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EFFICIENCY ANALYSIS OF CHINA'S OUTWARD FOREIGN DIRECT INVESTMENT IN ASEAN COUNTRIES

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This study aims to compute the efficiency scores of China's ODI in ASEAN countries over the period from 2005 to 2016 and identify the inefficiency determinants that affect the efficiency scores. A stochastic frontier gravity model was employed in the study. The overall performance of China's ODI in ASEAN countries is at inefficiency level, meanwhile the low performance of China's ODI indicated a higher potential level to improve. The second-stage analysis is a panel model regression to identify the inefficiency determinants by using the efficiency scores that derived from first-stage analysis as the dependent variable. The variables used in the second-stage analysis are language, voice and accountability, political stability, government effectiveness, regulation quality, rule of law, and control of corruption. The results showed language, voice and accountability, regulation quality, rule of law, and control of corruption are statistically significant towards the efficiency scores. ASEAN countries are recommended to enhance their government ability thus improve the regulation quality and rule of law.
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International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
871
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
EFFICIENCY ANALYSIS OF CHINA’S OUTWARD
FOREIGN DIRECT INVESTMENT
IN ASEAN COUNTRIES
Vikniswari Vija Kumaran1, Pang Wei Song2*, Zam Zuriyati Binti
Mohamad3, Tan Kock Lim4, Kong Yin Mei5, Kuek Thiam Yong6, Foo
Chuan Chew7
1,2,3,5,6,7Faculty of Business and Finance, Universiti Tunku Abdul Rahman
4KDU Penang University College
Abstract
This study aims to compute the efficiency scores of China’s ODI in ASEAN
countries over the period from 2005 to 2016 and identify the inefficiency
determinants that affect the efficiency scores. A stochastic frontier gravity
model was employed in the study. The overall performance of China’s ODI
in ASEAN countries is at inefficiency level, meanwhile the low performance
of China’s ODI indicated a higher potential level to improve. The second-
stage analysis is a panel model regression to identify the inefficiency
determinants by using the efficiency scores that derived from first-stage
analysis as the dependent variable. The variables used in the second-stage
analysis are language, voice and accountability, political stability,
government effectiveness, regulation quality, rule of law, and control of
corruption. The results showed language, voice and accountability,
regulation quality, rule of law, and control of corruption are statistically
significant towards the efficiency scores. ASEAN countries are
recommended to enhance their government ability thus improve the
regulation quality and rule of law.
Keywords: Efficiency, China’s ODI, Inefficiency, ASEAN, Stochastic
Frontier Gravity Model, Second stage analysis
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
872
1. INTRODUCTION
According to Fan, Zhang, Liu, and Pan (2016), whether the initiative that
China was currently conducting can be beneficial for its ODI depends on the
performance of the ODI and its determinants. However, as a relative
newcomer to global ODI market, the performance and potential of China’s
ODI in ASEAN remain uncertain. More to the point, there is lack of the
efficiency study on China’s ODI in ASEAN. According to UNCTAD
(2017), FDI remains as the largest and least volatile of a key source of
finance for developing economies. In fact, after the Global Financial Crisis
in the year 2008, global ODI shows a sluggish trend. At present, the global
ODI recovery remains bumpy, where both developed and developing
economies contributed a weak ODI flow (UNCTAD, 2017). In such
situation, China plays a crucial role in the developing economies, because it
has surpassed Japan became second largest ODI contributor, and the
expansion of China’s ODI never stopped after the crisis
As a relative newcomer of ODI contributor, there is a gap between China
and those developed economies regarding the aspect of quality investment.
According to Jiang and Liu (2018), China facing several issues such as
inequal distribution of investment area, prominent investment risk, and slow
upgrading of investment industry structure. For instance, the total share of
China’s ODI allocated to ASEAN countries who involved in China’s “Belt
and Road” initiative has a significant dropped. In other words, this proven
the uneven distribution of investment area, where Asia region is the largest
China’s ODI recipient but its sub-region, ASEAN only received the smaller
portion of China’s ODI.
According to UNCTAD (2017), the geopolitical risk and political
uncertainty might hamper the recovery of global FDI. In the meantime,
geopolitical uncertainty is one of the main macroeconomic factors that
agreed by most MNE’s executives that it would lead to a decrease in FDI
flow globally (UNCTAD, 2017). Besides that, according to Tong, Singh,
and Li (2018), host country with a good macro-corporate governance
structure has a positive impact on China’s ODI decision making. In other
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
873
words, host-country with a relatively stable political environment will attract
more China’s ODI. In fact, regional instability remains a serious concern for
ASEAN countries, where ASEAN countries facing internal struggles such
as crisis of Rohingya Refugees, South China Sea dispute, IMDB scandal in
Malaysia, Pattani insurgency in Thailand, and terrorism in the Philippines
(Kurniawan, 2017). Moreover, investing behaviour of China’s MNEs is
largely affected by the variation of policy (Tong, Singh, & Li, 2018).
