EXTERNALITIES OF FDI: EVIDENCE BY CHAN YUEN TUNG A …lib-sca.hkbu.edu.hk/trsimage/hp/12006866.pdf · externalities of fdi: evidence from china’s eastern coastal and central provinces

EXTERNALITIES OF FDI: EVIDENCEFROM CHINA’S EASTERN COASTAL

AND CENTRAL PROVINCES

BY

CHAN YUEN TUNGSTUDENT NO. 12006866

ECONOMICS CONCENTRATION

A PROJECT SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OF

BACHELOR OF SOCIAL SCIENCES (HONOURS) DEGREEIN CHINA STUDIES

HONG KONG BAPTIST UNIVERSITY

APRIL 2015

Page of Acceptance

April 2015

We hereby recommend that the Project by Mr. CHAN Yuen Tung entitled

“Externalities of FDI: Evidence from China’s Eastern Coastal and Central Provinces.” be

accepted in partial fulfillment of the requirements for the Bachelor of Social Sciences

(Honours) Degree in China Studies in Economics.

____________________ ____________________

Dr. Erin SO Pik Ki ____________________

Project Supervisor Second Examiner

1

Acknowledgement

I would like to thank my supervisor Dr. Erin SO Pik Ki for guiding and

enlightening me through out the entire study. Without her generous care and support, this

paper is hardly finished. Thanks are also due to Dr. CHAN Hing Lin for his teaching of the

econometric theories and applications and to Dr. LUK Sheung Kan for his pragmatic

comments on the regression models.

____________________

China Studies Degree Course

Economics Concentration

Hong Kong Baptist University

15.04.2015

2

Table of Content

PAGE OF ACCEPTANCE 1

ACKNOWLEDGEMENT 2

ABSTRACT 4

1. INTRODUCTION 5

2. LITERATURE REVIEW 9

3. METHODOLOGY AND REGRESSION MODEL 14

4. DATA 21

5. REGRESSION RESULT AND INTERPRETATION 23

6. CONCLUSION AND POLICY IMPLICATION 37

REFERENCES 40

3

Abstract

Using the panel data across 14 provinces in China’s Eastern coastal and Central

regions from 2002 to 2011, this paper finds that there are different levels of positive and

significant externalities spilled out from the labors hired by FDI and HKMT firms in

various sectors as well as the capital stocks invested by FDI firms. Moreover, export-led

growth does exist in China’s industry sector, but is limited to the domestic firms only. In

addition, the export shares of FDI and HKMT firms do not affect the domestic economic

growth. Lastly, an interesting finding is that increasing the capital inputs in construction

sector does not necessarily generate efficient GDP output growth.

4

1. Introduction

To take the advantages of the foreign direct investment (FDI), it is not uncommon

to see that the less developed countries’ governments are competing with each other to

offer different preferential policies, such as rental discounts, tax holidays and some special

subsides, to attract the overseas investors. Doubtlessly, China is a case in point. For

instance, in some sectors, the FDI firms can enjoy a 2-year tax holiday starting from the

first year that they can make profit and after these 2 years, they can still have a 50%

discount of the tax for the following 3 years. The major reason for doing so is that Chinese

government realizes that there will be externalities brought from the FDI inflows, which

will finally benefit the domestic economy. Thus, in the 1990s, with the government’s

efforts, China has become the largest recipient of the FDI among all other developing

countries.

Back to the late 1970s, China government has already set attracting the overseas

capital as one of the economic reform strategies. Since the Law on Sino-Foreign Equity

Joint Ventures1 published in 1979, the annual inflow of FDI has stepped up steadily. In

early 1992, China’s top leader Deng promised to further open up the country and to

accelerate the economic reform in his tour to the Southern provinces. Right after his speech,

the annual FDI inflows of 1992 and 1993 have increased for more than the double and

reached a peak of U.S. 44.2 billion in 1997. After China entered the World Trade

Organization (WTO) in 2001, according to Figure I below, the inflows of FDI kept

expending explosively until 2008, which was the year of global financial tsunami, to a level

1 It is a legal framework for FDI, which allows foreign investors to have equity joint ventures together with partners from China.

5

of U.S. 186.8 billion. Starting from 2009, the figure recovered and rebounded rapidly from

U.S. 167.1 billion to another peak of U.S. 331.6 billion in 2011.

Figure I – China’s FDI inflows (1990 - 2011). Source: The World Bank.

Although there are many FDI inflows in China, not every province can get the same

amount of benefits. As Figure II below illustrates, the regional distribution of FDI is not

even. The majority part, up to 85%, went to the Eastern coastal region; this is because in the

beginning of the open door policies, the Eastern area acted as a ‘white mouse’, especially

Guangdong province, as it is near Hong Kong and close to the coastal line, it has a better

linkage with the overseas investors. As a result, Guangdong alone shared 25.3% of the total

FDI inflows from 1990 to 2011. While the central region accounted for 10% during this

period. Although the West regions shared only 5% of the total FDI from 1990 to 2011, this

percentage indeed has already been increasing slowly from 3% in 1979 to 1998.

0

50

100

150

200

250

300

350

Bill

ion

(U.S

.)

FDI inflows in China (1990 to 2011)

6

Figure II – Regional Distribution of FDI Inflows in China (2002 - 2009). Source: China Trade and External Economic Statistical Yearbook. Eastern coastal region: Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong,

Guangdong and Hainan. Central Region: Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan. West Region: Shaanxi, Sichuan, Guizhou, Yunnan, Tibet, Gansu, Qinghai, Ningxia and Xinjiang.

