
coming
years
3500
3000
Since
2000
2500
2000
Unskilled
wage
1500
1000
500
0
1978
1983
1988
1993
1998
2003
2007
Year
But, the rise in wages
is now happening
in China …
Collective
Other
Wage have risen rapidly recently …
In coming years … projected to rise even faster …
Future growth of GDP (5, 6, 7 or 8 %/year) 
demand for labor will increase
FigureVII-1: CrudeBirthandDeathRates
today
60
50
Vital Ratesper 1000
40
Birth rates
30
Births
Deaths
20
10
Death rates
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
0
Supply of labor is falling …
In 1990 ≈ 25 million babies/year /
In 2010 ≈ 15 million babies/year … and falling
Summary: Implications
• China continues to grow: RISING DEMAND
• Size of labor force falls: FALLING SUPPLY
Rising wages in the future
By 2025 to
2030 
Changing industrial structure
$10-15/hour
Are rising wages bad?
• Of course not…“good riddance to sweat shop jobs.”
• But, with higher wages, China will have to move
itself up the productivity ladder …
So: China’s real human capital challenge is coming …
– Can China become competitive in industrial sectors
requiring medium and high skilled human capital?
– Can it maintain relative equality/equity in the process?
“Achievements” so far in Education
• 1990s: universal 9-year compulsory education
• Late 1990s: higher education expansion
– GER went from 5-6% in the mid-1990s to 29% in 2009
– Largest higher education system in the world
• 2000s: high school expansion
– GER went from 40% in late 1990s to 79% in 2009
– mandated 50:50 ratio between regular and vocational
What about Educational Quality?
• Shanghai PISA Results 2009
(PISA is for up to age 15 years old)
• Quality in the rest of China?
• Not much is known about quality in terms of
student outcomes.
CIEFR’s Research: Three Areas
(1) Vocational Education and Training
(2) Academic High School
(3) Higher Education (financial aid)
(1) Investment in Vocational vs.
General Schooling:
Evaluating China’s Expansion of
Vocational Education and
Training (VET)
Research Questions about VET
How to balance investments in vocational vs. general education to
support economic growth and reduce social inequality?
(1) What are the returns to VET? if negligible, policymakers may
consider slowing the expansion or improving the quality of VET.
(2) What are the factors that keep junior high school graduates in
poor, rural areas from continuing with their studies? A fairly low
proportion of them go on to any type of high school.
(3) What is the quality and cost-effectiveness of VET programs?
 Few mechanisms to evaluate the quality of VET programs
(2) Supporting Disadvantaged Junior High Students
• Randomized trials involving junior high students in poor areas:
– Vouchers high school (academic or VET)
– Edu/career counselling (returns to school, career awareness)
• Outcomes:
– Persistence/dropout in junior high school
– Academic (exam) performance
– High school matriculation rates
• Baseline and Follow-up Surveys:
– 2 provinces
– 132 rural public junior hi schools, 473 classes
– 19,832 seventh-grade students
Vouchers ($$$) + LONG
OR SHORT Counseling
Interventions
Long :
22 JHSs
41 classes:
no training
164 poor
students
– no $$$
Long+$$$:
22 JHSs
$$$:
22 JHSs
132 junior high
schools first year
students (~20000)
4 poor students X
473 classes =
1892 students
Short+$$$:
22 JHSs
Short:
22 JHSs
Control: 22 JHSs,
308 poor stus
35 classes:
long
training
40 classes:
long
training
43 classes:
no training
79
classes
36 classes:
no training
39 classes:
no training
36 classes:
short
training
47 classes –
no training
140 poor
students—
no $$$
80 of
160 poor
students
got $$$
86 of
172 poor
students
got $$$
158 of
316 poor
students
got $$$
72 of
144 poor
students
got $$$
78 of
156 poor
students
got $$$
144 poor
students
—no $$$
188 poor
students
—no $$$
Migrant JHS students in Beijing
• 200+ million rural migrants in China, many adults bring their
children to the urban areas
• Household registration system restricts the educational options
of these families/children
• We conducted RCTs to examine the effect of vouchers and
education savings plans on student persistence and academic
performance.
• Preliminary Results: Vouchers has some effect on reducing
dropouts. Especially for students in poor, rural areas, less so for
migrants in Beijing.
(3) Assessing VET High School Quality
• Nov., 2011: baseline survey of students in computer
application majors. Gave math and computer operation exams
to students in ~110 VET high schools in 2 provinces.
• May, 2012: math and computer exams to the same students
to assess the value-added of their programs.
• Collected other quality indicators from teachers, schools
 Compare VET schools
 Compare VET and academic HSs
• After May, 2012: RCTs – training and incentives on how using
data to improve student performance.
• May, 2013: Post-intervention math and computer exams
Academic High Schools
General Concerns
• Human capital formation
• Academic high school is a sort of bottleneck in
the pathway to college  who gets there?
implications for equality
• Variation in academic high school quality may
be great.
Study in one NW province in China
1) Sorting/Inequality in Education from High School to College
2) The Effects of Attending Different Academic High Schools
3) The Impact of Building Free, Elite High Schools For Students
From Disadvantaged Areas
Policymakers provided admin data for up to ten years
– 6 years: HS entrance exam data for select counties
– 10 years: college admissions data for all counties
– HS expenditures and revenues (select schools)
(1) Sorting/Inequality in the Province
Stage of Education
HS Entry Exam Attendance
HSEE Performance
HS Admissions
Elite HS Admissions
Dropouts by end of Junior Year
Dropouts by CEE
CEE Performance
College Admissions %s
Key College Admissions %s
poor area rural minority female
↓
↓
↑
age
low scorer
(2) High School Quality
• How to evaluate school quality is a difficult question. Value-added
models are one common method. (We also tried regression
discontinuity, but that’s another story…)
Why Value-Added?