Therefore, these political instability situations and policy variation are
believed will impacting the FDI inflow in ASEAN and affecting the
efficiency of China’s ODI in ASEAN as well.
The purpose of our study is to examine the performance and potential of
China’s ODI by assessing the efficiency of China’s ODI. Next, inefficiency
determinants are able to identify the magnitude to which the initiative
implementing by China government can improve China’s ODI. Therefore,
this study attempts to compute the efficiency score of China’s ODI in each
ASEAN member and identify the inefficiency determinants that affect
efficiency score by employing a stochastic frontier gravity model.This paper
is organized as follows. Section 2 discusses the methodology, theoretical
and empirical models. Section 3 presents the empirical findings and
discussion of the analysis. Section 4 presents the recommendations and
conclusion.
2. LITERATURE REVIEW
In order to access the efficiency score of China’s ODI in ASEAN countries,
the author needs to define what ODI efficiency in this study is. Indeed, in
existing study, ODI efficiency could also refers to FDI efficiency
(Armstrong, 2011; Fan et al., 2016; Mourao, 2018) or macro-level
investment efficiency (Jiang & Liu, 2018). However, there is lack of the
scholars have defined on FDI efficiency or macro-level investment
efficiency. Therefore, before defines what ODI efficiency is, there is a need
to first explain what efficiency is. According to Farrell (1957), in a firm
context, efficiency refers to the success in producing large amount of an
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
874
output by given a set of inputs. In fact, efficiency can be defined as the rate
of actual value to potential value (Kalirajan and Shand, 1999).
The study by Farrell (1957) claims that the efficiency has two components,
which are allocative efficiency and technical efficiency. In this study, the
present study following the previous studies (Armstrong, 2011; Fan et al.,
2016; Jiang & Liu, 2018; Mourao, 2018) that uses the technical efficiency as
ODI efficiency. Therefore, the author defined ODI efficiency as the ratio
between the actual level of ODI and the potential level of ODI that from a
given set of inputs. In a simple word, the author will compute an output-
oriented technical efficiency which using a set of inputs with an output
(Kumbhakar & Tsionas, 2006). Moreover, a number of studies have
indicated that ODI efficiency is used to explain the performance and
potential of ODI (Armstrong, 2011; Fan et al., 2016; Jiang & Liu, 2018;
Mourao, 2018), where a lower ODI efficiency means the lower ODI
performance, but at the same time, it has a higher potential to further
improve.
According to Armstrong (2011), there is no any model that widely used to
explain the FDI flows, meanwhile, unlike the international trade having the
theoretical model such as gravity trade model, FDI does not have any FDI
model that theoretical underpinnings of. However, the strong
interdependencies between FDI and international trade led to a considerable
number of studies in which using gravity trade model to explain the flow of
FDI, and surprisingly those model applications are relatively successful
(Armstrong 2011).
Besides that, the study by Fan et al (2016) has supported the above
statement which claimed that gravity model is widely used to explain
bilateral FDI flows among different geographical economies. In fact,
according to Hai and Thang (2017), the conventional gravity model could be
biased due to the model unable to control the resistances (inefficiency
factors) that under unobserved disturbance term. Therefore, a stochastic
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
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frontier gravity model was introduced to solve the problem (Hai & Thang,
2017).
3. METHODOLOGY
3.1 Stochastic Frontier Analysis
Stochastic frontier gravity model refers to the integration between stochastic
frontier analysis and the gravity model. In a simple form, the stochastic
frontier analysis is a methodology that applied to estimate a gravity model.
Stochastic frontier analysis is developed by Aigner, Lovell and Schmidt
(1977) and Meeusen and van den Broeck (1977). According to Kumbhakar
and Tsionas (2006), it is a parametric econometric analysis that estimate the
production function or technical efficiency.
The stochastic frontier analysis focus on two components which it will
derive a stochastic production frontier that act as the benchmark against the
efficiency scores is measured, and a one-sided non-negative error term that
which follows an independent and identical normal distribution across
observations to capture inefficiency term across production units (Aigner,
Lovell, & Schmidt, 1977). The stochastic frontier analysis has two common
estimation methods, which are maximum likelihood estimation and methods
of moments (Maurao, 2018).
In the present study, the author will use the maximum likelihood estimation
in stochastic frontier analysis instead of methods of moments. The reason
behind is that the present study having 120 observations but methods of
moment is used to indicating small number of sample size (n < 25).
Therefore, the maximum likelihood estimation is preferred because it allows
a larger sample observation (Maurao, 2018).
As the second-stage analysis of this study follows Armstrong (2011) that
using the panel OLS method to estimate the equation (8), so that the author
will discuss the panel regression method in this subsection. According
Gujarati and Porter (2009), there are three types of panel model to regress
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the panel data estimation, which are Pooled OLS regression (POLS), Fixed
Effect Model (FEM), and Random Effect Model (REM).

