1 | P a g e
An Empirical Investigation into the Effects of Foreign
Direct Investment on Economic Growth in South
Asian Economies, and what this means for the ‘Belt
and Road Initiative’
2020
Author: Alexander Plant
2 | P a g e
Contents Chapter 1 – Introduction ............................................................................................................................................... 4
1.1 Introduction ............................................................................................................................................................ 4
1.2 Foreign direct investment ................................................................................................................................ 4
1.3 Belt and Road Initiative ..................................................................................................................................... 5
1.3.1 Routes ............................................................................................................................................................... 5
1.3.2 Objectives ........................................................................................................................................................ 5
1.4 FDI trends in South Asia .................................................................................................................................... 6
Chapter 2 - Literature Review .................................................................................................................................. 10
2.1 Pre-Belt and Road Initiative .......................................................................................................................... 10
2.2 What brought about the Belt and Road Initiative? .............................................................................. 10
2.3 Going Global Strategy vs Belt and Road Initiative ............................................................................... 11
2.4 The Belt and Road Initiative ......................................................................................................................... 11
2.5 China Pakistan Economic Corridor (CPEC) ............................................................................................ 13
2.6 Belt and Road and Economic Growth ....................................................................................................... 14
2.7 Foreign Direct Investment and Economic Growth .............................................................................. 15
2.8 Conclusion of Literature ................................................................................................................................. 18
Chapter 3 – Methodology ........................................................................................................................................... 19
3.1 Theoretical model specification .................................................................................................................. 19
3.1.1 Production function ................................................................................................................................. 19
3.1.2 Augmented Production function ........................................................................................................ 19
3.1.3 Data and Variables.................................................................................................................................... 19
3.2 Functional Model Specification ................................................................................................................... 20
3.2.1 Cobb-Douglas Model Specification .................................................................................................... 20
3.2.2. Linearization of Cobb-Douglas model ............................................................................................. 21
3.3 South Asia Panel Data Analysis.................................................................................................................... 22
3.3.1 The GMM Method ...................................................................................................................................... 22
3.3.2 Difference GMM ......................................................................................................................................... 24
3.3.3 System GMM ............................................................................................................................................... 25
3.3.4 GMM Model Diagnostics test ................................................................................................................ 26
3.3.5 Granger Causality test ............................................................................................................................. 27
3.3.6 Deciding between Dynamic and System GMM ............................................................................. 28
3.3.7 South Asia Method conclusion ............................................................................................................ 28
3.4 Pakistan Data Analysis .................................................................................................................................... 28
3.4.1 Ordinary Least Squares method ......................................................................................................... 28
3.4.2 Unit Root test .............................................................................................................................................. 29
3.4.3 Model Diagnostics test ............................................................................................................................ 30
3 | P a g e
3.4.4 Pakistan method conclusion ................................................................................................................ 31
Chapter 4 – Empirical Results .................................................................................................................................. 32
4.1 South Asia ............................................................................................................................................................. 32
4.1.1 Variance Ratio Test .................................................................................................................................. 32
4.1.2 Difference GMM ......................................................................................................................................... 33
4.1.3 Model Diagnostic Tests ........................................................................................................................... 36
4.1.4 Granger Causality ...................................................................................................................................... 37
4.2 Pakistan ................................................................................................................................................................. 38
4.2.1 Unit Root testing ....................................................................................................................................... 38
4.2.2 OLS regression ........................................................................................................................................... 39
Chapter 5 - Conclusion ................................................................................................................................................ 41
5.1 Results conclusion ............................................................................................................................................ 41
5.2 What this means for the Belt and Road .................................................................................................... 42
References ........................................................................................................................................................................ 43
4 | P a g e
Chapter 1 – Introduction
1.1 Introduction
The Belt and Road Initiative (BRI) introduced by the Chinese government in 2013 is
possibly the largest global development strategy of the 21st century. Involving 150+
countries (Wang 2016); the strategy promotes both domestic and foreign investment,
and infrastructure development to help achieve greater integration, trade and growth
between nations. Foreign Direct Investment (FDI) is a form of cross boarder investment,
being the sum of equity capital, and long and short run capital flows into a country. It can
be seen as a flow of capital from one country to another. If countries follow the
encouragement by the BRI, to increase investment between the nations involved, FDI
should increase. Further, if the countries involved become more advanced in terms of
both physical and digital infrastructure as a result of the BRI, they may attract more FDI
from developed western nations. This paper investigates the effects of FDI on South Asian
countries over the post-BRI period being 2013-2018. These countries are some of the
main targets for investment and development, being located near or bordering China, and
being involved in two dominant routes – China-Pakistan Economic Corridor and the
China, Bangladesh, India, Myanmar Economic Corridor. The investigation will produce
results on the effects of FDI on economic growth in South Asian countries, allowing for
conclusions to be made regarding the potential effects of the ‘Belt and Road Initiative’.
This paper builds a theoretical framework based on classical economic theory, and then
builds a functional model to generate the results. Studies on FDI in South Asia generally
only date to the year 2000, this body of research concerns itself with the effects of FDI
post 2013, meaning the results are directly relevant to the Belt and Road Initiative.
1.2 Foreign direct investment
Globalisation led to a surge in foreign direct investment (FDI). Since the 1990’s world FDI
has risen by great volumes, from US$ 150billion in 1990, to US$ 2.6trillion in 2016 (World
Bank 2020). FDI in neoclassical economic theory brings a multitude of benefits to the host
country, all leading to a growth in real economic GDP per capita. This growth is induced
by the accumulation of capital stock in an economy. However, even though globalisation
led to world GDP growth, there is still no unanimous decision on the real effects of FDI on
an economy. Solow growth theory would state that capital inflows should increase
output, with less developed economies experiencing greater effects on output than
5 | P a g e
capital flows into a developed economy (Solow 1956). Currently, there remains a large
gap in world infrastructure which is acting as a bottleneck to trade and growth (OECD
Business and finance outlook 2018), this gap exists between developed and developing
nations. South Asian countries are considered developing nations; Solow would predict
that the capital accumulation from FDI would lead to great GDP growth in South Asia.
1.3 Belt and Road Initiative
The Belt and Road Initiative emphasizes holistic investment and development
throughout Asia to work towards select goals.
1.3.1 Routes
The Belt and Road Initiative is made up of 6 main routes. 1 – New Eurasia Land bridge, 2
– China, Mongolia, Russia Economic Corridor, 3 – China, Central Asia, West Asia Economic
Corridor, 4 – China IndoChina Peninsula Economic Corridor, 5 - China, Pakistan Economic
Corridor (CPEC), and 6 – China, Bangladesh, India, Myanmar Economic Corridor (OECD
Business and Finance Outlook 2018). South Asian countries are involved in the latter two
routes. CPEC stands out from the others as the route which is predicted to have the
greatest effect on the countries involved (Hillmam 2018).
1.3.2 Objectives
The below objectives have been published and detailed by the Chinese government in
their 13th Five-Year Plan as part of their efforts to inform other nations about the
intentions for the BRI.
• To increase trade and investment in the BRI – trade facilitation strategies will be
implemented across the Belt and Road in the form of greater communication and
cooperation when creating and enforcing policies. Trade inducing infrastructure
will be developed to reduce physical friction and time across the routes. Reducing
regulation on capital flows to allow for more efficient capital allocation.
• Free Silk Road trade zones – be proactive in the negotiation of free trade zones
with those countries along the Belt and Road routes.
• Financial Cooperation for infrastructure funding – work on tightening the
relations and cooperation with large international financial organisations to
increase the flow of capital into BRI projects. The Promotion of the Asian
Infrastructure Investment Bank is also key.
6 | P a g e
• Natural Resource extraction – to improve the cooperation of countries to make the
flow of energy and recourses more efficient from countries with abundance to
countries in need.
• Transport infrastructure improvements – increase the development of
progressive transportation infrastructure, and ensure the integration of which
helps facilitate the efficient transfer of goods and services across Belt and Road
countries.
• Cultural exchange – collaboration between nations in areas such as education,
sports, history, science, technology etc to increase cultural awareness.
(OECD Business and Finance outlook 2018; IMF Country Report, PRC, 2016)
1.4 FDI trends in South Asia
FDI into South Asian countries has increased substantially since the 1980’s. As our
analysis is concerned with data post BRI, we will take a look at the inward FDI flow into
South Asian countries in recent years. All FDI inflow data is measured in US$ at current
prices. The data has been transformed by taking the natural logarithm for data smoothing
and comparison purposes.
Figure 1 – Total Inward FDI South Asia
Here we have the aggregate foreign direct investment net inflows (current US$) into
South Asia, (world bank, 2020).
7 | P a g e
Figure 2 – Inward FDI Afghanistan
Figure 3 - Inward FDI Bangladesh
Figure 4 - Inward FDI Bhutan
Figure 5 - Inward FDI India
8 | P a g e
Figure 6 - Inward FDI Maldeives
Figure 7 - Inward FDI Nepal
Figure 8 - Inward FDI Pakistan
Figure 9 - Inward FDI Sri Lanka
9 | P a g e
Above are the individual foreign direct investment net inflows (current US$) for each of
the South Asian countries. With visual analysis of these graphs, there is a clear constant
uptrend for Bangladesh, India, Maldives, Pakistan and Sri Lanka, implying that between
the years 1990-2018, there has been FDI growth. With Afghanistan, Bhutan and Nepal
however, due to the lack of data, a trend can be hard to visualise. However, as the analysis
in this paper is concerned with 2013-2018, the missing data will not affect the results.
Figure 10 – Chinese FDI into Pakistan
In figure 10, we have the percent of China FDI into Pakistan to Pakistan’s total foreign
direct investment inflow (Chinese FDI data transformed into a 2-year moving average for
clearer interpretation of trend), (Invest Pakistan statistics 2018, World bank 2020). Here,
there is a very significant increase in the proportion of Pakistan’s FDI made up by Chinese
investment. In 2011, China invested US$47 Million in Pakistan, by 2017 that number
reached US$2 Billion.
%
10 | P a g e
Chapter 2 - Literature Review
2.1 Pre-Belt and Road Initiative
China has grown to become the second largest economy in the world, from a GDP of USD
394bn in 1990 to USD 14,363bn (International Monetary Fund 2019). This growth has
been a result of China’s trade and investment reforms and the creation of incentives for
foreign investors, leading to a surge in investment from abroad (Morrison 2019). By 2010
445,244 foreign-invested enterprises were registered in China, being responsible for
15.9% of the urban employment (China statistical workbook 2012). This is significant, as
the labour in urban China is responsible for the majority of its recent economic growth.
During the Deng Xiaoping leadership in 1978, China adopted what is known as the open-
door policy, which led to a more liberal outlook on the global economy and the opening
up of China to foreign direct investment, both of which initiated the transformation of
China to the economic superpower we know today. This new outlook first led to the
development of Shenzhen, resulting in extreme growth rates; between the years 1981
and 1993, a 40% per annum growth rate was experienced (Ge 1999). This rate of
expansion continued throughout China; in 1978 exports from China made up less than
1% of total world exports, by 2010, it exported 10.4% of world goods and services. In
aggregate, the revenue from merchandise exporting totalled USD 1.5 trillion (Husted and
Nishioka 2013).
