LBPG 5018: Research Methodology

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FDI Inflow and Infrastructure: A
Comparison of the Nigerian
Economy with the BRICS
Economies (1995-2015)
LBPG 5018: Research Methodology
Word Count: 1,992
Abstract
Consensus amongst economists on the role of infrastructure on FDI is minimal making
ascertaining the degree of impact very difficult. The research will therefore be a crosssectional empirical study aimed at determining the impact of infrastructure on foreign direct
investment (FDI) by comparing data from 1995-2015 from Nigeria, Brazil, Russia, India,
China and South Africa (BRICS). The study will use the regression analysis economic model
to determine if the independent variable – infrastructure is correlated with the dependent
variable – FDI.
Key Words: Foreign Direct Investment (FDI), Infrastructure, Nigeria, BRICS
1. Introduction
1.1 Background statement
Economic growth is a major goal of countries around the world and amassing capital from
either domestic or foreign sources is one way to achieve this growth. Capital from foreign
sources can be in form of grants from developed countries, “financial flows arising from
multiple sources such as the World Bank, regional banks and foreign direct and indirect
investment.” (Nouri, Asgarnezhad and Milad, 2016, pp1)
Foreign Direct Investment (FDI) which is the focus of this paper is believed and has been
proven to foster economic growth both in the home and host countries. (Selaya and Sunesen,
2012, pp2155) Chen (2000, pp6) defines it as “investment which a firm acquires a substantial
controlling interest in a foreign firm (above 10-percent share) or sets up a subsidiary in a foreign
country” and goes further to state that in recent times, it has grown faster than international
trade.
FDI has several covariates also referred to as determinants but there is little consensus amongst
economists as to what they are. (Blonigen and Piger, 2014, pp778) Infrastructure is one of such
covariates with contending literature on its impact on FDI, hence, this research will attempt to
share more light on it by carrying out a comparative study between Nigeria and the BRICS
countries – Brazil, Russia, India, China and South Africa.
1.2 Research Aim
The aim of this research is to compare the Nigerian economy with the BRICS economies to
determine the impact of infrastructure on FDI. For several years, Nigeria has been identified as
the biggest economy in Africa yet it ranks third in Africa in FDI stock behind South Africa and
Egypt. Asiedu, (2003 cited in Olokoyo, 2012, 7) says that, ‘the level of FDI attracted by Nigeria
is mediocre compared with the resource base and potential need.’ Infrastructure is believed to
be one of the reasons for the low FDI hence; this research will attempt to compare Nigeria with
the BRICS to see if there is a relation between FDI and Infrastructure.
1.3 Research objectives
i. To study the patterns of FDI inflow in Nigeria and the BRICS nations
ii. To ascertain if infrastructure is a determinant of FDI inflow
iii. To assess the level of impact infrastructure has on FDI inflow
1.4 Limitations
A possible limitation to this study may be the lack of access to adequate time series data
especially from Nigeria. This is due to the fact that it lacks behind in having rich official online
databases. Nevertheless, information from international databases like the WTO, IMF,
UNCTAD, World Bank, etc will be used to complement the data.
1.5 Managerial implications
This project will be highly essential to policy makers especially in Nigeria as identifying the
relative importance of infrastructure in driving FDI inflow can result in positive policy
measures that can make Nigeria a more attractive FDI destination. It can also bridge the gap in
FDI literature by confirming whether or not infrastructure plays an essential role in attracting
FDI inflow.
1.6 Ethics
The research will adopt quantitative methods and although, it is less invasive than qualitative
methods, ethical issues must still be considered. This research is devoid of human subjects;
hence issues of confidentiality and harm do not apply. However, proper procedures to avoid
ethical issues on data analysis and reporting must be observed. (Saunders, Lewis and Thornhill,
2012, pp139) This research therefore abides to the code of conduct provided in the UK Data
Protection Act of 1998 in ensuring that data is used accurately, ‘fairly and lawfully’, ‘for
limited, specifically stated purposes’, ‘in a way that is adequate, relevant and not excessive’.
