1. 中部崛起计划
“Rise of Central China Plan”:
An impact evaluation of a regional development campaign
Nicholas Swallow Beaudoin
March 15, 2015
Professor Jennifer Burney
Professor Craig McIntosh
IRCO 468: Evaluating Technical Innovation
Abstract
This paper analyzes the effect the Rise of Central China Plan (RCCP) had on regional
economic growth between the periods 2003 and 2011. Data was collected on 266 cities at
the prefectural level from 1996 to 2012. A shock in fixed asset investment was used to
proxy for treatment uptake while a difference-in-differences (DID) regression estimated
the treatment on the treated (TOT) effect. Counterfactual cities were created using
propensity score matching on common support and covariates included demographic,
economic and investment variables, as well as dummy variables to control for competing
regional development programs. The DID estimators determined that investment
incentives in RCCP increased gross domestic product (GDP) growth in the treatment
group by a marginally significant 0.23 percent. This study helps understand the role the
Chinese central government has on reducing regional inequality and for future macro-
economic policy-implementation at the regional level.

2. 2
Introduction
Since the implementation of market reforms in the late 1970s, the Chinese central
government has devolved much of its macro-economic implementation tools to the
provincial and local level. While China remains a unitary authoritarian state, the degree
to which the Beijing can initiate regional economic growth remains unclear.1
Due to the
centrally mandated investment drives of Deng Xiaoping in the early 1990s, coastal cities
received preferential treatment and investment incentives to expedite economic
development. Cities such as Shanghai, Tianjin and Shenzhen created wealth never before
seen in modern China. However, such growth has created regional inequalities as
evidence by lackluster gross domestic product (GDP) growth in Central China. Measures
to increase GDP in targeted regions through the promotion of fixed asset investment have
been used by Beijing to reduce regional economic inequality.2
Slow economic growth within the 74 cities in the six provinces of Central China
are at the core of this paper. On September 23, 2004, Premier Wen Jiabao of the State
Council discussed and passed the “Plan to Boost the Rise of Central Region” campaign.3
Provinces that were to receive the program were Henan, Shanxi, Anhui, Jiangxi, Hubei
and Hunan. The National Development and Reform Commission (NDRC), the primary
development agency under the State Council, established five policy areas to aid the
development of Central China: (1) increase grain production; (2) provide incentives for
cities to combine; (3) update aged industrial production facilities; (4) offer tax and
1
Lam, Willy Wo-Lap, Chinese Politics in Hu Jintao Era: New Leaders, 222.
2
Francoise, Nicolas, China and Foreign Investors: The End of a Beautiful Friendship?, 1
3
“The meeting proposes to implement the Plan to Boost the Rise of Central Region and achieve the goals of the central region in
significantly increasing the level of economic development, further strengthening the developing vitality, remarkably enhancing the
sustainable development capacity and making new progress in building a harmonious society by 2015.” Expo Central China

3. 3
investment incentives to attract foreign direct investment (FDI) and (5) increase spending
on education.4
While these five policy goals are at the center of the RCCP campaign, this
study will focus on the overall economic growth as seen in the annual percentage change
in GDP.
Official media outlets have contradicted the opinions of academia in regards to
the RCCP and its effect on targeted regional growth. According to a 2012 article in the
Shanghai Flash, the RCCP campaign has seen visible success due to a higher overall
GDP in Central China.5
While examples of stated causality post-RCCP are rampant
amongst news outlets, a thorough review of academic literature fails to offer empirical
evidence that the centrally mandated RCCP caused or even contributed to a higher GDP
amongst the six target provinces. Two studies have reviewed regional development
programs in China, yet have not attempted to solve this problem of causal inference. The
first is an article in China: An International Journal by Hongyi Lai titled, “Developing
Central China: A New Programme.” Chung et al. posit that as of 2012 (using data that
was valid as of 2009), it is too early to identify the RCCP campaign’s success.6
Similarly, Chung, Lai and Joo in China Quarterly’s “Assessing the ‘Revive the Northeast
Programme: Origins, Policies and Implementation,” describes specific goals of the RCCP
campaign, yet finds that validation of the RCCP campaign is too early to tell.7
So far, any
rigorous study of the RCCP campaign has focused on the Intention to Treat (ITT) to
determine an Average Treatment Effect (ATE) and not on the cities that actually took the
4
Hongyi Lai, “Developing Central China: A New Regional Programme.” 122.
5
Min Marti, “The ‘Rise of Central China” Plan: Objectives and Impacts with Special Focus on Anhui Province.’” In: Shanghai Flash
2012(4). p. 1-5.
6
Jae Ho Chung, Hongyi Lai and Jang-Hwan Joo “Assessing the "Revive the Northeast" (zhenxing dongbei) Programme: Origins,
Policies and Implementation,” 108
7
Ibid.

