An examination of the ccp’s strategies to alleviate discontent after the great recession of 2008

1

An Examination of the CCP’s Strategies to Alleviate
Discontent after the Great Recession of 2008.
Luigi Caloi, Sahaj Sood and Alon Mor
Dr. Christina Jenq
Political Economy of East Asia
December 12, 2016
Abstract
In 2007, per the World Bank, China’s heavily export-oriented economy posted a growth
rate of 14.195%.1
The onset of the Great Recession in the fall of 2008, following the collapse of
the Lehman Brothers investment bank, significantly reduced world demand for Chinese exports.
This contributed to a significant rise in unemployment in China which, in turn, led to an increase
in discontent among the population. The political ramifications of this could have been
catastrophic for the Communist Party (CP) as strong economic performance is one of the
regime’s main sources of legitimacy. However, the CP successfully used its Hukou-based system
of migration and instituted a RMB 4 trillion stimulus package to minimize the probability of
political instability. We define political instability as the probability of the CP’s collapse2
. While
literature exists that examines the ways in which the government used these two instruments to
alleviate the rising levels of discontent after the crisis, no other paper appears to provide an
empirical explanation behind the CP’s actions to endure the fallout from the recession. We
employed a theoretical framework (another point of differentiation from the existing literature)
and conducted a series of correlations and regressions to test many of the existing literature’s
findings. Our tests show that urban provinces in China are more unstable than their rural
counterparts and that the CP made rural provinces the primary beneficiaries of its generous
stimulus package. Our theoretical framework explains these findings by considering investments
in rural provinces as investments in political control and investments in urban provinces as
investments in economic growth. At first glance it may appear that since good economic
performance is an important source of legitimacy for the CP, the goals of political control and
economic performance are mutually reinforcing. However, during periods of severe external
shock such as the recession in 2008, our theoretical framework assumes this to certainly not be
the case, underlining the importance for the CP to find the optimal allocation of its investment
budget between economic performance/urban provinces and political control/rural provinces.
1. Introduction

1
http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2009&locations=CN&start=1978
2
Alesina, Alberto, Sule Ozler, Nouriel Roubini, and Phillip Swagel. “Political instability and economic growth.”
Journal of Economic Growth 1.2 (1996): 189-211.

2

China embarked on a period of market-oriented reform in 1978 under Deng Xiaoping, but
did so using a “gradualist” approach as opposed to the “Big-Bang” approach of rapid reform
adopted by many former countries of the Soviet Union.3
The gradualist approach aligned itself
with the CP’s interests as it allowed the regime to initiate selective economic reform while
creating new patronage resources4
—necessary to ensure the allegiance of newly created
autonomous interest groups to the regime.5
In 1993, the change in the structure of power caused
by the death of the eight elders saw a shift towards a more decisive approach towards reform.6
Economic performance has since become an important source of legitimacy for the CP.
However, it is important to remember that the goals of good economic performance and
maintenance of political control are neither mutually exclusive, nor completely mutually
reinforcing. This means that there may be times when the CP must prioritize the maximization of
political control over economic performance to remain in power. The most recent example of
such a time was during the Great Recession of 2008.
1.1 The Great Recession
China’s route to economic growth was heavily export-oriented, helped by the regime’s
artificial devaluation of the renminbi.7
At the time of the crisis, the export sector employed an
estimated 100 million people.8
These jobs were largely concentrated in China’s more affluent

3
Naughton, Barry. “Four: A Political Economy of China’s Economic Transformation. 1st
ed. N.p.: Cambridge, n.d.
91-135. Print.
4
ibid
5
ibid
6
ibid
7
Wallace, Jeremy L. Cities and Stability: Urbanization, Redistribution, and Regime Survival in China. New York,
NY: Oxford UP, 2014. Print.
8
Tong 2012

3

coastal areas, described by Wallace as “the economic vanguard of the country.”9
Between
September 2008 and January 2009, the number of Chinese exports fell from approximately $140
billion to $90 billion.10
The average number of goods shipped out of China fell from
approximately 4.5 billion in September 2008 to less than 1.5 billion in January 2009.11
Of interest to us was the difference between the severity of the impact of the recession on
the coastal regions and the rural interior. Since most of the export activity was centered in the
coastal regions, the negative impact of the recession would be greater felt in those regions. This
is clearly illustrated in the following maps, which show the relative fall in 2009 exports relative
to 2008 GDP by province (left) and industrial employment losses by province in 2009 (right).12
Furthermore, Tong found that China’s top five most export-oriented provinces for 2008
of Guangdong, Fujian, Shanghai, Zhejiang, and Jiangsu were among the following year’s worst
performers in industrial activities, writing that “while industrial employment declined by 0.1%

9
10
ibid
11
ibid
12
ibid

4

nationwide, that in Guangdong and the three Yangtze River delta provincial units declined
significantly more (by 3.8 percent in Guangdong, 6.5% in Shanghai, 3.3% in Zhejiang, and 7.1%
in Jiangsu).”13
The less-export oriented provinces of Anhui, Hubei, Inner Mongolia and Sichuan
showed comparatively better industrial growth in 2009.14
Given the importance of exports to the
Chinese economy, unemployment soared during the recession. About two-fifths of the newly
unemployed in the world economy following the crisis were Chinese.15
Kim Wang Chan
estimated the number of unemployed Chinese to be greater than 20 million16
while Huang et al.
place their estimate at around 50 million.17
The graph below shows the number of protests
recorded per province in 2008, illustrating the rising discontent within the population. As shown,
Guangdong, Fujian and Shanghai recorded among the highest levels of protests. These three,
together with Jiangsu and Zhejiang, were among China’s most export-oriented sectors of 2008
per Tong.18