Besides the uneven geographic distribution, the sectorial distribution of FDI inflows

is also uneven. As the production costs in China are relatively cheap; therefore, according

to the Figure III below, the secondary sector, especially the industry, benefited the most and

accumulated for more than half of the FDI inflows; only industry alone got 56% of the total

FDI inflows from 2002 to 2009.

As above data shows, the FDI inflows after China entered WTO in 2001 have been

increasing rapidly and majorly concentrates on the 2nd sector, especially the industry sector,

and in the Eastern coastal and the central regions. Therefore, this paper collects a panel data

crossing 14 provinces, namely: Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong,

Guangdong, Hainan, Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan, from 2002 to 2011

to analyze the externalities brought by these investments to different levels of the domestic

economic growths and will put more attentions on the industry sector. The current study

Eastern Coastal Region 85%

Central Region 10%

West Region 5%

Regional Distribution of FDI in China (2002 to 2009)

7

will also analyze and compare the effects brought from 2 different origins of the FDI,

which are the investments from the foreign and HKMT investors.

Figure III – Sectorial Distribution of FDI Inflows in China (2002 to 2009). Source: China Trade and External Economic Statistical Yearbook.

The rest of this paper is organized as follows. The previous literatures of the related

topics are summarized in section 2. The regression models are shown in section 3. Section

4 describes the data and the processing procedures. The econometric results and the related

interpretations are contained in section 5. The concluding remarks and some policy

implications are in the final section 6.

1st Sector 1%

2nd Sector - Industry 56%

2nd Sector - Construction

1%

3rd Sector 42%

Sectorial Distribution of FDI in China (2002 to 2009)

8

2. Literature Review

According to Qi and Li (2008), there are 3 main ways to increase the technology,

innovation and creativity level, which are:

I. Creating knowledge independently by research and development (R&D);

II. Purchasing advanced technology and know-how from international trade;

III. Spilling over the knowledge from FDI enterprises to the host countries;

and hence, they will increase the productivity of the society and the economic growth as

well.

Unlike the former two ways, the externalities in form of knowledge spillover

generated by FDI are relatively indirect and there are 5 major channels for it to carry out.

First and foremost, the local partners can learn the production processes and technology

directly from the investors. Labor mobility effect can be the second channel. The turnover

of the trained and skilled labor from the FDI firms to the domestic related counterparts will

also bring the technological know-how to their ‘new’ domestic firms. Demonstration effect,

in which the products and the inventions of the FDI companies can stimulate and enlighten

the local R&D activities, can be another channel (Jianhong Qi & Hong Li, 2008). The

fourth channel is the competitive effect. Last but not least, there will be the vertically

knowledge spillover through the forward and backward linages of supply chains. For

example, according to Smarzynska (2002), the local suppliers in Lithuania will have higher

productivities when there is a greater presence of the FDI companies.

9

Economists generally agree that FDI does insert an externality - the positive

knowledge spillover effect and as a result, the production technology, efficiency and thus

the economies will grow in the host countries, which are usually the Lesser-Developed

Countries (LDCs), can be enhanced. For instance, Findlay (1978) suggests that the speed of

technical progress in the host country can be increased infectiously by the more advanced

know-how and management methods used in the FDI corporations. Walz (1997) claims that

the multinational companies in LDCs will spill their knowledge over to the domestic R&D

sectors and causing the economic growths in the host countries happen eventually.

Although most of the related theoretical literatures show the positive relations

between the levels of presence of the FDI firms, the technical progresses and the

productivities of the host countries, interestingly, the results of empirical studies are

somehow mixed and diverse. Major views from the empirical literatures are summarized as

follow:

FDI has a positive effect on the technology progresses and productivities in the host

countries.

Many scholars find out that the domestic firms are likely to absorb the

knowledge spillover by the FDI and learn from them to increase their own

productivities and competitiveness in the market.

Rhee and Belot (1989) discover that the creations as well as the growth of

the local-owned textile firms in Bangladesh and Mauritius are stimulated by the

entry of a few foreign-owned firms. Hanel (2000) assumes that there are relations

between the shares of sales of the foreign subsidiaries in 19 industries of Canada

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and those subsidiaries’ related knowledge stocks; then calculates how R&D in those

subsidiaries affect the growth of the domestic factor productivity; and draws the

conclusion that the knowledge creation and the productivity growth can be

contributed by those non-local knowledge stocks. Branstetter (2000) figures out that

FDI is an important way for knowledge spillovers between both the indigenous

companies in US and the investing companies from Japan using the data of patent

citations.

More recently, Lee (2006) summarizes that the global knowledge spillover

via FDI is obvious after analyzing the data of 16 Organization for Economic

Cooperation and Development (OECD) countries from 1981 to 2000. Wang et al.

(2006) find that the inflows of FDI are one big incentive for the domestic companies

in China to lift their R&D inputs in order to maintain their competitiveness under

the international and globalization pressures.

FDI does not have a positive effect on the technology progresses and productivities

in the host countries.

Some scholars think that the spillover effect from the FDI is not strong and

robust enough, whereas some may even deem that the effect does not exist at all. As

a result, the domestic parties do not learn or adopt the more advanced technology,

know-how or managements used by the FDI firms to increase their productivities.

A case in point is that the orders about assembly of electronic devices

received by the local firms from the foreign investors do not help the former to learn

any new or advanced technology besides the simple and low-value-added assembly

work. Another case in point is that, according to Xian and Yan (2005), in order for

11

the spillover effects from FDI to occur, the local firms have to achieve certain basic

technology levels; yet, the provinces in the middle and west part of China have not

pass through the ‘doorsill’ and thus, no robust spillover effects can be observed.

Chen (2007) argues that the influences from FDI to China’s regional innovation

capabilities are feeble and furthermore, the FDI inflows may even crowd out the

domestic R&D activities.

The spillover effect of FDI on the technology progresses and productivities in the

host countries is unclear.