  
(1)
The technical efficiency of FDI undertaken by country i to country j over
the t period is defined as
 

 
(2)
When    measures FDI’s efficiency level. High-efficiency scores
suggest ODI from home economy to host economy is reaching closely to its
maximum level of potential, while low-efficiency scores are implying the
room of potential to strengthen regional integration between home and host
economy further
Besides, equation (2) shows that TE is a function of the one-sided
inefficiency element. As the results, if   , means the actual FDI lies
on the frontier due to there are no any frictions of FDI from home to host
economy. However, if   , means that the actual level of FDI falls
short of the frontier level, where indicated there are investment resistances
to FDI.
Indeed, equation (1) can be transformed into a linear equation which written
as:
         
(3)
To explain the FDI inefficiency, a technical inefficiency model is written as
below:
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 

(4)
Where,
is a vector of unknown coefficients;
 is a vector of explanatory variables associated with FDI’s technical
inefficiency over time;
 is the random error which is defined by the truncation normal
distribution. The point of truncation is   , for example:    

Lastly, a full linear stochastic frontier gravity model for FDI from country i
and country j can be written as below by combining equation (3) and (4):
       
 (5)
3.2 Two Stage Analysis
The two-stage approach actually is separated the stochastic frontier gravity
model into two parts. The first part of the model to compute the efficiency
scores of China’s ODI in ASEAN countries, while the second part of the
model to identify the inefficiency factors (investment resistances) that affect
the efficiency scores of China’s ODI.
First-Stage Model as follows:
     
     
        
(6)
 = the ODI flow from China to host country j over the t period.

 = China’s GDP and each ASEAN country’s GDP
respectively.
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 = the relative geographic distance between China and ASEAN
countries.
 = China’s GDP per capita and each ASEAN
country’s GDP per capita respectively.
 = a dummy variable that to indicate whether China and host
country j are contiguous.
 = two-sided error element and the one-sided inefficiency
element respectively.
The second-stage model as follows:
      
 

 
(7)
Where,

= dummy variable, Language.

= Voice and Accountability of host country j.

= Political Stability of host country j.

= Government Effectiveness of host country j.

= Regulation Quality of host country j.

=Rule of Law of host country j.

= Control of Corruption of host country j.