13
Tong 2012, 103. N.b. Industrial Employment data.
14
ibid
15
International Food Policy Research Institute (IFPRI) 2009; Xinhua Net 2009; Chan 2010d, 660.
16
Chan 2010c.
17
Huang et al. 2010.
18

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1.2 The CP’s Response
The CP used the Hukou-based system of migration and instituted a RMB 4 trillion
stimulus package to alleviate the rising discontent.19
After the recession eradicated much of the
economic incentive for migrants to work in the coastal areas, the Hukou-system created
incentives for the so-called “temporary population” to return to their homes in the rural interior
where, even without the relatively higher wages in the coast, the newly unemployed would have
shelter and some land to cultivate for food-growing.20
Land, therefore, was the rural man’s social
security. Huang et al. estimated that between 20 million and 40 million people migrated back to
their Hukou-registered homes.21

19
20
ibid
21
Huang et al. 2010
0

50

100

150

200

250

Protests
(2008)

Protests
(2008)

6

Meanwhile, the CP’s stimulus package was designed to benefit export-oriented firms on
the coast in the short-term but boost the long-term employment prospects of the rural interior.22
One could perhaps be forgiven for assuming, given how the coastal cities were more adversely
affected by the recession, that they would be the main beneficiaries of the stimulus package. The
following maps show that this was not the case:23
The map on the right shows that the provinces of the rural interior were the main
recipients of the center-approved debt in 2008.24
The map on the left, shown previously,
illustrates the difference in severity of the recession between the coast and the rural interior. It
shows that export losses in 2009 were greater in the coastal regions than in the rural interior.25
The decision to direct the stimulus package primarily towards rural provinces reflects a
reality that will be developed further in the next section: the CP’s marginal benefit of investment
in maintaining political control was greater than the CP’s marginal benefit of investing in
economic performance.

22
23
ibid
24
ibid
25
ibid

7

2. Theoretical Framework
We assume that at any given time the CP’s main aim is to remain in power. The payoff to
the CP (probability of political stability) can be denoted by the following function,
πg = f (e, c)
where π, g, e and s stand for payoff to the government, government, economic performance, and
political control respectively. As mentioned earlier, we define political instability as the
probability of a CP collapse and we define political stability as the CP’s maintenance of political
control. It is important to remember the important distinction between political stability and
political control.
The CP’s objective is to maximize the probability of political stability by finding the
optimal allocation of its budget, the stimulus package, between economic performance and
political control. This optimization can be denoted by the following function,
MBe = MCe
MBc = MCc
where MBe and MCe refer to the CP’s marginal benefit and marginal cost of investment in
economic performance, respectively and MBc and MCc refer to the CP’s marginal benefit and
marginal cost of investment in political control, respectively. We assume that:
i. Prior to the recession, the CP was optimally allocating its investments between its goals
of economic performance and political control. We call this time t0 and is represented by
the above optimization function.

8

ii. The instability caused by the recession increased both the CP’s marginal benefit of
investment in political control and the CP’s marginal benefit of investment in economic
performance, but that the former rose by more than the latter. Simultaneously, the CP’s
marginal cost of investment in economic performance also increases to reflect the rising
opportunity cost of NOT investing in political control. We call this time t1, shown below:
MBe < MCe
MBc > MCc
iii. The budget constraint faced by the CP that prevents it from dedicating unlimited
resources to economic performance and political control is represented by the following
function:
Y = Ie + Ic
where:
Y = amount of the government’s budget that can be allocated to investment in economic
performance and political control.
Ie = investment in economic performance
Ic = investment in political control
iv. To maximize payoff/probability of political control during the recession, the CP must
reallocate its budget/stimulus package to match the changes in the CP’s marginal benefits
and marginal costs described earlier. This would mean increased investment into political
control relative to economic performance. As investment into political control increases,
the marginal benefit of doing so naturally decreases while the marginal cost of doing so

9

increases. Simultaneously, the marginal cost of investing in economic performance
decreases to reflect the falling opportunity cost of NOT investing in political control. At
some optimal allocation of the budget, the CP will again maximize its payoff/probability
of political stability. This occurs at t2:
MBe = MCe
MBs = MCs
Graphical Representations of t0, t1, and t2:
Change from t0 to t1

10

So far, we have restricted our discussion of the sources of discontent to only include the
unemployment caused by the recession. In the next section, we explore alternative sources of
discontent.
3. Related Literature/Alternative Sources of Discontent
From our research, we have gathered the following possible alternative sources of discontent,
including unemployment:
i. GDP per capita per province
ii. Percentage change in GDP per capita per province
iii. Social security as a percentage of GDP
iv. Unemployment
v. Unmarried men as a percentage of total population of men province
Anticipations of future income are direct factors of current happiness. This helps explain
China’s apparent social stability despite the “immense socioeconomic transition that previous
Change from t1 to t2