Besides the R&D stocks, economists generally understand and accept that

the capital stocks of the FDI firms can also spill over and enhance the productivities

of the local companies. However, Todo (2006) refutes that, basing on the data of

Japanese manufacturing industries from 1995 to 2002, there is a positive effect of

R&D stocks, but not capital stocks, on the knowledge and productivity growths of

the host countries, and that result can be interpreted as the daily production practices

of the FDI firms do not generate spillover effects, but only the R&D activities do.

This uncertain spillover effect is somewhat similar in China in the eyes of

Jiang and Xia (2005); using the data of China’s hi-tech industries, they claim that

FDI does provide some positive effects towards the domestic counterparts, yet, their

own R&D inputs and the numbers of the scientific and technical researchers, staff

employed are the more important factors contributing the knowledge creations of

the domestic firms; in addition, they find that the competitive effect does not only

bring goods to the firms, while the local firms may also lose their markets as well as

their own strategic R&D resources due to the intensive competition pressures.

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As mentioned above, the empirical studies towards this issue come with different

and diverse opinions. And the related studies towards China’s regional situations after 2001,

which is the entry year of WTO, comparing the direct investments from different origins

are relatively rare. Hence, this current paper tries to analyze the externality, which can drive

the economic growth, brought by the FDI from the foreign investors and HKMT investors

in the Eastern coastal and the central regions of China.

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3. Methodology and Regression Model

The Gross Domestic Product (GDP), measures the total gross values added by all

the residents participated in productions, which also is the aggregate production level of a

country. It is commonly used to measure to the economic performances of the countries as

well as various sectors’ contributions.

In this current study, instead of Nominal GDP (NGDP), Real GDP (RGDP) is used

as it separates the effects of inflation or deflation from NGDP and therefore, it can better

indicate the economic performances. In order to observe the real economic growth caused

by the externalities, taking the natural logarithm of the RGDP is an essential step to change

it into the growth rate. Thus, lnGDP, which is the natural logarithm of the deflated NGDP,

will act as the dependent variables in following 9 regression models.

The basic components and regression model to measure the externalities, according

to Chen (2011) in his book2, are as follow:

lnGDP = Constant + αlnL + βlnK + γ (Other independent variable) + ε

where α, β and γ are the coefficients that capture the impacts from the growth of L (labor),

the growth of K (fixed capital stock) and the other independent variable related to the

externalities. ε represents the random error term of the model.3 And lnL and lnK are the

growth rates of labor number and capital stock respectively. While other independent

2 Indeed, the model originally comes from the production function: Y = ALαKβ.

3 The mean of this term should be zero.

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variable can be any variable that can insert an impact on the dependent variable, such as the

FDI inflows growth.

This current paper mainly focuses on the labor and the export side; since, comparing

with the spillover effect brought by the capital stock, these two factors’ effects are

relatively less studied and indirect in the sense that the knowledge or know-how basically

has to spill through learning and adapting but not directly to acquire through purchasing.

Basing on the above production function, 9 gradually established regression models

are shown below (see Table I for definitions of the subscripts); to avoid the multi-collinear

problem, expect model (7) and (8), independent variables of the domestic firms are not

placed into the models.

InGDPp,t = C + β1InLp,t + β2InKp,t + β3In(LF,p,t / Lp,t)

+ β4In(LHKMT,p,t / Lp,t) + ε (1)

Subscript Definition p Pinvince t Time Period (Year) t-1 The Last Time Period (Lagged 1 Year) F Foreign Direct Investment Firms HKMT Hong Kong, Macau and Taiwan Investment Firms D Domestic Firms 1st Sector The First Economic Sector 2nd Sector The Second Economic Sector 3rd Sector NON 2nd Sector Industry Construction TEV TPV

The Third Economic Sector The First and the Third Economic Sectors Industry Sector Construction Sector Total Export Value Total Production Value

Table I – Definitions of the Subscripts.

15

where In(LF,p,t / Lp,t) and In(LHKMT,p,t / Lp,t) are the growth rates of the ratios of labors

employed by FDI firms as well as HKMT investment firms among the total employed

labors respectively; while the coefficient β3 and β4 measure the magnitudes of the impacts

from the growths of the FDI and HKMT investment firms’ labors to the total employment

ratio severally. This model is set to study whether changes of the portions of labors hired by

the FDI and HKMT investment firms will affect the GDP growth.

Model (2) is somehow similar with model (1), except it is set to study whether

changes of the portions of capital stocks, instead of the employed labors, invested by the

FDI and HKMT investment firms will affect the GDP growth.

InGDPp,t = C + β1InLp,t + β2InKp,t + β3In(KF,p,t / Kp,t)

+ β4In(KHKMT,p,t / Kp,t) + ε (2)

where In(KF,p,t / Kp,t) and In(KHKMT,p,t / Kp,t) are the changes of the ratios of fixed capital

stocks invested by FDI firms as well as HKMT investment firms among the total fixed

capital stock respectively; while the coefficient β3 and β4 indicate the degrees of the

impacts from the growths of the FDI and HKMT investment firms’ fixed capital stocks to

the total fixed capital stock ratio severally.

In order to further study the spillover effects through the employed labors in

different economic sectors, model (3) and (4) are gradually developed from model (1)

above.