4. 4
treatment. This paper will focus on the Treatment Effect on the Treated (TOT) through
the following methodology.
I hypothesize that the Rise of Central China Policy had a minimal effect in the
range of -1 to 1 percent. Due to similar regional investment drives, I believe that the
RCCP campaign’s investment incentives are nullified since coastal, western and
northeastern regions develop programs have their own respective investment policies.
The methodology used to study if the RCCP increased GDP growth is
straightforward; fixed asset investment was used to proxy for the uptake of the policy that
was offered to the six provinces. This solves the ITT problem; we know which cities
decided to take the treatment rather than looking at an ATE for all cities that received the
program, regardless if they took it or not. Causal identification of the RCCP’s success
will be found if a difference-in-differences estimator of treatment versus control (baseline
in 2003 and endline in 2011) shows an upward linear trend that is statistically significant
at the 95% level. Checks for robustness using matching with common support and
running four separate regression models to check the validity of RCCP’s effect on GDP
in Central China are offered. Issues of endogeneity and threats to validity are discussed in
the conclusion. Finally, a policy recommendation is made for the State Council and
NDRC to improve validity in future impact evaluations of regional development
programs.

5. 5
Data Collection
The data used for this natural experiment were obtained through the University of
Michigan’s All China Data Center in Ann Arbor, Michigan.8
Data on 266 cities at the
prefectural and provincial level were collected. These data include city-level economic
inputs and outputs, ranging from population and employment statistics to various types of
investment (fixed asset, real estate, transportation), material outputs and local
government financial figures. While the years 1996 to 2012 are available, the most
consistent data begins in 1999 and ends in 2011. The RCCP was announced in March
2004 by the Beijing central government. 74 cities are in Central China and determined to
have been offered the program
Methodology
Since this is an observational study, a non-experimental design was taken to
estimate the treatment effect of the Rise of Central China Policy on the cities that were
offered the program and decided to implement the program (designated as treatment
uptake). The RCCP’s primary objective is to decrease regional inequality through
increasing production in agriculture, constructing new transportation networks, increasing
spending on education, updating factories and increasing the extraction of minerals. It is
well beyond the scope of this study to find a causal impact on the five previously
mentioned policy goals. Chinese sources of information are not transparent and many of
the appropriation decisions happen at the provincial and local level, where data is more
difficult to acquire. Therefore, the operational dependent variable is the annual
8
All
China
Data
Center

6. 6
percentage change in GDP. This takes the most important indicator of economic success
for China (GDP growth determines advancement of local leaders under the cadre
responsibility system).9
While the program was offered in 2004 to the cities of six provinces in Central
China, it is unclear when individual cities chose to take the treatment. Therefore, a proxy
variable was chosen to indicate if a city had taken the treatment and to separate those that
did not. This variable is fixed asset investment (FAI) and is a sound representation of a
city having taken the central government program to attract investment. Any shock in
FAI (see Appendix I), represented by a two-standard deviation increase from year-to-
year, is identified as having taken the treatment. Cities that did not take the treatment that
are also in the program were included in the control group.
Control variables include unemployment rate, number of employees in urban
areas, GDP Index and cultivated land and population statistics. Additionally, there are
two regional development programs that need to be accounted for through dummy
control variables. First, the 2000 “Revitalization of Northeastern China Plan targeted
Northeastern provinces (Heilongjiang, Jilin, Liaoning, including cities Chifeng, Tongliao,
Hinggan, Hulubuir). This plan is coded as “0” for years prior to 2003 and “turned on” as
a “1” after the 2003 program start. Second, in 2003 the “Western Development Plan” was
implemented. This includes Inner Mongolia, Shaanxi, Ningxia, Xiangjiang, Qinghai,
Gansu, Tibet, Sichuan, Chongqing, Yunnan, Guizhou, Guangxi provinces and is
controlled by a similar dummy variable that is turned on for the entirety of the study upon
implementation of the respective program. However, a TOT design was not used and a
9
The
cadre
responsibility
system
is
how
village,
municipal,
county
and
provincial
leaders
are
promoted.
The
three
hard
targets
are
GDP
growth,
social
stability
(less
than
3
demonstrations
per
year)
and
China’s
population
policy.

