Financial Development and Economic Growth:

Evidence from China*

Jin Zhang,a Lanfang Wangb and Susheng Wangc

January, 2012

Abstract: Using data from 286 Chinese cities over the period 2001–2006, this paper investi-

gates the relationship between financial development and economic growth at the city level in

China. Our results from both traditional cross-sectional regressions and first-differenced and

system GMM estimators for dynamic panel data suggest that most traditional indicators of

financial development are positively associated with economic growth. This result runs con-

trary to the existing conclusion that a state-ruled banking sector, such as that in China, hin-

ders economic growth because of the distorting nature of the government. Since we focus on

the years after China’s accession to the World Trade Organization (WTO) in 2001 while the

existing studies mainly covered the years before 2001, our finding suggests that the financial

reforms that have taken place after China’s accession to the WTO are in the right direction.

To examine the sensitivity of our results, different conditioning information sets are experi-

mented with. Our results are shown to be robust.

Keywords: Financial development, Economic growth, Emerging market

Classifications: N2, O1, O43

* We gratefully acknowledge helpful comments and suggestions from anonymous referees and financial sup-

port from the Program of Humanities and Social Science of Chinese MOE (No. 11YJC790271), the Fundamental

Research Funds for the Central Universities (No. 11YJC790271) and the National Natural Science Foundation of

China (No. 71102134).

a, c Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Email addresses: zhangjin@ust.hk and s.wang@ust.hk.

b Institute of Accounting and Finance, Shanghai University of Finance and Economics. Email address:

wang.lanfang@mail.shufe.edu.cn.

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1. Introduction

This paper investigates the relationship between financial intermediation and economic

growth in China. China has been experiencing fast economic growth and rapid expansion of

financial intermediation in the last thirty years. Since the start of its reforms in 1978, the Chi-

nese economy has maintained an annual growth rate of 9.8% in real terms (China Statistical

Yearbook 2007), while the total loans outstanding in its financial institutions relative to GDP

has increased from 51% to 107% (China Compendium of Statistics, 1949–2004; China Statis-

tical Yearbook 2007). As the largest emerging market and with many years of uninterrupted

fast growth, China presents us with an interesting case for study. One fundamental question is:

what is the relationship between financial development and economic growth in China? A

unique feature of this paper is that our empirical investigation is based on a rich set of city-

level data, in contrast to existing studies that are based on national or provincial datasets.

In the finance-growth literature, China attracts great interest as a unique case. According

to Allen et al. (2005), China is an important counterexample to the common finding in the

finance-growth literature, since China has enjoyed fast economic growth for more than 30

years while its financial sector is very much under state control and is still quite under-

developed today. The literature on the relationship between finance and growth in China gen-

erally finds a negative relationship. Using provincial data over the period 1990–1999,

Boyreau-Debray (2003) found that financial inter-mediation has a negative impact on local

economic growth. She attributed the negative influence to the banking sector’s support of

loss-making state-owned enterprises. Hasan, Wachtel and Zhou (2009) also found that the

financial sector has a negative influence on economic growth using provincial data over the

period 1986–2002. In contrast, using provincial data for the period 1985–1999, Chen (2006)

showed that China’ financial development contributes to economic growth. He further identi-

fied two channels for the financial sector to contribute to the economy: mobilization of savings

and the substitution of loans for budget appropriation. In addition, using provincial data for

the period 1995–2003, Cheng and Degryse (2007), who studied the impact of the develop-

ment of banks and non-bank financial institutions on local economic growth, found that bank-

ing development has a significant positive effect on economic growth. Guariglia and Poncet

(2008) used data from 1989 to 2003 and two different sets of indicators of financial develop-

ment to examine the relationship between finance and growth in China. They found that their

China-specific indicators measuring state intervention in finance are negatively associated

with economic growth, while the indicators measuring market-driven financing are positively

associated with economic growth. Finally, Ayyagari, Demirgüç-Kunt and Maksimovic (2008)

used firm-level data to examine the relationship between firm growth and firm financing pat-

terns, i.e., formal versus informal finance. They concluded that it is the formal financial sys-

tem that spurs firm growth, while fundings from informal channels do not. In addition, Park

and Sehrt (2001) found that policy lending by state banks did not fall during the period 1991–

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1997, and consequently the financial reforms in the mid-1990s were not able to turn the trend

of worsening bank performance around.

We have a unique dataset from 286 Chinese cities over the period 2001–2006. Our data-

set has two features: (1) Unlike the existing empirical studies on China’s finance and growth

that employ provincial data, we choose to use city-level data which have more local observa-

tions and information; (2) We focus on the period after China’s accession to the World Trade

Organization (WTO) in 2001 so as to investigate the effect of recent financial reforms. Results

from both traditional cross-sectional regressions and first-differenced generalized method of

moments (GMM) and system GMM estimators for dynamic panel data suggest that most tra-

ditional indicators of financial development are generally positively associated with economic

growth. This result runs contrary to the existing conclusion that a state-ruled banking sector,

such as that in China, hinders economic growth because of the distorting nature of the gov-

ernment. Since we focus on the years after China’s accession to the World Trade Organization

(WTO) in 2001 while the existing studies mainly covered the years before 2001, our finding

suggests that the financial reforms that have taken place after China’s accession to the WTO

are in the right direction. To examine the sensitivity of our results, we experiment with differ-

ent conditioning information sets. In addition, we conduct a sensitivity analysis by introduc-

ing a dummy variable to indicate coastal cities, capital cities and the cities that have hosted

foreign bank entries. Our results are shown to be robust.

The rest of this paper is organized as follows. Section 2 presents a literature review. Sec-

tion 3 briefly describes the development of the Chinese financial sector and provides some

background information about financial intermediation in China. Section 4 describes the da-

taset, defines the variables, and presents summary statistics. Section 5 presents the results

using purely cross-sectional data, while Section 6 discusses and presents the first-differenced

and system dynamic panel results. Section 7 concludes the paper with a summary.

2. Literature Review

Financial intermediaries serve as the medium of the savings-investment process. One

fundamental question is: will development of financial intermediaries exert a positive effect

on economic growth? For a long period of time, economists have had very different views on

this. For example, Robert Lucas (1988) believed that “the importance of financial matters is

very badly over-stressed in popular and even much professional discussion,” while Merton

Miller (1998) countered with “that financial markets contribute to economic growth is a prop-

osition almost too obvious for serious discussion.” Amid such disagreements, the literature on

finance and growth continues to expand with new theoretical models and advanced empirical

methods. Recently, a large body of research, especially empirical work, suggests that devel-

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opment of financial intermediaries exerts a positive effect on economic growth, rather than

following economic growth passively.

A variety of theoretical models have been proposed to analyze the connection between fi-

nancial development and economic growth. Levine (2005) presented a survey of theories on

the issue and listed five possible channels through which finance may influence growth. These

channels include: (i) providing information about possible investments so as to allocate capi-

tal efficiently; (ii) monitoring firms and exerting corporate governance; (iii) ameliorating risk;

(iv) mobilizing and pooling savings; and (v) easing the exchange of goods and services.

There is also a vast empirical literature on the issue. Early cross-country studies based on

cross-sectional regressions documented a positive correlation between financial development

and economic activity (Goldsmith, 1969; King and Levine, 1993; Levine and Zervos, 1998; La

Porta et al., 2002). Goldsmith (1969) did a ground-breaking empirical study using data from

35 countries. Although a positive link between finance and economic growth was found, the

question on whether there is a causal relationship between financial development and growth

was not addressed. Besides, his work did not systematically control for other relevant explana-

tory variables. King and Levine (1993) added more control variables to their regression model

and employed a dataset containing more countries. They ran regressions on a cross-country

sample of 77 countries over the period 1960–1989, after controlling for other factors affecting

economic growth, such as trade, education and political stability. However, the causality issue

was again not formally dealt with. Levine and Zervos (1998) further added measures of stock

markets to their regression model and systematically controlled for other factors affecting

long-run growth including banking development. They showed that stock market liquidity and

banking development can predict economic growth. However, none of these cross-country

studies gave a satisfactory answer to the causality question.

To answer the question of whether financial development is a leading indicator or a fun-

damental factor of economic growth, instrumental variables were employed in several cross-

country studies. Levine (1998, 1999) and Levine, Loayza and Beck (2000) identified a coun-

try’s legal origin as a valid instrumental variable and found that financial development has a

significant positive impact on economic growth. Levine, Loayza and Beck (2000) further ap-

plied a more advanced econometric technique, the generalized moments method (GMM) for

dynamic panel data, on a panel of 71 countries over the period 1960–1995. This advanced

technique yielded the same result as the traditional cross-sectional instrumental variable re-

gressions. That is, the exogenous component of financial development is positively associated

with economic growth. Beck, Levine and Loayza (2000) also used GMM estimators for dy-

namic panel data and found that financial development has a large and positive effect on total

factor productivity growth. Benhabib and Spiegel (2000) found that the indicators of financial

development that are correlated with total factor productivity growth are different from those

that stimulate investment using GMM. Dynamic panel models permit the use of instrumental

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variables for all the explanatory variables so that more precise estimates can be obtained.

Thus, quite a few studies have examined the relationship between finance and growth using

dynamic panel models in recent years. For example, Rousseau and Wachtel (2002) examined

whether the relationship between finance and growth varies with inflation. Rioja and Valev

(2004a) examined the effects of financial development on the sources of growth in different

groups of countries with panel data of 74 countries. Rioja and Valev (2004b) further found

that the impact of financial development on growth may be nonlinear. Rousseau and Wachtel

(2000) and Beck and Levine (2004) applied dynamic panel techniques to their regression

analyses after adding measures of stock markets to their models. Their results suggested that

some exogenous components of bank and stock market development can have a large impact

on economic growth. Finally, many time-series studies on the relationship between finance

and growth have also documented financial development’s positive impact on economic

growth (Jung, 1986; Demetriades and Hussein, 1996; Neusser and Kugler, 1998; Arestis et al.,

2001; Xu, 2000; Christopoulos and Tsionas, 2004; Bekaert et al., 2005).