InGDP2nd Sector,p,t = C + β1InL2nd Sector,p,t + β2InK2nd Sector,p,t

+ β3In(L2nd Sector,F,p,t / L2nd Sector,p,t) + β4In(L2nd Sector,HKMT,p,t / L2nd Sector,p,t) + ε (3)

16

InGDPNON 2nd Sector,p,t = C + β1InLNON 2nd Sector,p,t + β2InKNON 2nd Sector,p,t

+ β3In(LNON 2nd Sector,F,p,t / LNON 2nd Sector,p,t) + β4In(LNON 2nd Sector,HKMT,p,t / LNON 2nd Sector,p,t)

+ ε (4)

Unlike model (1), model (3) and (4) above are set to test whether the growths of the ratios

of L employed by the FDI firms, HKMT investments firms in the secondary and non-

secondary sector to the total employment in these 2 sectors will affect the growths of GDP

in these sectors respectively.

If model (3) shows significant and robust coefficient β3 or β4, then by using model

(5) and (6), it is possible to predict whether the spillover effects mainly occur in the

industry or the construction sector.

InGDPIndustry,p,t = C + β1InLIndustry,p,t + β2InKIndustry,p,t

+ β3In(LIndustry,F,p,t / LIndustry,p,t) + β4In(LIndustry,HKMT,p,t / LIndustry,p,t) + ε (5)

InGDPConstruction,p,t = C + β1InLConstruction,p,t + β2InKConstruction,p,t

+ β3In(LConstruction,F,p,t / LConstruction,p,t) + β4In(LConstruction,HKMT,p,t / LConstruction,p,t) + ε (6)

The coefficient β3 and β4 in model (5) are set to capture the impacts from the growths of the

labors working in industry sector, which are hired by FDI firms as well as HKMT

investment firms respectively, over the total industrial employment to the industry’s GDP

growth; while β3 and β4 in model (6) are not far-off but targeted at the construction sector.

Besides studying L and K, the export of industry sector is also worthwhile to pay

attention as the export sector, which is mainly composed by the industrial export sector in

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China, can cause the export-led growth in many developing countries.4 Therefore, model (7)

below is established to test the relation between the GDP growth of the industry sector and

the changes of the ratio of total export value to total production value.


+ β3In(TEVIndustry,p,t / TPVIndustry,p,t) + ε (7)

If β3 in model (7) is significant and robust, then, by using model (8), whether the

GDP change of the industry sector, which is driven by the export, is mainly from the

growths of the export portions of the FDI firms, HKMT investment firms or domestic firms

to their own production values can possibly be identified.


+ β3In(TEVIndustry,F,p,t / TPVIndustry,F,p,t) + β4In(TEVIndustry,HKMT,p,t / TPVIndustry,HKMT,p,t)

+ β5In(TEVIndustry,D,p,t / TPVIndustry,D,p,t) + ε (8)

Apart from the export to production ratio, the growths of the shares of exports from

FDI firms as well as from HKMT investment firms among the total export value may also

drive the GDP growth in industry sector, and thus, model (9) below is set to test the above

statement.


+ β3In(TEVIndustry,F,p,t / TEVIndustry,p,t) + β4In(TEVIndustry,HKMT,p,t / TEVIndustry,p,t) + ε (9)

4 See Tiwari and Mutascu (2011).

18

In order to prevent the problem of reverse causality, another 9 regression models are

also formulated with the subscript ‘t’ of all the independent variables in above 9 models

replaced by ‘t-1’ (lagged for 1 year) for robustness checking as follows:

InGDPp,t = C + β1InLp,t-1 + β2InKp,t-1 + β3In(LF,p,t-1 / Lp,t-1)

+ β4In(LHKMT,p,t-1 / Lp,t-1) + ε 1(t-1)

InGDPp,t = C + β1InLp,t-1 + β2InKp,t-1 + β3In(KF,p,t-1 / Kp,t-1)

+ β4In(KHKMT,p,t-1 / Kp,t-1) + ε 2(t-1)

InGDP2nd Sector,p,t = C + β1InL2nd Sector,p,t-1 + β2InK2nd Sector,p,t-1

+ β3In(L2nd Sector,F,p,t-1 / L2nd Sector,p,t-1)

+ β4In(L2nd Sector,HKMT,p,t-1 / L2nd Sector,p,t-1) + ε 3(t-1)

InGDPNON 2nd Sector,p,t = C + β1InLNON 2nd Sector,p,t-1 + β2InKNON 2nd Sector,p,t-1

+ β3In(LNON 2nd Sector,F,p,t-1 / LNON 2nd Sector,p,t-1)

+ β4In(LNON 2nd Sector,HKMT,p,t-1 / LNON 2nd Sector,p,t-1) + ε 4(t-1)

InGDPIndustry,p,t = C + β1InLIndustry,p,t-1 + β2InKIndustry,p,t-1

+ β3In(LIndustry,F,p,t-1 / LIndustry,p,t-1) + β4In(LIndustry,HKMT,p,t-1 / LIndustry,p,t-1) + ε 5(t-1)

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InGDPConstruction,p,t = C + β1InLConstruction,p,t-1 + β2InKConstruction,p,t-1

+ β3In(LConstruction,F,p,t-1 / LConstruction,p,t-1)

+ β4In(LConstruction,HKMT,p,t-1 / LConstruction,p,t-1) + ε 6(t-1)


+ β3In(TEVIndustry,p,t-1 / TPVIndustry,p,t-1) + ε 7(t-1)


+ β3In(TEVIndustry,F,p,t-1 / TPVIndustry,F,p,t-1)

+ β4In(TEVIndustry,HKMT,p,t-1 / TPVIndustry,HKMT,p,t-1)

+ β5In(TEVIndustry,D,p,t-1 / TPVIndustry,D,p,t-1) + ε 8(t-1)


+ β3In(TEVIndustry,F,p,t-1 / TEVIndustry,p,t-1)

+ β4In(TEVIndustry,HKMT,p,t-1 / TEVIndustry,p,t-1) + ε 9(t-1)

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4. Data

The econometric analyses in this paper are based on data of 14 provinces across the

Eastern coastal and central region of China from 2002 to 2011. The annual provincial data

is collected from various statistical reports: CHINA STATISTICAL YEARBOOK, CHINA

POPULATION & EMPLOYMENT STATISTICS YEARBOOK, CHINA LABOUR

STATISTICAL YEARBOOK, STATISTICAL YEARBOOK OF THE CHINESE

INVESTMENT IN FIXED ASSETS, CHINA INDUSTRY ECONOMY STATISTICAL

YEARBOOK, CHINA REAL ESTATE STATISTICS YEARBOOK, CHINA EXTERNAL

ECONOMIC STATISTICAL YEARBOOK and different provincial statistical yearbooks,

such as JIANGSU STATISTICAL YEARBOOK.