7. 7
simple dummy variable is coded only for the cities offered the program, irrespective if
they took the treatment or not.
The treatment effect on the treated (TOT) is determined using a difference-in-
differences estimator. The model that I propose, after having tested four regression
models including pooled OLS, fixed effects with and without clustering at the provincial
level and unit-specific effects, is the only model that provided a clear and statistically
significant (albeit at the 90 percent significance level) of the TOT:
𝑦!"# = 𝛼! + 𝛿𝑇!" + 𝛽𝑋′!"# + 𝜆 ∗ 𝑦𝑒𝑎𝑟!"# + 𝜌 ∗ 𝑁𝐸!" + 𝜃 ∗ 𝑊!" + 𝜀!"
𝑦: %ΔGDP
𝑇: 𝐶𝑖𝑡𝑖𝑒𝑠 𝑡ℎ𝑎𝑡 ℎ𝑎𝑑 𝑎 2 𝑠𝑡𝑑. 𝑑𝑒𝑣. 𝑗𝑢𝑚𝑝 𝑖𝑛 𝐹𝐴𝐼 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑦𝑒𝑎𝑟
𝑟!: 𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (𝑦𝑒𝑎𝑟 2003)
𝑟!: 𝐸𝑛𝑑𝑙𝑖𝑛𝑒 (𝑦𝑒𝑎𝑟 2011)
𝑋: 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
→ 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛, 𝐺𝐷𝑃 𝐼𝑛𝑑𝑒𝑥, 𝑐𝑢𝑙𝑡𝑖𝑣𝑎𝑡𝑒𝑑 𝑙𝑎𝑛𝑑, 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒, 𝑢𝑟𝑏𝑎𝑛 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒
𝑁𝐸: 𝑑𝑢𝑚𝑚𝑦 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑅𝑒𝑣𝑖𝑡𝑎𝑙𝑖𝑧𝑒 𝑡ℎ𝑒 𝑁𝑜𝑟𝑡ℎ𝑒𝑎𝑠𝑡 𝐶𝑎𝑚𝑝𝑎𝑖𝑔𝑛
𝑊: 𝑑𝑢𝑚𝑚𝑦 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑊𝑒𝑠𝑡𝑒𝑟𝑛 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡 𝐶𝑎𝑚𝑝𝑎𝑖𝑔𝑛
𝜀: 𝑢𝑛𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚
Parallel Trends Analysis/ Propensity Score Matching
An initial parallel trends analysis of GDP growth before 2004 shows strong
upward bias, and clear violation of parallel trends, towards larger cities in coastal
provinces. Beijing, Tianjin and Shanghai all have extraordinary growth rates, while cities