2.2 What brought about the Belt and Road Initiative?
In recent years, the Chinese economy has begun to change again, but deliberately. China’s
development model has started to shift its objectives more externally. During the past
decade, multiple events have occurred leading to this shifting philosophy (Liu and
Dunford 2016). One being the increasing labour costs in China’s manufacturing industry
(Yang, Chen and Monarch 2010), reducing their international competitive advantage.
Second, due to the high resource dependent growth which has been at the forefront of
China’s high growth rate, it has recently become a major target for environmental
regulation and control. Both of which are somewhat bottlenecking the growth of China.
Since the 2008 financial crisis, the growth rates in China have been declining. Although
the Chinese economy wasn’t as affected by the financial crisis compared with the US,
partly due to a stimulus package announced to alleviate the negative effects of the
financial crisis (Barboza 2008), their economy has still experienced a slowdown. In 2007,
11 | P a g e
GDP growth rate reached 14.3%, by 2013 GDP growth was at 7.8%, the lowest rate since
1999 (World bank 2019). During this period of declining growth rates, the administration
was quick to respond, with Xi Jinping announcing the Belt and Road Initiative in
September 2013 during his visit to Kazakhstan (Xinhua 2015).
2.3 Going Global Strategy vs Belt and Road Initiative
China’s ‘Go out’ or ‘Going Global’ strategy may seem to have similarities with the BRI in
terms of the philosophy of making the Chinese economy more interconnected with the
rest of the world (Bellabona and Spigarelli 2007). This ‘Going Global’ strategy was
introduced in 1999 by the Chinese government to promote outward foreign direct
investment (OFDI) by Chinese firms. Since the introduction of this strategy, appetite for
foreign investments by Chinese firms significantly increased, with a 1000% increase from
1991 to 2003, then another 300% increase from 2003 to 2007, reaching US$92 billion
(Xinhua 2007). However, whilst this strategy focusses on OFDI into firms and general
business development internationally (Mumuni and Murphy 2018), the BRI focuses on a
more holistic approach to a global development strategy, with infrastructure as a
foundation, President Xi stating “Infrastructure connectivity is the foundation of
development through cooperation. We should promote land, maritime, air and
cyberspace connectivity” (Hillman 2018). Focussing on this area of infrastructure should
facilitate the creation of trade due to the removal of technical and institutional
bottlenecks (Liu and Dunford 2016).
2.4 The Belt and Road Initiative
The Belt and Road Initiative is a development strategy/philosophy introduced by the
Chinese government in 2013. The strategy incorporates 152 countries from Asia, the
Middle East, Africa and Europe. It is argued that the BRI represents a shift in China’s
foreign policy from a defensive, reactive policy to a more proactive approach to
international relations (Wang 2016; Yu 2017). The name is making reference to the
ancient trade routes used by China, which in a sense will be revitalised throughout this
initiative.
China has been very active since the announcement of the BRI in taking action to make
the BRI to materialise. China’s Vision and Actions document outlines key areas which
should be focussed on, along with more broad objectives for the BRI. The promotion of
greater infrastructure is a key focus, with energy, port and information infrastructure
12 | P a g e
being emphasized. Information infrastructure such as optical fibre cable and
telecommunication equipment will advance the ‘Information Silk Road’ which plays a
major part in the integration of economies across Asia.
One view is that the BRI is a strategy which has been implemented to aid the development
of the West China regions (Ferdinand 2016; Summers 2016). These regions have been
slower to develop due to political factors. Frictions in politics between East and West
China, and also frictions between West China and bordering countries led to problematic
trade relationships. The major bottleneck in the growth of Western China however is the
geographical location, being that it is far from the coast. Most of China’s growth in the
past few decades have been export led, with the majority of the demand of China’s exports
being sea-borne, this gave regions located along the coast of China a large advantage over
landlocked regions. In 2015 it was estimated that the western regions will need 30-50
years to catch up with the eastern regions (Bing 2015). With the BRI though, the
developments in transportation infrastructure may allow for goods to be transported to
the coast then exported from there, but also may allow for exportation of goods westward
to bordering nations, especially if the BRI leads to greater international political
integration.
China has pledged to invest up to US$1 trillion to develop and innovate domestic
transportation infrastructure, and the China Development Bank has reserved US$890
billion for the investment into countries along the corridor (Ferdinand 2016). This
amount should lead to significant development of infrastructure along the ‘Belt’. The total
amount invested across the Belt and Road economic routes and corridors has been
estimated at the top end of US$6 trillion (Zhai 2018). By 2016, 30 countries have singed
‘memorandums of understanding’ (MOUs) with China, to collectively work towards the
goals of the BRI. More than 40 other countries have publicly advocated the BRI,
acknowledging the importance of a collective development strategy (Liu and Dunford
2016). Due to the undefined nature of the initiative, the project time scale is open to
interpretation, one estimate is set at 35 years, which was produced in 2014 by the China
economic publishing house.
With the grandeur of the BRI, financial backing is needed. This has been recognised by
China, leading to the incorporation of the ‘Asian Infrastructure Investment Bank’ (AIIB)
and ‘Silk Road Fund’. The AIIB, is solely devoted to the lending of funds for infrastructure
13 | P a g e
projects relating to the BRI. The bank has backing by 76 members, and over 20 more
prospective members. By 2015, already over US$160 billion worth of infrastructure
projects have begun construction, or are close to starting (Wan 2016). The Silk Road Fund
is a later development in the BRI timeline, being announced in Q4 2014. This is a
development fund, with capital of US$40 billion. This fund would be focussed on the
investment in firms rather than infrastructure. Finally, China’s Export Import Bank
(EXIM) has high exposure to the BRI projects with US$140 billion of outstanding loans
(Xinhua, China state council information office 2019).
Already, 18 Chinese cities have developed and opened direct railway links to European
cities (Yang, Jiang and Ng 2018). The first ever London to China direct rail route has been
opened as a result of the BRI; the Yiwu-London railway line was opened in 2017,
establishing a modern-day silk road. This 12,000km route is a material representation of
the BRI. It shows that large scale infrastructure work being built as a result of the BRI.
There are many other large-scale projects outside of China, such as the US$4 billion
railway line connecting Ethiopia to Djibouti, being the first of its kind in East Africa: a
750km fully electrified line, complete and operating in Q1 2018. The largest project
between China and Sri Lanka is a US$1.4 billion port. The port is expected to be complete
in July 2020, and to create 83,000 jobs over the next 20 years for locals. Further, in 2016
there were 1683 trains running to Europe using the Eurasian rail line, and by 2017 that
number had more than doubled to 3637 trains (Xinhua 2017). A major development was
the sale of Piraeus Harbour to China Overseas Shipping Company, after pressure from the
Greek government. Since this sale, the port has been operating very heavily, with a
highest ever amount of container traffic reached in 2017, at 453,264 TEU (twenty-foot
equivalent unit), which was a 70.6% increase in 2016’s traffic (Yang, Jiang and Ng 2018).
These events are proof the BRI is real, and having an impact today.
2.5 China Pakistan Economic Corridor (CPEC)
The trade relationship between China and Pakistan has been a cooperative one for many
years; in 2006 they signed a bilateral agreement for the facilitation of free trade (FTA),
with the figure of US$ 20billion being the aim of trade between the two nations FTA
(Irshad, et al 2015). Phase two of this FTA has been implemented during Q1 2020, with
the further lowering of tariffs, and the normalization of trade procedure designed to
reduce time and friction of trade. The geographical location of both the countries also
plays a role, with the gravity trade model implying that the two countries trade will be
14 | P a g e
amplified due to China and Pakistan bordering each other (Isard 1954). As there is
political and legislative cooperation between the two countries, trade is fluid between
them, however, with Pakistan’s low-tech infrastructure, the movement of goods could be
bottlenecked in many cases.
The China – Pakistan Economic Corridor (CPEC) is possibly the most significant route of
the BRI. As China is already Pakistan’s second largest trading partner (Irshad, et al 2015),
the CPEC should further strengthen this close relationship, and help facilitate further
trade growth. It is intended to develop infrastructure in Pakistan such as transportation
networks and energy infrastructure (Ramachandran 2016). It has been valued at US$46
billion (Irshad, et al 2015), being raised to US$62 billion two years later (Hussain 2015),
but this figure seems to vary. The ‘Early Harvest’ roadway is a US$6.1 billion roadway
project, the largest road infrastructure project in terms of value in Pakistan (Rana 2015).
Another major development in the CPEC is the Gwadar Port and City, this development
started in 2002, but has been substantially improved and focussed on since the
announcement of the BRI. By 2017, over US$1 billion worth investments are to be
developed in and around the port of Gwadar (Saran 2015). The idea is to connect the
Gwadar port to the landlocked Chinese province Xinjiang, thus creating mass export
potential for Xinjiang. This investment may create benefits for Pakistan in the form of
trade facilitation and therefore economic growth; the transportation infrastructure
should increase the capacity and efficiency of physical exports, removing any bottlenecks
which existed in the past.
2.6 Belt and Road and Economic Growth
One of the objectives of the Belt and Road Initiative is to develop transportation
infrastructure with the aim to help expediate trade and growth within BRI nations (IMF
Country Report, PRC, 2016). There is a small amount of papers which analyse the effects
of the investment on infrastructure on the economic growth in BRI countries. It is argued
that the more developed the infrastructure in a country is, the more it helps to facilitate
trade and growth (Zhang 2001). This is acknowledged again in Falki (2009), stating that
countries with low quality infrastructure such as Pakistan, should aim to develop its
quality so that trade can be promoted. Meaning that before the benefits of FDI in the form
of technology transfer can be gained, the level of infrastructure needs to be sufficient, or
else high quality FDI from multinational companies will be deterred.
15 | P a g e
One study uses a Structural Computable General Equilibrium Model (CGE) to analyse and
predict the effects of a reduction in trade costs as a result of the BRI investment (de
Soyres, Mulabdic and Ruta 2020). Firstly, they find that the investment reduces trade
friction (transportation costs and time) between BRI countries. This generates an
increase in trade between BRI countries by 7.2%, and for South Asia, 6%. They find that
the average increase in GDP in BRI countries is approximately 1.5%. In addition to de
Soyres’ paper, a second paper uses a Global CGE Model (Zhai 2018). Using the assumption
that US$ 1.4trillion will be invested over a 15 year period (2015-2029), which could be
considered a conservative estimate, they find that the average increase in GDP over the
15 years for BRI countries will be 2.9%, this factors in the investment increase, the
reduction in trade costs and improvements in energy efficiency. It is also found that
Pakistan experiences the highest increase in investment out of all BRI counties, with an
increase of 5% total annual investment for the 15-year period, this is in line with Jonathan
Hillmam’s paper, where it states that the CPEC is the most active corridor (Hillman 2018).
With this limited amount of research, it can be concluded that the investment in
transportation infrastructure induced by the Belt and Road Initiative should have a
positive impact on trade and GDP within countries involved with the BRI.