Since NGDP cannot reflect the actual economic performance, GDP used in this

paper is the NGDP deflated by the GDP deflator, which takes 1978 as the base year. Labor

is measured as the employed labor number at the end of the year. The export and

production value are counted in the provinces which product the final goods. For example,

if Guangxi produces a car and Guangdong acts as the exporter, the production value as well

as the export value will both count into the former’s account.

As Liu (2002) mentions, the capital stock measurement is a well-recognized

problem in the empirical studies; it does not only exist in the studies of China, but also

other countries. There are several approaches to estimate the total capital stock in a country;

unlike some fellows who will use the fixed capital investment figures directly as the capital

stock, this paper uses a scientific method used by Kim and Lau (1994) and Lei and Yao

(2009), which the initial capital stocks of China, Hong Kong and Macau, are assumed to be

21

5 times of the real gross fixed capital formation in the same year and the real gross fixed

capital formation after the initial year can be used as a proxy for the change in the capital

stock each year. And China’s annual depreciation rate of the capital, as used by Perkins

(1988), Woo (1998), Meng and Wang (2000) and Wang and Yao (2003), is assumed to be

5%. Therefore, in the current study, the nominal fixed capital formation of the initial year

(2002) is firstly deflated by the investment in fixed assets price index, which takes 1978 as

the base year; then by multiplying the value 5 times, the initial capital stocks can be found.

And the capital stock of the year after 2002, i.e. 2003, is the sum of 95% of the capital

stock of 2002 plus the newly real fixed capital formation of 2003 (deflating the nominal

fixed capital formation by the investment in fixed assets price index of 2003).

Furthermore, as Holz (2004) claims that China’s official statistics are of

questionable quality and inaccuracy, the inconsistency among the data used by this study is

relatively apparent. To cope with this problem, if there is inconsistency of a data point, the

data from a later time period will be given the first priority and the data from a higher

authority will be given the second priority in this study.5

5 This practice is in a belief that the data announced later may be amended and therefore they should be generally more consistent and competent; while the data published by a higher authority might be more accurate because the data may be processed more seriously.

22

5. Regression Result and Interpretation

As there are various institutions and characteristics of each province after the de-

centralization, fixed-effects model should be used to process the panel data to separate the

differences and let each province has a different constant term. However, probably due to

the insufficient sample size as the time limitation, the results from 18 models are almost

statistically insignificant. Hence, pooled ordinary least squares (pooled OLS) is used to

carry out the regressions in the current paper. And the results are shown below.

From the results of 9 models as well as 9 (t-1) models, generally speaking, the

coefficients of lnL and lnK, which are β1 and β2, are positive and statistically significant at

the 1% level. And the means of the all coefficients of them are 0.5981 and 0.3871

respectively. Therefore, it implies that, on average, every 1% of labor increase will lead to a

0.60% growth of GDP, while every 1% increase of capital stock will lead to 0.39% growth

of GDP. And the results above are indeed within the expectation as L and K are two basic

elements that have positive relations with the output in the production function. Moreover,

it suggests that, from a macro view, putting 1% extra labor in economic activities will give

a higher output growth then putting 1% more of the capital stock. And it denotes that in

these regions, economic activities and growths are still relying more heavily on labor

instead of using capital such as machines and computers and it can also be interpreted as

the economy of China’s Eastern coastal and central areas is relatively labor-intensive.

However, one thing to be highlighted is that the coefficients of the InKConstruction in

model (6) and its (t-1) model, unlike all other coefficients of lnL and lnK in remaining

models, are not statistically significant. This suggests that the increase in fixed capital stock

23

in the construction sector of those provinces does not push up the GDP growth of the

related field and this can also be interpreted as the capital investments in China’s

constriction sector are inefficient to generate positive outputs.

Table II – Estimation of GDP growth effect by the changes of (LF/L), (LHKMT/L), (KF/K) and (KHKMT/K).

a) Models with (t-1) mean that the independent variables are lagged for 1 year for robustness checking. b) The italic numbers in the table above are the p-values. c) *** stands for p-value < 0.01; ** stands for p-value between 0.01 & 0.05; * stands for p-value between 0.05 & 0.1.

According to Table II, the results of model (1) show that if the ratio of labors hired

by the FDI firms to the total employment of a province increase 1%, the GDP of that place

will have a 0.12% increase; while the labor hired by HKMT investment firms to the total

employment ratio of a province increase 1%, it will lead to a 0.06% rise in GDP of that

24

province. And both findings above are significant at the 1% level and the results still hold

their significances and robustness in the (t-1) model. The empirical results reveal that the

increases of ratios of labors from FDI firms and HKMT firms can both have positive effects

on the GDP growth; but with the same 1% increase in the ratio, the labor hired by the FDI

firms will give a nearly 100% higher return then the labors hired by HKMT firms to the

GDP growth. It denotes that the spillover effects are stronger through the labors hired by

the FDI firms to the domestic GDP growth. It may due to different reasons and for example,

the worker training sections are better and/or the standards of production, such as the know-

hows, technical requirements in the working environment, are higher in the FDI firms,

comparing with HKMT ones; as there is a hypothesis that when the multinational firms

decided to enter a country, i.e. China, they will have ensured that the revenues brought by

their competitive advantages, such as high technologies, are big enough to cover the huge

costs. And when the labors in the FDI firms go to domestic firms, the local firms can enjoy

the relatively high-skilled labors. Hence, the general productivities and contributions to the

local GDP growth of the labors in FDI firms are higher than those in HKMT firms.

