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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. - PowerPoint PPT Presentation
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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 than16 times that in 1978
It took the US nearly 100 years from 1870 to 1970 … to grow by 10 times!
Percent of Population in the Agricultural Sector
Income per Capita
US and other OECD nations
Ethiopia, Rwanda, etc.
“Iron Law of Economic Development”
Data from the World Bank
This is sometimes called the Kuznet’s curve
Percent of Pop’n in Ag. Sector
Income per Capita
Development = IndustrializationModernization = Urbanization
Zero: there are no high income countries in world with more than 10% of their populations that live in agriculture10-20%
Percent of Pop’n in Ag. Sector
Income per Capita
“Miracle Development—with Korean Characteristics”
Korea—1950s
Korea—1974
Korea—today
Korea—1987
Percent of Pop’n in Ag. Sector
Income per Capita
In 1980, China was:
• Poor
• Rural
• Agricultural
China in 1980s
China at the start of Economic Reforms
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
0%
20%
40%
60%
80%
100%
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 ti me farm onl y
In 2008 more than 90% of households have at least 1 family member (or son / daughter) working off the farm
In 1980: only 4% worked full time off the farm
63%
2008
Transformation Path
Percent of Pop’n in Ag. Sector
Income per Capita
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”
The movement of this labor … in vast quantities is what helps drive growth in the early stages of development …
0.7
23.65
21.76
13.56
24.91
2.634.09
0.52
27.52
0
4
8
12
16
20
24
28
中国 美国 日本 15欧盟 国 韩国 澳大利亚 墨西哥 巴西 斯里兰卡
/美
元小
时
Hourly Wage, 1990s
China US Australia Mexico Brazil Sri Lan.Japan EU Korea
0.50
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!
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!
0
500
1000
1500
2000
2500
3000
3500
1978 1983 1988 1993 1998 2003
Year
Ann
ual w
age
(197
8 re
al y
uan)
Collective Other
2007
Unskilled wage
Since 2000
Wage rises in coming years
But, the rise in wages is now happening in China …
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
Figure VII-1: Crude Birth and Death Rates
0
10
20
30
40
50
601953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
Vit
al
Rate
s p
er
1000
Births
Deaths
Birth rates
Death rates
Supply of labor is falling …
In 1990 ≈ 25 million babies/year /
In 2010 ≈ 15 million babies/year … and falling
today
Summary: Implications
• China continues to grow: RISING DEMAND
• Size of labor force falls: FALLING SUPPLY
Rising wages in the future
Changing industrial structure
By 2025 to 2030
$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 VETHow 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
132 junior high schools first year students (~20000)
Control: 22 JHSs, 308 poor stus
Long :22 JHSs
41 classes: no training
164 poor students – no $$$
Long+$$$: 22 JHSs
86 of 172 poor students got $$$
80 of 160 poor students got $$$
140 poor students—no $$$
35 classes: long training
43 classes: no training
40 classes: long training
$$$:22 JHSs
79 classes
158 of 316 poor students got $$$
Short+$$$:22 JHSs
36 classes: no training
72 of 144 poor students got $$$
39 classes: no training
78 of 156 poor students got $$$
Short: 22 JHSs
36 classes: short training
47 classes – no training
144 poor students—no $$$
188 poor students—no $$$
4 poor students X 473 classes = 1892 students
Vouchers ($$$) + LONG OR SHORT Counseling Interventions
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 schoolsCompare 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 poor area rural mi nori ty femal e age l ow scorer
HS Entry Exam Attendance ↓ ↓ ↑HSEE PerformanceHS AdmissionsElite HS AdmissionsDropouts by end of Junior YearDropouts by CEECEE PerformanceCollege Admissions %sKey College Admissions %s
(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 cost-sharing 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 “science-track” class
•Science-track classes were given the intervention or treatment (T).
•Confirmed that T and C groups were balanced
GREEN = INTERVENTIONBLUE = CONTROL
Intervention•Info booklet:
– 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
Needs-Based Grants
Green Channel
Home-based Loans
National Loans
Poverty Subsidy
Treatment
1.36*(.24)
[.08]
2.13**(.73) [.03]
2.72** (.81) [.00]
.60 (.25) [.22]
.92 (.23) [.72]
GEE TREATMENT EFFECT ESTIMATES FOR VARIOUS FINANCIAL AID OUTCOMES (SCIENCE TRACK STUDENTS)(Without Covariate Adjustments, using non-Imputed Data)
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!