A few recent papers studied the relationship between finance and growth in individual

countries. Compared with cross-country studies, in studies of individual countries, research-

ers can design specific measures of financial development according to the particular charac-

teristics of the country. These studies can also avoid dealing with country-specific factors in

regression analysis. In a study of the United States, Jayaratne and Strahan (1996) found that

the branch deregulation boosted bank-lending quality and accelerated economic growth. They

also found evidence that financial development stimulated economic growth. By examining

individual states of the United States from 1900 to 1940, Dehejia and Lleras-Muney (2003)

also confirmed that a well-functioning banking system boosts economic growth through im-

proving capital allocation. Beck, Levine and Levkov (2010) assessed the impact of bank de-

regulation on the distribution of income in the United States from 1970s through 1990s. They

found that deregulation reduces income inequality in the U.S. Rousseau and Sylla (2005) set

up a set of multivariate time series models that relate banking and equity market activity to

investment, imports and business incorporations of the United States from 1790 to 1850. They

found strong support for the hypothesis of “finance-led growth” in the U.S. Rousseau (1999)

studied Japan over the period 1880–1913. Through a set of vector autoregressive models,

their results offered evidence that financial factors played a leading role in promoting Japan’s

rise to world power during the Meiji period. Guiso et al. (2004) studied the effect of local fi-

nancial development in Italy. Their results indicated that financial development promotes

firm growth and enhances the probability that an individual starts her own business.

To improve the understanding of the relationship between financial development and

economic growth, more detailed micro-level data have been employed in recent years, both at

the industry and firm levels. Rajan and Zingales’ (1998) influential paper tested the hypothe-

sis that, at the industry level, the sectors that are more dependent on external financing will

Page 6 of 36

have higher growth in the countries with more-developed financial markets. Their results

supported the hypothesis and confirmed that financial development has a positive influence

on industrial growth. Wurgler (2000) employed industry-level data to examine the impact of

financial development on economic growth through the channel of capital allocation. Cetorelli

and Gambera (2001) investigated the impact of bank concentration on industrial growth using

a sample of 36 industries in 41 countries. They found that bank concentration promotes the

growth of industries that are more dependent on external finance. Kumar et al. (1999) showed

that the average firm size in industries that are heavy users of external finance is larger in

countries with better financial markets. Therefore, they concluded that financial development

has a positive effect on external finance and hence on firm size. Claessens and Laeven (2003)

further examined the joint impact of financial development and property rights on growth in

different industries. They provided evidence that financial development improves financial

access and better property rights foster growth through better asset allocation. Beck at al.

(2005) used firm-level survey data covering 54 countries to evaluate the impact of financing

obstacles on firm growth and found that the negative impact of financial obstacles on growth

is more substantial for small firms. Beck et al. (2008) showed that industries with a larger

share of small firms grow faster in economies with well-developed financial systems.

3. China’s Financial System: A Historical Review

China has been experiencing very rapid and stable economic growth since the start of its

reforms in 1978. Its economy is now the second largest just behind the U.S. and it may be-

come the largest economy in the world in 10 years based on the Purchasing Power Parity (PPP)

(Allen, Qian and Qian, 2005). Although China is playing an increasingly significant role in the

world economy and its financial system has been subject to substantial structural reforms, its

financial system is still quite underdeveloped, lagging far behind other parts of the economy

in terms of transformation from central planning to market-based operations. To understand

this situation, we need to go back to the history of China’s financial system.

In this section, we review the institutional history, regulation evolution, and economic

environment of the Chinese financial system. We divide the history into three periods: before

1994, from 1994 to China’s WTO entry in 2001, and after the WTO entry.

3.1. Before 1994

Before the start of its transition in 1978, there was no market-based financial system in

China. One single bank, the People’s Bank of China (PBOC), functioned as both the central

bank and the only commercial bank for all banking transactions in China. This highly central-

ized financial system was transformed into a two-tier system when the four state-owned

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commercial banks (the Big Four) were formally established. Since 1984, the PBOC began to

function as the central bank and the Big Four state-owned commercial banks (SOCBs) took

over commercial banking business from the PBOC. During this period, these four banks were

known as specialized banks as shown in Table 1. They generally extended loans to state-owned

enterprises regardless of profitability. Because of this policy lending, there was very limited

competition among the Big Four until the mid-1990s.

Table 1. The Big Four and Their Designated Sectors Before 1994

Name of SOCB Designated sectors to serve Bank of China (BOC) Foreign exchange, foreign trade and the national economy China Construction Bank (CCB) Construction sector Agricultural Bank of China (ABC) Rural banking business Industrial and Commercial Bank of China (ICBC) Commercial and industrial activities in urban areas

Source: PBOC.

However, following economic reforms in the 1980s, the establishments of new banks and

other financial institutions became a source of competition in the financial sector. Bank of

Communications (BOCOM) was the first state-owned joint-equity bank, which was estab-

lished in 1986. Further, foreign banks were gradually allowed to become an integral part of

China’s banking sector. In the meanwhile, some non-banking financial institutions started to

enter China’s financial system in the mid-1980s, including trust and investment companies,

financial companies, financial leasing companies, and urban and rural credit cooperatives.

Also, starting from the mid-1980s, the old way of centrally planning the allocation of financial

resources was gradually phased out. To some degree, local governments began to have the

rights to decide on their own resource allocation via loans or self-raised funds. Cull and Xu

(2000 and 2003) showed that banks were more efficient in allocating resources than state

budgetary appropriation over the period 1980–1994 in China. However, during this period,

China’s banking sector was overwhelmingly dominated by the Big Four. Their total assets ac-

counted for 64% of the total assets of China’s entire financial system (Almanac of China’s Fi-

nance and Banking, 1995). But, the Big Four were renowned for their low efficiency and had

been burdened with a large amount of non-performing loans (NPLs).

3.2. From 1994 to China’s WTO Entry in 2001

Sweeping reforms on China’s financial system were initiated in 1994. A series of financial

reforms from 1994 to 2000 entailed a progressive move toward less administrative and more

independent banking operations. First, in order to relieve the Big Four from policy lending,

three policy banks were established in 1994. Second, China’s monetary policy was shifted to-

wards indirect monetary control. Starting in 1998, credit planning for SOCBs was abandoned.

Accompanied with the enhanced independence of the PBOC, asset-liability management and

indirect monetary instruments began to take over the role of credit planning. Third, in 1995,

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the Commercial Bank Law of China was passed and enacted, which provides details of the

requirements for operations of commercial banks. Lastly, the restructuring of the SOCBs was

initiated in late 1990s when four Asset Management Companies (AMCs) were established to-

gether with an injection of 270 billion yuan by the government into the Big Four. In 1999, the

state-owned AMCs bought 1.4 trillion yuan of NPLs from the Big Four, which amounted to

roughly 20% of their total loans (Almanac of China’s Finance and Banking, 2000).

Meanwhile, more and more new banks appeared in the mid-1990s. China Minsheng Bank

Corporation (CMBC)’s establishment in 1996 made it the first privately-owned national bank

in China. By the end of 1999, there were 11 national joint-equity commercial banks, with total

assets of 1,447.7 billion yuan (PBOC 2000). Together with two recently established banks,

there are 13 national joint-equity commercial banks in China as of the end of 2005. Their

names, headquarter locations and year of establishment are listed in Table 2. Established

mostly in the late 1980s and early 1990s, these joint-equity commercial banks have gradually

increased their collective market share while the Big Four’s market share has gradually de-

creased.

Table 2. Joint-Equity Commercial Banks

Name of national joint-equity commercial banks Headquarter location Year of establishment Bank of Communications Shanghai 1986 China Merchants Bank Shenzhen 1987 Hengfeng Bank Yantai 1987 Shenzhen Development bank Shenzhen 1987 CITIC Industrial Bank Beijing 1987 Guangdong Development Bank Guangzhou 1988 Industrial Bank (Formerly Fujian Industrial Bank) Fuzhou 1988 Huaxia Bank Beijing 1992 China Everbright Bank Beijing 1992 Shanghai Pudong Development Bank Shanghai 1993 China Minsheng Banking Corporation Beijing 1996 Huishang Bank Hefei 2005 Bohai Bank Tianjin 2005

Source: PBOC’s and the respective banks’ websites.

In the mid-1990s, a few city commercial banks were established by consolidating local ru-

ral and urban cooperatives. They take the form of joint-equity banks with their business re-

stricted to their location cities. By the end of 1999, 90 such banks were operating in China,

with total assets of 554.7 billion yuan (PBOC 2000). China’s city commercial banks are re-

ferred to as “the third tier of China’s banking industry”. In a field survey of Chinese banks,

Ferri (2008) found that the “New Tigers” (including state-owned joint-equity banks and city

commercial banks) are better performing than the SOCBs. The unhealthy link between state-

owned entities (SOEs) and SOCBs still negatively affects the performance of SOCBs today.

Furthermore, the restriction on foreign bank entries was relaxed in 1994. During this pe-

riod, the PBOC began to make recommendations to improve bank risk controls and to follow

the Basel requirements. Lastly, two stock exchanges in China—the Shanghai Stock Exchange

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and the Shenzhen Stock Exchange were established in 1990 and 1991, respectively. China’s

stock market began to take shape although it was renowned for its lack of transparency and

fairness throughout the 1990s.