11

rapid growth had brought.” The same simulations used to come to this conclusion also imply that
a reversal in income expectations would lead to a quick sharp fall in levels of happiness, which
could cause social instability (Frijters). Historical evidence support these findings; “social protest
becomes more likely when people’s aspirations, influenced by recent achievements, exceed their
subsequent achievements.”26
Implication 1: Lack of economic growth can lead dissatisfaction and to political instability
Between 1990 and 2010, per capita consumption has increased fourfold in China. While
incomes have increased for all income groups, income inequality has increased, causing a
disparity in life satisfaction. Increasing levels of economic growth and urbanization have in fact
changed China from one of the most egalitarian countries to one of the least. China’s shift
towards economic liberalization and privatization has led to the removal social safety nets, in
turn, increasing levels of income inequality and insecurity.27
In general, life satisfaction has become more dependent on income and perceived income.
Several surveys have been conducted by Pew Research Center, the Chinese Academy of Social
Sciences, Gallup, and the World Values Survey (WVS), measuring subjective well-being (SWB)
of the Chinese population. Figure 1 displays a U-shaped pattern where life satisfaction in the
Chinese population declined from 1990 to around 2000-2005 and then turned upward.28
This is
consistent with Mancur Olson’s proposition that rapid economic growth in recently
underdeveloped countries is a destabilizing force. As rural people move to urban centers they
become aware of the high level of inequality and social injustice. Those in poverty become

26
Knight, John. “The Economic Causes and Consequences of Social Instability in China.” China Economic Review
25 (2013): 17-26. Web. 11 Dec. 2016.
27
Easterlin, Richard, Morgan, Robson, Switek, Holgozata, and Wang, Fei. “China’s Life Satisfaction, 1990-2010.”
PNAS. 109.25 (6 Apr. 2012): 9775-9780. Web. 11 Dec. 2016.
28
ibid

12

aware of not only their inequities but also of their prospects for change and begin to aspire
higher, staying in cities and continuing growth. The downside is an intermediary rise in
dissatisfaction.29
Implication 2: Rapid economic growth could lead to higher dissatisfaction
Rather than steadily increasing along with GDP, the U-shaped pattern mirrors an inverted
U-shape in the urban employment rate. Comparing these trends to the European transition
countries, the fact that life satisfaction did not increase as markedly as income and output,
indicates the fundamental importance of employment and the social safety net in determining
social well-being. According to Pew surveys, in 2002, when unemployment was near its high
point, nearly 48% of respondents indicated that their economic situation was “somewhat bad” or
“very bad.” In 2010 when unemployment dropped, life satisfaction rose from 5.27 in 2002 to
5.85 in 2010. This indicated that not only the unemployed experience decreased in life
satisfaction during troubling economic times.30
Implication 3: Unemployment can be a source of dissatisfaction and instability.
Amongst the dissatisfied, migrants show to be the most affected by economic
circumstances. As migrants increasingly move to urban areas, the life satisfaction of the urban
population as a hole tends to decrease. 31
Surveys show that long staying migrants have a higher

29
Olson, Mancur. “Rapid Growth as a Destabilizing Force.” The Journal of Economic History 23.04 (1963): 529-52.
Web. 11 Dec. 2016.
30
PNAS. 109.25 (6 Apr. 2012): 9775-9780. Web. 11 Dec. 2016.
31
PNAS. 109.25 (6 Apr. 2012): 9775-9780. Web. 11 Dec. 2016.

13

coefficient on the income variable than average.32
They are also more sensitive to average urban
income per capita in their destination provinces. The China Household Income Project (CHIP)
national survey of 2002 found that the income coefficient is highest in urban areas; the effect of
income on satisfaction is greatest in urban cities.33
When migrants move from rural to urban
areas, they become more dependent on income as a source of satisfaction.
Implication 4: GDP per capita and rapid economic growth impacts the decision making of a
migrant.
Implication 5: Employment opportunity can impact the decision making of a migrant.
According to two studies on the role of “perceived change in income over the previous
five years” current satisfaction is sensitive on perceived change in income. A past rise in income
increases satisfaction and a past fall in income decreases current satisfaction.34
Respondents in
the categories of “unhappy” and “not at all happy” stated income as their primary reason for
unhappiness. The next most important reason, reported by over 11% is “uncertainty about the
future.” This suggests that insecurity is a problem.35
Migrant households settled in the city having rural hukou apparently have the lowest
mean satisfaction among city dwellers. There are two reasons for this: (1) Rural people have
narrow reference groups, with 68% of rural dwellers reporting that they primarily compare
themselves to their “fellow villagers and neighbors.” (2) Migrants experience economic and

32
Knight, John, and Ramani Gunatilaka. "Aspirations, Adaptation and Subjective Well-Being of Rural–Urban
Migrants in China." Adaptation, Poverty and Development (2012): 91-110. Web. 11 Dec. 2016.
33
25 (2013): 17-26. Web. 11 Dec. 2016.
34
25 (2013): 17-26. Web. 11 Dec. 2016.
35