Model (2)’s results in Table II above show that when the percentage of the capital

stock owned by FDI firms among the total amount of the capital stock increases 1%, the

GDP will have a rise of 0.22% and this finding is robust and significant at the 1% level

both in Model (2) and its (t-1) model. Whereas the coefficients of the ratio of capital stock

owned by HKMT firms to the total amount of the capital stock are statistically insignificant

in Model (2) and also its (t-1) model, suggesting that there are no association between this

ratio and the GDP growth. By assuming that the more advanced capital stocks can generate

a higher output value and contribute more to the GDP, the above findings reveal that, 25

comparing to the fixed asset capital stocks owned by HKMT firms, the ones owned by FDI

firms are more advanced and with higher productivities and this finding is also in line with

the hypothesis mentioned above. Therefore, if the ratio of the capital owned by the FDI

firms to the total capital stock amount in a province increases, there will be a positive

spillover effect to domestic sector as well as a positive growth of the GDP.

Table III – Estimation of GDP growth of 2nd sector and NON 2nd sector effects by the changes of (L2nd Sector,F /

L2nd Sector), (L2nd Sector,HKMT / L2nd Sector) as well as (LNON 2nd Sector,F / LNON 2nd Sector) and (LNON 2nd Sector,HKMT / LNON

2nd Sector).


26

Model (3) and (4) in Table III are further developed from model (1) to see how

spillover effects occur through the labors employed by the FDI and HKMT firms in the

secondary and the non-secondary sector. Basing on the regression results of model (3) as

well as the related (t-1) model, the coefficients of the ratio of labors hired by the FDI firms

within the secondary sector to the total employment number of the secondary sector are

positive and statistically significant at the 1% level, showing that when there is a 1%

increase in this ratio, the GDP of the secondary sector will increase by 0.04%. On the other

hand, model (3) also shows that there are no statistical relation between the growth of GDP

in secondary sector and the change of the ratio of labors hired by HKMT firms within the

secondary sector to the total employment number of the secondary sector, since the

coefficients of this ratio are insignificant in both model (3) and its (t-1) model. The

empirical results above reveal that, within the secondary sector, the spillover effects to the

domestic economic growth will transmit through the labors hired by the FDI firms but not

through those who are hired by HKMT firms. And thus, it implies that the productivities of

the labors employed by the FDI firms in the 2nd sector are averagely higher; thence, when

more and more labors work in the FDI firms, with the total labor number of the 2nd sector

unchanged, more and more labors may have a better knowledge and productivity and

contribute more to the 2nd sector’s GDP growth. A reason behind may be the high standard

and technology productions of the FDI firms, but not HKMT ones, in the industry sector

that can spill the technical skills and the knowledge to the domestic labors and this reason is

further confirmed in model (5), which will be discussed later.

According to model (4) and its (t-1) model in Table III, there is a 99% confidence

level to claim that when the ratio of labors employed by HKMT firms within the non-2nd 27

sector to the total employment number of the non-secondary sector goes up 1%, it will lead

to a 0.08% increase of the non-secondary GDP. However, in the non-2nd sector, as the

coefficients of the ratio of labors hired by the FDI firms to the total employment number

are insignificant in model (4) as well as the (t-1) model, there are no statistical relations

between this ratio and the GDP growth of the non-2nd sector. It discloses that, in the non-

secondary sector, which is mainly composed by 3rd sector6, the workers hired by HKMT

firms have a higher contribution to the GDP growth of the related sectors, comparing with

the ones hired by the FDI firms; and there are some possible reasons to explain. Closer

Economic Partnership Arrangement (CEPA) involving Hong Kong and Macau may be one

of the reasons as it liberates various high value-added 3rd sectors, such as banking sector,

insurance service sector and security markets, to the companies from Hong Kong and

Macau. The labors of HKMT firms can work in the higher economic output fields, which

also require higher human capitals, than the ones work in FDI firms by assuming that

higher value added sectors need higher human capitals, and consequently, the workers in

HKMT firms of non-2nd sector will conduct a stronger spillover effect to the domestic

economic growth. Culture may also be another reason to explain. Since the 3rd sector is

majorly composed by the service sector, unlike the industry and construction sector in 2nd

sector, it is much more ‘human’ and hence, with the same service quality, the culture of a

company is relatively important. By assuming that HKMT firms will have a more similar

cultural background with Chinese consumers; one can claim that HKMT firms will be more

6 The size and the economic output value from the 3rd sector are much bigger than the 1st sector. For instance, China 3rd sector’s GDP was around 4.6 times higher than the 1st sector one in 2013.

28

popular than the FDI firms in the service sector7; in another words, the labors in these

HKMT firms can adapt to the Chinese service market environment better and thus, there

can be greater spillover effects regarding the knowledge and the skills, such as selling skills,

which can generate higher outputs. And when these workers go to domestic firms, the skills

they learnt could then spill to the domestic firms.8

Table IV – Estimation of GDP growth of industry sector and construction sector effects by changes of

(LIndustry,F / LIndustry), (LIndustry,HKMT / LIndustry) as well as (LConstruction,F / LConstruction) and (LConstruction,HKMT / LConstruction).