3.3. After the WTO Entry

China formally entered the WTO on December 11, 2001. The years thereafter are charac-

terized by an impressive financial liberalization process, including more interest rate liberali-

zation, less restrictions on ownership takeovers, and greater freedom to foreign banks, etc.

Interest rate liberalization is an important element of China’s effort in financial liberaliza-

tion and marketization of resource allocation. Before 1999, interest rates in money markets

and bond markets were first liberalized. After the WTO entry in 2001, China began to take

quick steps to relax restrictions on interest rates of loans and deposits.

Another supervisory institution, the China Banking Regulatory Commission (CBRC), was

established in 2003. With its establishment, there have been several improvements in asset

quality, capital adequacy, risk control and general supervision. Banks in difficulties or in cre-

dit crisis can be taken over or restructured by the CBRC. The CBRC issued the document

“Chinese Banking Sector’s Reform, Opening, and New Progress of Regulations” on December

5, 2005, which pointed out that one of the major disadvantages or problems associated with

state ownership in the banking sector is that the lack of incentives in monitoring state-owned

banks creates “black holes” in corporate governance. To alleviate this problem, the CBRC dis-

closes individual bank data regularly, including their NPLs, and makes peer comparisons.

Foreign investment in domestic banks first appeared in 1996, when Asian Development

Bank (ADB) bought a 1.9% share in China Everbright Bank. This practice has been intensified

since 2003, when the CBRC announced guidelines to encourage and facilitate foreign share

holdings. This reflects the general perception that Chinese banks need improvements in cor-

porate governance, operation technologies and risk management through foreign strategic

investors. While Chinese banks have started to allow foreign ownership with minority owner-

ship, they are also taking initiatives to offer their shares to both domestic and foreign market

participants. This can be seen in the initial public offerings (IPOs) made by the Big Four. In

addition, according to the CBRC’s regulation issued in February 2006, all newly-established

shareholding commercial banks are required to have at least one foreign strategic investor,

while any form of ownership by local governments is explicitly forbidden. Berger, Hasan and

Zhou (2009) found that minority foreign ownership strengthens the efficiency of Chinese

banks.

Since the accession to the WTO in 2001, foreign banks’ presence in China has increased

dramatically. At the end of 2006, 223 foreign banks from 42 countries and regions established

242 representative offices and 312 operational institutions, including branches, sub-branches

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and wholly-foreign-owned banks (Wang and Zhang, 2009). As part of the entry agreement,

China pledged a five-year time table to fully open up its domestic banking sector for foreign

competition, as shown in Table 3.

Table 3. The Opening-Up Schedule for the Banking Sector after the WTO Accession

2001 2002 2003 2004 2005 2006 Geography All of China Foreign

currency business Customers All individuals and

enterprises

Geography Dalian, Shanghai, Shenzhen and Tianjin

Guangzhou, Nanjing, Qingdao, Wuhan and Zhuhai

Chengdu, Chongqing, Fuzhou and Jinan

Beijing, Kunming and Xiamen

Ningbo, Shantou, Shenyang and Xi'an

All of China Local currency business

Customers All foreign individu-als and enterprises

All Chinese enterprises

All Chinese clients

Source: World Trade Organization.

In sum, reforms during 1978–2006 are largely based on three main pillars: bank restruc-

turing, financial liberalization, and regulation and supervision strengthening.

4. Variables and Data

This section defines variables and describes data. We are going to use city-level data to

examine the relationship between financial development and economic growth in China.1 To

measure financial development, we construct a number of financial indicators. In the mean-

while, we control for different conditioning information sets in our growth regression model.

4.1. Variables

To investigate whether the exogenous component of financial development positively in-

fluences economic growth, a growth regression model is set up with the annual growth rate of

real per capita GDP as the dependent variable. The independent variables include a variable

representing financial development and a conditioning information set controlling for other

factors.

1 We carry out the Feldstein-Horioka (1980) test to test capital mobility among Chinese cities as a justification

for the use of city-level data. The results from both cross-sectional and panel regressions support the view of a low

degree of capital mobility which makes the analysis meaningful. As of the panel regression, we use a fixed-effects

estimator where city dummies and year dummies are included. Boyreau-Debray (2003) and Boyreau-Debray and

Wei (2004) found evidence that the degree of China’s inter-provincial capital mobility is low.

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Indicators for development of financial intermediation

Traditional indicators of financial development have been employed by existing cross-

country studies, such as the value of credits provided by financial intermediaries to the private

sector divided by GDP (Levine, Loayza and Beck, 2000). However, China’s statistical data do

not provide exact information for us to calculate such indicators at the city level. Hence, re-

searchers who study the finance-growth relationship of China have developed a set of indica-

tors to represent China’s financial development, taking the data constraint into consideration.

This paper makes use of these indicators to examine the relationship between financial devel-

opment and economic growth in China. Here are the five indicators measured at the city level:

(1) Credit is the ratio of total loans in the financial system (banking institutions and non-

banking financial institutions) to GDP, which measures the overall depth of financial in-

termediation.

(2) Deposit is the ratio of total deposits in the financial system to GDP, which measures the

overall size of financial intermediaries.

(3) Savings is the ratio of total household savings deposited in the financial system to GDP,

which serves as a proxy of China’s financial development in terms of mobilizing house-

hold savings.

(4) Loans Over Appro is the share of fixed asset investment financed by domestic loans

relative to that financed by state budgetary appropriation. Fixed asset investment comes

from different sources including domestic loans, state budgetary appropriation, foreign

investment and self-raised funds. Among these sources, loans are considered more effi-

cient than state budget appropriation in terms of capital allocation. Thus we follow the li-

terature (Liu and Li ,2001; Guariglia and Poncet, 2008; Chen, 2006) and make use of this

ratio to measure the substitution of more market and profit-oriented financial transac-

tions for state budget appropriation in order to allocate capital more efficiently.

(5) Corporate is the ratio of corporate deposits to total deposits in the financial system.

This measures China’s financial development in providing corporate banking services.

Conditioning information sets

To use conditioning information sets to capture the influence of factors other than the fi-

nancial indicators on economic growth, we collect data for those control variables that are

traditionally used in the finance-growth literature. To examine the sensitivity of our empirical

results further, we divide these variables into four different conditioning information sets. The

sets are defined as follows:

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(1) Simple Set: the constant, the logarithm of the initial per capita GDP2 (Initial PCGDP)

to capture the convergence effect, and the logarithm of the initial level of education3

(Education) to control for human capital accumulation.

(2) Medium Set: the simple set plus the share of state-owned entities in total fixed asset

investments (SOE) as an inverse proxy for the progress of economic reforms, and the

Consumer Price Index (CPI) to control for inflation.

(3) Policy Set: the medium set plus the ratio of foreign direct investment to GDP (FDI) to

measure the degree of openness of the local economy, and government expenditure over

GDP (Government) to control for city government size.

(4) Full Set: the policy set plus business volume of postal and telecommunication services

(Postal&Telecom) to indicate the status of information transmission, and the density of

roads4 (Infrastructure) as a proxy for local infrastructure.

Note that, due to the possibility of a nonlinear relationship between economic growth and

the explanatory variables, we use natural logarithms of these variables in regressions.

4.2. Data

By the Chinese government administrative classification, there are three levels of cities in

China: municipalities, prefecture-level cities,5 and county-level cities. There are 4 municipali-

ties: Beijing, Shanghai, Tianjin and Chongqing. They are governed directly by the central gov-

ernment and they are not subject to the administration of any provincial government. Each

province in China has about 10 prefecture-level cities,6 which are governed directly by the

provincial government. And, a county-level city is usually governed by a prefecture-level city.

In this paper, a “city” refers to either a prefecture-level city or a municipality.

We collect a set of panel data from 286 Chinese prefecture-level cities and municipalities

over the period 2001–2006. The data is from the China City Statistical Yearbook for various

years. The yearbook provides two kinds of statistical data for each variable. One set of data is

from municipalities and urban regions of prefecture-level cities only, and the other set of data

2 The “initial per capita GDP” is per capita GDP in yuan of the previous year.

3 The “initial level of education” is the percentage of students in the total population enrolled in secondary

schools in the previous year.

4 The “density of roads” is defined as the total length of roads in kilometers per one million square kilometers.

5 Some prefecture-level cities (15 in total by the end of 2006) are further defined as sub-province cities. How-

ever, in terms of administrative statistics, they are still considered as prefecture-level cities.

6 There are three special cases: by the end of 2006, there were 21 prefecture-level cities in Guangdong prov-

ince, while Qinghai province and Tibet each had only 1 prefecture-level city.

Page 13 of 36

is from municipalities, urban and rural regions of prefecture-level and county-level cities. We

use the latter so as to take into account any economic activity that might have occurred in any

region of China.

Due to data limitation, we use the provincial data for the variables Loans Over Appro，

CPI and Government. This means that all the cities in the same province have the same value

for these variables. The provincial data is from the China Statistical Yearbook for various

years.

Table 4 presents the descriptive statistics and their correlations for the dependent vari-

able and financial indicators. We can see that there is considerable variation across cities.