2.7 Foreign Direct Investment and Economic Growth
The effects of FDI on Growth in an economy is a topic of much interest, both theoretically
and empirically. There seems to be a disparity between theory and empirical evidence. A
neo-classical economic model such as the Solow Growth model (Solow 1956) would
imply that FDI should lead to an increase in real GDP per capita due to the capital
accumulation effects; an increase in capital will increase output. With the diminishing
returns to capital in the Solow model, FDI into poorer countries should have a greater
impact than FDI into richer ones. On the other hand, it has been theorised that if a nation
depends on FDI for economic growth, then its GDP growth may actually be negatively
affected, this is known as the dependency theory. Stating that FDI leads to the
development of monopolies in domestic markets, reducing competition, leading to under-
utilisation of host country labour and capital (Adams 2009).
Ronald Findlay builds on the idea that FDI affects economies differently, stating that if
FDI comes from a developed high-income country, it will bring advanced technology
which previously isn’t available to the host (Findlay 1978), leading to a spill over effect.
The causal relationship between technology and growth has been outlined in a study on
16 | P a g e
developing economies (OECD 1991). The spill over effect would help progress the
knowledge in the host country, resulting in higher output (Durham 2004; Nair-Reichert
and Weinhold 2001). Thus, helping the low income, low knowledge countries grow faster.
FDI can also affect growth by the creation of competition in the host country, leading to a
productivity spill over (Blomstrom, persson 1983). When a foreign firm starts production
in the host country, the domestic firms now have more competition, creating incentives
to improve the efficiency and productivity of production (Kokko 1994), this is in direct
contradiction to the dependency theory, where it states competition is reduced. Other
Positive externalities FDI can produce are, for example, multinational firms may invest in
transportation infrastructure to help export their goods, which the host country could
use and benefit from. They may spread information about international markets, culture,
export techniques etc, all improving the domestic firm’s knowledge regarding the export
market (Blomstrom and Kokko 1998).
In general, classical economic theory would imply that FDI has a positive effect on an
economy, in terms of economic growth. However, the literature is divided when it comes
to FDI and economic growth. Multiple studies show that FDI has positive spill over
effects, leading to economic growth. In the transitioning economies of Eastern Europe,
there is a significant positive correlation between FDI and economic growth (Campos and
Kinoshita 2002). The analysis in this paper is robust, however is not flawless. The sample
size consists of only 8 years, meaning the results cannot be interpreted as long-run effects
of FDI. Nevertheless, FDI proved to be beneficial for economic growth in this sample. A
second study suggests similar results for European countries (Kherfi, Soliman 2005).
They find that countries in Central and Eastern Europe (CEE) experience more economic
growth per capita when they have a higher inflow of FDI, however, this is only when the
CEE countries have access to the European Union. That is, the growth generated by FDI
is export driven growth, and when the bottlenecks on trade are reduced, the effect of FDI
becomes significant (Borensztein, et al 1998). This hypothesis is confirmed by
(Balsubramanyam et al 1996), where trade liberalisation and reforms are important
factors if FDI is to stimulate growth. Again, if infrastructure and liberal trade policies are
present, FDI should have a positive effect on economic growth (Zhang 2001).
Similar studies have been carried out on developing countries. It is found that in Sub-
Saharan African countries, FDI has a positive impact on GDP growth (Tsatsaridis 2017).
The interaction between FDI and human capital is also studied here. It is found that as
17 | P a g e
the quality of human capital increases, the effect of FDI reduces, and even becomes
negative. Meaning, that only when the labour force is very low skilled, and education level
is low, FDI will have a positive impact, and as education improves, FDI’s impact reduces.
This finding is in-line with economic theory, in that FDI impacts less developed
economies more positively. A similar study concerns itself with the role of Chinese OFDI
into African economies. It is found that inflow of Chinese FDI had a causal relationship
with GDP growth in the years before the 2008 financial crisis (Whalley, Weisbrod 2012).
However, the results were very uneven, and only due to the large sample size of countries,
a conclusion can be made. This implies that the effects of FDI should be looked at on a
more case by case basis, or with a cross section of comparable countries for any useful
conclusion to be made.
Analysis on FDI in South Asia is limited. One study investigates the effects of FDI into
South Asia (Sahoo 2006), using data from 1970 to 2003. Only a few regressions lead to
the conclusion that FDI in South Asia has a positive effect on GDP, a very small but
significant coefficient is calculated. Therefore, the analysis carried out leads to the
conclusion that FDI leads to GDP growth in South Asia. The analysis also indicates that
exports have a high determination over GDP, this finding is supported in the previous
literature, where FDI is more impactful in nations where growth is export led (Bhagwati
1994). Another study on FDI in South Asia has contradictory results. Based on an
augmented production function, it was found from 1965-1996, FDI has a negative impact
on GDP growth, however the relationship has very low validity, (Agrawal, 2000). During
a period from 1990-1996, there was a very weak statistical relationship indicating FDI
effects GDP growth positively, this could indicate that in more recent years, South Asian
economies are experiencing different forms of FDI, thus affecting growth differently
(positively).
A study centred around Pakistan, located in South Asia, investigated the effects of FDI on
Pakistan GDP growth over a 30-year period from 1970-2001 (Atique et al. 2004). It
concluded that FDI in isolation had a negative correlation with GDP growth, however the
coefficient was very small, the FDI and trade openness coefficient (variables multiplied
together) was larger, positive and significant. This is further evidence that trade openness
in an economy is needed if FDI is going to be beneficial. A second paper on Pakistan found
dissimilar results (Saqib, Masnoon, Rafique 2013). The analysis is carried out using an
ordinary least squares method, including variables such as debt and trade. From 1981 to
18 | P a g e
2010, there was a negative correlation between GDP per capita growth and inflows of
FDI. This result is not in line with neoclassical growth theory; however, the result is
similar to the above paper (Atique et al. (2004) and also (Khan 2007). Further, a similar
study of FDI in Pakistan shares the same conclusion (Falki 2009). An OLS method is used
to show that FDI and GDP growth are negatively correlated, however in this paper, the
results are statistically insignificant and therefore has no validity when making a
conclusion. Again, this result is shared by the papers mentioned above, and also a paper
by Agrawal (2004).
2.8 Conclusion of Literature
To conclude, there are multiple economic theories regarding the effects of foreign direct
investment on economic growth, and the empirical analysis published and discussed
above lead to no consistent conclusion, even when centred around South Asia.
The analysis of this study will concern itself with the levels of FDI post BRI, to see whether
this FDI is more affective to economic growth than general FDI experienced in the last
few decades. Literature tends to conclude that better infrastructure is needed for FDI to
have positive effects on GDP. As the BRI aims to develop infrastructure, the FDI in South
Asian countries may be more likely to lead to positive economic growth if they have better
infrastructure as a result of the BRI. The BRI may also induce more FDI in the form of
intra-BRI country investments, what sets this apart from previous/other investment is
that there is a clear path to follow, with outlined goals. Having a clear path to follow may
lead to more efficient investment and possibly more economic growth.
19 | P a g e
Chapter 3 – Methodology
3.1 Theoretical model specification
This chapter will discuss the model used for analysis, including the theory behind the
model, how the functional form of the model is derived, and what data will be used. The
regression methods will be discussed in great detail, along with diagnostics tests used for
the models. The theoretical framework used will the inspired by classical economics, and
the variables will be chosen with influence from the literature above.
3.1.1 Production function
Starting with the simple two-factor production function, a key model in neo-classical
economics, and one used in many empirical studies of FDI effects (Romm 2006); Sahoo
2006). This traditional model only includes capital stock (K) and Labour (L). As this study
is investigating the impact of Foreign Direct Investment, capital stock will be separated
into domestically owned and foreign owned.
𝑌 = 𝑓(𝐾𝑑 , 𝐾𝑓 , 𝐿)
Where Y denotes output, Kd denotes domestically owned capital, Kf denotes foreign
owned capital, and L denotes Labour in the economy.
3.1.2 Augmented Production function
Based on empirical studies, the production function will be augmented to include extra
variables to make the function relevant and practical.
𝑌 = 𝑓(𝐾𝑑, 𝐾𝑓 , 𝐿, 𝑌−1, 𝐺, 𝑂, 𝐷, 𝐼𝑁𝐹, 𝐻𝐷𝐼)
Where Y-1 denotes the lagged output by one year. Domestically owned capital denoted as
Kd, Kf denotes foreign owned capital, and L denotes Labour in the economy. Where G
represents government spending. Where O denotes the trade openness of the country.
Total Debt is denoted using D. Infrastructure is denoted with INF. And HDI, which is a
holistic representation of the quality of human capital in the economy. This equation aims
to include all significant variables which could affect output, thus removing any chance of
under-specification of the model.
3.1.3 Data and Variables
The data used is the closest proxy which best represents the theoretical variables in the
above equation. All data will be taken from the World Bank Development Indicators, for
20 | P a g e
all eight countries, from 2013 to 2018. Output will be estimated using Real GDP (Current
US$). Domestic capital will be estimated using the Gross Domestic Savings in the economy
(Current US$). Foreign owned capital will be estimated using the Foreign Direct
Investment inflows (Current US$). Labour will be estimated using Total Labour Force.
Government spending will be estimated using Government Final Consumption
Expenditure (Current US$). Openness will be estimated using Total Trade (Current US$),
estimated using Total Trade as % of GDP * GDP to transform the data from percentage to
US$ amounts. Debt will be estimated by the Total External Debt Stock (Current, US$).
Infrastructure will be proxied using the Logistics Performance index (measured 1=low to
5=high). Finally, for HDI, we will use the Human Development Index. For the OLS
regression focussing on Pakistan, Telephone access and Mobile Cellular access per 100
population will be used as a proxy for infrastructure.
As we now have all the data to be incorporated into the function, we can re-write with
updated abbreviations.
𝐺𝐷𝑃 = 𝑓(𝐺𝐷𝑃−1, 𝑆𝐴𝑉, 𝐹𝐷𝐼, 𝐿𝐹, 𝐺𝑂𝑉, 𝑇𝑅𝐴𝐷𝐸, 𝐷𝐸𝐵𝑇, 𝐼𝑁𝐹, 𝐻𝐷𝐼)
3.2 Functional Model Specification
The augmented version of the production function specified above is for theoretical
representation only, it has no real mathematical function. The neoclassical production
function above will be specified in the functional form of the Cobb-Douglas model (Cobb,
Douglas 1928).
3.2.1 Cobb-Douglas Model Specification
The Cobb-Douglas can be used as a non-linear functional form of the simple two variable
production function.
𝑌 = (𝐾𝛼1𝐿𝛼2)
And the expanded simple production function.
𝑌 = (𝐾𝑑𝛼1𝐾𝑓
𝛼2𝐿𝛼3)
Here, the function is the same, but Kd, Kf, and L are all to the power of α1, α2 and α3
respectively. α1, α2 and α3 represent the output elasticities of the inputs. That is, the
responsiveness of output to a change in the level on an input. Now, writing the modified
production function in Cobb-Douglas format produces:
21 | P a g e
𝐺𝐷𝑃𝑖,𝑡 = (𝐺𝐷𝑃𝑖,𝑡−1𝛼1 𝑆𝐴𝑉𝑖,𝑡
𝛼2 𝐹𝐷𝐼𝑖,𝑡𝛼3 𝐿𝐹𝑖,𝑡
𝛼4 𝐺𝑂𝑉𝑖,𝑡𝛼5 𝑇𝑅𝐴𝐷𝐸𝑖,𝑡
𝛼6 𝐷𝐸𝐵𝑇𝑖,𝑡𝛼7𝐼𝑁𝐹𝑖,𝑡
𝛼8 𝐻𝐷𝐼𝑖,𝑡𝛼9)
This is a mathematically functional model. However, this model is non-linear, therefore it
will not be able to use any linear regression methods.