• Currently, China uses college entrance exam (CEE) scores or college
entry rates to judge the quality of high schools.
• But these are absolute “status” indicators and not relative “growth”
indicators.
• Value-added scores are “growth” indicators which reflect the
learning that students gain from the time of entering to the time of
leaving high school  more realistically reflect instructional quality.
Analysis and Findings
• We conducted student growth percentile SGP analysis on 50 high
schools using high school entrance and college entrance exam results.
Findings:
• The rankings of high schools change depending on whether you use
“absolute” indicators or these “value-added” indicators.
• We also found that some students (poor, older, urban, non-minority,
male, low scorers) tend to have lower SGP scores.
• Because we can see the value-added scores for each student, we can
see which students need additional support.
(3) Impact of Building Free, Elite High Schools
For Students From Disadvantaged Areas
• Academic High School is expensive and selective.
• Policymakers used built two large, free, elite high
schools (HSs) for students in disadvantaged areas.
• From 2003-2010: Spent ~ 150 million US dollars
LPS
(established in
2003; gradually
expanded to
5084 students
by 2009)
YC
(established in 2006;
gradually expanded
to 6281 students by
2009)
Request for Impact Evaluation
• Policymakers requested an impact evaluation.
• Questions: What were effects of the policy on
disadvantaged areas’ students’:
– college admissions (any college, first 2 tiers, elite)*
– high school entrance exam (participation,
performance)**
Quasi-experimental Methods
• Linear and Censored Quantile DID
• Short-interrupted time series (SITS) with
comparison group design
• “Augmented” administrative data with Census
data to account for censoring of observations
Findings
• The policy positively impacts students even before they
get to high school.
• The policy increases the likelihood that students from
disadvantaged areas can attend college and selective
colleges – equalizes opportunities across the province.
• Free, elite high schools are most effective when they
provide opportunities to medium & medium-high scoring
students (not just top students).
• high internal ROR for poor counties from the initiative
 government investment “recovered” after a few cohorts.
(3) Higher Education (Financial Aid)
Background
• In 1997, China’s government instituted a costsharing policy  many poor students couldn’t pay.
• In 2007, the State Council increased aid a lot: 27.3
billion yuan, mostly for low-income students.
• Social organizations, local governments and
universities also increased aid.
Potential Problems in Allocating Financial Aid
• Difficult for the government and universities to assess students’
financial need  lack of a universal income tax system.
• Each institution relies on information reported by students.
• In China, as well as in other developing countries, there is concern
however that students may not report their information accurately.
• There is also no specific standard for how administrators at
different institutions use this information to assess student need.
Research Questions
(1) What is the current distribution of aid across the
HE system?
(2) How is aid currently distributed by universities?
(3) Is aid reaching students from households with
lower socioeconomic status (SES)?
(4) Is aid given to students on characteristics
besides SES?
(5) What is the bottom line (in terms of net costs
and subsidies) for students?
Data
In 2008, we collected a 17% simple random sample
of senior college students from one province who
attended a four-year university in that province.
We had an extremely high response rate in general
(about 98%).
We constructed special SES measures using various
types of household information.
(2) How well was aid distributed in
each university to poor students?
• Government needs-based aid given more to
low social class students within institutions.
• Some universities give university aid more to
lower social class students...and some do not.
Finally: RCT on the effects of providing
college cost and fin aid information
Students, especially in disadvantaged areas
may not have heard of the State policy on
financial aid.
What is the effect of providing high school
students with user-friendly college cost and
financial aid information on their likelihood of
receiving financial aid?
Sample and Assignment
•41 poor counties – went to the best
high school in each county
•Randomly assigned 20 as
“treatment” counties/schools
•Randomly chose one “sciencetrack” class
•Science-track classes were given
the intervention or treatment (T).
•Confirmed that T and C groups
were balanced
GREEN = INTERVENTION
BLUE = CONTROL
•Info booklet:
Intervention
–Financial Aid
–College Costs
–Application Process
–Hotline Numbers
–Other Resources
–Rights/Obligations
•20 minute presentation
•3 to 4 minutes for Q&A
•5 minute feedback form
Results: Types of Financial Aid
GEE TREATMENT EFFECT ESTIMATES FOR VARIOUS FINANCIAL AID
OUTCOMES (SCIENCE TRACK STUDENTS)
(Without Covariate Adjustments, using non-Imputed Data)
Treatment
Needs-Based
Grants
Green
Channel
Home-based
Loans
National
Loans
Poverty
Subsidy
1.36*
2.13**
2.72**
.60
.92
(.24)
(.73)
(.81)
(.25)
(.23)
[.08]
[.03]
[.00]
[.22]
[.72]
Notes: 1) Effects reported as odd-ratios, robust standard errors in parentheses, p
values in brackets 2) **significant at the 5% level; *significant at the 10% level. 3) N =
2331.
Conclusions about Policy-Related
Research Work
• Much of our work is empirical so far:
– Descriptive
– Impact Evaluation using RCTs, quasi-experimental methods
Future work:
• Often look at local level – need more representative data for the
country as a whole or from several provinces.
• Discuss with policymakers the possibilities of implementing pilot RCTs.
• Strengthening reporting and collection of administrative data.
• Continue to find out why things work or don’t work in education.
Thank you!