2. Literature Review
Varghese (2016, pp1) suggests that over the last two decades, a striking development has been
the rapid spread of FDI on the global economic scale. Developing and emerging economies to
become part of this development strive to increase their share of FDI inflow by making their
countries attractive to MNCs. (UNCTAD, 2012, cited in Cezar and Escobar, 2015, pp714)
Several theoretical and empirical determinants influence the investment location of MNCs says
Busse and Hefeker (2007, pp398) and these theoretical determinants can be classified into
microeconomic and macroeconomic theories. The latter considers macroeconomic variables
like infrastructure to explain FDI flows. (Dua and Garg, 2015, pp135)
Dunning’s Electric Paradigm approach suggests that location advantages, ownership and
localization work together to achieve international production. (Blomstrom, Kokko and Zejan,
2000, pp4) The approach states that location advantages deals with readily available inputs for
all established firms in a host country like natural resources, government policies, political and
cultural environment, infrastructure, etc. (Blomstrom, Kokko and Zejan, 2000, pp4)
Blonigen and Piger (2014, pp775) state that, “empirical studies of bilateral foreign direct
investment (FDI) activity show substantial differences in specifications with little agreement
on the set of included covariates.” This drove them to adopt Bayesian statistical strategies to
determine which covariates are vital in driving FDI flows. Their research suggested that
variables like “traditional gravity variables, cultural distance factors, relative labour
endowments and trade agreements” have not only high but consistent inclusion probabilities in
FDI flows unlike “multilateral trade openness, most host-country business costs, host-country
infrastructure and host-country institutions” variables that showed little support. (Blonigen and
Piger 2014, pp775) The above results are not only in contrast with Dunnimg’s Electric
Paradigm approach but is also in contrast with the researches of Offiong, Atsu and Amed
(2014); Kotenkova, Larionova and Varlamova (2016); Varghese (2016) and (Dua and Garg,
2015) that suggest that pull factors – that show correlation between host country-specific
conditions and FDI inflow – like infrastructure, openness to trade, legal system, business costs
of host country, etc. influence FDI inflow.
Dua and Garg (2015, pp137) suggest that infrastructure is vital for driving FDI inflow as it
promotes maximum use of the labour force which results in cost reduction and profit
maximisation. In Varghese’s (2006, pp9) analysis of top 10 sectors attracting FDI using the
Student T test, he discovered that in India, infrastructure is ranked second in the attraction of
FDI inflow. He also stated that, “state- wise FDI inflows show that Maharashtra, New Delhi,
Karnataka, Gujarat and Tamil Nadu received major investment from investors because of the
infrastructural facilities…” (Varghese, 2006, pp9)
In Africa, Nigeria is ranked third with FDI stock of $86.6billion and South Africa first with
$145 billion. (Santander trade, 2016) In the global sphere, China is top of the chart followed
by Brazil, Mexico, Russia and India.( Kotenkova, Larionova and Varlamova, 2016, pp4) The
position of infrastructure as a determinant of FDI inflow is contested by Blonigen and Piger,
and although there are evidences in literature as to the influence of infrastructure on FDI, this
research will test the authenticity of Dunning’s Electric Paradigm approach and prove whether
or not infrastructure influences FDI as well as show its level of influence. This research will
bridge the gap by using case studies that possess high FDI stocks but different infrastructural
characteristics to determine the role of infrastructure in FDI inflow.
3. Methodology
3.1 Research Design
Rugg and Petre (2007, pp61) defined research design as, “…finding things out systematically
– map making as opposed to treasure hunt.” It is mapping out the overall structure of the
research. This is so because the research design is selected to fit the aim and research questions
of the research thereby influencing the review of literature, methodology, data collection as
well as analysis.
3.1.1 Research Paradigm and Approach
Research process according to TerreBlanche and Durrheim (1999 cited in Antwi and Hamza,
2015, pp218) have three core dimensions; “ontology, epistemology and methodology”. They
state that research design is thinking and practice surrounded by all three dimensions. Two very
contrasting paradigms are objectivism and constructionism says Neuman (2003 cited in Antwi
et al, 2015, pp218) Positivists/objectivism perceive knowledge as based on empirical and
experimental evidence whereas ‘interpretivist’/constructivist perceive knowledge as creations,
experiences and interpretations of people in their interaction with their environment. (Neuman,
2003 and Maxwell, 2006 cited in Antwi et al, 2015, pp28)
Approaches to research are linked to paradigms as positivists tend to take a deductive approach
and constructivists tend to take an inductive approach however both approaches can be applied
in a single research. (Saunders, et al, 2012, pp88) When predictions are made from specific
objectives or outcomes to general conclusions, it is said to be inductive and when general ideas
are used to make specific conclusions then it is said to be deductive. (Graziano and Raulin,
2007, pp35)
This research will thereby apply positivism and the deductive approach as it deals with
analyzing empirical data to find the connection between infrastructure and FDI.