3.2.2. Linearization of Cobb-Douglas model
The Cobb-Douglas is a multiplicative exponential function, and to be regressed using
linear methods, it needs to be transformed into a linear model. The Cobb-Douglas model
can be linearized by taking the natural logarithm of all variables. The linear form of the
model is:
𝑙𝑛𝑌 = 𝛼0 + ∑ 𝛼𝑖
𝑖
ln(𝑋𝑖)
Where Y is the output, Xi are the input variables, αi are the model coefficients. Thus, the
linearized model becomes:
𝑙𝑛𝐺𝐷𝑃𝑖,𝑡 = 𝛼0 + 𝛼1𝑙𝑛𝐺𝐷𝑃𝑖,𝑡−1 + 𝛼2𝑙𝑛𝑆𝐴𝑉𝑖,𝑡 + 𝛼3𝑙𝑛𝐹𝐷𝐼𝑖,𝑡 + 𝛼4𝑙𝑛𝐿𝐹𝑖,𝑡 + 𝛼5𝑙𝑛𝐺𝑂𝑉𝑖,𝑡
+ 𝛼6𝑙𝑛𝑇𝑅𝐴𝐷𝐸𝑖,𝑡 + 𝛼7𝑙𝑛𝐷𝐸𝐵𝑇𝑖,𝑡 + 𝛼8𝑙𝑛𝐼𝑁𝐹𝑖,𝑡 + 𝛼9𝑙𝑛𝐻𝐷𝐼𝑖,𝑡 + 𝜇𝑖,𝑡
The above equation is the final form of the model used in the analysis before it gets
manipulated any further due to regression analysis methods. The variable µt is the error
term, which is the difference between the model estimated values of lnGDPi, t and the true
values of lnGDPi, t.
The main reasons to linearize the model is so that it can be regressed using linear
regression methods. This is because taking the natural logarithm transforms growth
trends like the ones witnessed in GDP over time to linear trends. It smooths exponential
growth, allowing for the variables to better fit a linear model. Logging also helps reduce
heteroskedasticity (meaning that the variance is now more constant over the time
period). Secondly, it allows all data, which previously may have been measured in
different units to be in the same comparable universe, thus allowing for comparison
between different types of variables and data.
ln(𝑋𝑡) − ln(𝑋𝑡−1) ≈ 𝑋𝑡 − 𝑋𝑡−1
𝑋𝑡−1
This means that the change in the natural log of X is approximately equal to the
percentage change in X. For example, our Labour force data is measure in terms of people,
22 | P a g e
whereas the GDP data is measured in terms of US$, taking the natural log allows for these
two variables to be regressed and compared with each other.
3.3 South Asia Panel Data Analysis
The Generalized Method of Moments (GMM) method to estimate the parameters of the
model will be used. Using the GMM is suitable for this data set, which has a long cross
section of counties, but only a relatively short time period, as with this type of sample, the
GMM performs well, providing statistically significant output, allowing for valid
interpretation and conclusions to be made. Another advantage of the GMM method is that
it controls for problems created by endogeneity of regressors. The GMM method accounts
for endogeneity by including instrument variables (IV). When running the GMM model,
two regressions will be carried out, equation (1) consists of explanatory variables from
the simple production function, equation (2) will contain the extra variables introduced.
3.3.1 The GMM Method
The GMM is a method of estimating parameters in a model, using ‘moment conditions’ or
‘moment restrictions’, these moment conditions are derived from the dataset. The GMM
aims to estimate parameters of a statistical model which would best fit the data set used.
The unknown parameter is denoted at 𝜃, Yt denotes an individual observation which is a
multivariate random variable with n dimensions. Parameters are estimated using the
vector function of g(y, 𝜃):
𝑚(𝜃0) ≡ 𝐸[𝑔(𝑌𝑡, 𝜃0)] = 0
The expectation is denoted by E. The sample data is now substituted giving:
�̂�(𝜃) ≡1
𝑇∑ 𝑔(𝑌𝑡, 𝜃)
𝑇
𝑡=1
The expression is now minimised with respect to 𝜃, this minimised value is the GMM
estimate of parameter 𝜃0.
For a more intuitive explanation, the regression model used in the GMM method is now
constructed. Starting with a linear regression model with an endogenous regressor:
𝑦 = 𝒙′𝛽 + 𝑒
𝐶𝑜𝑣(𝑒, 𝒛) = 0
23 | P a g e
Where, 𝛽 denotes a column vector of coefficients, y and e are random variables, 𝑥 is a
column vector of k regressors; 𝒙 = (𝒙1, … , 𝒙𝑘)’, z is a column vector of j instruments; 𝒛 =
(𝒛𝑗, … , 𝒛𝑗)’. Using X, Y, and Z to denote the matrices of N observations(countries) for x, y,
and z. E is defined as 𝑬 = 𝒀 − 𝛽𝑿. The estimate of 𝛽 will be �̂�, giving �̂� = (�̂�1, … , �̂�𝑁)′ =
𝒀 − 𝑿�̂�. The above explanation has been used throughout papers on GMM methods
(Roodman 2009). The x variable consists of explanatory variables and the z variable
consists of instruments.
Now the above model will be expanded to be used in this study, however, to go any
further, the assumptions made by the GMM about the data generation process should be
outlined.
1. Firstly, A dynamic process may be present in the data, with current observations
of the dependent variable being correlated with past observations. That is, lnGDPi,
t may be influenced by lnGDPi, t-1.
2. Individual fixed effects may be arbitrarily distributed.
3. Some regressors may be endogenous to the model. That is, some explanatory
variables may be correlated with the error term in the model. For example, Cov
(lnFDIi, t, µi, t) ≠ 0
4. The errors/residuals other than the fixed effects may have serial correlation and
be heteroskedastic.
5. There is no correlation between the errors across the individual cross sections
(countries)
6. Some regressors can be can be correlated with past observations, but not strictly
exogenous. For example, the lagged dependent variable; lnGDPi, t.
7. The time periods observed can be smaller than the cross sections. However, this
is not the case with our data, with 30 years, 8 countries, and 7 variables.
8. It is also assumed that instrument variables (defined on the next page) can only
come from within the dataset available, that is, the instruments used must be lags
of the regressors.
The above assumptions have been detailed in Roodman, (2009).
24 | P a g e
Below is the common model used for the data generation process, which is much like the
one above.
𝑦𝑖,𝑡 = 𝛼𝑦𝑖,𝑡−1 + 𝛽𝒙′𝑖,𝑡 + 𝜂𝑖 + 𝛾𝑡 + 𝑢𝑖,𝑡
Where y is the dependent variable, in this case, the natural log of GDP; lnGDP. Where yi,t-1
is the lagged dependent variable; the lagged lnGDP. Where x denotes the vector of
explanatory variables, which to reiterate in the model used in this study are the natural
logs of; FDI, SAV, GOV, DEBT, TRADE, INF, HDI. And 𝜂 denotes the cross-sectional specific
effects, in this case, the fixed country specific effects. And, u denotes the time varying
error term. Finally, 𝛾 denotes the fixed time specific effects. For explanatory
simplification, from this point, 𝛾 will be omitted, and time period dummies which are also
included in the model to catch year specific effects will be omitted too. (Kosack, Tobin
2006).
Instrument variables (IV) are used in the GMM method to account for endogeneity. An
Instrument Variable is a variable which isn’t itself a valid explanatory variable, thus it
does not belong in the regression equation, however it is be correlated with an
endogenous regressors. A valid instrument variable has two conditions; (1) there must
be a correlation between the endogenous regressor and the IV, (2) there must be no
correlation between the IV and the error term in the regression equation (Newey, Powell
2003).
3.3.2 Difference GMM
Arellano and Bond developed a GMM method to produce a dynamic model used for panel
data, which is known as the Difference GMM (Arellano and Bond 1991). In this initial
model, an endogeneity problem arises (Nickell 1981). Endogeneity is when there is a
correlation between an explanatory variable, and the error term. The cause of the
problem is the lagged dependent variable being correlated with the error term. Using the
lagged dependent variable as an explanatory variable means that the regressors
distribution cannot be independent to the error term. This leads to a bias during the
estimation of the coefficient of the lagged dependent variable; 𝛼. To account for this
problem, the variables are transformed by taking the first difference. The Arellano and
Bond GMM also accounts for the fixed time and country effects, therefore, freeing the data
to show true correlations, independent of the fixed effects.
25 | P a g e
Starting with the initial model:
𝑦𝑖,𝑡 = 𝛼𝑦𝑖,𝑡−1 + 𝛽𝒙′𝑖,𝑡 + 𝜂𝑖 + 𝑢𝑖,𝑡
Now, the model is transformed by taking the first difference, as did Arellano and Bond.
𝑦𝑖,𝑡 − 𝑦𝑖,𝑡−1 = 𝛼(𝑦𝑖,𝑡−1 − 𝑦𝑖,𝑡−2) + 𝛽(𝒙𝑖,𝑡 − 𝒙𝑖,𝑡−1) + (𝜂𝑖 − 𝜂𝑖) + (𝑢𝑖,𝑡 − 𝑢𝑖,𝑡−1)
𝑒𝑖,𝑡 − 𝑒𝑖,𝑡−1 = (𝜂𝑖 − 𝜂𝑖) + (𝑢𝑖,𝑡 − 𝑢𝑖,𝑡−1)
As 𝜂𝑖 = 𝜂𝑖 , taking the first difference removes this fixed effects from the model and by
taking the first difference of the error term, endogeneity of the lagged dependent variable
is accounted for. The model can be re-written as follows;
∆𝑦𝑖,𝑡 = 𝛼∆𝑦𝑖,𝑡−1 + 𝛽∆𝒙′𝑖,𝑡 + ∆𝑢𝑖,𝑡
The instrument variables used in the Difference GMM will be in levels form. Levels form
refers to untransformed variables. The most common IV are lagged levels of the
explanatory regressors, as assumption 8 states that IV have to come from within the
dataset.
Blundell and Bond (1998) critique the difference GMM saying it uses weak instruments,
and the model is not good when the depended variable y follows a random walk (as the
lagged variables and instruments will provide little information regarding the future
changes) (Roodman 2009). However, only when the regressed variables themselves
follow a random walk as well, is when the instruments are weak.
3.3.3 System GMM
To address problems in the Difference GMM, Arellano and Bover in 1995 and then
Blundell and Bond in 1998 made further modifications to the GMM method creating what
is known as the System GMM method (Arellano, Bover 1995; Blundell and Bond 1998).
This GMM method builds a system of two equations; one similar to the Difference GMM,
and one new equation in levels. The System GMM corrects endogeneity by introducing
more instrument variables which should improve the efficiency of the model. They also
control endogeneity by building an equation in levels, and then transforming the
instrument variables, making the IV exogenous to the fixed effects in the explanatory
regression. Another modification which the System GMM brings is rather than
transforming data by taking the first difference, it transforms using orthogonal deviations
26 | P a g e
instead. An orthogonal deviation is when the difference is taken by subtracting the
average of all future values away from the current observation, this can be written in the
following format:
∆𝑦𝑖,𝑡 = 𝑦𝑖,1 −1
𝑇 − 1(𝑦𝑖,2 + ⋯ + 𝑦𝑖,𝑇)
Taking the orthogonal deviation means that if one time period of data is missing, then
only one piece of transformed data will be missing.