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social discrimination in the city.36
With economic well-being and perceived income being
determinants of social satisfaction, it is natural that upon moving to urban cities, rural migrants
experience decreases in satisfaction.
Implication 6: Lack of social security can cause dissatisfaction and instability
Yet even though migrants in urban areas are less satisfied on average than rural residents,
56% of migrants feel that urban living gives greater happiness. There is a notion, originating
from sociological literature and furthered in companion papers on subjective well-being, that
aspirations depend on income relative to reference groups. Migrants compare themselves to their
urban counterparts, drawing inference about their future income. Since 1988 the national Gini
coefficient of household income per capita rose from 0.38 to 0.49 in 2007 (then the highest in
Asia). The apparent inequality in cities and attachment to relative income help explain why
happiness scores fail to show a strong positive correlation between increase in happiness and
rapid growth of incomes in China.37
Insecurity is another factor of unhappiness. Before the reform of state-owned enterprises
in the mid-1990s, urban residents enjoyed lifetime employment, coined the “iron rice bowl.” The
collapse of social welfare systems lead to unemployment, coinciding with U-shape analysis in
figure 1. Figure 3 shows that since 1990 the disparity in life satisfaction between the upper third
income group and the lower third has increased. In 1990 the proportion of respondents reporting
a high level of life satisfaction was 68% vs 65% respectively. In 2007 this shifted to 71% vs 42%
(Easterlin). China’s economic restructuring undermined the health care system Health care has

36
25 (2013): 17-26. Web. 11 Dec. 2016.
37

15

become increasingly privately financed, though still mostly public. However health care costs
have risen, pricing out the lower tiers of the population and in some cases causing people to fall
into poverty.38
China’s market-oriented economic reforms has made the employment-based social
security system inadequate and inefficient. Previously state-owned enterprises (SOEs) provided
workers with health care, job security, and pension funds. But SOEs’ disappointing performance
after China’s liberalization has led to cuts in SOE support, fundamentally changing China’s
industrial structure. Specifically, the elderly are under-cared for. In 2000 71.4% of older people
relied on their children for support while only 16.9% received pension.39
In response, the
government has since focused on getting more people employed and revamping the pension
system. The goal is to fund the elderly with money from the payroll tax (9% of wages) and to
create a defined contribution system for current employees’ pension.40
Our data analysis finds
that the government has invested more heavily into regions with decreasing pension funds,
indicating that employment and economic growth may be a priority in ameliorating
dissatisfaction from decreasing pensions.
Another source of insecurity is the uneven sex ratio among young people. In 2008 the
ratio of males to females was 1.21, 1.15, and 1.14 among young people aged 5-9, 10-14, and 15-
19 respectively. The result is intense marriage competition not only among young men, but also

38
Organization for Economic Development and Cooperation (2010) China in the 2010s: Rebalancing Growth and
Strengthening Social Safety Nets (Organization for Economic Development and Cooperation, Beijing
39
Leung, Joe. “Social Security Reforms in China: Issues and Prospects.” International Journal of Social Welfare
12.2 (2003): 73-85. Web. 11 Dec. 2016.
40
Feldstein, Martin. “Social Security Pension Reform in China.” China Economic Review 10.2 (1999): 99-107.
Web. 11 Dec. 2016.

16

among their parents. Wei and Zhang (2011) find that households with a son “increase their
savings in a competitive manner to improve their son’s relative attractiveness in marriage.41
Furthermore, rural households with a son have higher conditional income, attributed to provide
for their son.42
Implication 7: Unmarried men as a percentage of total population of men can be a source of
dissatisfaction and instability.
In the next sections, we provide empirical support for the theory that investments in rural
provinces represent investments towards political control while investments in urban provinces
represent investments towards economic performance.
4. Data Description
Protest data is for obvious reasons a very sensitive data in China. For that reason, one can
hardly have access to it. In order to solve this issue, we collected protest data from Google
Database of Events, Language, and Tone (GDELT). GDELT has a computer algorithm that scans
the web for all print, web, and radio news articles mentioning a given event for any regions. We
used GDELT to gather protest data for China.
Official Chinese GDP figures have faced criticism for inaccuracy43
and so must be used
with caution. However, we trust that for our purposes—to compare the GDP among provinces—
the data should be valid. We collected GDP per capita per province data and unmarried men data

41
Wei, Shang-Jin, and Xiaobo Zhang. "The Competitive Saving Motive: Evidence from Rising Sex Ratios and
Savings Rates in China." Journal of Political Economy 119.3 (2011): 511-64. Web. 11 Dec. 2016.
42
Knight, John, Li Shi, and Deng Quheng. "Son Preference and Household Income in Rural China." Journal of
Development Studies 46.10 (2010): 1786-805. Web. 11 Dec. 2016.
43
Cary, Eve. "The Curious Case of China's GDP Figures." The Diplomat. The Diplomat, 2013. Web. 13 Dec. 2016.

17

from China Data Online.44
Percentage or urban population was taken from the annual data per
province from the National Bureau of Statistics.45
Data on the registered rate of unemployment
in urban areas was taken from the the Reporting Form System on Training and Employment
Statistics, which provided by the Ministry of Human Resources and Social Security. We used the
CEIC China Database to gain access to this data.46
Finally, data on pension fund per province,
which we used as a proxy to social security, was taken from the Department of Population and
Employment Statistics of the National Bureau of Statistics, again using the CEIC China
Database.47
5. Methodology
In this section, we outline our methodologies to test the following two hypotheses:
I. Hypothesis: In 2008, the more urbanized a province, the more unstable the province
The stimulus package was primarily intended to stabilize China’s political economy by
creating new employment opportunities in the rural interior to counter the brimming discontent.48
These measures were based on the premise that dispersing discontent among the larger area of
the rural interior represented a more manageable proposition for the CP than if the discontent
were to be clustered/concentrated within a few coastal provinces and cities.49
Our empirical
analysis to test the notion that clustered/concentrated urbanization shows a positive relationship
with the level of discontent (using number of protests as a proxy) is divided into two steps:

44
"China Yearly Macro-Economics Statistics(Provincial)--All China Data." China Yearly Macro-Economics
Statistics(Provincial)--All China Data. N.p., n.d. Web. 13 Dec. 2016.
45
"NBS Statistical Data." National Bureau of Statistics of China. N.p., n.d. Web. 13 Dec. 2016.
46
"体验最完整的一套超过128个国家的经济数据." Compare Economic Data for over 120 Countries CEIC. N.p.,
n.d. Web. 13 Dec. 2016.
47
ibid
48
49
ibid

18

Step 1: We performed a correlation between number of protests per province in 2008 and
urban population as a percentage of total population per province in 2008. We also
conducted correlations between the number of protests per province in 2008 and the other
following sources of discontent to uncover any possible alternative relationships:
i. GDP per capita per province (2008)
ii. GDP per capita growth per province (2008)
iii. Unmarried men as a percentage of total population of men per province (2008)
iv. Registered unemployment in urban areas per province (2008)
v. Pension fund as a percentage of GDP per province (proxy for social security fund)
(2008)
Step 2: We substantiated our correlations with the following OLS regression model:
Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 xi
We then added the other variables into the model to test the possible alternative relationships.
For that, we ran the following regressions:
1. Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 GDP growth
(2008)
2. Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 percentage of
unmarried men (2008)
3. Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 registered urban
unemployment rate (2008)

19

4. Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 social
security/people>65 (2008)
5. Protests in 2008 = β0 + β1 percentage of urban population (2008) + β2 GDP per capita
(2008)
II. Hypothesis: In 2008, the government directed its stimulus package primarily towards
the rural interior to mitigate discontent
After testing the first hypothesis, we attempt to show how the CP’s stimulus package was
strategically directed to further incentivize the reverse-migration of the so-called “temporary
population” back to their Hukou-registered jurisdictions.50
According to our theoretical
framework, after the recession, the CP’s marginal benefit of investment in political control
increased relative to that of economic performance. Since our findings support the argument that
more urbanized provinces are more unstable than their rural counterparts, it follows that any CP
investment towards rural regions represents an investment in increased political control.
Step 1: Our first step was to correlate percentage change in fixed asset investment (same proxy
used by Wallace for the stimulus package)51
from 2008 to 2009 with the following factors
influencing migration:
i. GDP per capita per province (2008)
ii. Percentage change in GDP per capita per province (2007 to 2008)
iii. Rural population as a percentage of total population per province (2008)

50
51

20

iv. Social security as a percentage of GDP per province (2008)
v. Registered urban unemployed per province (2008)
vi. Total unemployment rate per province (2008)
vii. Unmarried men as a percentage of total population of men per province (2008)
Step 2: We substantiated our correlation analyses with an OLS regression model. Our hypothesis
is that the two main factors that explain the direction of the stimulus package are (i) low
urbanization (rural areas) and (ii) low GDP per capita. We face the obvious challenge that GDP
per capita and urbanization rate are correlated. Thus, of the two variables, we picked one—GDP
per capita—to construct the following model:
Percentage change in fixed asset investment = β0 + β1 GDP per capita + β2 xi
We then ran the following regressions to test for alternative explanations and variables:
1. Percentage change in fixed asset investment (2008 to 2009) = β0 + β1 GDP per capita
(2008) + β2 social security/people>65 (2008)
(2008) + β2 registered urban unemployment rate (2008)
(2008) + β2 GDP per capita percentage change (2007 to 2008)
(2008) + β2 percentage of urban population (2008)
(2008) + β2 percentage of unmarried men (2008)

21

(2008) + β2 percentage on non-employed (2008)
6. Empirical Analysis
Testing Hypothesis I: We conducted a series of correlations and regressions between number of
protests per province in 2008 and an index of sources of instability in 2008 to test the plausibility
of the notion that urban areas are more unstable than rural areas.
A. Correlations
Finding 1: The figure below clearly indicates that the number of protests per province
recorded in 2008 was greater than the same recorded in 2006 and 2007 for all twenty-nine
provinces shown except for the following seven: Tianjin, Hebei, Liaoning, Jiangxi, Henan,
Yunnan, and Ningxia. Beijing and Tibet, outliers, were excluded from the graph altogether.
Beijing regularly sees a disproportionately larger number of protests per year than other
provinces, while we believe it is reasonable to assume that most protests in Tibet are in some
way related to the Central Tibetan Authority’s (CTA) protracted struggle for independence from
the People’s Republic of China (PRC).

22

The figure below illustrates the same phenomenon, including the disproportionate number of
protests recorded in Beijing and Tibet:
0

50

100

150

200

250

Tianjin

Hebei

Shanxi

Inner
Mongolia

Liaoning

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Guangdong

Guangxi

Hainan

Chongqing

Sichuan

Guizhou

Yunnan

Shaanxi

Gansu

Qinghai

Ningxia

Xinjiang

Number
of
Protests
per
Province
(2006,
2007,
2008)
excl.