Table 4. Descriptive Statistics and Correlations

Growth Credit Deposit Savings Loans over Appro Corporate Descriptive Statistics Mean 0.127 0.809 1.131 0.713 5.032 0.337 Maximum 0.377 3.288 5.590 1.797 23.127 0.965 Minimum -0.078 0.059 0.083 0.067 0.660 0.012 Stand.Dev. 0.127 0.426 0.510 0.224 4.110 0.129 Observations 1680 1644 1644 1648 1607 1644

Correlations Growth 1.000 Credit 0.017* 1.000 Deposit 0.030 0.846*** 1.000 Savings -0.089*** 0.548*** 0.730*** 1.000 Loans over Appro 0.160*** -0.06** 0.048* -0.063** 1.000 Corporate 0.228*** 0.549*** 0.552*** -0.082*** 0.078*** 1.000

Notes: The significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

5. Finance and Growth: Cross-Sectional Analyses

A purely cross-sectional analysis focuses on the “initial value” regressions, where we use

the values of the financial indicators and control variables contained in conditioning informa-

tion sets in 2001 and the average value of the dependent variable over 2001-2006. Conse-

quently, there is one observation per city. The basic cross-sectional regression model is:

[ ] ,i i i iGrowth Finance Conditioning Information Seta b g e¢= + + + (1)

where the dependent variable Growth is the real per capita GDP growth rate, Finance takes

each of the five financial indicators described in Subsection 4.1, and Conditioning Informa-

tion Set takes each of the four control sets defined in Subsection 4.1.

While this analysis does not resolve the issue of causality, the initial value regressions al-

leviate two critical weaknesses of the “contemporaneous” regressions where dependent and

explanatory variables are all averaged over the same period. First, it is possible that a common

shock to the dependent and explanatory variables during the same period drives the empirical

findings of the “contemporaneous” regressions. Second, the “contemporaneous” regressions

Page 14 of 36

overlook the potential endogenous determination of the dependent and the explanatory vari-

ables (Levine and Zervos, 1998).

Table 5 summarizes the least squares results with different conditioning information sets.

The results indicate a very strong positive association between the development of financial

intermediation and economic growth. For brevity, we report only the coefficients on the fi-

nancial development indicators. Each of the five financial indicators is significantly correlated

with economic growth at the 5% significance level in the simple, medium, policy and full con-

ditioning information set regressions, except that Corporate is significant at the 10% signifi-

cance level in the simple set regression. In addition, the results show that the strong relation-

ship between financial development and economic growth does not merely reflect contempo-

raneous shocks. Furthermore, financial development does not simply follow economic growth.

Table 5. Finance and Growth: OLS Estimators (Initial Value Regressions)

Conditioning Information Set Credit Deposit Savings Loans over Appro Corporate Simple Coefficient 0.010** 0.008** 0.011** 0.010*** 0.007*

Standard error (0.004) (0.004) (0.005) (0.003) (0.004) R2 [0.060] [0.049] [0.046] [0.081] [0.039] Observation 236 236 239 234 235

Medium Coefficient 0.013*** 0.011*** 0.012** 0.010*** 0.010** Standard error (0.004) (0.004) (0.005) (0.003) (0.004) R2 [0.103] [0.089] [0.081] [0.100] [0.074] Observation 236 236 239 234 235

Policy Coefficient 0.011*** 0.010*** 0.012** 0.008** 0.009** Standard error (0.004) (0.003) (0.005) (0.003) (0.004) R2 [0.138] [0.135] [0.131] [0.149] [0.121] Observation 231 231 233 228 230

Full Coefficient 0.012*** 0.012*** 0.011** 0.008** 0.009** Standard error (0.004) (0.004) (0.005) (0.003) (0.004) R2 [0.141] [0.141] [0.129] [0.158] [0.124] Observation 231 231 232 227 230

Notes: Cities in Tibet are excluded from the sample due to missing data. The significance levels at the 1%, 5% and

10% are identified by ***, ** and *, respectively.

6. Finance and Growth: Dynamic Panel Analyses

6.1. Model Specification

Our panel data analyses use data over the period 2001–2006 from 286 cities. To investi-

gate the relationship between financial development and GDP growth,7 our basic regression

model is:

, , , ,[ ] ,i t i t i t i t i tGrowth Finance Conditioning Information Seta b g h l e¢= + + + + + (2)

7 We will explain how to derive this dependent variable using GMM estimation in the next subsection.

Page 15 of 36

where the subscript i is a city index and t is a time index. { }ih are unobserved city-specific

effects, { }tl are time fixed effects, and ,i te is an idiosyncratic error term.

6.2. Methodology

The generalized-method-of-moments (GMM) estimators for dynamic panel data (Arella-

no and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998) have been applied

widely in recent years, especially in deriving the impact of financial development on economic

growth. There are several advantages of using GMM panel estimators over purely cross-

sectional estimators. First, we are able to control for time fixed effects and city-specific effects.

Second, we can use appropriate lags of the independent variables as instrumental variables to

deal with possible endogeneity in the regressors. In our case of growth regressions, a simulta-

neity bias caused by the joint determination of financial development and economic growth

may produce inconsistent estimators. Also, the variables in conditioning information sets may

suffer from an endogeneity problem. The GMM panel estimators allow us to address these

econometric problems using lagged observations of the explanatory variables as instruments

(internal instruments). As such, we can reliably examine the impact of the exogenous compo-

nent of financial development on economic growth in China.

Specifically, let y be the logarithm of real per capita GDP and X be a set of explanatory va-

riables including one of the financial indicators and control variables contained in one of the

conditioning information sets but excluding the lagged dependent variable. Consider the fol-

lowing regression equation:

, , , 1 , ,( 1) .i t i t i t i t i t i ty y y Xa b h l e-¢- = - + + + + (3)

We can rewrite Equation (3) as follows:

, , 1 , , .i t i t i t i t i ty y Xa b h l e-¢= + + + + (4)

The existence of city-specific effects ih makes the within-group estimators inconsistent even if

,i te is not serially correlated, because ih is correlated with the lagged dependent variable , 1i ty - .

Thus, to eliminate city-specific effects, we take the first difference of Equation (4) to obtain:

, , , 1 , 2 , , 1 1 , , 1( ) ( ) ( ) ( ).i t i t i t i t i t i t t t i t i ty y y y X Xa b l l e e- - - - -¢- = - + - + - + - (5)

Now, instrumental variables are needed to deal with two issues: (a) endogeneity of the regres-

sors; (b) correlation between the new error term , , 1i t i te e -- and the lagged dependent variable

, 1 , 2i t i ty y- -- of Equation (5).

The first-differenced GMM estimators use lagged explanatory variables as the instrumen-tal variables under two assumptions: (a) the idiosyncratic error term ,i te is not serially corre-

lated; (b) the variables contained in ,i tX are weakly exogenous. The following moment condi-

tions are used by the first-differenced GMM estimators:

Page 16 of 36

, , , 1( ) 0, for 2; 3,..., ,i t s i t i tE y s t Te e- -é ù- = ³ =ë û (6)

, , , 1( ) 0, for 2; 3,..., .i t s i t i tE X s t Te e- -é ù- = ³ =ë û (7)

In our case, these moment conditions imply that the twice and further lagged values of the

real per capita GDP, the financial indicators and the variables contained in a conditioning

information set can be used as instrumental variables to obtain the first-differenced GMM

estimators.

However, as Alonso-Borrego and Arellano (1996) and Blundell and Bond (1998) pointed

out, the instruments available for the first-difference equation are weak instruments when the

explanatory variables are persistent over time. Weak instruments can result in serious finite

sample biases. The variance of the coefficients gets larger asymptotically. To deal with the

potential bias and imprecision of the first-differenced GMM estimators, additional moment

conditions are proposed for an equation expressed in levels (Arellano and Bover, 1995; Blun-

dell and Bond, 1998). When an equation in differences and an equation in levels are combined

as a system, the estimators based on the moment conditions associated with this system are

called system GMM estimators. The instruments for the equation in levels are the lagged dif-

ferences of the explanatory variables. One additional assumption needs to be made to ensure

the validity of the additional instrumental variables: the first differences of the independent

variables in Equation (4) are uncorrelated with city-specific effects ih . In this case, we have

the following moment conditions for the equation in levels:8

( ), , 1 ,( ) 0, for 1; 3,..., ,i t s i t s i i tE y y s t Th e- - -é ù- + = = =ê úë û (8)

( ), , 1 ,( ) 0, for 1; 3,..., .i t s i t s i i tE X X s t Th e- - -é ù- + = = =ê úë û (9)

In our case, these moment conditions imply that the first lagged differences of the real per

capita GDP, the financial indicators and the variables contained in the conditioning informa-

tion sets can be used as additional instruments. Indeed, Bond et al. (2001) and Hauk and

Wacziarg (2009) pointed out that the system GMM estimators should be employed for growth

regressions to generate consistent and efficient parameter estimates.

Two specification tests suggested by Arellano and Bond (1991), Arellano and Bover (1995)

and Blundell and Bond (1998) are to be carried out: (1) The Sargan test9 for the over-

8 As pointed out by Arellano and Bover (1995), only the most recent differences can be used as instrumental

variables for an equation in levels. Otherwise, we will have redundant moment conditions, since lagged variables

are already used as instruments for the equation in differences.

9 We report the Sargan test statistics rather than Hansen J tests, because Sargan and difference-in-Sargan

tests are not so vulnerable to instrument proliferation as they do not depend on an estimate of the optimal weight-

ing matrix (Roodmand, 2009). We acknowledge the drawback of the Sargan test that it assumes homoskedasticity.

But because we consistently find the Sargan test to be more conservative than the Hansen test which easily pro-

duces J statistics with implausibly perfect p-values of 1.000, we choose to report the Sargan test statistics.