7 This assumes that the service qualities of the FDI firms and HKMT firms are the same.

8 This is because the service qualities of the FDI and HKMT firms are generally better than the local ones.

29

Model (5) and (6) are built up from model (3) to see and compare the spillover

effects through the labors hired by the FDI firms and HKMT firms in 2 sectors of the 2nd

sector, namely the industry sector and the construction sector.

From the regression results of model (5) in Table IV above, the estimated

coefficient of the ratio of labors employed by FDI firms in the industry sector to the total

labor employment number in the industry sector is positive and significant at the 10% level,

which means that, in China’s Eastern coastal and central areas, every 1% increase of this

ratio, it will lead to a 0.08% growth of the industry’s GDP. Yet, this finding, indeed, is

relatively feeble comparing with other findings in this paper as the coefficient of above

ratio in its (t-1) function is insignificant statistically; more works have to be done to solidify

this finding. On the other hand, significant and robust relation does not exist in the labors

employed by HKMT firms according to the results above. In both model (5) and its (t-1)

model, the results shows that there are no relations found between the GDP growth of the

industry sector and the change of the ratio of the industrial labors hired by HKMT firms to

the total industrial employment number as both coefficients in these 2 models are

insignificant.

The aim of setting model (6) is to find out the relations between the GDP growth of

the construction sector and the ratios of the workers in construction sector employed by

FDI firms as well as HKMT firms to the total employment number of the construction

sector. However, as the coefficients of both ratios are statistical insignificant in model (6)

as well as in its (t-1) model. It denotes that no matter how the portion of workers in

construction sector employed by the FDI firms or by HKMT firms among the total

30

construction sector employment changes, the GDP growth rate of the construction sector

will not be affected. This may be explained by construction sector’s situation. As in the

construction sector, it is common to see that the large infrastructure projects are indeed

launched by the state; therefore, the contracts are usually got by the domestic firms. As a

result, the numbers of labors hired by the FDI firms as well as HKMT firms are small, i.e.

the ratios of them to the total employment in construction sector are generally below 5%

from 2002 to 2011. Thus, even if there are spillover effects through these labors, the effects

might not be statistically significant.

As illustrated previously in model (3) of Table II, if there is a 1% increase of the

ratio of labors hired by the FDI firms in the 2nd sector to the total employment of the 2nd

sector, it will lead to a 0.04% increase of the GDP of 2nd sector. One possible reason behind

is that the FDI firms in industry sector are using relatively higher technologies and/or

having higher productivities and the labors working there should have absorbed the skills

and technical know-hows. As a result, when this kind of labors’ portion becomes relatively

bigger in the society, it will lead to a higher economic growth and their knowledge will also

spill to the domestic firms someday later. And this reason is now solidified by the results of

model (5) and (6). Increasing the number of labors employed by the FDI firms in the

industry sector with total employment number of the industry sector unchanged does have a

positive relation with the GDP growth of industry sector. As a major part of GDP growth of

the 2nd sector is in fact coming from the GDP growth of the industry sector9; therefore, one

9 From 2012 to 2013, 2011 to 2012 and 2010 to 2011, the increases of the GDP of the industry sector accounted for 76%, 76% and 84% of the GDP growth of the 2nd sector, while the rest of 24%, 24% and 16% of the GDP growths of the 2nd sector are contributed by the construction sector respectively.

31

of the sources for the spillover effects to carry out through the labors hired by FDI firms in

2nd sector to the domestic economic growth is probably from the spillover effects through

labors hired by the FDI firms in the industry sector but not in the construction sector; yet, to

further ensure this statement, new models as follow should be set and tested:


+ β3In(LIndustry,F,p,t / LIndustry,p,t) + β4In(LIndustry,HKMT,p,t / LIndustry,p,t) + ε

and


+ β3In(LConstruction,F,p,t / LConstruction,p,t) + β4In(LConstruction,HKMT,p,t / LConstruction,p,t) + ε

Table V – Estimation of GDP growth of industry sector effect by changes of (TEVIndustry / TPVIndustry),

(TEVIndustry,F / TPVIndustry,F), (TEVIndustry,HKMT / TPVIndustry,HKMT) and (TEVIndustry,D / TPVIndustry,D).


32

Unlike previous models, model (7) aims at figuring out the relation between the

GDP growth of the industry sector and the ratio of the total industrial export value to the

total production value in order to see whether there will be export-led growth10 in China’s

Eastern coastal and central areas’ industry sectors. From the results of model (7) in Table V,

the estimated coefficient of the total industrial export value to the total industrial production

value is positive and significant at the 1% level. It reveals that when this ratio goes up by

1%, the GDP growth of the industry sector will increase 0.09% and this finding is also

robust in the (t-1) model. Besides L and K, the growth of export to production ratio of the

industry sector can also drive the GDP growth of the industry sector positively; it denotes

that with the total industrial production value unchanged, one can increase the economic

growth of the industry sector by increasing the export value of the industrial goods.

According to Grossman and Helpman (1991), trade can promote technology diffusion and

knowledge spillover and hence lead to a faster productivity growth. Therefore, as China

exports more with the total production level unchanged, there should be a bigger spillover

effect from the trade to the domestic economic sector.

10 Indeed, to see the export-led growth of the whole country should use the model below:

InGDPp,t = C + β1InLp,t + β2InKp,t + β3In(TEVp,t / TPVp,t) + ε

but not only limited to the industry sector. Yet, the export sector is only composed of primary goods and

manufactured goods in China and the export from the industry sector accounts the majority part of the export

sector; i.e. in 2013, 2012 and 2011, manufactured goods’ export values account for 95%, 95% and 94% of

China’s total export values. The relations between the export-led growth of the whole society and the change

of the ratio of the total export value (which is mainly from the industry sector) to the total production value

may not be obvious as the spillover effects have to be strong enough to spill to the non-industry sector in a

short period of time. Since the models set in this paper are only the present year and the lagged one year (t-1),

this paper focuses only on the industry sector’s export-led growth effect.