Beijing
and
Tibet

2006
2007
2008

0

500

1000

1500

2000

2500

3000

Beijing

Tianjin

Hebei

Shanxi

Inner
Mongolia

Liaoning

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Guangdong

Guangxi

Hainan

Chongqing

Sichuan

Guizhou

Yunnan

Shaanxi

Gansu

Qinghai

Ningxia

Xinjiang

Tibet

Number
of
Protests
per
Province
(2006,
2007,
2008)
incl.
Beijing

and
Tibet

2006
2007
2008

23

Finding 2: The scatterplot below is a graphical representation of the statistically
significant positive correlation between number of protests per province in 2008 and the
percentage of urban population per province in 2008. This finding suggests that the coastal and
urban regions saw more protests in 2008 than did rural regions. Correlation: 0.585766819; p-
value: 0.001326126
0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0
50
100
150
200
250

%
of
urban
populaZon
(2008)

Protests
per
province
(2008)

Number
of
protests
per
province
(2008)
and
%
of
urban
populaZon
(2008)
excl.

Beijing,
Tibet,
Tianjin,
and
Gansu

24

Finding 3: We observe a statistically significant negative correlation between GDP
growth per province from 2007 to 2008 and the number of protests recorded per province in
2008, suggesting that rural provinces, which are more likely to have higher GDP growth rates,
are less conducive to protests than urban provinces, which are more likely to have lower GDP
growth rates. We exclude outliers Beijing and Tibet. Correlation: -0.54089; p-value:
0.00244867.
Finding 4: The correlation between GDP per capita per province in 2008 and number of
protests per province in 2008 is positive and statistically significant, implying that urban
provinces, whose residents are likelier to have higher incomes, tend to record a greater number
of protests than rural provinces, whose residents are likelier to have lower incomes. We exclude
outliers Beijing, Tibet, Tianjin, and Gansu. Correlation: 0.58037796; p-value: 0.00150527.
Finding 5: The correlation between the number of unmarried men as a percentage of the
number of total men per province in 2008 and the total number of protests per province in 2008
was found to be positive and statistically insignificant. The correlation implies that the higher the
number of unmarried per province, the greater the level of frustration among the men in society
regarding their inability to find a spouse, leading to greater protest. We exclude outliers Beijing
and Tibet. Correlation: 0.15779432; p-value: 0.41363673.
Finding 6: We found a small, positive correlation between the percentage of registered
urban unemployed residents per province in 2008 and the number of protests per province in
2008, suggesting that the higher the registered unemployment rate per province, the greater the

25

number of protests in the year 2008. We exclude outliers Beijing and Tibet. Correlation:
0.11765383; p-value: 0.1176
Finding 7: Social security per population size older than 65 was found to be positively
correlated to the number of protests per province for 2008. The statistically significant positive
correlation implies that the greater the proportion of people over the age of 65 benefiting from
social security, the greater the number of protests. While this correlation is counter-intuitive, it
could simply be reflecting the correlation between social security and GDP per capita per
province. We exclude outliers Beijing, Tibet, Gansu, and Fujian. Correlation: 0.509351926; p-
value: 0.00665499.
B. Regressions
The empirical evidence supports the premise that percentage of urban population is the
biggest factor for the growth of protests in 2008.
First, when we added registered urban unemployment rate, social security or GDP per
capita to the model (regressions 3, 4 and 5 respectively), all showed unreliable results. For the
three, the variables’ p-values were extremely high (e.g. in regression 5, 2008 urban percentage of
population p-value was 0.409, and 2008 GDP per capita p-value was 0.499). This could be
explained by the fact that these measures are correlated with each other.
Regression 1, on the other hand, was our most reliable result. We found that:
Percentage change in protest (2008 -2009) = 36.33742 + 1.9961 urban percentage of total
population – 3.8384 GDP growth.

26

In other words, the regression analysis shows us that if we held the urbanization rate of
two provinces constant, a 1% increase in GDP growth will result in a 3.84% decrease in the
number of protests. This result is consistent with the hypothesis that GDP growth is correlated to
stability and high rates of urbanization are linked to high numbers of protests. Moreover, this
regression analysis recorded the highest adjusted R2
among all our regression analyses (0.3947).
However, the p-value for GDP growth was 0.051, which fails our statistical significance test.
We face some limitations due to the natural fact that most of the alternative variables that
could explain a rise in protests—lower GDP growth, unemployment, social security—were
correlated with urbanization rates in China in 2008. Yet, our empirical analysis gives support to
the premise that higher rates of urbanization in a province increases the likelihood of protests in
that province.
Our results are summarized in the following table:

27

Red: P-value > 0.05 Green: Adjusted R2
> 0.3168 (the
Yellow: Lowest P-value, but > 0.05 Adjusted R2
when Urbanization rate is the
only variable)

28

Testing Hypothesis II: We conducted a series of correlations and regressions between
percentage change in fixed asset investment per province between 2008 to 2009 and an index of
sources of discontent in 2008 to test the plausibility of the notion that the CP strategically formed
an incentive scheme for the newly unemployed population in coastal cities to migrate back to
their Hukou-registered jurisdictions. We expect investment to be negatively correlated to
urbanization rates and positive correlated to GDP per capita.
A. Correlations
Finding 1: The percentage change in investment from 2008 to 2009 per province was negatively
correlated with number of protests in 2008 per province.
At first glance, one could doubt our hypothesis that the government prioritized political
control during the recession, because of the statistically significant negative relationship of the
stimulus package with the protest data. Correlation: -0.62; p-value = 0.000557565.
0