Therefore, with these alterations listed above, the two System GMM equations are as
follows:
Equation in differences: ∆𝑦𝑖,𝑡 = 𝛼∆𝑦𝑖,𝑡−1 + 𝛽∆𝒙′𝑖,𝑡 + ∆𝑒𝑖,𝑡
Equation in levels: 𝑦𝑖,𝑡 = 𝛼𝑦𝑖,𝑡−1 + 𝛽𝒙′𝑖,𝑡 + 𝑒𝑖,𝑡
Instrument variables used in the difference equation will consist of lagged levels, and the
instruments for the levels equation will consist of lagged differences (Kosack, Tobin
2006).
3.3.4 GMM Model Diagnostics test
Using the GMM method will always generate a set of coefficients. However, to know that
these are accurate and efficient, there are two main tests used within the literature to test
the model. First being the Sargan Hansen J Test (Hansen 1982; Sargan 1988). The second
being the Arellano-Bond Serial Correlation Test (Arellano and Bond 1991; Arellano and
Bover 1995).
The Sargan-Hansen J test tests whether the model is over-identified. That is, if the number
of moment conditions is greater than the dimension of the vector of parameter θ.
Essentially, it is testing whether the moments condition �̂�(𝜃) is adequately close to 0,
meaning that the model with the estimated parameters fits the data set sufficiently. The
test works by removing an instrument variable one at a time, then estimating the error
term to see if the instrument variable is correlated with the error term. If the instrument
variable is not correlated with the error term, then it is a good instrument variable. The J
statistic is of the Chi-squared distribution, with 𝑘 − 𝑙 degrees of freedom, The J statistic is
calculated as follows:
27 | P a g e
𝐽 ≡ 𝑇 ∙ (1
𝑇∑ 𝑔(𝑌𝑡, 𝜃)
𝑇
𝑡=1
)
𝑇
�̂�𝑇 (1
𝑇∑ 𝑔(𝑌𝑡, 𝜃)
𝑇
𝑡=1
)
With the 𝜃 is the estimation of the parameter 𝜃0. Where l denotes the number of estimated
parameters, and k is the dimension of vector g(Yi,t, θ). Yt are individual observations.
H0 : 𝑚(𝜃0) = 0 (the model is valid, and with the correct number of moment conditions)
H1 : 𝑚(𝜃0) ≠ 0 (the model is invalid, the model does not fit the dataset)
The null hypothesis will be rejected if 𝐽 > 𝑞0.99
𝑋𝑘−𝑙2
, at the 99% confidence level. If the model
is incorrectly specified, then J will converge to ∞.
The second specification test is Arellano-Bond serial correlation test (Arellano, Bond
1991). This tests for an autocorrelation in the error term. The dynamic GMM model takes
the first difference of the error term, which in levels, is assumed to have no serial
correlation. Taking this first difference may lead to a first-order serial correlation due to
the inclusion of the lagged dependent variable, but this shouldn’t alter the estimator’s
consistency. However, if a second-order serial correlation is present, it indicates the
lagged dependent variable’s instruments are endogenous, and therefore not valid.
Leading to inconsistency in the estimates.
3.3.5 Granger Causality test
The Granger Causality Test will explain the direction of causality between variables
used in the model above. It works by testing the ability to predict future values of a time
series variable, with lagged values of itself, and another variable. The dependent
variable Y is regressed with lagged values of Y, and a second regression is carried out,
this time, Y is regressed with lagged values of Y and lagged values of X. If, the regression
using both Y and X values is the superior model at predicting Y, then we can say variable
X Granger-causes the Y variable. The two equations are as follows:
𝑦𝑡 = 𝛼0 + 𝛼1𝑦𝑡−1 + 𝑎2𝑦𝑡−2 … 𝛼𝑚𝑦𝑡−𝑚 + 𝑒𝑡
𝑦𝑡 = 𝛼0 + 𝛼1𝑦𝑡−1 + 𝑎2𝑦𝑡−2 + ⋯ + 𝛼𝑚𝑦𝑡−𝑚 + 𝛽1𝑥𝑡−1 + ⋯ + 𝛽𝑝𝑥𝑡−𝑝 + 𝑒𝑡
Here, 𝑦𝑡 will represent GDP, and 𝑥𝑡 represents FDI. If the second regression has a higher
explanatory power over 𝑦𝑡, then we can say a change in FDI Granger-causes GDP to
change. Tests between FDI and Trade will also be conducted.
28 | P a g e
3.3.6 Deciding between Dynamic and System GMM
It is important to note that even though the System GMM can correct for certain issues
with the Difference GMM, it does not indicate complete superiority. In some cases, the
Difference GMM should be used. Thus, it is very important to decide which GMM method
to apply. The test used to decide is one proposed by Blundell and Bond in 1998.
The method involves the investigation of the dependent variable, and its time varying
properties (Blundell, Bond 1998). Starting with the initial model:
𝑦𝑖,𝑡 = 𝛼𝑦𝑖,𝑡−1 + 𝛽𝒙′𝑖,𝑡 + 𝜂𝑖 + 𝑢𝑖,𝑡
Blundell and bond test if the variable 𝑦 is close to being a random walk. If this is the case,
then the lagged levels will provide little information regarding the future changes
(differences). Therefore, the untransformed lagged instrument variables used in the
Difference GMM will be weak. It will provide both biased and inefficient estimate of 𝛼.
However, if the dependent variable y doesn’t follow a random walk, then lagged
instrument variables have explanatory power over future changes, making them strong
IV’s, meaning the Difference GMM method is appropriate. The Variance Ratio test will be
used to test for the presence of a random walk in 𝑦.
3.3.7 South Asia Method conclusion
This section describes in great detail how the GMM method estimates the parameters of
a model, the two types of the GMM method, how to decide which method to employ, and
the diagnostics tests used.
3.4 Pakistan Data Analysis
As this study is also interested in the effects of FDI on the economic growth in Pakistan,
a second regression will be carried out to investigate these effects. The Ordinary Least
Squares method will be used to estimate the unknown parameters of the linearized
model. Note, due to data availability, Infrastructure has been estimated using Telephone
subscriptions and Cellular mobile subscriptions per 100 population (thus
communication infrastructure is a focus).
3.4.1 Ordinary Least Squares method
The OLS method works in a simple way, by estimating parameters for a model, then
generating a dataset, then comparing the generated dataset with the true dataset, and
repeating this process until the error between the generated data and true data is
29 | P a g e
minimised. In the following section, the method of estimating the model coefficients is
presented.
The regression model is formulised as follows:
𝑦𝑖 = 𝛽0 + ∑ 𝛽𝑗𝑥𝑖𝑗 + 𝜀𝑖
𝑝
𝑗=1
Where 𝑦𝑖 is the dependent variable, being lnGDP .Tthe constant term in the model is
denoted with 𝛽0. The coefficient for the jth variable is denoted as 𝛽𝑗 . The ith observation
of the jth variable is denoted as 𝑥𝑖𝑗 . And finally, 𝜀𝑖 is the error term. Individual
observations are represented as i (single piece of data). A variable in the sample, such as
lnFDI, or lnSAV is represented by j.
The OLS equation with the estimated parameters is as follows:
�̂�𝑖 = �̂�0 + ∑ �̂�𝑗𝑥𝑖𝑗 + 𝜀𝑖
𝑝
𝑗=1
𝜀�̂� = 𝑦𝑖 − �̂�𝑖
Deriving the OLS estimators is rather straightforward, with only the covariance and
variance used:
�̂�𝑗 =∑ 𝑥𝑖𝑗𝑦𝑖𝑗−
1
𝑛∑ 𝑥𝑖𝑗 ∑ 𝑦𝑖𝑗
∑ 𝑥𝑖𝑗2 −
1
𝑛(∑ 𝑥𝑖𝑗)2
= 𝐶𝑜𝑣[𝑥𝑗,𝑦𝑗]
𝑉𝑎𝑟[𝑥𝑗]
�̂�0 = �̅� − �̂�𝑗�̅�𝑗
Above, are the formula the OLS method uses to produce the coefficients in the model.
3.4.2 Unit Root test
Data stationarity is generally vital for a regression to be valid, for GMM however, during
a small T (years), large N(countries), non-stationarity of the data does not lead to
spurious regression results, this is because the problem of autocorrelation will be much
less severe, and can be controlled by controlling for time specific effects. Therefore, it is
not necessary to ensure the data is stationary in the South Asia model. Further, in the
difference GMM, the variables will likely become stationary during the first
transformation, by taking the first differences.
30 | P a g e
However, the same cannot be said for the multiple regression test used for Pakistan. An
OLS regression, cannot account for non-stationary data, especially when the number of
years included is large, which in this case, is 29 years. To test for data stationarity, the
Augmented Dickey-Fuller test will be used; an improved version of the Dickey-Fuller
test (Dickey, Fuller 1979). The Dickey-Fuller test tests for a unit root, which is when a
lagged value of the dependent variable is correlated with the current dependent
variable. That is, last years lnGDP will have explanatory power over current lnGDP.
When the dependent variable contains a unit root, it leads to spurious regression
results, and they are therefore uninterpretable. The test is as follows:
∆𝑦𝑡 = 𝛼 + 𝜌𝑡 + 𝛽𝑦𝑡−1 + ∑ 𝛾𝑖∆𝑦𝑡−1 + 𝜀𝑡
𝑘−𝑖
𝑖=1
This equation contains ∆𝑦𝑡and 𝑦𝑡−1, which represents differenced lnGDP and lagged
lnGDP. The constant term is 𝛼, and 𝜌𝑡 is a time trend term. The coefficient 𝛽 represents
how much of a change in GDP is explained by the previous year’s GDP, if 𝛽 is significantly
different from 0, then the variable contains a unit root. The 𝜌 coefficient represents the
change in GDP associated with the change in year; that is, as each year increases, GDP will
increase by 𝜌 amount. The final term in the equation was introduced as a modification to
Dickey-Fuller equation to remove any autocorrelation in the formula. If autocorrelation
is present in the original variables, then 𝜀𝑡will be autocorrelated too, but adding
∑ 𝛾𝑖∆𝑦𝑡−1𝑘−1𝑖=1 will removes effect (Cheung, Lai 1995).
3.4.3 Model Diagnostics test
The R2 statistic is used to see how well the OLS estimated model fits the data set.