3.1.2 Research Method and Strategy
Graziano et al, (2007, pp56) describes research methods as techniques applied for the process
of answering specific research questions. Bryman (1984, pp76) states that two core classes of
research methods exist; qualitative and quantitative methods and these methods have diverse
data gathering strategies.
Quantitative methods lay emphasis on measurement. It focuses on explaining a phenomenon
by collecting numerical data. (Babbie, 2010, pp86) It includes strategies like surveys,
experiments, correlation analysis, case methods, etc. (Leedly and Ormrod, 2010, pp182-189)
On the other hand, qualitative research methods focus on the how and why questions. HesseBiber and Leavy (2004, pp39) states that whereas “quantitative research focuses on the number
or percentage of respondents doing a particular thing, qualitative research is focused on why
they do what they do”.
The choice of research methods and strategies determine the value of data collected hence, this
research will adopt a mono quantitative method. It will use case studies (Nigeria, Brazil,
Russia, India, China and South Africa) to determine if there is correlation between
infrastructure and FDI. Jupp (2006) describes case studies as “an approach that uses in-depth
investigation of one or more examples of a current social phenomenon, utilizing a variety of
sources of data.” ‘A case study is especially suitable when there is a desire to understand and
explain a phenomenon in a field which is not yet well understood.’ (Hakim, 2000) They could
be descriptive, exploratory or explanatory (Yin, 1984).
This research is an empirical study that aims to analyse the impact of infrastructure on FDI.
For reliability and validity, five countries have been chosen as case studies. This method is best
because of the limited time frame available for the research.
3.2 Approach to Data Collection and Analysis of Findings (approx. 10% length)
The research will be a secondary research with a combination of time series and cross-section
data that have been gathered over time by government agencies like the World Bank, WTO,
UNCTAD, IMF and Economic agencies of respective countries. Data for 20years, 1995-2015
for Nigeria and BRICS countries will be taken as the sample size. The time frame has been
selected because developing and transitioning economies made some structural and economic
reforms in the early 1990’s that fostered rapid economic growth-relations. (Fischer et al, 1998
cited in Olokoyo, 2012, 13) These countries have been chosen because of their high FDI stock
and rankings globally and within their continents.
Statistical and econometric method of analysis will be used in the data analysis. Specifically,
regression analysis will be used to test the correlation between the independent variable
(infrastructure) and dependent (FDI) variable.
4. Conclusion
Foreign Direct Investment plays a vital role to economic growth and development.
Understanding what covariates drive FDI is essential to policy makers as they can structure
their policies to improving these covariates, thereby making their country an attractive FDI
location. Infrastructure is one of such covariates unfortunately; its position is controversial in
the economic sphere. This research using economic model will analyse existing data from
Nigeria and BRICS nations in the bid to determine the impact of infrastructure on FDI.
Research timetable
The research timetable has been illustrated using the Gantt chart below. It is made up of
timelines of tasks involved in the dissertation process. It covers the duration of thirty (30)
weeks from March 1st until the dissertation submission date, September 19th.
2017 Mar April May June July Aug Sept
Discussion
with
Supervisor
Preparator
y Study for
Data
Collection
Data
Collectio
n
Week
1
Dissertatio
n
Submission
Week 30
Presentation of
Findings and Final
Dissertation Draft
Weeks 25 ‐
Identify 27
Researc
h Topic
Holiday
Weeks 16 ‐
17
Discussion
with
Supervisor
Week
21
Proposal
Phase
4
weeks
Reviewing of
Existent Literature
4
weeks
Data Collection
and analysis
phase
6
weeks
Presentation of
Findings and
Recommendation
1
week
Final
Draft
2
Weeks
Submissio
n of Draft
Proposal
Weeks 19 ‐
20
Week
5
Week 4 Weeks 7‐9 Weeks 21 ‐
24
Data
Analysis
References
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CEZAR1, R. AND ESCOBAR, C., R. (2015), Institutional distance and foreign direct
investment, Rev World Econ, Vol. 151, pp713–733
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