8. 8
that are also in the control group, such as those further west, lack such a robust stage of
development.
Propensity score matching is used to get around this violation of parallel trends.
This “curse of dimensionality” was inherent by attempting to use other propensity score
matching methods, namely radius and exact matching. Since Chinese cities have myriad
observed means regarding similar economic indicators related to development, finding an
exact or close match was impossible. Common support solves this problem. By taking
the treatment and control groups and estimating the probability that given variables used
for estimation (see V for a full list of variables used for propensity score matching) this
linear probability method finds the probability that cities would have been included in the
RCCP investment campaign had it not been for their geographical location outside of
Central China. In words, these cities were within the two distributions of probabilities to
make this study as close to randomly selected as possible (see Appendix IV).
Based upon this propensity score model, 138 cities were matched for the control
group that either never received the program or received the program and decided not to
take the investment incentives, regardless of their location. The treatment group only
included cities that were offered the program in Central China and took it. Again, uptake
of treatment is proxied by the FAI shock discussed earlier (49 cities) (see Appendix I).
By matching on pre-treatment status, parallel trends are seen in Appendix IV. The
qualities matched on were treatment status and 20 economic variables that acted as litmus
tests for similar investment and development status (see Appendix V for all variables
used for propensity score matching calculations and the area of common support in
Appendix IV). Appendices III, VI and VII show baseline means and standard deviations

9. 9
during the pre-program phase (1999-2003) of both the unmatched and matched cities,
respectively. VII shows the improvement of the propensity score matching design for
each variable.
The variables that were most important to have similar means between control
and treatment groups were GDP Index, FAI, annual change in percentage of GDP per
capita, and annual percentage change in FAI. All of these baseline indicators improved
upon the common support matching method used. Closeness in means between control
and treatment groups improved for GDP Index by 98 percent, FAI by 98 percent, annual
percentage change GDP by 12 percent, and annual percentage change in FAI by 3 percent
(see Appendix VII). Having more precise baseline indicators is appropriate to any
observational study since it will increase the precision and decrease bias in the regression
estimators.
Parallel trends analysis was used to compare pre-program economic growth,
categorized by annual percentage change in GDP per capita. Appendix VIII demonstrates
parallel trends from 1999 to 2002, with the year 2003 showing a one to two percent
increase in control group growth beyond the treatment group. Finally, a staggered entry
model (Appendix VIII) shows the cities that took the treatment (experiencing a shock in
FAI, see Appendix I). The staggered entry model demonstrates the annual percentage
change in GDP for five years prior to and five years after treatment uptake.

10. 10
Treatment Estimation
Determining the treatment effect I use a treatment on the treated (TOT) model.
This is different than past studies, such as the 2011 UC San Diego EmPac paper by
Jeffrey Warner at the School of International Studies and Pacific Affairs. In this study,
titled “Evaluating China’s Western Development Strategy,” the China Western
Development Program was analyzed using an impact evaluation.10
In the Warner study,
the author utilized an intention to treat (ITT) model where any city that was offered the
program was designated as having taken the treatment.
ITT Model:
Ε 𝛿! = 𝑌!! 𝑇! = 1) − Ε 𝑌!! 𝑇! = 0)
However, this study is flawed since it ignores compliance; the ITT model would bias the
results of the treatment (given the treatment worked) downwards. Therefore, a TOT
model is used where the estimated treatment effect of those treated is subtracted from the
estimated effect of those that are not treated. A litmus test for compliance is seen as C.
TOT Model (includes compliance):
Ε 𝛿! = 𝑌!! 𝑇! = 1, 𝐶! = 1) − Ε 𝑌!! 𝑇! = 0, 𝐶! = 0)
This TOT model assumes that there is a valid counterfactual for those that could have
received treatment based on the propensity score matching. In sum, the hypothesized
model will be a DID estimator of baseline (program offered, t+1), minus endline
(program offered, t+8), calculations for each group, then comparing treatment and control
10
The China Western Development Strategy was a regional investment campaign that began in 2003, covering 6 provinces (Gansu,
Guizhou, Qinghai, Shaanxi, Sichuan, and Yunnan), 5 autonomous regions (Guangxi, Inner Mongolia, Ningxia, Tibet, and Xinjiang),
and 1 municipality (Chongqing)