Essentially, it represents how much of the true dataset is estimated by the model. Using
the following:
𝑇𝑆𝑆 = ∑(𝑦𝑖𝑗 − �̅�𝑗)2
𝑛
𝑖=1
𝐸𝑆𝑆 = ∑(�̂�𝑖𝑗 − �̅�𝑗)2
𝑛
𝑖=1
𝑅𝑆𝑆 = ∑(𝑦𝑖𝑗 − �̂�𝑗)2
𝑛
𝑖=1
31 | P a g e
Where SST is the total sum of squares, SSR is the residual sum of squares and SSE is the
estimated sum of squares. Individual observations are denoted as 𝑦𝑖𝑗 , the mean
observation is denoted as �̅�𝑗 , and �̂�𝑗 denotes estimated observation. The overall aim of
the OLS method is to minimise the residual sum of squares and therefore increase the
R2. The R2 is calculated by:
𝑅2 =𝐸𝑆𝑆
𝑇𝑆𝑆=
∑ (�̂�𝑖𝑗 − �̅�𝑗)2𝑛
𝑖=1
∑ (𝑦𝑖𝑗 − �̅�𝑗)2𝑛
𝑖=1
However, there is a problem with the R2 , in that, it cannot decrease when extra
explanatory variables are added to the model, even if they provide no explanatory
power whatsoever, which may not seem like an issue, but adding extra variables goes
against the idea of a parsimonious model, meaning, the fewer variables the better. An
adjustment can be made to R2 as follows:
�̅�2 = 1 −
𝑅𝑆𝑆𝑛 − 𝑘𝑇𝑆𝑆
𝑛 − 1
Another statistical test used to test for model adequacy is called the F test. It tests with
statistical significance, if the model fits the data adequately. The null and alternative
hypothesis are; the model with no independent variables fits the model the same as our
model, and the model fits the data better than a model with no variables. The statistic is
as follows:
𝐹 =
𝑅2
𝑘 − 11 − 𝑅2
𝑛 − 𝑘
3.4.4 Pakistan method conclusion
This chapter describes the methods used to generate the coefficients and therefore the
regression model. The preliminary tests of data suitability through the use of the
Augmented Dickey-Fuller test. Finally, the model diagnostics tests employed.
32 | P a g e
Chapter 4 – Empirical Results
4.1 South Asia
Here, are results from the tests detailed in Chapter 3.3. The data is tested to indicate
which GMM method would be suitable for the dataset, then the GMM is constructed and
the model is run producing the regression output, then the robustness of this model is
tested, and therefore the validity of the results.
4.1.1 Variance Ratio Test
The Variance Ratio test was conducted on the depended variable 𝑦𝑖,𝑡, representing GDP
(linearized through the natural logarithm, as every variable in the study is), of each of the
South Asian countries. This was to test the properties of the variable over time. Both of
the combined tests conclude with the same result, rejecting the Null Hypothesis. Four
individual periods have also been tested, all producing statistical significance to reject the
null hypothesis. Meaning, that the dependent variable GDP does not follow a random
walk, therefore, lagged variables will carry explanatory power over future changes.
According to Blundell and Bond (1998), employing the Difference GMM method to a non-
random walk dependent variable generates statistically valid results. Therefore, the
Difference GMM method is used in this study.
Variance Ratio Test Null Hypothesis: LGDP follows a random walk
Combined Test Test Statistic Probability
Max |z| 3.86 0.0003
Wald 22.69 0.0000
Individual test Test Statistic Probability
Period (2) 1.98 0.0479
Period (3) 2.87 0.0041
Period (4) 3.86 0.0001
33 | P a g e
4.1.2 Difference GMM
Difference GMM Dependent Variable: Economic Growth (∆lnGPD) Null Hypothesis: coefficient is significantly different than zero; 𝜷 ≠ 𝟎
Regressor Equation (1) Equation (2)
GDPt-1 0.855*** 0.054
FDI 0.0274* 0.024**
Savings 0.084*** 0.038*
Labour 0.042 0.416
Government Spending - 0.276*
Trade - 0.232**
Debt - 0.131
Infrastructure - 0.115
Human Development - 4.94
*Significant at 10% level **Significant at 5% level ***Significant at 1% level
Here are the core results produced from the augmented production function, regressed
using the Arellano and Bond (1991) Difference GMM Method. Equation 1 being regressed
using the lagged dependent variable ∆𝑙𝑛𝐺𝐷𝑃𝑡−1, and the core variables in the production
function being capital and labour. Where capital has been expanded into both domestic
(savings) and foreign (FDI). The first explanatory variable, the lagged GDP has great
significance at the 1% level, with quite a large coefficient, which seems to contradict some
studies (Naveed, Shabbir 2006). As this variable is external to the production function, it
can be theorised that the equation 1 is underspecified, meaning that other explanatory
variables should be added to increase the model’s estimation performance. In equation
2, when extra variables are added, the lagged GDP coefficient reduces in size, and
becomes statistically insignificant, implying that its significance in equation 1 maybe due
to specification error.
The focus of this study is the effects of FDI on economic growth. The FDI coefficients in
equations 1 and 2 are both significant, indicating that the FDI into South Asian countries
from 2013-2018 has had a significant and positive impact of economic growth. Therefore,
the output (GDP) elasticity of FDI varies between 0.024-0.027. Further, in the more
34 | P a g e
robust model of equation 2, the significance of the coefficient increases to the 5% level,
whilst the coefficient size remains similar. The FDI may induce economic growth from
transfer of knowledge to increased competition, but it is difficult to estimate how FDI
causes growth in South Asia without investigating micro level FDI. These results are
promising in light of the BRI. As there’re mixed results produced in the literature
regarding FDI and economic growth in South Asia, meaning no unanimous prediction can
be made regarding the effects of the investment induced by the BRI. With respect to the
results above however, one could conclude with statistical confidence that if the BRI
increases the amount of foreign investment into South Asia, then it should lead to an
increase in the economic output.
A similar result is generated with the effect of Savings (used as a proxy for domestic
investment) on economic growth. In both equation 1 and 2, the coefficient for saving is
positive and statistically significant. Indicating that as people or institutions (companies)
save more capital in the banks, it can then be loaned out (allocated) to be invested. This
is the basic principle behind banking, the act of borrowing money from people who have
excess, and lending it to people who can invest it efficiently. This result is in line with
expectations from theoretical economics, and from previous literature from studies on
both Pakistan and South Asia (Sahoo 2006; Falki 2009).
The results of the two aforementioned variables; FDI and Saving, are strong evidence of
the validity of the simple production function, and the Solow growth model (Solow 1956).
The results indicate that the capital accumulation within the economies of South Asia has
a positive effect on the output of said economies. However, the next variable; labour does
not continue this trend of significance. The effect of labour growth, measured by the total
labour force in each of the countries, and in both equation 1 and 2 was insignificant. Even
though the coefficient is positive, as predicted by economic theory, is has no statistical
validity meaning no conclusion about the effect of labour force growth can be made
regarding its effect on economic growth in South Asia. This result is to be expected, as
multiple studies on economic growth consistently find labour to be either insignificant
(Atique, et al. 2004) or to even have a significant negative effect on economic growth
(Sahoo 2006). The only conclusion which can be made is that the two-factor production
function is not applicable to South Asia over the time of this study.
35 | P a g e
The additional variables from this point are only present in equation 2. They are the
variables added to the augmented production function (influenced by past literature and
economic theory) to develop a more robust model at estimating economic growth. The
first being government spending within the economy. This variable is both positive and
significant, indicating that as government expenditure increases, so does GDP.
Government spending could be in many forms, from short run expenditure such as
subsidies and public sector employment, to long term spending such as education. Both
are theorised to impact economic growth, but as the time period investigated in the model
is in the short run, the effects of government spending on long run projects such as
education will not be represented. Nevertheless, the government spending in South Asian
economies over the period analysed impacted growth positively. This result is expected
in theory, however in past literature, the effects are somewhat ambiguous, with results
of positive but insignificant effects found in transitioning economies (Campos and
Kinoshita 2002), and also results of significant negative effects of government
expenditure on GDP per capita growth in developed economies (Naveed, Shabbir 2006).
The final significant variable in the model is Trade (used as a proxy for openness). Being
significant at the 5% level, trade has the second largest coefficient. This is to be expected
when making predictions based on past literature; in South Asia from 1975-2003 it was
found that exports have a significant impact on GDP growth (Sahoo 2006). A later study
for the same time period and region found results consistent with this studies results,
with total trade being significant at the 5% level (Sahoo, Dash 2012).
Debt, Infrastructure and HDI are all insignificant variables in determining economic
growth. To start with, the coefficient for Debt is positive which contradicts literature
(Panizza, Presbitero, 2012) however, as the coefficient has no statistical significance, we
cannot conclude that the variable shouldn’t be in the model. The variable for
Infrastructure is also insignificant, which isn’t the case in some economic growth centred
literature (Sahoo, 2006; Sahoo and Dash 2012). As the infrastructure variable used is a
logistics performance index number and as the study only investigates 6 years, there may
not be sufficient time for current infrastructure developments incorporated into the
index number to be reflected in economic growth. Finally, HDI is another insignificant
variable, it is possible that the Labour and HDI variable share explanatory power, making
them both insignificant, however, literature can be found including both (Campos and
Kinoshita 2002).
36 | P a g e
4.1.3 Model Diagnostic Tests
Sargan-Hansen J Test Null Hypothesis: model is valid, with no over-identification.
Equation (1) Equation (2)
J Statistic 11.27 3.46
J P-Value 0.186 0.839
The Sargan-Hansen J test tests whether the model is over-identified, essentially testing
whether the �̂�(𝜃) is close enough to zero, meaning the model satisfactorily fits the data
set. The J statistics produced both accept the null hypothesis, being that the model is valid
and correctly identified. This test provides validity to the interpretation used above.
Equation 2 provides a very low J statistic, and whilst both accept the null hypothesis, this
J statistic accepts with which much more significance, indicating a higher level of validity
of the model used in equation 2.
The results for the Arellano-Bond test for Serial correlation test indicate no second order
serial correlation. The Difference GMM structures the model such that the error term is
differenced and one of the explanatory variables is the differenced lagged dependent
variable, this could – but not always will – cause first order serial correlation, which can
be seen in the table, that equation 1 (but not equation 2) contains first order serial
correlation, but only being significant to the 10% level. The presence of first order serial
correlation doesn’t make the estimates inconsistent. However, the presence of second
order serial correlation will lead to inconsistent estimates. The test above accepts the null
hypothesis for both equations 1 and 2 when testing for second order serial correlation,
implying the model’s error is not serially correlated, thus producing consistent estimates
of the coefficients.
Arellano-Bond Serial Correlation Test Null Hypothesis: No Serial Correlation present.
Equation (1) Equation (2)
AR (1) Probability 0.0878 0.338
AR (2) Probability 0.503 0.919
37 | P a g e
4.1.4 Granger Causality
Granger Causality Test
Null Hypothesis F-Statistic Probability
GDP does not Granger Cause FDI 3.46 0.985
FDI does not Granger Cause GDP 0.839* 0.0604
Trade does not Granger Cause FDI 1.62 0.229
FDI does not Granger Cause Trade 2.95* 0.0694
*Significant at 10% level
The results above summarise the direction of causality between FDI and GDP, and also
for FDI and Trade. This study is interested in whether FDI causes GDP growth. Looking at
the results, it can be concluded that FDI Granger Causes GDP, as this test is rejecting the
null hypothesis at the 10% level. This conclusion indicates lagged values of FDI influence
values of GDP. For example, a foreign firm investing in South Asia due to better business
opportunities such as free trade brought about due to the BRI, may only start affecting
GDP after a year of business trading. This FDI will be represented in current FDI figures,
but the FDI may take time to affect the economic growth.