50

100

150

200

250

0
5
10
15
20
25
30
35
40
45

Protests

%
of
investment
change

%

change
of
investment

(2008
-‐
2009)
and
protests

(2008)

29

However, as we saw in part 1, protests are negatively correlated to rural areas. Since our
first hypothesis is that the stimulus package should go to rural regions to mitigate discontent, this
first correlation does not contradict our hypothesis.
Finding 2: Percentage change in investment from 2008 to 2009 per province was negatively
correlated with percentage of urban population in 2008 per province
When analyzing the investment change data, Sichuan stands as an outlier (the percentage
change on fixed asset investment for Sichuan was 59.54%). This already gave us an intuitive
idea that the investment had indeed gone to rural regions. We then excluded Sichuan and Tianjin
from our data set, and ran the correlation of percentage of urban population and the stimulus
package. The correlation confirms our initial hypothesis. Correlation: -0.564403209; p-value =
0.00142623.
0

5

10

15

20

25

30

35

40

45

0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00

Investment
%
Change

%
of
Urban
popula>on

%
Urban
populaZon
(2008)
v.
Investment
%
change
(2008-‐2009)

(excl.
Sichuan
and
Tianjin)

30

Finding 3: We found that the correlation between the GDP per capita and the percentage
investment change was -0.708771096.
Again, we excluded Sichuan and Tianjin because both were outliers. The correlation was
statically significant, with p value = 0.0000168 and it also had the highest R-squared of all the
correlations we ran (R2
= 0.502356467) and the lowest standard error (8.82413E-05). This result
supports our hypothesis that investment should be directed to poorest areas to create a
compelling incentive scheme for migration. Correlation: -0.708771096; p-value: 0.0000168;
R2
: 0.502356467; SE: 8.82413E-05
Finding 4: We found that the GDP per capita growth from the year 2007 to 2008 and the
percentage investment change was positively correlated and statistically significant.
This positive relationship was neither surprising, nor did it add a new element to our explanation.
Due to the negative correlation of GDP per capita and GDP growth in we expected that the
stimulus package would be directed towards areas that were growing (considering that they are
usually poorer/more rural). We test this explanation in our forthcoming regression analysis.
Correlation: 0.484540105; p-value: 0.005738978.
Finding 5: We found that the correlation between social security and the investment change was
0.477737384. The p-value was 0.007586965. We used pension fund divided by total population
above the age of 65 as our proxy for social security.
This relationship supports our previous finding that China lacks a robust social safety net, and
that safety net plays a role in decision making of an unemployed migrant of whether to stay in an

31

urban area or go back. In other words, this indicates that the government might have opted to
invest in regions that had a lower safety net, to maximize the incentive scheme for migration.
On the other hand, this could also be reflecting the positive correlation of GDP per capita and
social security. To further test these two possibilities, we run a regression analysis with both
variables—GDP per capita and social security. Correlation: 0.477737384; p-value:
0.007586965
Finding 6: We found a statically significant correlation between registered urban unemployment
rate and the percentage investment change from 2008 to 2009 of 0.44993708, and a p-value of
0.014325681. Most notably, the coefficient here was the highest among all of the findings, equal
to 7.632114755. The standard error here, however, was 2.915358632, which made us cautious
about the validity of this finding.
Considering the role that the existence of job opportunities play in the decision-making of
a recent unemployed migrant, it would not be surprising if the CP targeted provinces with high
urban unemployment. The possibility of an unemployed migrant finding a job in his/her Hukou-
based jurisdiction directly increases the probability that he/she will migrate back. Correlation:
0.44993708; p-value: 0.014325681
Finding 7: We found that the correlation between the percentage of unmarried men to total
population of men per province in 2008 and the percentage investment change from 2008 to
2009 was -0.108551701. However, the p-value for this correlation was statistically insignificant.
Correlation: -0.108551701; p-value: 0.561054883

32

Finding 8: Finally, we found that the correlation between the percentage investment change
from 2008 to 2009 per province and the percentage of non-employed per province was 0.03. Yet,
the p-value here was remarkably high: 0.876615829.
We calculated the percentage of non-employed of a province with the following formula:
((Population > 15 years old and < 64 years old) – total employed)
______________________________________________________________________________________________________________________________
(Population > 15 years old and < 64 years old)
This formula takes into account those that are currently not working but also not seeking
a job. In other words, one explanation for the correlation’s unreliability is because the formula
accounts for the non-employed, for instance, women who opt not to work and stay home.
Correlation: 0.03; p-value: 0.876615829.
Our findings are summarized in the following table:

33

B. Regressions
Regression Analysis:
Finding 9: In all six regressions, on the one hand, all variables became statistically insignificant
when paired with GDP per capita. On the other hand, GDP per capita remained statistically
significant in all six. This increases the robustness of the conclusion we reached from our
correlation analyses: GDP per capita was the main variable driving the decision making of the
CP with regards to the direction of the stimulus package.
Moreover, in the regression that included GDP per capita as the sole variable, its
coefficient was -0.000461. When we included the other six variables, its coefficient varied
between -0.000844 and -0.000407, but remained mostly close to its initial value. This low
variation increased our confidence in the robustness of the result.
Next, percentage of urban population was the second most important variable. From all
six regressions we ran, percentage of urban population was the only variable that increased the
adjusted R2
of the regression (from 0.484 to 0.527) and it had the smallest p-value when paired
with GDP/capita. However, its p-value was still above 0.05 (p-value = 0.0748) so we concluded
that it was, nevertheless, statistically insignificant.
Although we reached a single-variable regression formula, this result shouldn’t be
downplayed. Existing literature discussing the CP’s stimulus package also show the CP’s
prioritization of investing in rural areas over urban areas. Our results agree with the existing
literature, but also empirically indicate that GDP per capita was the main variable driving the
CP’s decision making regarding the direction of the stimulus. This result reflects the CP’s
strategy to not only invest in the rural regions, as already discussed in the existing literature, but

34

to ensure investment in the poorest of the rural regions, in order to maximize the efficiency of its
incentive scheme for migration.
Yet, we must be cautious when interpreting these results. Instead of indicating the
relevance of GDP per capita to explain the direction of the stimulus package, the regressions
could be misleading because some of the variables used are correlated, leading to possible
alternative explanations for causality. For instance, GDP per capita is usually higher in provinces
that have higher urbanization rates. See below for a summary of our analyses:

35

Red: P-value > 0.05 Green: Adjusted R2
> 0.4839
Yellow: Lowest P-value, but > 0.05 Adjusted R2
when GDP per capita is the
only variable)
7. Conclusion
To summarize, the purpose of our paper is to test and support two hypotheses:

36

I. In 2008, the more urbanized a province, the more unstable the province
II. Therefore, in 2008, the government directed its stimulus package primarily towards the
rural interior to alleviate discontent.
Our theoretical framework shows that this was rational for the CP because of its aim of
minimizing political instability, which is defined as the probability of the CP’s collapse. In our
payoff optimization function, we define the payoff as the probability that the CP remains in
power (political stability) and it can use economic performance and the maintenance of political
control as its instruments to achieve this. In economic terms, during the recession, the CP’s
marginal benefit of investment towards political control increases relative to the CP’s marginal
benefit of investment towards economic performance because of the rising discontent among the
population. Simultaneously, it follows that the CP’s marginal cost of investment towards
economic performance rises relative to the CP’s marginal cost of investment towards political
control to reflect the rising opportunity cost of not investing in political control. To achieve the
optimal allocation of resources that maximizes government payoff/minimizes political
instability, the CP must dedicate a larger portion of its resources towards political control than
economic performance until such time that the following conditions are satisfied:
MBe = MCe
MBc = MCc
where MBe and MCe refer to the CP’s marginal benefit and marginal cost of investment in
economic performance, respectively and MBc and MCc refer to the CP’s marginal benefit and

37

marginal cost of investment in political control, respectively. We develop this framework to
eventually conclude that during the recession, the CP viewed investments in rural areas as
contributing more towards political control and investments in urban areas as contributing more
towards economic performance. We reach this conclusion empirically, by running a series of
correlation and regressions that support it. We also find that, not only did the CP choose to invest
more in rural areas during the recession, they chose to invest in the poorer of the rural regions in
order to maximize the efficiency of their incentive scheme to further encourage the reverse-
migration of the “temporary population” back to their Hukou-based jurisdictions.
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Cambridge, n.d. 91-135. Print.
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978

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Alesina, Alberto, Sule Ozler, Nouriel Roubini, and Phillip Swagel. “Political instability and
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Wallace, Jeremy L. Cities and Stability: Urbanization, Redistribution, and Regime Survival in
China. New York, NY: Oxford UP, 2014. Print.
International Food Policy Research Institute (IFPRI) 2009; Xinhua Net 2009; Chan 2010d, 660
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Knight, John. “The Economic Causes and Consequences of Social Instability in China.” China
Economic Review 25 (2013): 17-26. Web. 11 Dec. 2016.
Easterlin, Richard, Morgan, Robson, Switek, Holgozata, and Wang, Fei. “China’s Life
Satisfaction, 1990-2010.” PNAS. 109.25 (6 Apr. 2012): 9775-9780. Web. 11 Dec. 2016.
Olson, Mancur. “Rapid Growth as a Destabilizing Force.” The Journal of Economic History
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Knight, John, and Ramani Gunatilaka. "Aspirations, Adaptation and Subjective Well-Being of
Rural–Urban Migrants in China." Adaptation, Poverty and Development (2012): 91-110. Web.
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Organization for Economic Development and Cooperation (2010) China in the 2010s:
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Feldstein, Martin. “Social Security Pension Reform in China.” China Economic Review 10.2
(1999): 99-107. Web. 11 Dec. 2016.
Wei, Shang-Jin, and Xiaobo Zhang. "The Competitive Saving Motive: Evidence from Rising Sex
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Knight, John, Li Shi, and Deng Quheng. "Son Preference and Household Income in Rural
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Cary, Eve. "The Curious Case of China's GDP Figures." The Diplomat. The Diplomat, 2013.
Web. 13 Dec. 2016.
"China Yearly Macro-Economics Statistics(Provincial)--All China Data." China Yearly Macro-
Economics Statistics(Provincial)--All China Data. N.p., n.d. Web. 13 Dec. 2016.
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