Page 17 of 36

identification restrictions, which is to test the overall validity of the instruments. Under the

null hypothesis that the instruments are valid, the test statistic is asymptotically distributed as

chi-square with the degree of freedom being equal to the number of instruments minus the

number of parameters estimated; (2) a second-order serial correlation test, which is to exam-

ine the hypothesis that the error term ,i te is not serially correlated. Under the null of no sec-

ond-order serial correlation, the test statistic is asymptotically distributed as standard normal.

6.3. Techniques for Reducing the Instrument Count

The instrument count easily grows large relative to the sample size as the time period

T or the number of explanatory variables rises when differenced GMM or system GMM esti-

mators are performed. Roodman (2009), Windmeijer (2005) and Arellano (2002) pointed out

that numerous instruments can overfit endogenous variables leading to biased estimators.

Roodman (2009) further discussed another problem of instrument proliferation. Specifically,

a high instrument count can make the Sargan test for instrument validity weak and mislead-

ing. He also pointed out weak Sargan tests are particularly misleading for system GMM esti-

mators.

To address the issue of “too many instruments”, we follow the techniques suggested by

Roodman (2009). The first is to use only one or two lags instead of all available lags for in-

struments. This strategy to limit the number of instruments generated from first-differenced

and system GMM estimators has been adopted by several researchers (Levine, Loayza and

Beck, 2000; Giedeman and Compton, 2009; Demir and Dahi, 2009). The second is to collapse

the instruments through additions into smaller sets. To effectively mitigate the problems

caused by instrument proliferation, Roodman (2009) suggested combining the two strategies.

In this paper, we follow Roodman’s (2009) approach to both collapse instruments and limit

the lag depth in first-differenced and system GMM estimators.

In the meanwhile, we carry out difference-in-Sargan tests to examine the validity of the

system GMM instruments, i.e., the validity of the first lagged differences of the explanatory

variables. In addition, we also perform difference-in-Sargan tests for the validity of instru-

ments based on lagged growth, which are viewed as problematic by Roodman (2009).

6.4. Regression Results

Results Using First-differenced GMM Estimators

Although system-GMM estimators have advantages over first-differenced GMM

estimators as mentioned in Subsection 6.2, weak Sargan tests are less problematic in first-

Page 18 of 36

differenced GMM estimations. The results from first-differenced GMM estimations are

summarized in Table 6,10 which suggest that financial development exerts a large positive

impact on economic growth. Four financial development indicators (Credit, Deposit, Loans

Over Appro, and Corporate) are significantly positive at the usually acceptable levels of

significance with various conditioning information sets. There is only one exception: the

coefficient of Corporate is insignificant with the full set. The coefficient of Savings is positive

with each information set, while it is significant only at the 5% significance level with the

simple set.

Table 6. Finance and Growth: First-differenced GMM Estimators

Conditioning Information Set Credit Deposit Savings Loans over Appro Corporate Simple Coefficient 0.082** 0.066** 0.095** 0.030* 0.094* Standard error (0.035) (0.031) (0.048) (0.017) (0.053) Sargan test (p-value) [0.474] [0.033] [0.846] [0.241] [0.288] Instruments 10 10 10 10 10 Observation 1092 1092 1092 1050 1090 Medium Coefficient 0.079** 0.070*** 0.100 0.038* 0.094* Standard error (0.037) (0.024) (0.064) (0.023) (0.056) Sargan test (p-value) [0.443] [0.022] [0.667] [0.491] [0.311] Instruments 12 12 12 12 12 Observation 1092 1092 1092 1050 1090 Policy Coefficient 0.099** 0.071* 0.021 0.042** 0.125* Standard error (0.033) (0.060) (0.063) (0.020) (0.060) Sargan test (p-value) [0.632] [0.565] [0.584] [0.507] [0.460] Instruments 14 14 14 14 14 Observation 1092 1092 1092 1050 1090 Full Coefficient 0.098* 0.079* 0.020 0.046** 0.115 Standard error (0.077) (0.050) (0.052) (0.018) (0.106) Sargan test (p-value) [0.516] [0.501] [0.449] [0.671] [0.450] Instruments 16 16 16 16 16 Observation 1063 1063 1063 1021 1061

Notes: The test statistics and standard errors are asymptotically robust to heteroskedasticity. Cities in Tibet are

excluded from the sample due to missing data. The significance levels at the 1%, 5% and 10% are identified by ***,

** and *, respectively.

The instrument count is quite low, ranging between 10 and 16. Most regressions pass the

Sargan test. There are only two exceptions: the coefficients of Deposit with the simple and

medium sets do not pass the test. For the models that pass the Sargan test, the p-values for

the validity of the full instrument set comfortably satisfy the conventional significance levels,

ranging between 0.241 and 0.846.11

10 We collapse instruments for each explanatory variable, including both the financial indicators and the vari-

ables contained in the conditioning information sets. We use all available lags to instrument the financial indica-

tors and use only one lag for each variable contained in the control sets. The purpose is to avoid exact identification

which makes the Sargan test unavailable.

11 All the first-differenced GMM regressions pass the difference-in-Sargan test for the validity of instruments

based on lagged growth. The p-values exceed the conventional significance levels substantially. These results are

available upon request.

Page 19 of 36

The regression estimates are also economically large. The signs and significance levels are

in line with the results from purely cross-sectional estimators.

Results Using System GMM Estimators

Tables 7–812 report system GMM estimates of Equation (2) with the policy set and the full

set respectively. To further ensure the credibility of system GMM estimators, we carry out two

different regressions for each financial development indicator by collapsing and un-collapsing

the instruments.13 Tables 7-8 present the results from the “collapsed” regressions. Tables A1-

A4 in the appendix present the results from both collapsing and un-collapsing instruments

with four different conditioning information sets (Simple, Medium, Policy and Full).

The regression results reported in Tables 7-8 show a significant positive relationship be-

tween the four financial development indicators (Credit, Deposit, Loan Over Appro, and Cor-

porate) and economic growth. These positive relationships are consistent with the ones from

OLS estimators and first-differenced GMM estimators. All of the regressions pass the second-

order serial correlation test. The null hypothesis that the error term is not serially correlated

cannot be rejected.

The regressions for the three financial indicators (Credit, Deposit and Corporate) pass

the Sargan test, except the Deposit regression with the policy set. Compared with the “un-

collapsed” variants, the instrument count of the “collapsed” regressions is reduced to a large

extent, ranging from 19 to 23. Most p-values for the Sargan test comfortably satisfy the con-

ventional significance levels with an average value of 0.354. The p-values for the difference-in-

Sargan test for the validity of the instruments based on lagged growth, which is suspected by

Roodman (2009),14 have an average value of 0.425. The validity of the subsets of instruments

is established for these regressions. We hence confirm that the overall size and depth of the

financial sector spur economic growth and the development of financial intermediation in

China positively influences economic growth through the mobilization of corporate deposits.

12 We limit the lag depth to one for every explanatory variable, including both the financial indicators and the

variables in the control sets.

13 The difference-in-Sargan tests for system GMM instruments are not available when we collapse the instru-

ments and use only one lag per instrumental variable. The model is under-indentified without system GMM in-

struments.

14 Roodman (2009) claims that “It seems likely that lagged growth is an invalid instrument in the LLB re-

gressions, that system GMM is invalid”(page 155). Our results confirm his findings. When the p-values of the dif-

ference-in Sargan test for the validity of system GMM instruments and instruments based on lagged growth are

lower than the conventional levels simultaneously or only the latter is lower, the regression fails to pass the Sargan

test for joint validity of the instruments.

Page 20 of 36

Page 21 of 36

Table 7. Finance and Growth: System GMM Estimators (Policy Set)

(1) (2) (3) (4) (5) Regressors Collapsed Collapsed Collapsed Collapsed Collapsed Credit 0.037* (0.021) Deposit 0.047** (0.019) Savings -0.092 (0.085) Loans Over Appro

0.019**

(0.007) Corporate 0.204* (0.119) Initial PCGDP 0.053*** 0.046*** 0.041 0.005 -0.023

(0.020) (0.014) (0.055) (0.015) (0.044)

Education 0.024 0.114** 0.044 0.015 0.134 (0.056) (0.054) (0.057) (0.017) (0.099) SOE -0.037 -0.060*** -0.006 -0.012 -0.066 (0.043) (0.017) (0.049) (0.023) (0.044) CPI 0.191 0.189 -0.143 -0.785 -5.003** (0.833) (0.406) (2.655) (0.722) (2.147) FDI -0.017* 0.002 -0.001 -0.007 0.007 (0.009) (0.005) (0.015) (0.005) (0.012) Government -0.028 0.004 -0.075 -0.040* -0.139*** (0.039) (0.030) (0.060) (0.022) (0.051) Dummy2002 -0.017 0.007 0.000 -0.042** -0.094** (0.021) (0.011) (0.071) (0.017) (0.042) Dummy2003 -0.008 0.003 0.010 -0.017*** 0.004 (0.007) (0.007) (0.023) (0.006) (0.012) Dummy2004 0.008 0.013 0.024 0.024 0.142*** (0.022) (0.012) (0.058) (0.018) (0.053) Dummy2005 0.002 0.003 0.006 -0.001 0.021** (0.005) (0.003) (0.007) (0.003) (0.009) Constant -0.610 -1.244*** 0.058 -0.014 -0.762 (0.516) (0.437) (0.815) (0.226) (0.742) Observations 1381 1381 1381 1339 1379 Instruments 19 19 19 19 19 Sargan test (p-value) 0.220 0.047 0.689 0.005 0.802

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.853 0.045 0.780 0.011 0.893

m2 test (p-value) 0.624 0.813 0.624 0.522 0.466

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null

of no second-order serial correlation. Cities in Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 22 of 36

Table 8. Finance and Growth: System GMM Estimators (Full Set)