The Granger-Causality test for Trade and FDI also has relevance. The result indicates that
FDI does Granger Cause Trade. This is relevant, as the BRI may create motives for more
FDI in places like Pakistan, but as the result below indicates, FDI isn’t that beneficial for
Pakistan. However, for example, FDI into Pakistan could lead to more Trade as the
Granger test indicates, and Trade is a significant variable at increasing economic growth.
38 | P a g e
4.2 Pakistan
Here, are the results from the tests detailed in Chapter 3.4. The data is tested for
stationarity, then the OLS method is employed to run the regression. The regression is
then tested using basic diagnostics tests.
4.2.1 Unit Root testing
Augmented Dickey-Fuller Null Hypothesis: The variable contains a unit root (None Stationary) Variable (logarithm) Levels
(Probabilities) First Difference
(Probabilities) Second Difference
(Probabilities)
GDP 0.939 0.005 -
GDP-1 0.748 0.006 -
FDI 0.395 0.045 -
Saving 0.4039 0.000 -
Labour 0.142 0.067 0.0001
Government Spending
0.798 0.004 -
Trade 0.742 0.009 -
Debt 0.204 0.018 -
HDI 0.498 0.077 0.000
Telephone Access 0.984 0.168 0.002
Mobile Access 0.989 0.037 -
Note: Data is considered stationary (no unit root) at the 5% significance level.
The results above generated using the Augmented Dickey-Fuller equation indicate that
all variables used in the Pakistan model are non-stationary, thus leading to spurious
regression, leading to results with no validity. After taking the first difference, 8 out of 11
variables reject the null hypothesis, indicating they are stationary and are now valid
variables ready to be regressed. Labour, HDI and Telephone Access are stationary after
the second difference. The transformed stationary variables will be regressed using the
OLS method.
39 | P a g e
4.2.2 OLS regression
OLS Regression Dependent Variable: Economic Growth (∆lnGPD) Null Hypothesis: coefficient is significantly different than zero; 𝜷 ≠ 𝟎 Variable (logarithm) Equation (1) Equation (2) Equation (3) Equation (4)
Constant 0.029*** 0.022 0.021 0.024
GDP-1 - 0.032 0.05 0.12
FDI -0.007 -0.006 -0.009 -0.005
Saving 0.147*** 0.172*** 0.173*** 0.18**
Labour - - -0.395 -0.528
Government Spending 0.206** 0.187** 0.198** 0.195*
Trade 0.386*** 0.406*** 0.391*** 0.361***
Debt - 0.101 0.103 0.087
HDI - - -1.75
Telephone Access - - -0.053
Mobile Access - - -0.02
R2 0.72 0.73 0.73 0.75
Adjusted R2 0.67 0.64 0.63 0.593
F-Statistic F-Critical Value
14.9*** (4.18)
8.85*** (3.67)
7.39*** (3.54)
4.79*** (3.37)
Note: F statistic compared to critical value at 1% *Significant at 10% level **Significant at 5% level ***Significant at 1% level
The results show four equations which have been generated using the OLS method. Each
equation removes one or multiple insignificant variables to make the model more
parsimonious. Firstly, the infrastructure variable used here had been proxied using
Telephone access and Mobile Cellular access. The result however is consistent with the
total South Asia model, indicating no statistical significance and therefore no conclusion
can be made. Data used in papers which were able to find linkages between infrastructure
and growth is different to this data(Sahoo, Dash 2012), meaning even though these two
variables are insignificant, it doesn’t mean that they were not relevant to the study, or
that the model is not regressed using the appropriate methods. HDI is another
insignificant regressor, again being consistent with the South Asia analysis above. These
three insignificant regressors were removed from the model, creating equation 3.
40 | P a g e
Equation 3 contains the same significant variables, being Government Spending, Saving
and Trade. Labour Force growth in this equation is negative but insignificant, meaning no
valid interpretation can be made. Some studies find labour growth to have significant
positive effects (Falki, 2009), however as this coefficient isn’t significant, it is removed
from the model to avoid causing problems with other coefficients.
Equation 2 follows the same pattern, with the significant coefficients from equations 3
and 4 remaining significant. Debt remains insignificant, which may seem to contradict
certain literature, however, there are varying results between developed and developing
countries. During the period used in this regression, Pakistan has changed significantly,
implying that debt may have affected growth differently over the sample period, thus
leading to no significant linear relationship, this is the view of the IMF (International
Monetary Fund, 2012). The lagged dependent variable also remains insignificant in
equations 2-4, and if therefore removed.
Equation 1 is the most parsimonious model, containing the highest proportion of
significant regressors. Starting with Savings, this variable went from significant at the 5%
level is equation 4 to being significant at the 1% level in equation 1. Savings in Pakistan
has a larger coefficient compared with the total South Asia results, implying Pakistan’s
GDP is impacted more by saving than the average throughout South Asia. Government
Spending’s coefficient reduced in size from that of South Asia’s results, however, it is
much more significant, being at the 5% level. It can be said with more confidence that
Pakistan’s GDP growth is impacted by its government expenditure. Finally, Trade is more
significant that the results from South Asia, rejecting the Null at 1%. These significant
variables are expected (Falki 2009; Saqib, Masnoon, Rafique 2013).
The result for FDI is in line with the majority of the literature above, being that the FDI
coefficient is insignificant and/or negative in Pakistan. The results from the South Asia
analysis can be interpreted with valid reference to the Belt and Road Initiative as the
period investigated is post BRI, however, Pakistan’s multiple regression is from 1990-
2018, thus much of the regression is influenced by pre-BRI data. This makes conclusions
and predictions regarding the effects of the Belt and Road Investment into Pakistan
difficult, and with low validity.
Finally, it should be mentioned that the more parsimonious the model is, the higher the
adjusted R2 produced was, moving from 0.59 in equation 4, to 0.67 in equation 1.
41 | P a g e
Indicating equation 1 has the highest adjusted explanatory power over determining
economic growth. The same pattern can be seen in the F-test, with all equations rejecting
the null hypothesis indicating the models have significant explanatory power, but when
moving from equation 4 to 1, the null hypothesis is rejected with more significance.
One reason FDI is insignificant in the Pakistan regression, but significant in the South Asia
panel data results could be due to the more effective Foreign Direct Investment during
periods after 2013. This could be due to multiple reasons, first being the policies and
legislation implemented after the BRI announcement such as Phase II of the China-
Pakistan FTA being introduced, making FDI more effective, according to Zhang (2001),
liberal trade policies are needed for FDI to have positive effects. Another reason could be
the infrastructure developed as a result of the BRI such as the ‘Early Harvest’ Roadway,
could help facilitate exports from foreign firms based in Pakistan, again Zhang (2001)
states infrastructure is needed if FDI is to have a positive effect. Thus, since this
regression is based on the past 29 years, the positive effects of FDI are only a small
proportion of the sample period.
Chapter 5 - Conclusion
5.1 Results conclusion
Due to the conflicting evidence regarding the effects of Foreign Direct Investment on
economic growth, and due to the outdated empirical analysis on the topic, this study did
not make any preliminary predictions. The results found that during the sample period
(2013-2018); Saving (domestic investment), Government expenditure, Trade and
Foreign Direct Investment all significantly contribute towards economic growth within
South Asia. The validity of these results is increased with the Sargan-Hansen J test, and
again with the Arellano and Bond Serial correlation test. The Granger causality test is
used to validify the direction of causality between FDI and GPD.
If Pakistan is regressed alone, the results are less conclusive. No definitive effect between
FDI and economic growth is found, which is consistent with the literature. However,
trade, saving and government spending are significant variables in causing economic
growth. The model increased in explanatory power moving from equation 4 to 1, with
both adjusted R2 and F-statistics increasing. This result is contradicting the South Asia
analysis above, however, the two regressions aren’t statistically comparable.
42 | P a g e
5.2 What this means for the Belt and Road
It can be concluded that, as the effects of FDI are positive and significant, the Belt and
Road Initiative should lead to economic growth within South Asia due to its promotion of
foreign investment. When looking at the trends, for example in Figure 8 and 10, it can be
seen that the proportion of China to total inward FDI into Pakistan is increasing, along
with Pakistan’s total inward FDI, indicating potential gains to economic growth from
Chinese FDI.
The Belt and Road Initiative aims to improve transportation infrastructure within the two
routes; China-Pakistan Economic Corridor, and the China, Bangladesh, India, Myanmar
Economic Corridor. This improvement in infrastructure will reduce trade bottlenecks.
This should firstly increase the amount of international trade possible within these
countries, thus leading to economic growth as indicated in the results of this study. This
improvement in infrastructure could help attract more FDI (Shah 2014), which again will
cause economic growth as illustrated in the results. Finally, when infrastructure is
developed, the bottlenecks of trade are reduced, allowing for FDI to be effective
(Borensztein, et al 1998; Zhang 2001). This means even though the Pakistan OLS
regression doesn’t indicate positive effects of FDI, once the CPEC infrastructure is
developed, then the FDI should have positive effects on GDP.
The reduction of regulation of capital flows to help facilitate investment is another
objective. Firstly, the effects new infrastructure as a result of more investment has been
detailed above. This objective should lead to more infrastructure projects being started,
such as the ‘Early Harvest’ roadway in Pakistan, or developments at the Gwadar Port due
to more capital available to invest. These projects act as incentives for foreign
construction/industrial firms to invest in countries with high project intensity such as
Pakistan, or other major BRI countries. Thus, directly increasing FDI.
Finally, a major objective of the BRI is to promote more liberal trade policies and reduced
regulation. Lower export regulation, and lower tariffs due to Free Trade Agreements
(FTA) like the China-Pakistan FTA should induce more trade. This could also be an
incentive for foreign firms to invest in countries with FTA’s due to giant export potential.
Both this increase in Trade and FDI would lead to economic growth in South Asia. Again,
liberalisation of trade policy is reducing regulatory bottlenecks, thus making the effects
of FDI more positive, according to Zhang (2001) and Borensztein (1998).
43 | P a g e
Flo
w c
har
t il
lust
rati
ng
effe
cts
of
the
Bel
t an
d R
oad
In
itia
tiv
e
Cre
ated
by
Z0
98
35
51
44 | P a g e
References
1. Agrawal, P. 2000, “Economic impact of foreign direct investment in south Asia”,
Working Paper, Indira Gandhi Institute of Development Research, Mumbai, India
2. Agrawal, P., 2000. Economic impact of foreign direct investment in South Asia.
India and the WTO, p.117.
3. Aitken, B. and A. Harrison, 1999, Do domestic firms benefit from foreign
investment? Evidence from Venezuela. American Economic Review, 89, No. 3, June
4. Alege, P.O. and Ogundipe, A., 2014. Foreign direct investment and economic
growth in ECOWAS: A system-GMM approach. Covenant Journal of Business and
Social Sciences, 5(2).
5. Arellano, M. and Bond, S. (1991). Some Tests of Specification for Panel Data: Monte
Carlo Evidence and an Application to Employment Equations. The Review of
Economic Studies, 58(2), pp.277–297.
6. Arellano, M. and Bover, O., 1995. Another look at the instrumental variable
estimation of error-components models. Journal of econometrics, 68(1), pp.29-51.