= error term.
3.3 Data Description
The dataset used in this study divides into two sets of determinants to
support the two-stage approach that will be conduct in following chapter
Data for this were retrieved from World Bank.
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. The first set of data, namely frontier determinants which use for first-stage
analysis to compute the efficiency scores of China’s ODI in ASEAN
countries. While the second set of data, namely inefficiency determinants
which will use for the second-stage analysis to identify the inefficiency
factors (investment resistances) that will affect the efficiency scores of
China’s ODI. Data for this were retrieved from World Bank.
4. RESULTS AND DISCUSSION
The two-stage approach is allowed to use in the present study is because of
the advantage of stochastic frontier analysis which distinct the error term to
non-negative error term and normal disturbance. The non-negative error
term captures the inefficiency terms, so that it can be used to construct the
empirical model for second-stage analysis. From the first-stage analysis, the
efficiency scores of China’s ODI has been computed by using Frontier 4.1
software.
A set of frontier determinants (output and inputs) that used to compute the
efficiency score are China’s ODI flows (output), China’s GDP (input), GDP
of host country (input), relative geographical distance (input), China’s GDP
per capita (input), GDP per capita of host country (input), relative natural
resources (input), and ontiguous (input). Based on the result, the overall
efficiency score of China’s ODI in ASEAN countries over the period 2005
to 2016 is 0.3494 which achieved at the inefficiency level (rank 7). This
result shows the performance of China’s ODI in ASEAN countries over the
years is lower, but there is high potential of China’s ODI in ASEAN
2005
200
6
2007
2008
2009
2010
2011
2012
2013
201
4
2015
2016
Mea
n
0.381
0.30
5
0.372
0.333
0.362
0.403
0.325
0.306
0.326
0.32
9
0.338
0.407
Min
0.137
0.00
0
0.105
0.210
0.233
0.132
0.105
0.059
0.097
0.11
9
0.074
0.070
Max
0.781
0.47
3
0.552
0.470
0.463
0.637
0.494
0.516
0.609
0.58
1
0.592
0.725
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countries in future because it has not achieved at frontier level yet.
Table 1: Efficiency Scores of China's ODI in ASEAN Countries, 2005-2016
Table 1 shown that the lowest China’s ODI efficiency score fall in 2006
(Meana = 0.3059), while the highest China’s ODI efficiency score achieved
in 2016 (Meana = 0.4076). However, both under the category of inefficiency
level. Malaysia is the ASEAN country that has the highest China’s ODI
efficiency score (Meanb = 0.5371) which achieved at low efficiency level
(rank 6), while the ASEAN country that has the lowest China’s ODI
performance is Myanmar (Meanb = 0.2270) which under the category of
inefficiency level (rank 7). Moreover, the ASEAN country that repeated to
has the lowest China’s ODI efficiency score is Philippines and Brunei, while
Malaysia and Thailand are the countries that repeated to has the highest
efficiency scores of China’s ODI. From the second-stage analysis, the
efficiency score computed in the first-stage analysis became the dependent
variable and using Eviews 10 software to estimate the panel model. A set of
inefficiency determinants (independent variables) that will affect the
efficiency score are introduced which are language, voice and
accountability, political stability, government effectiveness, regulation
quality, rule of law, and control of corruption.
Table 2: POLS Model Estimation Result
Variable
Coefficient
t-value
p-value
Constant
()
0.2280***
(0.0450)
5.0625
0.0000
Language
()
0.1126**
(0.04890)
2.3019
0.0232
Voice and
Accountability
()
-0.1207***
(0.03729)
-3.2375
0.0016
Political Stability
()
-0.0335
(0.02653)
-1.2618
0.2096
Government
-0.0513
-0.6641
0.5080
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Effectiveness
()
(0.07723)
Regulation Quality
()
0.1102**
(0.05568)
1.9797
0.0502
Rule of Law
()
0.3430***
(0.1123)
3.0557
0.0028
Control of Corruption
()
-0.3016***
(0.05566)
-5.4197
0.0000
R2
0.3305
F-Statistics
7.8973
Adjusted R2
0.2886
Prob (F-
Statistics)
0.0000
Durbin-Watson
Statistics
1.6488
Observation
120
Based on the result shown in Table 2, language, voice and accountability,
regulation quality, rule of law, and control of corruption are statistically
significant to affect the efficiency scores of China’s ODI in ASEAN
countries except for political stability and government effectiveness.
Language, regulation quality, and rule of law showed a positive sign which
are indicates that common languages shared by China and host countries,
high regulation quality and stronger rule of law can improve the efficiency
score of China’s ODI in ASEAN countries through the reduction of
economic distance that improve the bilateral FDI activities.
Furthermore, the voice and accountability and control of corruption showed
a negative coefficient are the surprising result in the present study. Most of
the time, people are thinking the country that having the best governance
environment will attract more FDI allocation. In fact, the multinational
enterprises might avoid the country that has the democracy constraint that
caused by higher score of voice and accountability. Meanwhile, they might