(1) (2) (3) (4) (5) Regressors Collapsed Collapsed Collapsed Collapsed Collapsed Credit 0.035* (0.021) Deposit 0.037* (0.020) Savings -0.162 (0.138) Loans Over Appro 0.014** (0.007) Corporate 0.102* (0.062) Initial PCGDP 0.054** 0.060*** 0.024 0.007 -0.001 (0.026) (0.021) (0.045) (0.013) (0.026) Education 0.029 0.142** 0.045 0.022 -0.010 (0.056) (0.062) (0.059) (0.019) (0.090) SOE -0.029 -0.053*** -0.012 -0.044 (0.041) (0.018) (0.057) (0.045) CPI 0.186 -0.294 -1.732 -2.638 (0.866) (0.478) (2.762) (2.018) FDI -0.019* -0.002 0.003 -0.001 -0.006 (0.010) (0.006) (0.021) (0.006) (0.011) Government -0.008 -0.028 -0.013 -0.031 -0.056 (0.033) (0.032) (0.077) (0.024) (0.044) Postal&Telecom 0.016 0.018 0.047 -0.013 0.009 (0.014) (0.013) (0.039) (0.013) (0.017) Infrastructure 0.001 -0.006 -0.003 -0.007 -0.002 (0.011) (0.010) (0.026) (0.008) (0.015) Dummy2002 -0.023 -0.001 -0.048 -0.021** -0.075* (0.019) (0.014) (0.064) (0.008) (0.043) Dummy2003 -0.011 0.006 0.001 -0.015** -0.007 (0.008) (0.009) (0.017) (0.006) (0.009) Dummy2004 0.004 0.023 0.050 0.007 0.080* (0.024) (0.014) (0.063) (0.005) (0.048) Dummy2005 0.000 0.003 0.011 -0.003 0.013* (0.006) (0.003) (0.010) (0.003) (0.007) Constant -0.726 -1.420*** 0.331 -0.036 0.040 (0.539) (0.492) (0.838) (0.204) (0.656) Observations 1377 1377 1377 1377 1375

Instruments 23 23 23 23 23 Sargan test (p-value) 0.217 0.300 0.883 0.011 0.369

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.367 0.247 0.485 0.091 0.478

m2 test (p-value) 0.718 0.673 0.576 0.811 0.553

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null

of no second-order serial correlation. Cities of Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 23 of 36

The Loan Over Appro regressions fail to pass the tests in any specification. Although we

are unable to infer a causality relationship between this financial indicator and economic

growth, their positive correlation is real. This implies that more active use of market-oriented

and profit-driven financial transactions, such as loans relative to state budget appropriation,

is positively associated with economic growth. In addition, this conclusion is in line with those

of Liu and Li (2001), Guariglia and Poncet (2008) and Chen (2006) who employ the same

measure with provincial data.15

However, the coefficient of Savings turns to be negative from system GMM estimations,

but loses its significance for economic growth. The difference-in-Sargan tests of the “un-

collapsed” variants indicate that the system GMM instruments are invalid. Considering the

positive relationship from the OLS and differenced GMM estimators, we consider the negative

coefficients from system GMM estimators as a fluke of problematic system GMM

instruments.16 However, it is difficult to conclude that Savings plays a significantly positive

role to promote growth from the above evidence. China has a very high savings rate of about

40%, which may cause low household consumption as a percentage of GDP and hurt eco-

nomic growth. This possibly explains the reported unclear relationship between Savings and

growth in China.

In summary, we find that the development of financial intermediation in China after the

WTO entry positively influences economic growth.17 This finding is consistent with most

cross-country studies on the relationship between financial intermediation and economic

growth. However, this finding is contrary to several existing studies on the finance-growth

relationship in China (Boyreau-Debray, 2003; Hasan, Wachtel and Zhou, 2007; Guariglia and

Poncet, 2008). These studies argued that financial deepening in China did not contribute to

economic growth, since banks, especially the SOCBs, continued to support loss-making SOEs

and, as a government policy to reduce poverty, capital was channeled to slow-growing regions.

This inefficient allocation of capital was blamed as the main cause of the negative effect of

15 As a robustness check, we replace Loan Over Appro with the share of fixed asset investment that is fi-

nanced by domestic loans. The OLS, differenced GMM and system GMM regressions produce significantly positive

coefficients.

16 The difference-in-Sargan tests of the “un-collapsed” variants, which are reported in the appendix, indicate

that the system GMM instruments are invalid. If the system GMM instruments are dropped, the regressions are

brought back to differenced GMM where Savings positively influences growth.

17 In order to investigate the issue of this causality further, we apply a panel Granger causality test (Granger,

1969) to our data. Freeman (1983) developed a “direct Granger method” to assess the Granger causality and de-

termine the causality direction of a relationship between two variables in a direct way: regress one variable on the

lagged values of the other variable and itself, and then use an F-test to examine the null hypothesis that all the

coefficients on the lagged values of the other variable are zero. We find no evidence that economic growth Granger-

causes financial development. These results are available upon request.

Page 24 of 36

financial development on economic growth in China. However, these studies mainly covered

the years before China’s accession to the WTO in 2001. Since our study focuses on the years

after 2001, the opposite results are not surprising. This suggests that the financial reforms in

China after 2001 are in the right direction, especially in accelerating financial deepening, bank

restructuring and financial liberalization.

Furthermore, the positive estimated coefficients are also economically significant (large

in value). For example, based on the coefficient reported in column (1) of Table 7 from the

“collapsed” system GMM estimation with the policy set, a city exogenously moving from the

25th percentile in the distribution of the ratio of total loans to GDP (53.96%) to the 75th per-

centile (92.99%) will have a 2.01% larger GDP growth rate.

Most control variables show expected signs although not always statistically significant.

We find evidence of convergence because the coefficient of the lagged per capita GDP, Initial

PCGDP, is significantly smaller than unity in most regressions. We find that human capital

has a positive impact on economic growth. The variable, SOE, which is the share of state-

owned entities in total investment, always shows a negative effect on economic growth. This

indicates that the relatively poor performance of state-owned entities hurts economic growth.

Also, inflation, CPI, negatively affects growth. Government size, Government, has a negative

impact on growth. The stock of a city’s communication facilities, Postal&Telecom, influences

growth positively. The infrastructure variable, Infrastructure, is always insignificant. The in-

dicator of openness, FDI, is also insignificant in almost all the regressions. The last result is in

line with Carkovi and Levine (2005) and Boyreau-Debray (2003), who found that foreign in-

vestment has no significant impact on economic growth after controlling for endogeneity.

6.5. Sensitivity Analysis: a City Dummy Variable

To ensure robustness of our findings further, we conduct a sensitivity analysis by intro-

ducing a dummy variable to indicate coastal cities, capital cities and the cities that have hosted

foreign bank entries. The reasons for introducing this city dummy are as follows: First, in

China, there is a huge economic development imbalance between coastal and hinterland re-

gions. Soon after the Chinese economic reforms started in 1978, an open door policy for for-

eign investment and trade led to the establishment of five special economic zones in coastal

areas (Shenzhen, Zhuhai, Shantou, Xiamen, and Hainan province) in 1980. Later, 14 coastal

cities (Dalian, Qinghuangdao, Tianjin, Yantai, Qingdao, Lianyungang, Nantong, Shanghai,

Ningbo, Wenzhou, Fuzhou, Guangzhou, Zhanjiang, and Beihai) were opened up for foreign

investment and designated as Economic and Technical Development Zones. The central gov-

ernment invested directly in many projects in these cities and offered many favorable policies,

including more autonomy and lower taxes. Song, Chu and Cao (2000) found that these poli-

cies exacerbated inter-city disparities in China in terms of per capita GDP and per capita in-

come. Boyreau-Debray (2003) introduced a coastal dummy to her growth-finance analysis to

Page 25 of 36

take into account the fact that the banking sector in coastal provinces is characterized by a

lower share of SOCB credit and less credit from the central bank. Second, each province in

China has a provincial capital city. Usually, the capital city enjoys the dominant status in

economic development and is the most populous city in the province. Lastly, by the end of

2006, a total of 31 cities in China have hosted foreign bank entries. A city’s banking sector

becomes more diversified after foreign bank entries. Thus, introducing a dummy variable to

indicate coastal cities, capital cities and cities having foreign banks18 allows us to check for any

omitted variable biases created by the special characteristics of these cities.

We conducted the sensitivity analysis using system GMM estimators with the policy set.

The results are presented in Table 11. We can see that adding this city dummy does not change

our conclusions. That is, the strong connection between financial development and economic

growth is not associated with whether a city is a coastal city, a capital city or a city with foreign

banks, a characteristic that was not taken into account in our baseline regressions. Moreover,

the “collapsed” Loans Over Appro regression passes the Sargan test and this result strength-

ens the conclusion in Subsection 6.4. This indicates that the development of financial inter-

mediation has a causal and positive impact on economic growth through the substitution of

loans for state budget appropriation.