7. Atique, Z., Ahmad, M.H., Azhar, U. and Khan, A.H., 2004. The impact of FDI on
economic growth under foreign trade regimes: A case study of Pakistan [with
comments]. The Pakistan Development Review, pp.707-718.
8. Barboza, D., 2008. China unveils sweeping plan for economy. New York Times, 9.
9. Beijing, China Economic Publishing House. 2014, The Eurasian era: Blue Book of
Silk Road economic study 2014–2015, pp. 23.
10. Bellabona, P. and Spigarelli, F. (2007). Moving from Open Door to Go Global: China
goes on the world stage. International Journal of Chinese Culture and
Management, 1(1), p.93.
11. Bhagwati, J. N. (1994) Free Trade: Old and New Challenges. Economic Journal
104:423, 231-246.
12. Bhargava, A., Franzini, L. and Narendranathan, W., 1982. Serial correlation and the
fixed effects model. The Review of Economic Studies, 49(4), pp.533-549.
13. Blomstrom, M. and Kokko, A. (1998). Multinational Corporations and Spillovers.
Journal of Economic Surveys, 12(3), pp.247–277.
45 | P a g e
14. Blomstrom, M. and Persson, H. (1983), Foreign investment and spillover efficiency
in an underdeveloped economy: Evidence from Mexican manufacturing industry.
World Development, Volume 11, pp. 493-501.
15. Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in
dynamic panel data models. Journal of Econometrics, [online] 87(1), pp.115–143.
16. Borensztein, E., De Gregorio, J. and Lee, J.-W. (1998). How does foreign direct
investment affect economic growth? Journal of International Economics, 45(1),
pp.115–135.
17. Cheung, Y.W. and Lai, K.S., 1995. Lag order and critical values of the augmented
Dickey–Fuller test. Journal of Business & Economic Statistics, 13(3), pp.277-280.
18. Chinadaily.com.cn. (2020). China’s direct investments abroad top $92b by 2007.
[online]
19. Cobb, C. W. Douglas, P. H. (1928) A theory of production. American Economic
Review. 139-165
20. de Soyres, F., Mulabdic, A. and Ruta, M. (2020). Common transport infrastructure:
A quantitative model and estimates from the Belt and Road Initiative. Journal of
Development Economics, 143, p.102415.
21. Dickey, D.A. and Fuller, W.A., 1979. Distribution of the estimators for
autoregressive time series with a unit root. Journal of the American statistical
association, 74(366a), pp.427-431.
22. Durham, J.B. (2004). Absorptive capacity and the effects of foreign direct
investment and equity foreign portfolio investment on economic growth.
European Economic Review, 48(2), pp.285–306.
23. Fedderke, J.W. and Romm, A.T. (2006). Growth impact and determinants of foreign
direct investment into South Africa, 1956–2003. Economic Modelling, 23(5),
pp.738–760
24. FERDINAND, P. (2016). Westward ho-the China dream and ‘one belt, one road’:
Chinese foreign policy under Xi Jinping. International Affairs, 92(4), pp.941–957.
25. Ferdinand, P., 2016. Westward ho—the China dream and ‘one belt, one road’:
Chinese foreign policy under Xi Jinping. International Affairs, 92(4), pp.941-957.
26. Findlay, R. (1978). relative backwardness, direct foreign investment, and the
transfer of technology: A simple dynamic model. Quarterly Journal of Economics,
92(1), 1-16
46 | P a g e
27. Ge, W. (1999). Special economic zones and the economic transition in China.
Singapore ; River Edge, Nj: World Scientific.
28. Hansen, L.P., 1982. Large sample properties of generalized method of moments
estimators. Econometrica: Journal of the Econometric Society, pp.1029-1054.
29. Hu Angang, Zhongguo, min sheng yu fazhan. 2008, China: people’s livelihood and
development, Beijing: Zhongguo jingji, pp. 99–117.
30. Husted, S. and Nishioka, S. (2013). China’s fare share? The growth of Chinese
exports in world trade. Review of World Economics, 149(3), pp.565–585
31. IMF, THE PEOPLE’S REPUBLIC OF CHINA 2016 ARTICLE IV CONSULTATION-
PRESS RELEASE; STAFF REPORT; AND STATEMENT BY THE EXECUTIVE
DIRECTOR FOR THE PEOPLE’S REPUBLIC OF
CHINA.(n.d.).[online]Availableat:https://www.imf.org/~/media/Websites/IMF/
imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16270.ashx.
32. Isard, Walter. (1954). “Location Theory and Trade Theory: Short-Run analysis”.
Quarterly journal of Economics, 68: 305-320.
33. Jonathan Hillmam, China’s Belt and Road Initiative is full of holes, September
2018, CSIS Briefs.
34. K.H. Zhang, How Does Foreign Investment Affect Economic Growth in China?
Economics of Transition, 9, (2001), 679-693.
35. Khan Arshad, 2007, “Foreign Direct Investment and Economic Growth: The role of
Domestic Financial sector”, PIDE Working Paper
36. Kokko, Ari, (1994), Technology, market characteristics, and spill overs, Journal of
Development Economics, 43, issue 2, p. 279-293,v:43:y:1994:i:2:p:279-293
37. Kosack, S. and Tobin, J., 2006. Funding self-sustaining development: The role of
aid, FDI and government in economic success. International organization, 60(1),
pp.205-243.
38. Liu, W. and Dunford, M. (2016). Inclusive globalization: unpacking China’s Belt and
Road Initiative. Area Development and Policy, 1(3), pp.323–340.
39. Morrison, wayne M. (2019). China’s Economic Rise: History, Trends, Challenges,
and Implications for the United States. Congressional Research Service.
40. Muhammad Saqib Irshad, Qi Xin & Hamza Arshad. (2015). One Belt
andOneRoad:DoseChina-Pakistan Economic Corridor Benefit for Pakistan's
Economy? Journal of Economics and Sustainable Development, vol. 6, no. 24, 2015.
47 | P a g e
41. Mumuni, S.M. and Murphy, S.A. (2018). China’s “Going Global” Policy in Africa: An
Exploratory Overview of an Evolving Policy Framework. Journal of Asian
Research, 2(1), p.11.
42. Nair-Reichert, U. and Weinhold, D. (2001). Causality Tests for Cross-Country
Panels: a New Look at FDI and Economic Growth in Developing Countries. Oxford
Bulletin of Economics and Statistics, 63(2), pp.153–171.
43. Naveed, A. and Shabbir, G., 2006. Trade openness, FDI and economic growth: A
panel study. Pakistan Economic and Social Review, pp.137-154.
44. Newey, W.K. and Powell, J.L., 2003. Instrumental variable estimation of
nonparametric models. Econometrica, 71(5), pp.1565-1578.
45. Nickell, S., 1981. Biases in dynamic models with fixed effects. Econometrica:
Journal of the Econometric Society, pp.1417-1426.
46. Nordås, H.K. and Piermartini, R., 2004. Infrastructure and trade.
47. Nuzhat Falki. (2009). Impact of Foreign Direct Investment on Economic Growth
in Pakistan. International Review of Business Research Papers Vol. 5 No. 5
September 2009 Pp. 110-120.
48. OECD (1991), Technology and Productivity: The Challenge for Economic Policy.
Paris.
49. Panizza, U. and Presbitero, A.F., 2012. Is high public debt harmful for economic
growth? New evidence. Austerity: Too Much of a Good Thing?, p.91.
50. Phillips, P.C., 1989. Lectures on Advanced Econometric Theory.
51. Ramachandran, Sudha (16 November 2016). "CPEC takes a step forward as
violence surges in Balochistan". www.atimes.com. Retrieved 19 November 2016.
52. Rana, S., 2016. Eastern CPEC route unfeasible: report. The Express Tribune.
Retrieved, 8.
53. Roodman, D. (2006). How to do Xtabond2: An Introduction to Difference and
System GMM in Stata. SSRN Electronic Journal.
54. S. Adams, (2009) Foreign Direct Investment, Domestic Investment, and Economic
Growth in Sub-Saharan Africa, Journal of Policy Modelling, Working Paper, (2009).
55. Sahoo, P. (2012). Determinants of FDI in South Asia: Role of Infrastructure, Trade
Openness and Reforms. The Journal of World Investment & Trade, pp.256–278.
48 | P a g e
56. Sahoo, P. and Dash, R.K., 2012. Economic growth in South Asia: Role of
infrastructure. The Journal of International Trade & Economic Development,
21(2), pp.217-252.
57. Saqib, N. Masnoon, M. Rafique, N. (2013). Impact of Foreign Direct Investment on
Economic Growth of Pakistan. Advances in Management & Applied Economics,
vol.3, no.1, 2013, 35-45 ISSN: 1792-7544
58. Sargan John, D., 1988. The Estimation of Economic Relationships Using
Instrumental Variables. Contributions to Econometrics: John Denis Sargan, 1.
59. Shah, M.H., 2014. The significance of infrastructure for FDI inflow in developing
countries. Journal of Life Economics, 1(2), pp.1-16.
60. Solow, M, Robert. (1956) A CONTRIBUTION TO THE THEORY OF ECONOMIC
GROWTH, QUARTERLY JOURNAL OF ECONOMICS, 65-94
61. Summers, T. (2016). China’s ‘New Silk Roads’: sub-national regions and networks
of global political economy. Third World Quarterly, 37(9), pp.1628–1643.
62. The historical PPP GDP figures of Mainland China and exchange rates of Chinese
yuan to Int'l. dollar are based on the World Economic Outlook Database October
2019 International Monetary Fund. October 2019.
63. Wan, M. (2016). The Asian Infrastructure Investment Bank : the construction of
power and the struggle for the East Asian international order. New York: Palgrave
Macmillan.
64. Wang, Y. (2016). Offensive for defensive: the belt and road initiative and China’s
new grand strategy. The Pacific Review, 29(3), pp.455–463.
65. Whalley, J. and Weisbrod, A. (2012). The Contribution of Chinese FDI to Africa’s
Pre Crisis Growth Surge. Global Economy Journal, 12(4), p.1850271.
66. Xinhua, "China plans 10 major steps to spark growth as fiscal, monetary policies
ease". News.xinhuanet.com. Retrieved 2008-11-14.
67. Xinhua, "Chronology of China's Belt and Road Initiative". China's State Council.
Archived from the original on 23 September 2018. Retrieved 23 September 2018.
68. Yang, D., Jiang, L. and Ng, A.K.Y. (2018). One Belt one Road, but several routes: A
case study of new emerging trade corridors connecting the Far East to Europe.
Transportation Research Part A: Policy and Practice, [online] 117, pp.190–204
69. Yang, D.T., Chen, V.W. and Monarch, R. (2010). RISING WAGES: HAS CHINA LOST
ITS GLOBAL LABOR ADVANTAGE? Pacific Economic Review, 15(4), pp.482–504.
49 | P a g e
70. Yu, H. 2016, Motivation behind China’s ‘One Belt, One Road’ Initiatives and
Establishment of the Asian Infrastructure Investment Bank. Journal of
Contemporary China, 26(105), pp.353–368.
71. Zhai, F. 2018, China’s belt and road initiative: A preliminary quantitative
assessment. Journal of Asian Economics, 55, pp.84–92.