33

Model (8) tries to find out whether the spillover effect found in model (7) is from

the FDI firms, HKMT investment firms or from the domestic firms. According to the

results in Table V, the coefficient of the ratio of industrial total export value of the domestic

firms to the industrial total production value of these local firms, unlike the ratios of the

FDI firms and HKMT firms, is the only robust finding and it is significant at the 1% level

in both model (8) and its (t-1) model. In fact, the findings above are not difficult to

understand. Comparing to the FDI and HKMT investment firms, domestic firms, without

doubt, are relatively less productive. As trade can increase the chances of technology

diffusions by letting the less advanced party to exposes to the more productive ones and to

learn from the latter, therefore, the spillover effect from the exports of industrial goods only

appears in the relatively backward Chinese domestic industrial firms according to the

regression results above.

34

Table VI – Estimation of GDP growth of industry sector effect by changes of (TEVIndustry,F / TEVIndustry) and (TEVIndustry,HKMT / TEVIndustry).


Unlike Model (7) and (8), model (9) is used to see whether the shares of the

industrial total export value of the FDI firms as well as HKMT firms among the total

industrial export value will affect the growth rate of GDP of the industry sector. As the

estimated results indicate that the coefficients of both ratios of the FDI firms as well as

HKMT investment firms are statistical insignificant in both model (9) and the (t-1) model,

the changes of the export shares amount the FDI firms and HKMT investment firms do not

affect the industrial GDP growth rate. One of the reasons to explain the above findings is

35

that there are basically no export quotas for many manufacturing goods in China after the

entering of WTO and thus, the export amount of the FDI firms and the export amount of

HKMT investment firms do not necessarily have relation and hence, the export amounts

solely depend on the firms’ decisions. As a result, only the changes of ratio of the export

value to the production value will matter and may affect the GDP growth, as model (7) and

(8) show, but not the relative export shares.

36

6. Conclusion and Policy Implication

Using the panel data across China’s 14 provinces in the Eastern coastal and the

central regions from 2002 to 2011, this study tries to find out the evidences of the relations

between labors, capital stocks and the exports of the FDI firms as well as HKMT

investment firms and the domestic GDP growths in different sectors.

Basing on the pooled OLS regression results from above 9 models, it is obvious to

observe that the growth of the labor force and the growth of the fixed asset capital stock are

two important and essential factors to drive the positive growth of the GDP as they are two

basic components of the production function; therefore, it is not hard to understand that

basically all the coefficients of the lnL and lnK in above regression results are positive and

significant at the 1% level, except one of the InK in the construction sector. It indicates that

the growth of the fixed asset capital stock in that sector does not have statistical relation

with the growth of the GDP and in another words; the input increase of the capital will not

lead to output growth in the construction sector.

As for the spillover effects through the labors, the results indicate that, with the total

labor employment number unchanged, both increases of the labors hired by the FDI and

HKMT firms will lead to a positive growth of the GDP. Within the 2nd sector, only the

growth of the labors hired by the FDI firms to the total employment level will lead to a

positive rise of the 2nd sector’s GDP, but not the ones employed by HKMT firms. Whereas

the situation is totally different in the non-2nd sector, the results reveal that, instead of the

FDI firms, the portion of the labors hired by HKMT investment firms to the total

employment number in non-2nd sector has a positive relation with the GDP of that sector.

37

To dig deeper in the 2nd sector, within the industry sector, only the ratio of the labors hired

by FDI firms to the total industrial employment number will positively drive the industry

sector’s GDP, but not the workers employed by HKMT firms. In the construction sector, no

spillover effects are observed from the labors hired by the FDI as well as HKMT firms.

As for the externalities from the capital stocks, when more capital stocks are

invested by the FDI firms, but not HKMT firms, with the total amount of the stocks the

same, it will lead to a positive GDP growth.

Last but not least, this paper also finds that export-led growth does exist in China’s

industry sector as the total export to total production value goes up, the related GDP will

also be driven up positively. Moreover, this situation will only appear in the domestic firms

but not the FDI firms nor HKMT firms. In addition, no statistical relations has found

between the ratios of the industrial export values of FDI firms as well as HKMT firms to

the total export value and the GDP growth of the industry sector; and this indicates that

there will be no crowding out effects between which parties export more.

Basing on the empirical results of the current study, there are some policy directions

towards the government policies of the Eastern coastal and the central regions of China.

Generally speaking, the government should welcome the investments from overseas

and HKMT as they can generate positive externalities to the domestic economic growth.

Comparing both kinds of the investments, FDI ones will have stronger spillover effects

than HKMT ones. And thus, once there are crowding out effects, government should limit

HKMT investments before limiting the FDI.

38

To capture the spillover effects through the labors as much as possible, the

government ought to encourage the FDI firms from the 2nd sector, especially the industry

sector, to hire more domestic workers; on the other hand, to encourage HKMT firms from

non-2nd sector to employ more local workers as well. For instance, giving tax rebates to the

companies hiring certain amounts of the domestic labors.

Encouraging the industry sector to export more is another way to enjoy the spillover

effects. Be that as it may, this can only apply on the domestic firms. Besides liberating the

export duties as the government does now, it, for example, can assist the domestic firms to

build up connections with overseas buyers by holding more expos and internationalize

RMB to facilitate the trading and so on. Although the government should stimulate the

industrial export of the domestic firms, there is not necessary to put a cap or heavy tariffs

on the exports of the FDI firms as well as HKMT firms’ goods.

39

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