Table 9. Finance and Growth with a City Dummy: System GMM Estimators (Policy Set)

Regressors (1) Collapsed

(2) Collapsed

(3) Collapsed

(4) Collapsed

(5) Collapsed

Credit 0.060** (0.028) Deposit 0.038 (0.028) Savings -0.033 (0.085) Loans Over Appro 0.030*** (0.010) Corporate 0.171* (0.103) Initial PCGDP 0.050** 0.045*** 0.049 -0.034 -0.018 (0.025) (0.016) (0.059) (0.027) (0.052) Education 0.019 0.124* 0.042 -0.007 0.090 (0.063) (0.070) (0.044) (0.020) (0.114) SOE 0.001 -0.066** 0.023 0.091*** -0.026 (0.035) (0.032) (0.046) (0.025) (0.041) CPI 0.694 0.205 0.200 -1.210 -4.443* (0.903) (0.448) (2.733) (0.769) (2.471) FDI -0.011 0.000 0.012 0.004 0.007 (0.010) (0.007) (0.017) (0.010) (0.016) Government -0.031 0.000 -0.069 -0.061* -0.130** (0.039) (0.036) (0.052) (0.033) (0.053) City Dummy (costal, capital, with foreign banks) -0.007 0.006 0.018 -0.062*** -0.087*

(0.024) (0.012) (0.074) (0.020) (0.050) Dummy2002 -0.011 0.003 0.015 -0.030*** 0.002 (0.009) (0.009) (0.023) (0.011) (0.014) Dummy2003 -0.007 0.011 0.019 0.025 0.128** (0.024) (0.014) (0.060) (0.020) (0.060) Dummy2004 -0.000 0.002 0.005 -0.002 0.020**

18 We can alternatively introduce three dummy variables for the three types of cities. However, applying three

separate dummies does not change our main conclusions. In fact, the dummies are insignificant in most cases.

Page 26 of 36

(0.005) (0.003) (0.008) (0.004) (0.010) Dummy2005 -0.101 0.046 -0.266* -0.167* -0.075 (0.078) (0.059) (0.159) (0.094) (0.089) Constant -0.632 -1.248** -0.229 0.500 -0.445 (0.607) (0.542) (0.822) (0.321) (0.766) Observations 1381 1381 1381 1339 1354 Instruments 19 19 19 19 19 Sargan test (p-value) 0.232 0.153 0.600 0.526 0.835

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.904 0.076 0.221 0.355 0.821

m2 test (p-value) 0.524 0.885 0.737 0.368 0.480

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. The city dummy takes the value of 1 on 44 cities (coastal cities, capital cities and cities with

foreign banks). m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null of

no second-order serial correlation. Cities of Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

7. Conclusions

This paper examines the relationship between financial intermediation and economic

growth in China, using data from 286 Chinese cities over the period 2001–2006. We investi-

gate the exogenous component of financial development on economic growth using system

GMM estimators for dynamic panel data. This technique yields the same results as the tradi-

tional cross-sectional estimators and the simpler first-differenced GMM estimators. We study

an empirical relationship between various measures of financial development and economic

growth with a unique city-level dataset. Our results suggest that traditionally used indicators

of financial development are generally positively associated with economic growth after con-

trolling for many factors associated with growth. The size and depth of the financial sector

spur economic growth. With more use of markets and profit-oriented financial transactions

and mobilization of corporate deposits, the development of financial intermediation in China

after the WTO entry positively influences economic growth in China. These results are consis-

tent with most cross-country studies on the relationship between financial intermediation and

economic growth, but run contrary to existing studies on China that suggests that financial

development hinders economic growth due to the distorting nature of the state-ruled banking

sector. Thus, our findings suggest that the banking reforms after China’s accession to the

WTO are in the right direction.

However, household savings are found to have an unclear effect on economic growth. The

results from OLS and differenced GMM estimators suggest a positive relationship between

household savings and economic growth, while results from system GMM estimators suggest

a negative but insignificant effect, which we consider may be a by-product of problematic in-

struments.

Page 27 of 36

We pay particular attention to the issue of “too many instruments” when first-differenced

GMM and system GMM estimators are employed. Techniques for reducing the instrument

count are applied, including limiting the lag depth for instruments and collapsing instruments.

Furthermore, we test for the validity of the full instrument set and subsets of instruments rig-

orously. The conclusions are drawn carefully based on both complicated estimators and sim-

ple estimators. To examine the sensitivity of our results, different conditioning information

sets are experimented with. We have also carried out a sensitivity analysis by adding a city

dummy to differentiate between coastal cities, capital cities and cities with foreign bank en-

tries. These analyses show that our results are robust.

More insights into the linkage between financial development and economic growth in

China can be obtained if more detailed city-level data are available, especially data on shares

of SOCBs and joint-equity commercial banks in total credit. Also, a firm-level study of the

impact of external finance on firm growth may produce more insightful results. These are on

our agenda for future research.

Page 28 of 36

Appendix

Table A1. System GMM Estimators: Two Variants (Simple Set)

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b)

Regressors Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Credit 0.026* 0.040* (0.016) (0.019) Deposit 0.050* 0.075* (0.027) (0.045) Savings -0.114 -0.079** (0.114) (0.033) Loans Over Appro 0.012* 0.013* (0.007) (0.004) Corporate 0.120** 0.072* (0.055) (0.039) Initial PCGDP 0.021** 0.018 0.062*** 0.059*** 0.056** 0.026 0.015 0.010* 0.041* 0.035** (0.009) (0.014) (0.022) (0.021) (0.028) (0.016) (0.013) (0.006) (0.021) (0.015) Education 0.037 0.088* 0.093 0.108 0.188 0.195** 0.019 0.018 0.091 0.089* (0.023) (0.052) (0.108) (0.085) (0.142) (0.082) (0.030) (0.017) (0.071) (0.051) Dummy2002 -0.026*** -0.029*** 0.005 0.007 0.008 -0.008 -0.022*** -0.024*** 0.008 -0.001 (0.007) (0.012) (0.013) (0.014) (0.015) (0.010) (0.006) (0.003) (0.011) (0.009) Dummy2003 -0.018*** -0.023*** 0.006 0.005 0.010 -0.004 -0.013** -0.014*** 0.010 0.003 (0.007) (0.011) (0.011) (0.01) (0.011) (0.007) (0.006) (0.003) (0.009) (0.008) Dummy2004 0.005 0.000 0.021** 0.020*** 0.017 0.008 0.007 0.005* 0.023*** 0.018*** (0.005) (0.006) (0.009) (0.007) (0.010) (0.007) (0.005) (0.003) (0.007) (0.005) Dummy2005 0.005 -0.003 0.004 0.004 0.002 0.000 -0.001 -0.000 0.008* 0.005 (0.003) (0.003) (0.005) (0.004) (0.006) (0.004) (0.003) (0.002) (0.005) (0.003) Constant -0.410** -0.762* -1.277* -1.459** -1.129 -1.037** -0.205 -0.150 -1.261** -1.019** (0.161) (0.394) (0.755) (0.725) (1.146) (0.495) (0.206) (0.116) (0.516) (0.430)

Observations 1381 1381 1381 1381 1381 1381 1339 1339 1379 1379

Instruments 11 30 11 30 11 30 11 30 11 30

Sargan test (p-value) 0.013 0.117 0.748 0.689 0.269 0.239 0.001 0.000 0.981 0.244

Difference-in-Sargan test for system GMM instruments (p-value) NA 0.050 NA 0.680 NA 0.094 NA 0.000 NA 0.928

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.010 0.034 0.519 0.427 0.384 0.542 0.012 0.000 0.641 0.080

m2 test (p-value) 0.817 0.923 0.877 0.737 0.613 0.607 0.707 0.674 0.992 0.949

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for second-order serial correlation, which is asymptotically N(0,1) under the null of

no second-order serial correlation. Cities in Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 29 of 36

Table A2. System GMM Estimators: Two Variants (Medium Set)

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b)

Regressors Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Credit 0.033** 0.037*** (0.015) (0.012) Deposit 0.039* 0.045* (0.023) (0.026) Savings -0.127 -0.066* (0.080) (0.083) Loans Over Appro

0.021* 0.014*

(0.011) (0.007) Corporate 0.195** 0.115* (0.094) (0.062) Initial PCGDP 0.022** 0.020* 0.064*** 0.045*** 0.032 0.029* 0.045*** 0.004 0.015 0.036 (0.011) (0.011) (0.019) (0.017) (0.033) (0.017) (0.017) (0.010) (0.030) (0.027) Education 0.049 0.071* 0.062 0.135** 0.033 0.142** 0.041 0.027 0.176 0.092 (0.042) (0.038) (0.074) (0.058) (0.036) (0.069) (0.047) (0.028) (0.143) (0.107) SOE -0.030 -0.029* -0.042* -0.052*** 0.010 -0.021* -0.041 -0.005 -0.060 -0.081*** (0.027) (0.015) (0.021) (0.019) (0.057) (0.012) (0.035) (0.041) (0.047) (0.029) CPI -0.527 -0.381 0.626 -0.146 0.072 0.824 -0.833 -0.918 -1.663 -0.669 (0.857) (0.724) (0.765) (0.693) (2.081) (0.817) (0.791) (0.897) (2.150) (2.303) Dummy2002 -0.035 0.034* 0.019 -0.003 -0.008 0.011 -0.020 -0.048** -0.025 -0.007 (0.022) (0.018) (0.022) (0.021) (0.058) (0.022) (0.020) (0.019) (0.049) (0.055) Dummy2003 -0.018** -0.020*** 0.009 0.000 0.001 0.000 0.001 -0.017*** 0.006 0.009 (0.007) (0.006) (0.010) (0.008) (0.016) (0.009) (0.008) (0.006) (0.012) (0.011) Dummy2004 0.020 0.012 0.008 0.018 0.013 -0.009 0.038** 0.026 0.061 0.039 (0.021) (0.019) (0.016) (0.017) (0.045) (0.020) (0.018) (0.023) (0.048) (0.052) Dummy2005 0.003 0.000 0.004 0.002 0.007 -0.001 0.009** 0.001 0.014* 0.011 (0.004) (0.004) (0.004) (0.004) (0.008) (0.004) (0.004) (0.005) (0.007) (0.007) Constant -0.510 -0.652** -1.036* -1.347*** 0.154 -0.777 -0.649* -0.143 -1.788* -1.170* (0.325) (0.301) (0.544) (0.459) (0.576) (0.473) (0.330) (0.207) (0.985) (0.700) Observations 1381 1381 1381 1381 1381 1381 1339 1339 1379 1379 Instruments 15 46 15 46 15 46 15 46 15 46