bribes the host country under certain circumstances to circumvent
regulations that burdensome and obstacles of bureaucratic which probably
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explain the higher score of control of corruption that associate lower
performance of China’s ODI.
5. CONCLUSION
Based on the findings, the overall efficiency scores of China’s ODI in
ASEAN countries is fall at inefficiency level. In other words, there is a
higher potential of China’s ODI in ASEAN countries can be improve
through the adjustment or improvement on the respective policies of China
and ASEAN countries based on the inefficiency determinants that have been
identified in the present study. Governance indicators like regulation quality
and rule of law have positive influence towards efficiency scores of China’s
ODI in ASEAN countries as well. It is no doubt that a country that has a
sound legal system can increase the confident level of investor. As the
regulation quality and rule of law used is from the host country (ASEAN
countries), so that the author the following policy suggestion is for ASEAN
countries. The core concern of regulation quality and rule of law is about the
government’s ability to implement and enforce the law and regulations. The
keyword here is the ability of the government because a good quality and
enforceable laws or policy changes is decision by each respective
government. Therefore, ASEAN countries needs to enhance their
government abilities for further improve in their legal system.
When the government’s ability is high then its control of implementation
and enforceability will be increase simultaneously. After the enhancement
of government’s ability, then ASEAN countries should focus and revise
their law and regulations once is outdated or any vulnerability exposure. The
world is changing every day which same goals to the changing of society
behaviour. There is no a perfect law and regulation that no need to revise.
Therefore, as the change of business environment and behaviour, the law
and regulation should be re-examining and revise according to the realistic
to protect the citizens and businesses. Not only that, the government of
ASEAN countries must strictly enforce the law and regulations to shape the
country in a good manner. As a result, the country who has a stronger
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
883
regulation quality and rule of law will increase the confident level of
investor as they feel be protected in the country thus encouraged ODI.
REFERENCES
[1] Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and
estimation of stochastic frontier production function models. Journal of
econometrics,6(1), 21-37. doi: 10.1016/0304-4076(77)90052-5
[2] Armstrong, S. (2011). Assessing the scale and potential of Chinese
investment overseas: An econometric approach. China & World
Economy, 19(4), 22-37. doi: 10.1111/j.1749-124X.2011.01248.x
[3] ASEAN. (2017). Overview of ASEAN-China Dialogue Relations.
Retrieved from http://asean.org/storage/2012/05/Overview-of-ASEAN-
China-Relations-October-2017_For-Website.pdf
[4] Centre for International Prospective Studies and Information. (2018).
a. GeoDist. [Data file].
[5] Fan, Z., Zhang, R., Liu, X., & Pan, L. (2016). China’s outward FDI
efficiency along the Belt and Road: An application of stochastic frontier
gravity model. China Agricultural Economic Review, 8(3), 455-479.doi:
10.1108/CAER-11-2015-0158
[6] Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th
International ed.). New York: McGraw-Hill Education.
[7] Hai, T. H. N., & Thang, N. D. (2017). The ASEAN free trade agreement
and Vietnam’s trade efficiency. Review of Business and Economics
Studies, (1). doi:10.5539/ass.v13n4p192
[8] Jiang, X. B., & Liu, L. M. (2018). China’s direct investment efficiency
toward the countries along the Belt and Road. Journal of Global
Economics, 6(1). 1-5. doi: 10.4172/2375-4389.1000284
[9] Kalirajan, K. P., & Shand, R. T. (1999). Frontier production functions
and technical efficiency measures. Journal of Economic surveys, 13(2),
149-172. doi: 10.1111/1467-6419.00080
[10] Kumbhakar, S. C., & Tsionas, E. G. (2006). Estimation of stochastic
frontier production functions with input-oriented technical
efficiency. Journal of Econometrics, 133(1), 71-96. doi:
10.1016/j.jeconom.2005.03.010
International Journal of Disaster Recovery and Business Continuity
Vol.11, No. 1 (2020), pp. 871-884
ISSN: 2005-4289 IJDRBC
Copyright 2020 SERSC Australia
884
[11] Li, Q., & Resnick, A. (2003). Reversal of fortunes: Democratic
institutions and foreign direct investment inflows to developing
countries. International organization, 57(1), 175-211. doi:
10.1017/S0020818303571077
[12] Mourao, P. R. (2018). What is China seeking from Africa? An analysis
of the economic and political determinants of Chinese outward foreign
direct investment based on stochastic frontier models. China Economic
Review, 48, 258-268. doi: 10.1016/j.chieco.2017.04.006
[13] Tong, T., Singh, T., & Li, B. (2018). Country-level macro-corporate
governance and the outward foreign direct investment: Evidence from
China.
[14] International Journal of Social Economics, 45(1), 107-123.doi:
10.1108/IJSE-09-2016-0243
[15] World Bank. (2018). The World Bank in China. Retrieved from
http://www.worldbank.org/en/country/china/overview
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