11. 11
to find a difference-in-differences. This is matched on common support of probability of
treatment, providing a valid counterfactual for the control comparison group.
Baseline Evaluation
An ordinary least squares (OLS) regression is run on each control variable. Identification
came from an insignificant t-value to imply no correlated between covariates and
treatment uptake.
Correlation regression:
𝑦! = 𝛽! + 𝛽! 𝑋! + 𝜀!
The results in Appendix II find that significance is found in population, unemployment
rates and urban employment rates, while GDP Index and area of cultivated land, two very
important determinants for treatment uptake, are not significant at the 95% level. The
significance in population is not of concern because there will be a relationship between
GDP per capita and any variable that includes population dynamics. This will not bias the
estimators or influence when a city decided to take the treatment.
Results (see Appendix X):
The first step in this analysis is to use a simple OLS estimator by pooling the
years 1999 to 2012, using the previously mentioned controls and adding a dummy for
treatment. The average change for each city using robust standard errors is presented. No
statistical significance in this simple model in regards to treatment’s effect on GDP per
capita. Since all the hetereogeneity between the treatment and the control cities cannot be

12. 12
observed, the residual difference between the groups is not coming solely from the
treatment group.
OLS regression:
𝑦! = 𝛽! + 𝛿𝑇! + 𝑋′! 𝛽 + 𝜌 ∗ 𝑁𝐸! + 𝜃 ∗ 𝑊! + 𝜀!
A fixed effects regression is used next. Building upon the parallel trends analysis
after having matched on propensity scores with common support, fixed effects helps find
causal identification through analyzing individual beta trends for each city. Two models,
one with clustering at the provincial level and the other without clustering, both
demonstrate that there is no statistical significance in either model for the uptake of
treatment. The clustered fixed effects model attempts to control for unobservable
characteristics between cities. These could include regional qualities in regional
personality that makes individuals predisposed to efficiency, climate, geographical
advantages, proximity to open ports and other covariates that could not be included. By
knocking out the residual differences in alpha (see fixed effects estimator below), a more
precise model can be made. However, results in Model 3 of Appendix X show that only
population, GDP Index, area of cultivated land are significant at the 95% level. Model 2
using clustering at the provincial level yielded less robust results, as seen in significance
only in the GDP Index variable.
Fixed Effects regression:
𝑦!" = 𝛼! + 𝛿𝑇!" + 𝑋′!" 𝛽 + 𝜆! + 𝜌 ∗ 𝑁𝐸!" + 𝜃 ∗ 𝑊!" + 𝜀!"
The fourth model utilized unit-specific effects. By creating a separate y-intercept
for each city, as well as an independent slope, the unit-specific effects model takes into
account individual characteristics for each city at each time period. Using unit specific

13. 13
effects yielded no statistical impact from treatment on change in GDP per capita. The
truncated version of these results is seen in Appendix X under “Model 4.”
Unit-Specific Effects regression:
𝑦!"# = 𝛼! + 𝛿𝑇!" + 𝛽𝑋′!"# + 𝜆 ∗ 𝑦𝑒𝑎𝑟!"# + 𝜌 ∗ 𝑁𝐸!" + 𝜃 ∗ 𝑊!" + 𝜀!"
The final estimating equation used is a difference-in-differences estimator with
base year 2003, one year before the implementation of the program. Appendix XI gives a
visual representation to the baseline and endline results. Visually, it appears that there
was about a less than 1 percentage jump in the treatment’s effect on GDP per capita when
subtracting 2011 from 2003 results. Both groups appear to show considerable gains in
GDP growth. Using the DID calculation, I hoped to find a statistically significant post-
treatment increase, when compared to the post-control data, of the RCCP campaign
helping economic growth in Central China treatment cities by 0.23 percentage points on
average in GDP per capita. The DID equation as seen previously:
DID regression:
𝑦!"# = 𝛼 + 𝛾𝑇! + 𝜆𝑟! + 𝛿 𝑇! ∗ 𝑟! + 𝑋′!"# 𝛽 + 𝜌 ∗ 𝑁𝐸!"# + 𝜃 ∗ 𝑊!"# + 𝜀!"#
Control Pre: 𝛼
Control Post: 𝛼 + 𝜆
Treatment Pre: 𝛼 + 𝛾
Treatment Post: 𝛼 + 𝛾 + 𝜆 + 𝛿
DID estimator: 𝛼 + 𝛾 + 𝜆 + 𝛿 − 𝛼 + 𝛾 − ( 𝛼 + 𝜆 − 𝛼)
After running the DID estimator, and controlling for the pre-mentioned city-level
covariates, development campaigns and baseline and endline dummies for years 2003 and
2011, cities that were offered the program in Central China, on average and holding other
covariates constant, increased at a rate of 5.011 percent.. This finding is statistically