Sargan test (p-value) 0.210 0.198 0.408 0.113 0.330 0.111 0.015 0.000 0.599 0.408

Difference-in-Sargan test for system GMM instruments (p-value) NA 0.161 NA 0.128 NA 0.051 NA 0.000 NA 0.408

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.451 0.478 0.537 0.148 0.829 0.877 0.590 0.000 0.641 0.977

m2 test (p-value) 0.942 0.958 0.761 0.815 0.662 0.474

0.618 0.484 0.592 0.918

Notes: The standard errors are in parentheses. Test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null

of no second-order serial correlation. Cities in Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 30 of 36

Table A3. System GMM Estimators: Two Variants (Policy Set)

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b)

Regressors Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Credit 0.037* 0.032* (0.021) (0.018) Deposit 0.047** 0.049** (0.019) (0.024) Savings -0.092 -0.099** (0.085) (0.039) Loans Over Appro

0.019** 0.018**

(0.007) (0.009) Corporate 0.204* 0.084* (0.119) (0.046) Initial PCGDP 0.053*** 0.025 0.046*** 0.064** 0.041 0.026* 0.005 -0.006 -0.023 0.039 (0.020) (0.020) (0.014) (0.030) (0.055) (0.025) (0.015) (0.020) (0.044) (0.021) Education 0.024 0.139* 0.114** 0.033 0.044 0.158 0.015 0.016 0.134 -0.027 (0.056) (0.076) (0.054) (0.063) (0.057) (0.123) (0.017) (0.020) (0.099) (0.074) SOE -0.037 -0.046 -0.060*** -0.040 -0.006 -0.009 -0.012 0.007 -0.066 -0.049* (0.043) (0.035) (0.017) (0.033) (0.049) (0.038) (0.023) (0.018) (0.044) (0.026) CPI 0.191 -0.675 0.189 -0.223 -0.143 -0.176 -0.785 -1.207 -5.003** 0.174 (0.833) (0.839) (0.406) (0.770) (2.655) (1.401) (0.722) (0.911) (2.147) (0.784) FDI -0.017* 0.000 0.002 -0.006 -0.001 -0.003 -0.007 -0.004 0.007 -0.007 (0.009) (0.006) (0.005) (0.007) (0.015) (0.008) (0.005) (0.007) (0.012) (0.006) Government -0.028 -0.021 0.004 -0.055* -0.075 -0.043 -0.040* -0.047** -0.139*** -0.078* (0.039) (0.037) (0.030) (0.033) (0.060) (0.055) (0.022) (0.022) (0.051) (0.042) Dummy2002 -0.017 -0.028 0.007 -0.011 0.000 -0.007 -0.042** -0.055** -0.094** 0.008 (0.021) (0.025) (0.011) (0.029) (0.071) (0.035) (0.017) (0.022) (0.042) (0.020) Dummy2003 -0.008 -0.012 0.003 0.011 0.010 0.002 -0.017*** -0.019*** 0.004 0.014* (0.007) (0.037) (0.007) (0.014) (0.023) (0.011) (0.006) (0.007) (0.012) (0.008) Dummy2004 0.008 0.023 0.013 0.020 0.024 0.016 0.024 0.032 0.142*** 0.024 (0.022) (0.022) (0.012) (0.017) (0.058) (0.038) (0.018) (0.023) (0.053) (0.021) Dummy2005 0.002 0.002 0.003 0.003 0.006 -0.002 -0.001 0.000 0.021** 0.007 (0.005) (0.004) (0.003) (0.004) (0.007) (0.008) (0.003) (0.004) (0.009) (0.005) Constant -0.610 -1.057* -1.244*** -0.737 0.058 -0.587 -0.014 0.117 -0.762 -0.117* (0.516) (0.602) (0.437) (0.562) (0.815) (1.039) (0.226) (0.300) (0.742) (0.631) Observations 1381 1381 1381 1381 1381 1381 1339 1339 1379 1379 Instruments 19 62 19 62 19 62 19 62 19 62 Sargan test (p-value) 0.220 0.179 0.047 0.758 0.689 0.116 0.005 0.000 0.802 0.158

Difference-in-Sargan test for system GMM instruments (p-value) NA 0.141 NA 0.241 NA 0.027 NA 0.002 NA 0.354

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.853 0.317 0.045 0.665 0.780 0.953 0.011 0.134 0.893 0.127

m2 test (p-value) 0.624 0.965 0.813 0.849 0.624 0.574 0.522 0.392 0.466 0.490

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null

of no second-order serial correlation. Cities in Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 31 of 36

Table A4. System GMM Estimators: Two Variants (Full Set)

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b)

Regressors Collapsed Un–

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Collapsed Un-

Collapsed Credit 0.035* 0.032* (0.021) (0.016) Deposit 0.037* 0.008 (0.020) (0.036) Savings -0.162 -0.085* (0.138) (0.048) Loans Over Appro 0.014** 0.019** (0.007) (0.009) Corporate 0.102* 0.086** (0.062) (0.040) Initial PCGDP 0.054** 0.005 0.060*** 0.043** 0.024 0.046* 0.007 -0.005 -0.001 0.022 (0.026) (0.012) (0.021) (0.021) (0.045) (0.027) (0.013) (0.017) (0.026) (0.014) Education 0.029 0.031 0.142** 0.037 0.045 0.060 0.022 0.020 -0.010 -0.052 (0.056) (0.050) (0.062) (0.059) (0.059) (0.065) (0.019) (0.020) (0.090) (0.069) SOE -0.029 -0.014 -0.053*** -0.019 -0.012 -0.043 0.001 -0.044 -0.066*** (0.041) (0.020) (0.018) (0.043) (0.057) (0.039) (0.019) (0.045) (0.024) CPI 0.186 -0.920 -0.294 0.736 -1.732 0.468 -1.015 -2.638 -1.582 (0.866) (0.839) (0.478) (0.905) (2.762) (0.932) (0.791) (2.018) (0.968) FDI -0.019* 0.000 -0.002 -0.008 0.003 -0.005 -0.001 0.002 -0.006 -0.003*** (0.010) (0.006) (0.006) (0.009) (0.021) (0.011) (0.006) (0.006) (0.011) (0.008) Government -0.008 -0.020 -0.028 -0.018 -0.013 -0.010 -0.031 -0.045* -0.056 -0.071* (0.033) (0.026) (0.032) (0.035) (0.077) (0.045) (0.024) (0.026) (0.044) (0.037) Postal&Telecom 0.016 0.009 0.018 0.028* 0.047 0.042* -0.013 -0.013 0.009 0.024 (0.014) (0.012) (0.013) (0.016) (0.039) (0.023) (0.013) (0.014) (0.017) (0.017) Infrastructure 0.001 0.005 -0.006 0.010 -0.003 -0.003 -0.007 -0.006 -0.002 -0.006 (0.011) (0.007) (0.010) (0.012) (0.026) (0.017) (0.008) (0.008) (0.015) (0.009) Dummy2002 -0.023 -0.040 -0.001 -0.004 -0.048 -0.007 -0.021** -0.046** -0.075* -0.046 (0.019) (0.014) (0.014) (0.013) (0.064) (0.019) (0.008) (0.020) (0.043) (0.029) Dummy2003 -0.011 -0.014* 0.006 -0.001 0.001 0.000 -0.015** -0.016** -0.007 0.003 (0.008) (0.007) (0.009) (0.005) (0.017) (0.007) (0.006) (0.007) (0.009) (0.009) Dummy2004 0.004 0.022 0.023 0.005 0.050 0.004 0.007 0.030 0.080* 0.064** (0.024) (0.014) (0.014) (0.014) (0.063) (0.019) (0.005) (0.020) (0.048) (0.031) Dummy2005 0.000 0.002 0.003 0.002 0.011 0.001 -0.003 0.002 0.013* 0.012** (0.006) (0.003) (0.003) (0.004) (0.010) (0.004) (0.003) (0.004) (0.007) (0.006) Constant -0.726 -0.203* -1.420*** -0.551 0.331 -0.323 -0.036 0.122 0.040 0.205 (0.539) (0.378) (0.492) (0.516) (0.838) (0.573) (0.204) (0.263) (0.656) (0.475) Observations 1377 1377 1377 1377 1377 1377 1377 1377 1375 1375

Instruments 23 78 23 78 23 78 23 78 23 78 Sargan test (p-value) 0.217 0.260 0.300 0.310 0.883 0.215 0.011 0.001 0.369 0.272

Difference-in-Sargan test for system GMM instruments (p-value) NA 0.128 NA 0.288 NA 0.145 NA 0.001 NA 0.537

Difference-in-Sargan test for instruments based on lagged growth (p-value) 0.367 0.870 0.247 0.122 0.485 0.798 0.091 0.098 0.478 0.572

m2 test (p-value) 0.718 0.744 0.673 0.692 0.576 0.906 0.811 0.495 0.553 0.787

Notes: The standard errors are in parentheses. The test statistics and standard errors are asymptotically robust to

heteroskedasticity. m2 is a test for a second-order serial correlation, which is asymptotically N(0,1) under the null

of no second-order serial correlation. Cities of Tibet are excluded from the sample due to missing data. The signifi-

cance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.

Page 32 of 36

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