14. 14
significant at the 99 percent significance level. More importantly, however, is the delta
coefficient on treated cities in Central China provinces. When compared to baseline, the
treatment cities gained an average of 5.247 percent increase in GDP regardless of when
they chose to take the treatment. These results are marginally significant since the p-value
is between 0.10 and 0.05, thus yielding a marginal 90 percent significance level. This
model also had one of the highest adjusted R-squared, second only to a non-significant
model 2 using fixed effects with clustering at the province level. An effect size of on the
treatment group in year 2011 (endline) is, which means that the effect size calculated in
Based on statistical significance and adjusted R squared, the DID estimator is deemed
most reliable to determine causal inference of treatment on GDP growth.
When taking the DID estimator’s results into account, this study finds that the
cities in Central China, proxied by a FAI shock, yielded a marginal increase over control
cities that either were not offered the program or decided not to take it. This 0.23
percentage point increase is near the hypothesized prediction of the baseline/endline
calculation performed visually before running the DID estimation.regional economic
growth.
Threats to Validity
The first threat to this study is the inherent demand-side endogeneity of treatment
uptake. The DID estimating regression, common support matching, parallel trends
analysis all helped provide the clearest picture of RCCP success. However, this picture
remains noisy, as we don’t know what motivates cities to take treatment when offered the
investment program. Are these cities better off economically? Are local-level

15. 15
administrators who are responsible for investment decision-making corrupt to local
businesses that want to funnel profits for themselves? These are important questions that
threaten causal inference of the RCCP campaign having a direct impact from the central
government.
The second issue of endogeneity in the DID estimator is omitted variable bias
(OVB). GDP relies on a plethora of factors. Controlling for all factors is nearly
impossible. Unforeseen events, such as the Global Financial Crisis in 2007-2008 had an
impact on China.11
In response to this global economic panic, China initiated a 2010
investment campaign that might have influenced our estimators since it was targeted to
construction and infrastructure development. Therefore, cities that were targeted by this
policy, if they were in the control group and within common support, will bias our final
DID estimator downwards.
Third, selection bias is evident in the implementation of the program. Within the
program, as evidenced in the Shanghai Flash article, certain cities, such as Wuhan,
received more incentives for foreign direct investment due to its strategic location in the
center of Central China. Since Wuhan was treated, this will cause the final TOT to be
biased upwards.
Conclusion
On a broad level, this paper addresses the Chinese central government’s ability to
initiate and implement regional development to reduce inequality between cities.
Operationally, this study finds that the RCCP had little effect on GDP growth in Central
11
China
was
not
impacted
as
harshly
by
the
Global
Financial
Crisis
as
Western
countries.
Due
to
their
late
entry
into
the
US
housing
market
and
subprime
securities
issuance,
China
was
spared
from
great
financial
turmoil.

16. 16
China. While there are issues of endogeneity in uptake of the treatment, having been
offered the program, all means were taken to reduce bias and create the most precise
estimators for the designated sample. Using propensity score matching on common
support, a difference-in-differences estimator for years 2003(baseline) and 2011(endline),
results are not as robust as hoped for. This is because a 90% statistically significant TOT
was found after running the DID regression. Therefore, the overall effect, taking into
account the trend of cities that could have been offered the program but were not, is a
0.23 percent increase in annual GDP.
For future regional development campaigns to have a more robust impact, I
suggest the following for Chinese central government policy-makers:
1. Target city-specific development. Devolve economic decision-making from the
provincial level and create city-to-central government task forces. These will help
Beijing determine the individual needs and treatment effects for each city.
2. Move away from GDP as a pre-requisite condition for promotion under the cadre
responsibility system.12
More robust methods, such as industry-specific output
can be used to determine promotion, or a formula that takes into account growth
in various sectors.
3. Increase surveying and data collection at the city-level. To implement nationally
targeted policy by the State Council in Beijing, Premier Li Keqiang, along with
the support of the Standing Politburo and General Secretary Xi Jinping, more
robust data collection will need to be available. As it currently stands, the prime
12
The
cadre
responsibility
system
is
how
village,
municipal,
county
and
provincial
leaders
are
promoted.
The
three
hard
targets
are
GDP
growth,
social
stability
(less
than
3
demonstrations
per
year)
and
China’s
population
policy.

17. 17
limitation to reliable and recent household and local-level surveys and statistics
are not available.

18. 18
APPENDIX 1
APPENDIX II

19. 19
APPENDIX III
APPENDIX IV

20. 20
Variables
used
for
the
propensity
score
calculation:
FAI, industrial
output, GDP index, urban employment
figures, government expenditures,
bank loans, population density,
proportion of employees in primary,
secondary and tertiary industries,
population, natural growth rate, pop, non-agricultural population, new contracts signed,
foreign capital signed, investment in buildings, investment in real estate, number of
farmers and the two dummy variables for the western and northeastern investment
campaigns.
APPENDIX V
APPENDIX VI

21. 21
APPENDIX VII

22. 22
APPENDIX VIII
APPENDIX IV

23. 23
APPENDIX X

24. 24
APPENDIX XI

25. 25
Bibliography
All China Data Center. University of Michigan. Accessed through subscription with
UCSD Geisel Library from January to March, 2015.
Ash, Robert. “Quarterly Chronicle and Documentation.” School of Oriental and African
Studies, University of London. June 2008.
“China to Boost Rise of Central Region.” Xinhua News Agency, February 17, 2006.
http://www.china.org.cn/english/null/158317.htm (Accessed March 1, 2015)
Francois, Nicolas “.China and Foreign Investors: The End of a Beautiful Friendship?”
IFRI April 2008.
Huang, N., J. Ma, and K. Sullivan. "Economic Development Policies for Central and
Western China." China Business Review. 01 Nov. 2010. Web. 09 Oct. 2013.
http://www.chinabusinessreview.com/economic-development-policies-for-
central- and-western-china/ (Accessed February 24, 2015).
Jae Ho Chung, Hongyi Lai and Jang-Hwan Joo . “Assessing the "Revive the Northeast"
(zhenxing dongbei) Programme: Origins, Policies and Implementation.” No. 197
(Mar., 2009), pp. 108-125.
Lai, Hongyi. “Developing Central China: A New Regional Programme.” China: An
International Journal. Volume 5, No. 1, March 2007.
Lam, Willy Wo-Lap. Chinese Politics in Hu Jintao Era: New Leaders, New Challenges.
East Gate Book. 2006. ME Sharpe, New York.
Marti, A. Min, X. “The “Rise of Central China” Plan: Objectives and Impacts with
Special Focus on Anhui Province.” In: Shanghai Flash 2012(4). p. 1-5.

26. 26
Shuja, Sharif. “The Limits of Chinese Economic Reform,” China Brief: A journal of
analysis and information. Volume 5, Issue 17.
“The Rise of Central China: Is There Any Suspense in the Struggle for the Central
City in Central China.” People’s Daily Online. http://www.people.com.cn. 19 Nov.
2006. (Accessed March 14, 2015).
“Three Difficult Spots in Fulfilling the Six Provinces’ Dream of the Rise of Central
China.” Wuhan Chenbao (Wuhan Morning Post), 21 Apr. 2005.
“The State Development and Reform Commission Tentatively Formulates Five Policies
to Support the Rise of Central China.” Changsha wanbao (Changsha Evening
Post), 7 Mar. 2005.
“Relief for Depressed Central Region.” Beijing Review. 21 Apr. 2005. (Accessed on
March 14, 2015).
Warner, Jeffrey. “Regional Development Discrepancies and Public Policy:
Evaluating China’s Western Development Strategy.” Center on Emerging Pacific
Economies. University of California, San Diego: School of International Relations
and Pacific Studies. 2011.